important chlorophyll levels in the ripe fruit. Color variations do not only change fruit pigmentation, but they also affect the entire set of tomato fruit attributes, adversely or positively impacting the organoleptic and nutritional properties. Indeed, ripening involves a number of physiological processes ranging from the chlorophyll breakdown to the consecutive carotenoid accumulation, but also of other compounds belonging to the secondary metabolism, which play functional roles in plants [5], and have a potential in preventing diseases and promoting health in animals and humans [6,7]. Among them, acids, tocochromanol, quinones, fatty acids, sugars and polyols, as well as glycoalkaloids vary during ripening, thus modifying the nutritional value and the antioxidant activity of the fruit [8]. Three tomato mutants, *yellow flesh* (*r*), *colorless fruit epidermis* (*y*) and *green flesh* (*gf*), which are representative of the main classes of color pigments, were chosen to be studied at metabolite level, with the aim of assessing the impact of these mutations on the fruit metabolome with emphasis on compounds that co-participate in defining fruit qualities.

Yellow-fruited tomatoes have been documented since the first introduction in Europe [4,9]. The yellow color is due to the *r* mutation, represented by loss-of-function alleles of phytoene synthase 1 (*Psy1*; *Psy1* catalyzes the first rate-limiting step in the carotenoid pathway, the condensation of two molecules of geranylgeranyl diphosphate in phytoene [10]), which results in the inhibition of the whole carotenoid biosynthesis [11]. Recently, *r* tomatoes have been meeting with an increasing success, for the color novelty and the peculiar organoleptic qualities [12].

The *y* fruit mutant was originally described as a monogenic recessive variant leading to the formation of a colorless fruit peel [13]. The mutation, mapped on the short arm of Chr1 [14], involves the *SIMYB12* transcription factor, causing the lack of naringenin chalcone, one of the major flavonoids in tomato fruit peel, which gives the yellow color and has been proposed to influence the characteristics and function of the cuticular layer [15,16]. The pink *y*-type fruit mutation has been identified in numerous cultivated varieties that are highly consumed in Asian countries.

Fruits of *gf* tomato mutant were described for their characteristic muddy brown color, resulting from the accumulation of lycopene coupled with the heterochronic presence of chlorophyll in the ripen fruit due to a lack of chlorophyll degradation [17]. In *S. lycopersicum*, the *Gf* locus maps on the long arm of Chr8. Further studies indicated that *gf* is a member of the SGR gene family, *SISGR1*, a Mg-dechecletase gene needed for chlorophyll catabolism [18,19]. Nowadays, many cultivated tomatoes, heirloom varieties but also modern hybrids, exhibiting the *gf* phenotype are commercially available; indeed, these varieties are appreciated and have been reported to be superior for taste-related compounds [20,21].

San Marzano (from now on referred to as SM) is a traditional variety grown in the region of Naples, Italy; it is considered an important model for fruit quality parameters, because of its intense and uniform red color, which revealed peculiar sensory profiles. Indeed SM fruits are covered by the leaves, which promotes the lycopene accumulation, but at the same time reduces the sugar content, giving birth to its typical bittersweet flavor; this is also mainly due to the ratio between the sugar content, where fructose and galactose are the main carbohydrates, and organic acids, mostly citric acid, a major factor in determining sourness, but also flavor intensity [22–24]. The effect of San Marzano extracts has also been the object of studies aimed at assessing nutraceutical properties [25].

The *r*, *y* and *gf* mutations, together with other variants affecting the tomato fruit phenotype, have been introgressed by backcrossing into the common genetic background SM; the single mutant lines have also been used to express two variants in double combinations [26].

In this work, we analyzed the effect of introducing color mutations in the metabolite complement of the SM fruit. Namely we compared a comprehensive set of fruit volatile compounds (VOCs), including those involved in flavor, and both non-polar (NP) and polar (P) specialized metabolites, including those involved in health properties, in the *r*, *y* and *gf* single mutant lines and their respective double mutants, to the ripe fruit of the SM genetic background. The double mutants expanded the effect on metabolism of the introduced single mutations by revealing additional additive or epistatic effects that could be further exploited for the improvement and diversification of the SM landrace by introducing innovation, while maintaining the characteristic SM typology.
## 2. Materials and Methods

### 2.1. Plant Material and Growth Conditions

Three single mutant lines harboring *r*, *y* and *gf* and the double mutants representing all the possible combinations in the SM background were studied in comparison with the corresponding wild-type (Table 1). Details on the backcross scheme used to obtain these introgression lines, as well as the growth conditions used were reported before [26]. Briefly, eight plants per accession were transplanted and cultivated in twin rows in an unheated tunnel following standard cultural practices for indeterminate tomatoes, using tutors and weekly removal of lateral shoots. Daily temperature was controlled by a ventilation system and plants were irrigated through a drop system. The trial was repeated with identical materials and methods for two consecutive years (2017 and 2018).

<table><caption>**Table 1.** Extended names, genetic symbols and description of the berry color of the seven studied genotypes, including San Marzano (SM), three single mutant and their respective double mutants.</caption><thead><tr><th>Class of Material</th><th>Class of Variation</th><th>Name</th><th>Genetic Symbol</th><th>Fruit Color</th></tr></thead><tbody><tr><td>Wild-type</td><td>-</td><td>San Marzano</td><td>SM</td><td>Red</td></tr><tr><td rowspan="7">San Marzano fruit variants</td><td>Chlorophyll</td><td>green flesh</td><td>gf</td><td>Muddy brown</td></tr><tr><td>Carotenoids</td><td>yellow flesh</td><td>r</td><td>Yellow</td></tr><tr><td>Flavonoids</td><td>colorless fruit epidermis</td><td>y</td><td>Pink</td></tr><tr><td rowspan="2">Double mutants</td><td>yellow flesh + green flesh</td><td>r_gf</td><td>Light yellow</td></tr><tr><td>colorless fruit epidermis + green flesh</td><td>y_gf</td><td>Wine-coloured</td></tr><tr><td>-</td><td>colorless fruit epidermis + yellow flesh</td><td>y_r</td><td>Green</td></tr></tbody></table>

### 2.2. Fruit Sampling

Before sampling, berries were visually inspected and only intact and healthy tomatoes were collected. Two biological replicates for genotype, each represented by four fully ripe berries, were harvested over a period of three days, during the first week of August, for each year. A longitudinal pericarp wedge was excised from each of the four appropriately washed berries and cut into cubes; each replica, consisting of about 30 g of fresh material, was immediately frozen in liquid nitrogen and homogenized until a fine powder was obtained. Aliquots of about 10 g of this material were freeze-dried for the analysis of non-volatile secondary metabolites. All samples, both frozen and freeze-dried, were stored at -80 °C until analysis.

### 2.3. Volatile Detection and Quantification

For volatile analysis, two biological replicates and two technical replicates were processed and analyzed independently for the two year experiments. Prior to the analysis, frozen fruit powder (0.5 g fresh weight) from each sample was weighed in a 15 mL vial, closed and incubated at 37 °C for 10 min. Then, 1.1 g of CaCl₂·2H₂O and 500 µL of EDTA 100 mM (pH 7.5) were added; samples were then gently shaken and sonicated for 5 min. Then, 1 mL of the homogenized mixture was transferred into a 10 mL screw cap headspace vial, where volatiles were collected by head space solid-phase microextraction as previously described [27]. A 65 µm PDMS/DVB SPME fiber (Supelco Analytical, Bellefonte, PA, USA) was used for all the analysis. Pre-incubation and extraction were performed at 50 °C for 10 and 20 min respectively. Desorption was performed for 1 min at 250 °C in splitless mode. Volatile extraction and injection were performed by means of a CombiPAL autosampler (CTC Analytics AG, Zwingen, Switzerland)). Separation and detection were performed by a 6890N gas chromatograph coupled to a 5975B mass spectrometer (Agilent Technologies Inc., Waldbronn, Germany) with DB-5ms fused silica capillary column (60 m, 0.25 mm, 1 µm) (J&W Scientific, Agilent Technologies Inc., Santa Clara, CA, USA). Oven temperature conditions were 40 °C for 2 min, 5 °C/min ramp until 250 °C and then held isothermally at 250 °C for 5 min. Helium was used as carrier gas
at 1.2 mL/min constant flow. Ionization was performed by electron impact (ionization energy, 70 eV; source temperature 230 °C). Data acquisition was performed in scan mode (mass range *m/z* 35–250; 6.2 scans per second). Chromatograms and spectra were recorded and processed using the Enhanced ChemStation software (Agilent). Untargeted analysis of all the compounds in the chromatogram was performed by means of the MetAlign 3 software [28]. Compounds were unequivocally identified by comparison of both mass spectra and retention time to those of pure standards (SIGMA-Aldrich, St. Louis, MO, USA). For quantification, peak areas of selected specific ions were integrated for each compound and normalized by comparison with the peak area of the same compound in a reference sample injected regularly, in order to correct for variations in detector sensitivity and fiber aging. This reference sample consisted of a homogeneous mixture of all the samples analyzed. Data for each sample were expressed as the relative content of each metabolite compared to those in the SM reference.

## 2.4. Non-Volatile Detection and Quantification

For both non-volatile P and NP metabolites, two biological replicates and two technical replicates, for two years, were processed and analyzed independently. Prior to analysis, 10 mg of freeze-dried fruit powder from each sample were weighed and extracted (i) with 0.75 mL cold 75% (*v/v*) methanol with for 0.5 mg/L formononetin as internal standard (IS) for P metabolites, as previously described [29]; and with (ii) 0.25 mL cold 100% (*v/v*), 1 mL of chloroform spiked with 25 mg/L α-tocopherol acetate as internal standard and 0.25 mL 50 mM Tris buffer (pH 7.5, containing 1 M NaCl) as described in [30]. Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) conditions were as previously reported for, respectively, polar [31] and non-polar [32] metabolomes.

Metabolite identification was performed by comparing chromatographic and spectral/MS properties with authentic standards, if available, and reference spectra, and based on the *m/z* accurate masses as found in the Pubchem database for monoisotopic masses, or in the Metabolomics Fiehn Lab Mass Spectrometry Adduct Calculator for adduct ions. Quantification of each metabolite was carried by calculating the relative contents to the formononetin (P) and α-tocopherol acetate (NP) IS levels.

## 2.5. Statistical and Bioinformatics Analyses

Raw data were firstly inspected and manually curated for the presence of outliers (e.g., when % st.dev./avg exceed 30%). For principal component analysis (PCA), the complete dataset after log2 transformation and including all replicates was considered. Untargeted analysis of VOC, and NP and P metabolomes was carried out as previously reported using, respectively, MetAlign and the SIEVE software (v2.2, ThermoFisher Scientific, Waltham, MA, USA; [33]).

As untargeted analysis revealed a consistent year effect, the “Gen*Year” interaction was investigated by two-way multivariate analysis of variance (MANOVA) on those metabolites than presented less than 30% missing values. The analysis was performed with the PROC GLM procedure and the MANOVA statement implemented in the SAS software package (v9.4M6, SAS® University Edition, SAS Institute Inc., North Carolina State, USA). Since “Gen*Year” interaction was found to be the less consistent source of variation, allowance was made for the existing interaction, data were mediated over the two years and all genotypes were presented with a single mean value. PCA was performed with SIMCA-P version 11 (Umetrics, Umea, Sweden) with Unit Variance normalization. The differences between each line and the SM reference were assessed using Student's *t*-test at the 5% significance level (*p* < 0.05). Graphs were elaborated with Excel (Microsoft Office 2013, Microsoft Corporation, Washington, DC, USA).

Venn diagrams were generated using Venny software (https://bioinfogp.cnb.csic.es/tools/venny/index.html, v2.1). Correlation networks were generated using average values over the two years under study, as previously described [34,35]. To better evaluate most robust metabolite-metabolite associations (e.g., significant correlations), the MCODE Cytoscape plugin was used [36].
3. Results

3.1. Untargeted Analysis of Volatile, Non-Polar and Polar Metabolites

Untargeted metabolomics aims to gather information on global metabolic profiles by retrieving,
in an unsupervised way, as many metabolites are detectable in a GC-MS/LC-HRMS chromatograms.
The comparison of the entire VOC, NP and P metabolome between the SM control and the six mutated
lines detected the total features of their metabolic profile differences, setting the stage for a more
specific targeted metabolomics study later in this paper. By using this approach, 263 VOCs, 746 NP and
110 P compounds were identified in the samples, many of which were differentially accumulated in, at
least, one pairwise comparison (Table S1). For VOCs, the first two principal components explained
over 51% of total variance; PC1 separated *gf* and *y* from the other lines, while PC2 kept *r* plus all
the combinations harboring the *y* mutation distinct from the others (Figure 1a). In Figure 1b, PCA
of the first two components for the NP compounds explained almost 53% of the total variance; SM
and two *green flesh* genotypes were clearly separated from *y* mutants plus *r* by the PC1, and *r* mutants
were grouped in the lower quadrants by the PC2. For P metabolites, the first two PCA components
explained the over 55% of total variance (Figure 1c); PC1 kept more clearly SM and the lines carrying *r*
separated from *y* mutants, and *y_r* was positioned exactly halfway between its parental lines. PC2
separated lines carrying *yellow flesh*, plus *y* from SM, which was in the upper side of the graph together
with *gf* and *y_gf*. Overall, the untargeted metabolomic analysis revealed that the mutations mostly
affected NP rather than VOC and P metabolome, with the *r* mutant showing the highest extent of
changes, including 112 NP compounds (106 down- and 6 up-regulated; Table S1). In addition, *r* also
displayed the highest epistatic attitude towards *y* (in the VOC and NP fractions) and *gf* (P metabolome),
while, notably, *y* was epistatic to *gf* in the NP untargeted metabolome.

<figure><img src="image_45.png" /><figcaption>**Figure 1.** PC1 × PC2 score plots of the six mutated lines plus San Marzano (SM) according to relative values of 263 VOCs (a), 746 NP (b) and 110 P (c) metabolites measured by GC-LS and LC-HRMS. Line symbols are explained in Table 1.</figcaption></figure>

3.2. Estimation of "Gen*Year" Interaction in the Quantification of Targeted Metabolites

Analysis of untargeted metabolites revealed the presence of a consistent Year effect (Figure S1). Seventy-eight VOCs, 33 NP and 69 P metabolites were independently subjected to multivariate ANOVA; the interaction was not significant for VOCs, but it was highly significant for NP and significant for P compounds (Table S2). "Gen*Year" interaction was found to be the least consistent source of variation; therefore, data were mediated over the two years and all genotypes were presented with a single mean value in targeted analyses.
### 3.3. Targeted Analysis of Volatile Compounds

In order to give a more specific characterization of the flavor, volatile composition of each of the mutated lines in comparison with the wild-type SM was carried out. The selected analytical strategy allowed the relative quantification of 68 VOCs unequivocally identified by both mass spectra and retention index with those of authentic standards (Table S3). Overall, eight compounds were related to benzenoids (B), ten to branched-chain amino acid-relatives (BCAA), nine to apocarotenoids (C), two to esters (E), twenty-four to fatty acids derivatives (L), four to phenylalanine derivatives (Phe), two to sulfur compounds (S) and six to monoterpenoids (T).

PCA of the volatile composition revealed that the first two components explained about the 54% of the total variance; the score plot showed the position of the double mutants, related to their parental lines, and with respect to SM (Figure 2a). Indeed, *r* and *y_r* were co-located in the same dial, in agreement with the VOC untargeted metabolome plot; conversely, *r_gf* placed halfway between *r* and *gf* according to PC1 (Figure 2a). Moreover, PC1 kept the mutants *r* and *y_r* separated from the other lines. PC2 placed *y_r* and its parental lines in the upper side of the graph, together with SM. The corresponding loading plot was able to identify groups of metabolites, often belonging to the same metabolic pathways, as apocarotenoids (C, in red) and lipids (L, in light blue), terpenoids (T, in green blue) and branched-chain amino acid derivatives (BCAA, in blue; Figure 2b). A comparison between the score and the loading plots revealed the overall compositional differences between the mutated lines.

<figure><img src="image_46.png" /><figcaption>**Figure 2.** PCA of log2 values of 68 volatile compounds measured by a solid-phase micro-extraction gas-chromatography coupled to mass spectrometry (HS-SPME/GC-MS). (a) PC1 × PC2 score plot of the six mutated lines plus San Marzano (SM). (b) PC1 × PC2 loading plot. Line (a) and metabolite class (b) abbreviations are explained in Tables 1 and 2 respectively.</figcaption></figure>

Indeed, one of the most obvious features was that all the lines harboring the *yellow flesh* mutation (*r*, *y_r* and *r_gf*) and, in a lesser extent, also the double mutant *y_gf*, were characterized by producing lower levels of volatile apocarotenoids, which was particularly dramatic in the case of some linear apocarotenoids such as 6-methyl-5-hepten-2-one (Figure 3). In the case of the mutants for *yellow flesh* this was in accordance with the scarcity of their carotenoid precursors. These lines also showed lower levels of several phenylalanine derivatives. A characteristic feature of the lines *y*, *r* and *y_r* was the high production of fatty acid derivatives together with low levels of branched-chain amino acid-related volatiles, conversely to double mutant lines *y_gf* and *r_gf*, which showed the opposite pattern. Finally, *y* and *gf* lines were characterized by higher levels of apocarotenoids and terpenoids (Figure 2b).
<table><caption>**Table 2.** Number of compounds in the different categories and classes of metabolites that are significantly different from San Marzano (SM) in each of the six lines under study.</caption><thead><tr><th rowspan="2">Metabolomics<br>Fraction</th><th rowspan="2">Metabolic Class</th><th rowspan="2">Abbreviation</th><th rowspan="2">No. of<br>Compounds</th><th colspan="6">No. of Differentially Accumulated Compounds Over SM</th></tr><tr><th>gf</th><th>r</th><th>y</th><th>r_gf</th><th>y_gf</th><th>y_r</th></tr></thead><tbody><tr><td rowspan="6">Volatile</td><td>Benzenoids</td><td>B</td><td>8</td><td>2</td><td>2</td><td>3</td><td>2</td><td>3</td></tr><tr><td>Branched-chain amino<br>acid derivatives</td><td>BCAA</td><td>10</td><td>3</td><td>3</td><td>2</td><td>4</td><td>1</td></tr><tr><td>Apocarotenoids</td><td>C</td><td>9</td><td>3</td><td>5</td><td>3</td><td>6</td><td>3</td></tr><tr><td>Esters</td><td>E</td><td>2</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Fatty acid derivatives</td><td>L</td><td>24</td><td>3</td><td>4</td><td>6</td><td>1</td><td>4</td></tr><tr><td>Others</td><td>Phe, S, T,<br>No ID*</td><td>15</td><td>6</td><td>4</td><td>8</td><td>4</td><td>6</td></tr><tr><td colspan="2">Total</td><td>VOCs</td><td>68</td><td>17</td><td>18</td><td>22</td><td>17</td><td>3</td><td>19</td></tr><tr><td rowspan="7">Non-polar</td><td>Carotenoids</td><td>CAR</td><td>15</td><td>2</td><td>5</td><td>3</td><td>7</td><td>5</td><td>7</td></tr><tr><td>Chlorophylls</td><td>CHL</td><td>8</td><td>2</td><td>1</td><td></td><td>2</td><td>3</td><td></td></tr><tr><td>Fatty acids</td><td>FA</td><td>14</td><td>1</td><td></td><td></td><td></td><td>1</td><td></td></tr><tr><td>Phospholipids</td><td>PHO</td><td>1</td><td></td><td></td><td>1</td><td>1</td><td></td><td></td></tr><tr><td>Quinones</td><td>QUI</td><td>6</td><td>1</td><td>2</td><td></td><td>2</td><td></td><td>3</td></tr><tr><td>Tocopherols</td><td>TOC</td><td>5</td><td>1</td><td>1</td><td>1</td><td></td><td>1</td><td>2</td></tr><tr><td>Others</td><td>STE,<br>No ID*</td><td>5</td><td>1</td><td>1</td><td></td><td>1</td><td></td><td></td></tr><tr><td colspan="2">Total</td><td>NP</td><td>54</td><td>8</td><td>10</td><td>5</td><td>13</td><td>10</td><td>12</td></tr><tr><td rowspan="8">Polar</td><td>Amino acids</td><td>AA</td><td>19</td><td>2</td><td>6</td><td>3</td><td>4</td><td>3</td><td>6</td></tr><tr><td>Acids</td><td>AC</td><td>17</td><td>5</td><td>5</td><td>6</td><td>5</td><td>4</td><td>4</td></tr><tr><td>Amines</td><td>AM</td><td>4</td><td></td><td></td><td>1</td><td>1</td><td></td><td>1</td></tr><tr><td>Sugars and polyols</td><td>SAP</td><td>15</td><td>3</td><td>3</td><td>3</td><td>2</td><td>4</td><td>2</td></tr><tr><td>Alkaloids</td><td>ALK</td><td>11</td><td>1</td><td>4</td><td>2</td><td>3</td><td>1</td><td>2</td></tr><tr><td>Phenylpropanoids</td><td>PHE</td><td>55</td><td>6</td><td>13</td><td>24</td><td>18</td><td>18</td><td>16</td></tr><tr><td>Vitamins</td><td>VIT</td><td>3</td><td>1</td><td>2</td><td></td><td></td><td></td><td>1</td></tr><tr><td>Others</td><td>A, NU, LI,<br>No ID*</td><td>5</td><td></td><td></td><td>1</td><td></td><td>2</td><td>1</td></tr><tr><td colspan="2">Total</td><td>P</td><td>129</td><td>18</td><td>33</td><td>40</td><td>33</td><td>32</td><td>33</td></tr><tr><td colspan="2">Gran total</td><td></td><td>251</td><td>43</td><td>61</td><td>67</td><td>63</td><td>45</td><td>64</td></tr></tbody></table>

To further investigate the volatile compounds content of each of the mutated line in comparison with their original parental SM, a *t*-test analysis was performed. Out of 68 VOCs identified, the line with the highest number of compounds significantly different from SM was *y*, mainly because of differences in fatty acid derivatives, among other VOCs (Table 2). The lines containing the *r* mutation strongly differed for apocarotenoid volatiles, a group of metabolites considered to be involved in tomato flavor [37], representing the metabolic pathways most notably altered in these ILs (Figure 3). Some volatiles, such as the benzenoid eugenol, had higher levels in all mutant lines (Figure 3a). Mutants carrying *yellow flesh* had lower levels of the apocarotenoid β-ionone (Figure 3b), as well as 6-methyl-5-hepten-2-one, where *y_gf* acquired lower levels too (Figure 3c). Furthermore, *r*, *y* and their double mutant had higher levels of many fatty acid derivatives, such as (E)-2-pentenal (Figure 3d) and 1-penten-3-one (Figure 3e). Lastly, *y* was enriched in phenylalanine derivatives, such as 2-phenylethanol (Figure 3f).

<footnote>* Undefined compounds.</footnote>
<figure><img src="image_47.png" /><figcaption>**Figure 3.** Relative log2 variation in selected volatiles involved in tomato flavor: (a) eugenol, (b) β-ionone, (c) 6-methyl-5-hepten-2-one, (d) (E)-2-pentenal, (e) 1-penten-3-one, (f) 2-phenylethanol in the six fruit mutant lines in San Marzano (SM) background compared with the recurrent parent. Line symbols are reported in Table 1. Bars colored in grey and black indicate means significantly lower and higher than SM for *p* ≤ 0.05 after Student's t-test, respectively.</figcaption></figure>

### 3.4. Targeted Analysis of Non-Polar Metabolites

To investigate changes at the NP specialized metabolome, LC-HRMS was used to determine the level of 54 known and previously validated compounds. They were divided in different metabolic classes, including 14 fatty acids (FA), one phospholipid (PHO), two sterols (STE), 15 carotenoids (CAR), eight chlorophylls (CHL), six quinones (QUI), and five tocochromanols (TOC; Table S4). The score plot of the first two PCA components explained about the 57% of the total variance, with double mutants differently spaced from their parental lines (Figure 4a). Indeed, PC1 kept the mutants carrying *r* separated from SM and the other lines. PC2 clearly separated *y_r* and its parental lines from the three mutants carrying *gf* plus SM, partially confirming untargeted metabolomics results. The loading plot grouped metabolites belonging to the same metabolic pathways (Figure 4b). A comparison between the score and the loading plots revealed the compositional differences between the mutated lines. Indeed, the chlorophyll group in the upper side of PC2 characterized the *green flesh* genotypes, while two quinones were in the lower side of PC2, in correspondence of the *yellow flesh* genotypes. The carotenoid group was split into early carotenoids (up to lycopene) and xanthophylls at the opposite sides of PC1; they were respectively decreased and increased in the *yellow flesh* mutant lines (Figure 4b).
<figure><img src="image_48.png" /><figcaption>**Figure 4.** PCA of log2 values of 54 NP (a,b) and 128 P (c,d) metabolites measured by LC-HRMS. (a–c) PC1 × PC2 score plot of the six mutated lines plus San Marzano (SM). (b–d) PC1 × PC2 corresponding loading plots. Line (a,c) and metabolite class (b,d) abbreviations are explained in Tables 1 and 2 respectively.</figcaption></figure>

To further investigate the metabolic changes of each mutated line in comparison with the original parental SM, a t-test analysis was performed (Table 2; Table S4), and we particularly focused on metabolites with sensorial (color, taste) and health-related properties. The lines carrying *r* showed the highest number of NP compounds different from SM, mainly due, as expected, to differences in carotenoids (Table 2). The lower number of differences was shown by the single mutant *y*, indicating that this genotype is more similar to SM for NP targeted compounds.

*r*, *r_gf* and *y_r* were characterized by levels of phytoene (Figure 5a), β-carotene (Figure 5b) and lycopene (Figure 5c) lower than SM and the other lines, in agreement with previous reports; *r*, *r_gf*, *y_gf*, and *y_r* reported higher levels of the xanthophylls all-trans-neoxanthin (Figure 5d) and luteoxanthin (Table S4). At chlorophyll metabolism level, *gf* was characterized by higher contents of both chlorophyll a (Table S4) and b, with the latter also higher in *y_gf* and *r_gf* (Figure 5e). Moreover, *r_gf* showed higher levels of pheophytin a and pheophorbide a (Table S4). Drawing the attention on quinones, plastoquinone increased in lines carrying *r* (Figure 5f) and plastoquinol-9 in *r* and *y_r* (Table S4). Lastly, δ-tocopherol amount was higher than SM only in *r_gf*, while γ-tocopherol and β-tocopherol enhanced in *r_gf* and *y_gf* (Figure 5g,h).
<figure><img src="image_49.png" /><figcaption>**Figure 5.** Relative log2 variation in (a) phytoene, (b) β-carotene, (c) lycopene, (d) all-trans-neoxanthin, (e) chlorophyll b, (f) plastoquinone, (g) δ-tocopherol, (h) β-/γ-tocopherol, of six fruit mutant lines in San Marzano (SM) background compared with the recurrent parent. Line symbols are reported in Table 1. Bars colored in grey and black indicate means significantly lower and higher than SM for *p* ≤ 0.05 after Student's *t*-test, respectively.</figcaption></figure>

### 3.5. Targeted Analysis of Polar Metabolites

The relative quantification of 128 polar metabolites allowed to complete the metabolomics characterization of the six mutants under study. P metabolites were divided into different metabolic classes, including 19 amino acids (AA), 17 acids (AC), four amines (AM), two lipids (LI), one nucleic acid (NU), 15 sugars and polyols (SAP), 11 alkaloids (ALK), 55 phenylpropanoids (PHE) and three vitamins (VIT; Table S5). The score plot of the first two components explained about the 52% of the total variance, with PC1 that particularly separated *r* and *r_gf* from all the other lines, and PC2 that identified a group including *gf* and *r_gf* together with SM (Figure 4c). Interestingly, P untargeted and targeted metabolomes differently separated the mutants under study, providing clues about a large extent of distinct metabolic components contributing to their chemical profiles. The loading plot grouped metabolites belonging to the same metabolic pathway (Figure 4d). By the comparison of the score and the loading plots, the position of some metabolites in relation to the lines studied was highlighted; indeed, many kaempferols and quercetins were in the PHE group corresponding to the area of the *y* mutants. On the contrary, many naringenins grouped in the opposite side. Most AAs were grouped together, matching with *r* and in opposition to the SAP group (Figure 4d).

To investigate the P metabolite content of each of the mutated lines in comparison with SM, a *t*-test analysis was performed (Table 2; Table S5), giving emphasis to nutritional- and sensorial attribute-related molecules. The line with the highest number of differentially accumulated polar
compounds was the *y* single mutant, with a preponderance of down-regulated metabolites, as expected; the PHE group mostly contributed to this diversity (Table 2). On the contrary, *gf* was the line more like SM. Notably, lines containing *r* showed a higher number of AA over SM (Table 2).

As already highlighted in the corresponding loading plot (Figure 4d), lines carrying *y* had lower levels of the PHE naringenin chalcone glucoside (Figure 6a), and conversely higher levels of kaempferol-hexose deoxyhexose-pentose compared to SM, a biochemical phenotype also observed in *gf* and *r_gf* too for the latter (Figure 6b). Regarding the ALK group, calystegine B1 resulted statistically lower in all the lines, with exception of *gf* and *y_gf*, which however displayed reduced amounts compared to SM (Figure 6c). In addition, a series of primary metabolites were characterized by higher levels in the mutants under study: for example, the AA proline in *r* and *y_r* (Figure 6d) and the VIT nicotinamide in *gf*, *r* and *y_r* (Figure 6e). Similarly, the SAP glucoheptulose was higher in *gf*, whereas all the other lines were more similar to SM (Figure 6f). The LI phosphocoline displayed lower levels in lines carrying the *y* mutation (Figure 6g), while the AC sinapinic acid was higher in all lines, with the only exception of *r* (Figure 6h).

<figure><img src="image_50.png" /><figcaption>**Figure 6.** Relative log2 variation in (a) naringenin chalcone glucoside, (b) kaempferol-hexose-deoxyhexose-pentose, (c) calystegine B1, (d) proline, (e) nicotinamide, (f) glucoheptulose, (g) phosphocoline, (h) sinapinic acid, of six fruit mutant lines in San Marzano (SM) background compared with the recurrent parent. Line symbols are reported in Table 1. Bars colored in grey and black indicate means significantly lower and higher than SM for *p* ≤ 0.05 after Student's *t*-test, respectively.</figcaption></figure>
3.6. *Bioinformatics to Investigate Metabolite-Metabolite Relationships*

Bioinformatic approaches, including Venn diagram visualization and correlation network analysis, were used in order to achieve a deeper understanding of the biochemical perturbations and relationships occurring in the SM mutants under study. Venn diagrams showed the degree of overlap for VOC, NP and P metabolites in each double mutant and its two parental single mutants. For line-specific metabolites in each group, all lines, except *y_gf* and *y_r*, showed a higher number of significantly up-regulated metabolites (Figure 7). When the overlaps between single mutants and the respective double combinations were considered, the epistasis of *r* over *gf* (29 metabolites were in common between *r* and *r_gf*) and *y* (28 metabolites in common between *r* and *y_r*) and of *y* over *gf* (21 metabolites in common between *y* and *y_gf*) were found (Figure 7).

<figure><img src="image_51.png" /><figcaption>**Figure 7.** Venn diagrams for (a) *gf*, *r* and *r_gf*; (b) *gf*, *y* and *y_gf*; (c) *r*, *y* and *y_r*. Red and black numbers correspond to up- and down-regulated metabolites, respectively.</figcaption></figure>

Furthermore, we used a correlation network approach to investigate mutation-induced alteration
at VOC, NP and P metabolome levels. To this purpose, three networks were built by integrating
all differentially accumulated metabolites in, at least, each single mutant and the corresponding
double mutants (Figure 8). Overall, the three force-directed networks allowed the achievement of
specific topologies according to the distribution of the significant correlation networks existing in each
metabolite-metabolite interaction; notably, a high extent of conservation was observed in each network,
either in the direct (PHE in the *y*- and CAR in the *r*-yielding networks) or not direct (L volatiles in *r*,
*y* and *r_y*; and AA in *r*, *gf* and *r_gf*) targets of the mutations. In order to evidence the more robust
and strongest correlations, the MCODE Cytoscape plugin was applied to each of the three networks
(Figures S2–S4). In this way, it was possible to identify the highly interconnected regions, which
resulted particularly abundant in the *r/gf* mutants, with a lower number of greatly dense clusters
including primary (SAP, AA) and secondary (isoprenoids as CAR, CHL, QUI, T and PHE) compounds;
on the contrary, the *y/r* mutants were characterized by a higher number of clusters with lower density,
and with a high representation of amino and organic acids, besides CAR and PHE.
<figure><img src="image_52.png" /><figcaption>**Figure 8.** Correlation networks of metabolomics data in (a) *y*, *gf* and *y*-*gf*; (b) *y*, *r* and *y*-*r*; (c) *r*, *gf* and *r*-*gf* mutants. All the data from volatile (VOC), non-polar (NP) and polar (P) analyses were used as fold change to the San Marzano (SM) reference. Network topology was directed by the force of Pearson correlation coefficient index (Table S6). Each node represents VOC (turquoise circle), a NP (diamond) or a P (triangle) metabolite. Lines joining the nodes represent positive (red) or negative (blue) correlations. Node sizes are proportional to the respective node strengths, which are shown in Tables S7–S9. Node color is depending on the metabolic class of each compound as indicated in the figure. Number of nodes (n) and network strength (NS) are shown at the top of each network. Only correlations with |*ρ*| > 0.95 are shown (*p* ≤ 0.05).</figcaption></figure>

**4. Discussion**

This study focused on the analysis of the volatile and non-volatile compounds of six tomato lines
introgressing mutations that have been selected as representative for affecting the main classes of
fruit pigments. Interest towards these mutations is proved by the increasing introduction of cultivars
with yellow, pink and brown fruits in the fresh tomato market and by scientific studies addressing
the properties of single [38,39] and multiple [40] mutants. The characterization of the biochemical
effects of these mutations was based on the comparison with the SM original parent, with the final aim
of defining whether the lines showing aesthetic novelty were also diversified for organoleptic and nutritional characteristics.

When the studied compounds were considered all together, *y* showed the highest level of variation. Venn diagrams indicated epistasis of *r* over both *gf* and *y*, as highlighted by the univariate analysis. Interestingly, when integrated and subjected to a correlation network analysis, differentially accumulated metabolites in any of the six single and double mutant ILs exhibited a strong level of coordination: indeed, irrespectively to the metabolic class object of the mutation, metabolites acting in the same pathway clustered together, thus indicating a great conservative capacity of the fruit metabolism in its mutation-derived reorganization. This finding is consistent with previous reports showing, either in tomato and grape, a general phenotypic-metabolic plasticity in response to genetic or environmental changes [24,30,41]. However, looking at the sub-clusters generated by each network, distinct and specific relationships were unraveled, with the mutants carrying the *y* and *r* mutations involving the largest number of metabolites belonging to highly diversified pathways.

### 4.1. *Biochemical Changes in Fruits of Yellow Flesh and of Its Combinations with y and gf*

Mutants containing *r* were clearly separated from the other lines by NP targeted metabolites, mainly due to the compositional differences of carotenoids. The double mutant *r_gf* mapped in a distinct position compared with *r* and *y_r*, indicating partially additive and partially synergistic effects of this combination. The primary effect of the *r* mutation in the carotenoid pathway was to strongly reduce phytoene, and the colored carotenoids β-carotene and lycopene, as expected [42]. Phytofluene was also reduced in *r* genotypes, as previously described in tomato accessions [43] and in ripening fruits of *Psy1* knockout lines [44]. In the latter case, a concomitant decrease in the volatile apocarotenoids was also observed [44]. In literature, the study of *r* metabolites was often limited to the analysis of carotenoids, including the xanthophyll lutein, whose levels, in line with our results, did not vary [11]. Intriguingly, we evidenced that all-trans-neoxanthin, the last xanthophylls in the carotenoid pathway, in *r* mutants showed levels higher than SM; it is possible that this represents a cellular strategy implemented to compensate for the decrease of other carotenoids. Indeed, xanthophylls act in flowing the energy through the photosynthetic apparatus and protecting organisms against damage caused by photosynthesis itself [45]. In addition, both violaxanthin and neoxanthin are key substrates in ABA biosynthesis, and their variation can influence fruit ripening and the attitude towards abiotic stresses [46]. In this context, enhanced levels of xanthophylls could provide enough metabolic flux to guarantee an adequate ABA production. The same hypothesis can be extended to the analysis of quinones, such as plastoquinone and plastoquinol-9, whose levels increased in *yellow flesh* mutants. The importance of quinones in basic metabolic processes, such as respiration and photosynthesis, has been established [47] and an increase in their content is plausible, in a scenario where it is necessary to compensate for the lack of carotenoids.

Important metabolic changes occurred in the *r* mutants for P compounds; several amino acids positively varied, as proline in the single mutant and in combination with *y*, and valine/norvaline in the single mutant or combined with *gf*. Proline and valine have significant functions in plant cells as plant stress sensor, ABA and polyamines interactors, and precursors of BCAA-derived volatiles [48,49]. Similarly, the vitamin nicotinamide, that plays a primary role in pyridine metabolism [50], also increased in *r*, alone or with *y*.

Carotenoid-derived volatiles have an important role in tomato flavor, as their levels positively correlate with tomato acceptability [51]. Loss-of-function of *Psy1* in *yellow flesh* mutants led to the lack of substrates for apocarotenoid production [52], thus justifying the low amount of C VOCs such as β-ionone [53] and 6-methyl-5-hepten-2-one [54]. Conversely, the L 1-penten-3-one and (E)-2-pentenal increased in *r* and *r_y*, suggesting a role for the lipoxygenase-linoleate (LOX) enzyme that catalyzes the oxidation of polyunsaturated fatty acids by molecular oxygen with the formation of unstable hydroperoxides which in turn oxidize carotenoid pigments [55].
In summary, compensating the depletion of the principal carotenoids, lines carrying the *r* mutation offer peculiar nutraceutical properties, for an increased content of amino acids, vitamins, xanthophylls and quinones. Xanthophylls have been highlighted in recent studies for their positive contribution towards total dietary carotenoid intake [56], whereas quinones showed positive properties in treating cardiovascular diseases, chronic gingivitis and periodontitis and a favorable impact on cancer treatment and human reproductive health [57]. Differently, tomato *r* lines will probably show a lower score for the aroma, considering that some of the apocarotenoids providing floral or fruity notes are less represented in yellow fruits.

## 4.2. Biochemical Changes in Fruits of Colorless Fruit Epidermis and of Its Combinations with *r* and *gf*

Multivariate analysis of targeted P compounds tightly grouped lines carrying the *y* mutation, an expected result as *y* is characterized by substantial modifications of the class of phenylpropanoids, mainly due to the lack of yellow pigment naringenin chalcone [15]. As also observed in the case of the NP fraction, untargeted and targeted metabolomes did not perfectly matched, suggesting the existence of larger, still unexplored, metabolic changes. The decrease or absence of naringenins also characterized *y* and its two double mutants, leading to the conclusion that *y* is epistatic on *gf* and *r* for this class of compounds. Compared to red SM fruits, kaempferol-hexose-deoxyhexose-pentose was higher in *y* mutants, even if many other kaempferols showed no differences. Similarly, several quercetins showed higher levels in lines carrying *y*. Variations in the PHE pathways could explain the reduced phosphocholine levels detected in the three *y* mutants, since increased activity of pathways generating cuticular lipids in tomato fruit peel precedes that of phenylpropanoid and flavonoid biosynthesis pathways [58].

No substantial differences in carotenoids were found between *y* and SM, except the higher all-trans-neoxanthin levels in *y_gf* and *y_r*. Conversely, tocochromanol metabolism was altered in *y* mutant berries as there was a decrease in tocopherols, mainly affecting *y* and *y_r*, confirming previous reports [15,59].

Among VOCs, differences were found in compounds derived from phenylalanine, such as eugenol and 2-phenylethanol, in line with the known alterations of *y* in the phenylpropanoid branch of phenylalanine catabolism [54]. Indeed, a significant number of VOCs derived from amino acids, are considered to have an influence on tomato flavor and liking, such as guaiacol and eugenol. In addition, 2-phenylethanol is a main contributor to tomato flavor, increasing floral aroma and the perception of sweetness [60,61], it is plausible that increase of these class of volatiles exerts a positive effect on the aroma in lines carrying the *y* mutation. In addition, *y* mutants were characterized by higher production of many fatty acid derivatives, which are the most abundant volatiles produced in the tomato fruit [62]. These VOCs include several C₅ compounds such as 1-penten-3-one or (E)-2-pentenal, and C₆ volatiles such as 1-hexanol, (Z)-3-hexenal, (E)-2-hexenal, or hexanal, among others, that are classified as "green leaf" volatiles due to their 'green' characteristic, with a fresh aroma of cut grass. Although some studies suggested a reduced impact on tomato flavor and no effect on consumer preference of these compounds [63,64], others claim their potential impact on overall flavor intensity and liking [37].

In addition to variation of the aroma, *y* mutants are endowed of higher kaempferol and quercetin levels, but also present the drawback of lower naringenin and tocopherols. Since all tocochromanols are potent lipid-soluble antioxidants and are essential dietary nutrients for mammals as vitamin E [65], this aspect represents a negative trait in *y* tomato lines.

Therefore, the *y* mutation is to be considered as a source of more aesthetic (color) and organoleptic (aroma) than nutritional novelty. Although no clear relationship between colorless epidermis and fruit shelf-life, peculiar mechanical properties of the *y* epicarp were manifested by the fact that the peel of the mutant fruit was richer of lignin [15,66]. Intriguingly, the SM lines carrying *y*, alone or in combination, showed a higher resistance to storage, indicating that pigment variation in the peel implicates different mechanical properties and post-harvest behavior of the fruit [26].
### 4.3. Biochemical Changes in Fruits of Green Flesh and of Its Combinations with r and y

The group of *green flesh* mutants showed no changes in carotenoids, but as expected, it was primarily affected in the content of chlorophylls. *gf* belongs to the so-called cosmetic-type SGR mutants, that have been described in several plant species to retain substantial amount of chlorophylls in fruits during ripening, maintaining other ripening-related metabolites, such as lycopene, unchanged [67]. Accordingly, all the SM lines containing *gf* showed fruits with higher amounts of chlorophylls. Among other NP metabolites, several tocopherols increased in *gf* single and double mutants, confirming previous data that showed higher tocopherol levels in *gf* [68,69] and *r_gf* (A. Mazzucato and G.P. Soressi, unpublished results) mutants.

For P metabolites, *gf* was similar to SM, with few notable exceptions involving flavonoids, such as kaempferols, and sugars, such as glucoheptulose.

No substantial variations were found for *gf* mutants in VOCs except for a high eugenol content that was paralleled by the other lines.

Because of the positive effects of chlorophylls and chlorophyll-related metabolites on cellular inflammation and as anti-mutagen and anti-carcinogen agent [70,71], and of the antioxidant function of vitamin E-producing metabolites, that reduce free-radical damage to membrane lipids by scavenging peroxyl radicals [72,73], tomato lines carrying *gf* may be considered an alternative to red tomatoes offering added nutraceutical value. In addition, *gf* may play important roles in enhancing organoleptic qualities. Although a direct, positive effect of *gf* on soluble solids content has not clearly been established, an SIGLK2-enhanced chlorophyll content in immature green fruits led to an increment in total soluble solid content in ripe fruits, possibly by the positive regulation of sugar metabolism enzyme-encoding genes [69]. This effect will be additive to that of other taste-related compounds, such as glutamate [20]. In our *gf* lines, glutamic acid was not significantly different from SM, but, considering only data in one year, it was substantially higher in all lines. All these properties justify the interest towards breeding new *gf* tomato lines and the present success in the market promises a further development in the near future.

## 5. Conclusions

This work was based on the study of the effects of mutations with an emerging commercial interest, compared within the traditional Italian tomato variety San Marzano. This biochemical and bioinformatics characterization has given further insights on the effect of each mutation on fruit aesthetic, flavor and nutritional composition. The analysis of the respective three double mutants offered an added value, making it possible to establish epistatic or synergistic effects between each pair of mutations and represented a starting point for breeding new tomato lines with different phenotypes. Although *r* and *y* cause the decrement of the most important classes of health-related pigments (carotenoids and flavonoids), the compensating increase of other metabolites with nutraceutical (xanthophylls, tocopherols, amino acids) or flavor-related (phenylalanine and fatty acid derivatives) positive properties make the studied lines worthy of attention for breeding novel and better tomatoes. Novel phenotypes could take advantage of new breeding techniques as genome editing to recapitulate the original mutations in different tomato backgrounds, or in different species, and obtain the wanted variation directly avoiding backcrossing and linkage drag effects. Ultimately, advanced breeding programs will convert the new lines into novel elite varieties of commercial interest.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2218-1989/10/3/110/s1, Figure S1: PC1 × PC2 score plots of the six mutated lines plus San Marzano (SM), sampled in two different years, according to relative values of 263 volatile (a), 746 non-polar (b) and 110 polar (c) metabolites measured by a solid-phase micro-extraction gas-chromatography coupled with a mass spectrometry instrument (HS-SPME-GC-MS), Figure S2: Subnetworks generated from the *y*, *gf* and *y_gf* global network of Figure 8, using the MCODE Cytoscape plugin. Each subnetwork highlights the highest densely connected regions present in the global network. Each node represents a VOC (turquoise circle), non-polar (diamond) or a polar metabolite (triangle). Lines joining the nodes represent correlations; direct and inverse correlations are shown in red and blue, respectively. Node sizes are proportional to the corresponding node strengths (nss), which are shown in
Supplemental Table S7. Node color is depending on the metabolic class of each compound as indicated in the figure. Number of nodes (n) and network strength (NS) are shown on top of each network. Only correlations with $|p| > 0.95$ are shown (*p*-value 0.05), Figure S3: Subnetworks generated from the *y*, *r* and *y*-*r* global network of Figure 8 using the MCODE Cytoscape plugin. Each subnetwork highlights the highest densely connected regions present in the global network. Each node represents a VOC (turquoise circle), non-polar (diamond) or a polar metabolite (triangle). Lines joining the nodes represent correlations; direct and inverse correlations are shown in red and blue, respectively. Node sizes are proportional to the corresponding node strengths (nss), which are shown in Supplemental Table S7. Node color is depending on the metabolic class of each compound as indicated in the figure. Number of nodes (n) and network strength (NS) are shown on top of each network. Only correlations with $|p| > 0.95$ are shown (*p*-value 0.05), Figure S4: Subnetworks generated from the *r*, *gf* and *r*-*gf* global network of Figure 8 using the MCODE Cytoscape plugin. Each subnetwork highlights the highest densely connected regions present in the global network. Each node represents a VOC (turquoise circle), non-polar (diamond) or a polar metabolite (triangle). Lines joining the nodes represent correlations; direct and inverse correlations are shown in red and blue, respectively. Node sizes are proportional to the corresponding node strengths (nss), which are shown in Supplemental Table S7. Node color is depending on the metabolic class of each compound as indicated in the figure. Number of nodes (n) and network strength (NS) are shown on top of each network. Only correlations with $|p| > 0.95$ are shown (*p*-value 0.05), Table S1: Number of volatile (VOC), polar (P) and non-polar (NP) untargeted metabolites significantly different from Sam Marzano (SM) in the six introgression lines. Line symbols are explained in Table 1, Table S2: Fisher's F test (F) and relative degree of significance (P) after two-way multivariate analysis of variance (MANOVA) for three main categories of metabolites analyzed, Table S3: Mean (M) and standard deviation (SD) of volatile compounds in two years for the fold change of individual values over the San Marzano value and significance of Student's t test between each mutant line and San Marzano. Line symbols are explained in Table 1, Table S4: Mean (M) and standard deviation (SD) of non-polar compounds in two years for the fold change of individual values over the San Marzano value and significance of Student's t test between each mutant line and San Marzano. Line symbols are explained in Table 1, Table S5: Mean (M) and standard deviation (SD) of polar compounds in two years for the fold change of individual values over the San Marzano value and significance of Student's t test between each mutant line and San Marzano. Line symbols are explained in Table 1, Table S6: Metabolite-metabolite significant correlations (Pearson coefficient; *r* > |0.95|, *p*-value < 0.05) in tomato single mutant and the corresponding double mutants (*r*, *y* and *r*-*y*; *r*, *gf* and *r*-*gf*; *y*, *gf* and *y*-*gf*), Table S7: Node table of *y*, *gf* and *y*-*gf* double mutant Network. For each node the corresponding node strength (ns) is reported, Table S8: Node table of *y*, *r* and *y*-*r* double mutant Network. For each node the corresponding node strength (ns) is reported, Table S9: Node table of *gf*, *r* and *r*-*gf* double mutant Network. For each node the corresponding node strength (ns) is reported.

**Author Contributions:** A.M., G.D. (Gianfranco Diretto) and A.G. conceptualized and designed the experiments, G.D. (Gabriella Dono) G.D. (Gianfranco Diretto) and J.L.R. performed the experiments, G.D. (Gabriella Dono), G.D. (Gianfranco Diretto), S.F. and J.L.R. performed statistical analysis, A.M. and G.D. (Gabriella Dono) drafted the manuscript. All the authors reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the European Commission through-H2020 SFS-7a-2014 TRADITOM (634561) and the COST Actions ROXy CA18210 and EUcarotene CA15136 for networking. The Italian Ministry for Education, University and Research (MIUR) Law 232/216, Department of Excellence and the Spanish Ministry project BIO2016-78601-R are also acknowledged.

**Acknowledgments:** The authors acknowledge M.E. Picarella and the Metabolomics Lab at IBMCP for expert technical assistance.

**Conflicts of Interest:** The authors declare no conflict of interest.

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Article

# Placenta, Pericarp, and Seeds of Tabasco Chili Pepper Fruits Show a Contrasting Diversity of Bioactive Metabolites

Felipe Cervantes-Hernández, Paul Alcalá-González, Octavio Martínez and José Juan Ordaz-Ortiz *

Unidad de Genómica Avanzada, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV), Km. 9.6, Libramiento Norte Carretera Irapuato-León, Irapuato, Gto. 36824, Mexico; felipe.cervantes@cinvestav.mx (F.C.-H.); paulpascual92@hotmail.com (P.A.-G.); octavio.martinez@cinvestav.mx (O.M.)

* Correspondence: jose.ordaz.ortiz@cinvestav.mx

Received: 26 August 2019; Accepted: 23 September 2019; Published: 28 September 2019

**Abstract:** Chili pepper (*Capsicum* spp.) is one of the most important horticultural crops worldwide, and its unique organoleptic properties and health benefits have been established for centuries. However, there is little knowledge about how metabolites are distributed throughout fruit parts. This work focuses on the use of liquid chromatography coupled with high resolution mass spectrometry (UHPLC-ESI-HRMS) to estimate the global metabolite profiles of the pericarp, placenta, and seeds of Tabasco pepper fruits (*Capsicum frutescens* L.) at the red mature stage of ripening. Our main results putatively identified 60 differential compounds between these tissues and seeds. Firstly, we found that pericarp has a higher content of glycosides, showing on average a fold change of 5 and a fold change of 14 for terpenoids when compared with other parts of the fruit. While placenta was the richest tissue in capsaicinoid-related compounds, alkaloids, and tocopherols, with a 35, 3, and 7 fold change, respectively. However, the seeds were richer in fatty acids and saponins with fold changes of 86 and 224, respectively. Therefore, our study demonstrates that a non-targeted metabolomic approach may help to improve our understanding of unexplored areas of plant metabolism and also may be the starting point for a detailed analysis in complex plant parts, such as fruits.

**Keywords:** *Capsicum frutescens* L.; non-targeted metabolomics; secondary metabolism; Liquid Chromatography coupled to Mass Spectrometry (LC-MS)

## 1. Introduction

Chili pepper (*Capsicum* spp.) is one of the most important crops worldwide. It is used as a main ingredient for many dishes in different cultures, such as Asian, Latin-American, and Mediterranean cultures, due to its organoleptic properties [1]. There are 40 accepted chili species but only five are considered domesticated: *C. annuum*, *C. chinense* Jacq, *C. frutescens*, *C. baccatum*, and *C. pubescens* [2]. In 2017, 36 million tons of chili peppers were produced globally, with Mexico being the second largest producer [3]. Wild pepper populations of Tabasco pepper (*C. frutescens* L.) and *C. annuum* can be found in some states of Mexico, increasing the relevance for collecting and characterizing these species as resources for the breeding of cultivated peppers [4,5]. Previous studies have extensively described metabolite diversity in *C. annuum* [6,7] but very little is known on *C. chinense* and *C. frutescens*. Most of these studies have undertaken a targeted approach, where the main focus has been to quantitate for specific metabolites, such as capsiate, dihydrocapsiate, capsaicin, dihydrocapsaicin, carotenoids, fatty acids, and amino acids [8,9]. To date, there are no studies with a comprehensive global profiling of tissue specific in *C. frutescens* and *C. chinense*.
*Capsicum* species are known to be rich in compounds such as capsaicinoids, capsinoids, carotenoids, flavonoids, vitamins, essential oils, and other phytochemicals, which provide a unique taste, aromatic properties, and health benefits [10–14]. Capsaicinoids consist mainly of two congeners, capsaicin and dihydrocapsaicin. Capsinoids also have two major analogues, capsiate and dihydrocapsiate. However, there are structural differences: Capsaicinoids are fatty acid amides linked with vanillylamine, whereas capsinoids are fatty acid esters linked to vanillyl alcohol [15,16]. Several compounds identified in the *Capsicum* species have been studied, because the medicinal potential examples of these compounds are icariside E5, capsosides, and capsianosides [17]; hydroxycinnamic derivatives, O-glycosides of quercetin, luteolin, and chrysoeriol [7]. The reported medicinal benefits are related to areas, such as anti-inflammatory [18], anti-cancer [19], anti-microbial, antioxidant properties [1,20,21], and those with weight-loss properties [22]. In addition, some epidemiological studies of a number of these antioxidants reported that they possess anti-atherosclerotic, antitumor, antimutagenic or anticarcinogenic activity [23–25]. It may be that these properties have the ability to help us to address the identification, isolation, and production of nutraceutical compounds or new natural medicinal compounds.

Nevertheless, the location and relative abundance of these metabolites and their precursors in different parts of the chili pepper fruit, such as the pericarp, placenta and seed, remain unclear. It is known that some compounds are synthesized and accumulated into specific tissues in the *Capsicum* genus [1,26]; for example, capsaicin is synthesized mainly in the placenta [27,28], while anthocyanins are described as being accumulated in pericarp during fruit development [29]. Materska, reported a placental and pericarp comparison in chili pepper fruit, where placenta was the richest in flavonoids, while the pericarp presented a larger diversity in glycosylated compounds. Despite this, little is known about the tissue specific spatial-temporal location of other classes of compounds and products of the secondary metabolism in *Capsicum* fruits [30]. Investigating the metabolic diversity on fruit tissues is essential in order to gain a comprehensive understanding of the function of specific parts of the fruit at the fundamental level. Consequently, this will enable the possible exploitation of natural products either for pharmaceutical or food products. In the past, these have been done with histochemical methods, by staining tissues sections with various chemical to reveal the presence of specific compounds either by visual or microscope inspection [31].

Metabolomics is defined as the comprehensive analysis of all low molecular weight organic compounds (<1500 Da) in a biological system [32]. Mass spectrometry has become the most widely applied platform for metabolomics, due to the wide range of molecules that can be analyzed on a single run [33]. Global profiling or non-targeted mass spectrometry-based metabolomics have gained importance in the study of crop species and have been applied to investigate potato, tomato, rice, wheat, strawberry, cucumber, and tobacco [34–37]. In the field of plant metabolomics, liquid chromatography coupled with electrospray ionization high resolution mass spectrometry (UPLC-ESI-HRMS) has emerged as the technique of choice for the putative identification of metabolites in complex matrices. This technique has been widely used, due to its sensitivity, selectivity, and analysis capability [38,39]. Nevertheless, metabolite identification for unknown compounds still remains a big challenge to overcome. In that respect, the recommendations by the Metabolomics Standards Initiative (MSI) recognize five different levels for metabolite confidence annotations. Level 0 requires the full compound 3D structure and stereochemistry information. Levels which are more common include: Level 1 identifications need a confirmation by two orthogonal parameters such as retention time and MS/MS spectrum, normally with match reference standards; and Level 2 requires at least two orthogonal pieces of information, including evidence that excludes all other candidates. Data for Level 2 should describe probable structure and be matched to literature data or databases by diagnostic evidence [40].

Combining existing bioinformatic tools with high resolution mass spectrometry data can reveal unclear relationships of metabolites and their possible function at a spatial-temporal distribution level. As a first attempt to construct the chili fruit metabolome, we produced a hand curated dataset,
that contains 60 putative identified metabolites, which include alkaloids and terpenoids that are unreported in *Capsicum*, with significant differences and relative abundances between three sections (pericarp, placenta, and seed) of Tabasco pepper fruit at the mature red stage, using an UHPLC-QTOF-HRMS platform, combined with the use of Progenesis QI for small molecules, as a tool for the pre-identification of unknown metabolites. Our results underline the global metabolic differences in complexity, mainly based on the secondary metabolism of these fruit parts.

## 2. Results

### 2.1. Non-Targeted Metabolomic Analysis

Our data for two tissues and the seed of Tabasco chili fruit comprised a total of 87 files or chromatograms per ionization mode (positive and negative polarities) as shown in Figure 1 and were uploaded into Progenesis QI. The dataset was first aligned (retention time): Each chromatogram was aligned against each other and automatically compared to a reference profile selected by the software that contained the highest number of features (potential compounds). Then, peak picking was performed by default parameters. A total of 1980 features were detected in the aqueous phase and 1481 were detected in the diethyl ether extract, for both ionization modes. Figure 1 shows a typical chromatographic profile in positive and negative ionization mode of placenta tissue, displaying some representative compounds of chili pepper fruit.

<figure><img src="image_56.png" /><figcaption>**Figure 1.** (a) Base peak intensity chromatographic profile of placenta tissue from chili pepper fruit on a Charged Surface Hybrid (CSH) C18 column obtained with Electrospray Ionization (ESI) positive mode on a mass range from 100 to 1500. 1. 2-[(1H-Indol-3-ylacetyl) amino]-4-methylpentanoate; 2. Indole-3-acetamide; 3. L-cis-Cyclo (aspartylphenylalanyl); 4. Capsicosin; 5. Yamogenintetroside B; 6. Capsaicin; 7. Dihydrocapsaicin; 8. Homodihydrocapsaicin; 9. β-Carotinal. (b) Base peak intensity chromatographic profile of placenta tissue from chili pepper fruit on a CSH C18 column obtained with ESI negative mode. 10. Oleandrigenin monodigitoxoside; 11. β-D-fructofuranosyl 6-O-octanoyl-α-D-glucopyranoside; 12. β-(1->6)-galactotriitol; 13. (2S,3R)-2-Azaniumyl-3-hydroxyoctadecyl phosphate.</figcaption></figure>
Distribution of the features between parts was compared using principal component analysis (PCA) of loading and score plots describing a significant grouping by part for both extraction phases (Figure 2). In addition, quality controls (QC samples) were also considered, as shown in Figure 2. The QC samples cluster tightly in comparison to the total variance in the projection, suggesting a dataset deemed to be of high quality. Tables 1 and 2 describe loadings that mostly contribute to principal components for each extraction. Putative organic compounds catalogued as saponins (SPNS), such as Tuberoside J, Matesaponin 5, and Asparagoside B significantly contributed to component 1 in the aqueous phase, while other important features for component 2 belong to flavonoid class (FLV) and SPNS compounds. Furthermore, both components in the organic phase were mainly composed of glycerolipids (GL) and terpenoids (TER), as well as a putative carotenoid, (5cis,5'cis,9cis,11'cis)-1,2,7,7',8,8'-Hexahydro-1,2-epoxy-ψ, ψ-carotene were important for contribution of component 1 in organic phase.

<figure><img src="image_57.png" /><figcaption>**Figure 2.** (a) Principal Component Analysis (PCA) Bi-plot of loadings (features: crosses) obtained in ESI positive mode and scores (samples: colored circles) extracted with methanol:water phase (component 1:33.10%; component 2: 15.72%; loadings = 1294 features; n = 26); (b) PCA Bi-plot of loadings (features: crosses) and scores (samples: colored circles) extracted with diethyl ether phase (component 1:37.15%; component 2: 15.14%; loadings = 1391 features; n = 24).</figcaption></figure>
<table><caption>**Table 1.** Loadings most contributing to principal components for the aqueous phase.</caption><thead><tr><th>Putative Identification</th><th>Class</th><th>PC1</th></tr></thead><tbody><tr><td>Tuberoside J</td><td>SPNS</td><td>0.2012</td></tr><tr><td>Asparagoside B</td><td>SPNS</td><td>0.1939</td></tr><tr><td>Matesaponin 5</td><td>SPNS</td><td>0.1871</td></tr><tr><td>Oleanolic acid</td><td></td><td></td></tr><tr><td>3-O-[O-β-D-glucopyranosyl-(1-&gt;4)-O-β-D-glucopyranosyl-(1-&gt;3)-O-α-1-rhamnopyranosyl- (1-&gt;2)-α-1-arabinopyranoside]</td><td>SPNS</td><td>0.1822</td></tr><tr><td>Capsicosin</td><td>SPNS</td><td>0.1650<br>PC2</td></tr><tr><td>(3"-Apiosyl-6"-malonyl) astragalin</td><td>FLV</td><td>0.1401</td></tr><tr><td>Pratenol B</td><td>BZD</td><td>0.0881</td></tr><tr><td>Asparagoside B</td><td>SPNS</td><td>0.0750</td></tr><tr><td>Matesaponin 5</td><td>SPNS</td><td>0.0673</td></tr><tr><td>Kaempferol 3-xylosylglucoside</td><td>FLV</td><td>0.0658</td></tr></tbody></table>

PC1: Principal Component 1; PC2: Principal Component 2; BZD: benzoyl derivate; FLV: flavonoid; SPNS: saponin.

<table><caption>**Table 2.** Loading most contributing to principal components for the organic phase.</caption><thead><tr><th>Putative Identification</th><th>Class</th><th>PC1</th></tr></thead><tbody><tr><td>Abietane</td><td>TER</td><td>0.1450</td></tr><tr><td>(5cis,5'cis,9cis,11'cis)-1,2,7,7',8,8'-Hexahydro-1,2-epoxy-ψ,</td><td>CARO</td><td>0.1429</td></tr><tr><td>ψ-carotene</td><td></td><td></td></tr><tr><td>Lycoperoside D</td><td>SPNS</td><td>0.1324</td></tr><tr><td>Phyllohydroquinone</td><td>TER</td><td>0.1302</td></tr><tr><td>2-Caprylooleomyristin</td><td>GL</td><td>-0.0796<br>PC2</td></tr><tr><td>α,α'-Trehalose 6-mycolate</td><td>GL</td><td>0.1505</td></tr><tr><td>2-Caprylooleomyristin</td><td>GL</td><td>0.1226</td></tr><tr><td>MG(14:0/0:0/0:0)</td><td>GL</td><td>-0.1284</td></tr><tr><td>Uralenneoside</td><td>BZD</td><td>-0.0757</td></tr><tr><td>Abietane</td><td>TER</td><td>0.0719</td></tr></tbody></table>

PC1: Principal Component 1; PC2: principal Component 2; BZD: benzoyl derivate; Caro: carotenoid; GL: Glycerolipids; SPNS: saponin; TER: terpenoid.

## 2.2. Level 1 and 2 Metabolomic Identification Analysis

We putatively identified approximately 270 compounds and classified them in 52 compound classes (Table S1). Putative identifications were taken into consideration with a high match score (>90%). Terpenoids, fatty acids, and glycosylated compounds were the most abundant groups. As was predicted, different capsaicinoid compounds were also detected with a high match score. Alkaloids, carotenoids, saponins, and phenolic compounds were also detected in our study.

Capsaicin and dihydrocapsaicin were validated by matching their retention times and MS/MS spectra with those of the analytical standard (Level 1 identification). Furthermore, commonly reported compounds, such as carotenoids and capsaicin related compounds, were detected and putatively identified in the same manner in our samples (Figure 3).
<figure><img src="image_58.png" /><figcaption>**Figure 3.** Mass spectrum of most common compounds in Tabasco chili pepper using Ultra High Pressure Liquid Chromatography MSMS Quadrupole Time of Flight (UHPLC-MS² Q-TOF; collision energy ramp: 20–40 eV) ESI positive ionization mode of placenta tissue, as putatively identified by Progenesis QI for small molecules. (a) Capsaicin; (b) β-carotinal; and (c) dihydrocapsaicin.</figcaption></figure>

### 2.3. *Different Metabolomic Profiles in Capsicum Sections*

Based on results from volcano plot comparisons, we found a total of 60 putative compounds with significant differences between the parts of the Tabasco pepper fruit. Figure 4 shows the distribution of features between placenta and pericarp tissues. As was expected [28], capsaicinoids were more abundant in placenta than pericarp. In contrast, pericarp was richer in glycosylated compounds and
terpenoids such as acalyphin, capsiate, and capsidiol. Some significant ions remain unknown that still need to be identified.

<figure><img src="image_59.png" /><figcaption>**Figure 4.** Volcano plot comparison of relative abundance between tissues of 1394 features in ESI positive ionization mode: Placenta (left), and pericarp (right), unchanged (green); one-way ANOVA *p* = 0.05 (dotted line) Y axis: p value, X axis: fold change.</figcaption></figure>

After feature screening and putative identification, Venn diagrams were generated (Figure 5) to show similarities and differences between fruit parts, according to the fold change values obtained in Table S1. Noticeably, around 30% of the complete dataset of identification was shared by all three fruit parts in almost all the extraction solvents, the exception being for the organic phase in the negative ionization mode, which only share 9.32% similarities. As can be seen in the Venn diagrams, a greater number of putative metabolites were identified in the positive ionization mode. Those easily detected in the positive ionization mode were the compound classes alkaloids, carotenoids, fatty acids, glycosylated compounds, terpenoids, and saponins; while in the negative ionization mode amino acid-derivate compounds, sphingolipids, and phospholipids were detected.

<figure><img src="image_60.png" /><figcaption>**Figure 5.** Venn diagrams of the complete dataset of putative metabolites in different fruit parts at the red mature stage of Tabasco chili pepper (C. frutescens); labels are in percent and number of metabolites.</figcaption></figure>

Listed in Table 3 is the putative identification that presents a differential abundance between fruit parts, including the fragmentation pattern (MS²) and adducts for putative annotation. Fold change values are shown in Table S1.
<table><caption>Table 3. Differential putative identifications in parts of Tabasco pepper fruit by UHPLC-MS² in both ESI modes.</caption><thead><tr><th>Compound Name</th><th>Formula</th><th>Class</th><th>Adduct</th><th>Precursor (m/z)</th><th>Fragments (m/z)</th></tr></thead><tbody><tr><td>α-camphalenaldehyde</td><td>C<sub>16</sub>H<sub>12</sub>O</td><td>TER</td><td>[M + H - H<sub>2</sub>O]</td><td>135.1180</td><td>100.1021(3.7)</td></tr><tr><td>Jasmonol</td><td>C<sub>14</sub>H<sub>10</sub>O<sub>2</sub></td><td>JASM</td><td>[M + H - 2H<sub>2</sub>O]</td><td>145.1027</td><td>133.1026(4.0), 121.1024(3.3), 107.0864(3.5)</td></tr><tr><td>2,4-Dipentadienylbenzene</td><td>C<sub>14</sub>H<sub>10</sub></td><td>BZD</td><td>[2M + NH<sub>4</sub>]<sup>+</sup></td><td>298.1669</td><td>177.0684(2.8), 145.0399(1.6), 117.0428(1.9)</td></tr><tr><td>Uroleucoside</td><td>C<sub>12</sub>H<sub>10</sub>O<sub>2</sub></td><td>BZD</td><td>[M + H] +</td><td>287.0755</td><td>287.0741(63.6), 285.0609(0.4), 257.0637(6.2), 203.0493(0.5), 153.0301(1.45), 155.0542(0.1)</td></tr><tr><td>Synephrine acetonide</td><td>C<sub>12</sub>H<sub>10</sub>NO<sub>2</sub></td><td>BZD</td><td>[2M + FA - H<sub>3</sub>]</td><td>459.2565</td><td>208.2805(8.9)</td></tr><tr><td>Cusohydrine</td><td>C<sub>13</sub>H<sub>10</sub>NO<sub>2</sub></td><td>AK</td><td>[M + H] +</td><td>225.1977</td><td>197.1340(5.5), 183.1184 (5.6)</td></tr><tr><td>Acetylphin</td><td>C<sub>13</sub>H<sub>10</sub>NO<sub>2</sub></td><td>GC</td><td>[M + Na<sup>+</sup>]</td><td>383.1044</td><td>325.0952(1.3), 299.0744(4.6), 165.0311(0.3)</td></tr><tr><td>Prakanol B</td><td>C<sub>13</sub>H<sub>10</sub>O<sub>2</sub></td><td>BZD</td><td>[M + H - H<sub>2</sub>O]</td><td>287.0546</td><td>153.0195(2.8), 131.0512 (2.3)</td></tr><tr><td>Lycopodane</td><td>C<sub>13</sub>H<sub>10</sub>N</td><td>AK</td><td>[M + H - 2H<sub>2</sub>O] +</td><td>220.3782</td><td>184.1841(5.8)</td></tr><tr><td>Pedailtin</td><td>C<sub>16</sub>H<sub>12</sub>O<sub>2</sub></td><td>FLV</td><td>[M + H - H<sub>2</sub>O]</td><td>299.06570</td><td>299.0568(7.3), 165.0197(0.3)</td></tr><tr><td>Nordihydrocaapsaicin</td><td>C<sub>17</sub>H<sub>12</sub>NO<sub>3</sub></td><td>CAPS</td><td>[M + H] +</td><td>294.2055</td><td>285.2240(3.6), 257.2282(2.8), 189.1653(3.9)</td></tr><tr><td>Nerolidyl acetate</td><td>C<sub>17</sub>H<sub>12</sub>O<sub>2</sub></td><td>TER</td><td>[M + H - 2H<sub>2</sub>O] +</td><td>229.1966</td><td>161.134 (12.7)</td></tr><tr><td>Capsaicin</td><td>C<sub>17</sub>H<sub>12</sub>O<sub>2</sub></td><td>CAPS</td><td>[M + H] +</td><td>306.2075</td><td>182.1559(0.2), 137.0695(1.4), 122.0371(5.8)</td></tr><tr><td>Dihydrocaapsaicin</td><td>C<sub>17</sub>H<sub>12</sub>NO<sub>3</sub></td><td>CAPS</td><td>[M + H] +</td><td>308.2240</td><td>175.0771(2.5), 169.0756(3.5), 163.0764(0.9), 160.0537(0.7), 137.0614(2.2), 131.0511(2.0)</td></tr><tr><td>Artocarbeane</td><td>C<sub>17</sub>H<sub>12</sub>O<sub>3</sub></td><td>PPN</td><td>[M + H] +</td><td>311.1301</td><td>131.0808 (0.3)</td></tr><tr><td>1-(4-hydroxyphenyl)-7'-phenyl-[6E]-6-hepten-3-ol</td><td>C<sub>17</sub>H<sub>12</sub>O<sub>2</sub></td><td>PPN</td><td>[M + Cl] -</td><td>317.1345</td><td>273.2222(60.9), 255.2121(53.2), 173.1399(3), 163.0664(4.3), 161.1336(28.8), 147.1183(8.0)</td></tr><tr><td>Steroidal acid</td><td>C<sub>17</sub>H<sub>12</sub>O<sub>2</sub></td><td>FAT</td><td>[M + H - H<sub>2</sub>O]</td><td>273.2235</td><td>273.2222(60.9), 255.2121(53.2), 173.1399(3), 163.0664(4.3), 161.1336(28.8), 147.1183(8.0)</td></tr><tr><td>Kaempfrol-3-O-arabinoside</td><td>C<sub>18</sub>H<sub>14</sub>O<sub>2</sub></td><td>FLV</td><td>[M + H - 2H<sub>2</sub>O] +</td><td>383.0783</td><td>325.0734(4.0), 299.0568(7.3), 165.0197(0.5)</td></tr><tr><td>all-trans-3,4-Dihydroxystearate</td><td>C<sub>18</sub>H<sub>14</sub>O<sub>2</sub></td><td>PRN</td><td>[M + H] +</td><td>281.1929</td><td>181.1024(21.3), 165.0731(9.1), 157.1027(23.0), 155.0870(37.3), 145.1027(27.6), 128.0636(66.9)</td></tr><tr><td>Clirnastiol C</td><td>C<sub>18</sub>H<sub>14</sub>O<sub>2</sub></td><td>TER</td><td>[M + H - H<sub>2</sub>O] +</td><td>363.1781</td><td>332.1368(0.6), 314.1253(0.5), 222.1141(0.6), 136.0677(2.1), 135.0456(0.2), 119.0495(0.8)</td></tr><tr><td>Isopimic acid</td><td>C<sub>18</sub>H<sub>14</sub>O<sub>2</sub></td><td>TER</td><td>[M + H - 2H<sub>2</sub>O] +</td><td>285.2239</td><td>284.2974(0.7), 257.2282(2.8)</td></tr><tr><td>2'-4-Hydroxyisooctrentin</td><td>C<sub>18</sub>H<sub>14</sub>O<sub>2</sub></td><td>FLV</td><td>[M + H] +</td><td>465.1051</td><td>303.0512 (7.4)</td></tr><tr><td>5,7,3'-trihydroxy-3,5'-dimethoxy-3'-[3'-methylbut-2-enyl]flavone</td><td>C<sub>18</sub>H<sub>14</sub>O<sub>2</sub></td><td>FLV</td><td>[M + H] +</td><td>399.1472</td><td>381.1379 (0.9)</td></tr><tr><td>Vestitone 7'-glucoside</td><td>C<sub>18</sub>H<sub>14</sub>O<sub>2</sub></td><td>PPN</td><td>[M + ACN] +</td><td>417.1577</td><td>221.0831 (2.6)</td></tr><tr><td>6-O-Acetylastrinulin</td><td>C<sub>18</sub>H<sub>14</sub>O<sub>2</sub></td><td>TER</td><td>[M + ACN] +</td><td>787.5307</td><td>733.4879 (7.0)</td></tr><tr><td>si-8-Acetylhydrosangunamine</td><td>C<sub>18</sub>H<sub>14</sub>NO<sub>3</sub></td><td>AK</td><td>[M + H - H<sub>2</sub>O]</td><td>372.1245</td><td>344.1288(0.8), 149.0352(0.3)</td></tr><tr><td>Quercetin 3-6'-[n-butyl]glucoside</td><td>C<sub>18</sub>H<sub>14</sub>O<sub>5</sub></td><td>FLV</td><td>[M + H] +</td><td>551.1061</td><td>303.0514 (2.3)</td></tr><tr><td>12'-apos-3-carbental</td><td>C<sub>18</sub>H<sub>14</sub>O<sub>5</sub></td><td>TER</td><td>[M + FA - H]</td><td>745.5259</td><td>685.5227(23.1), 539.4294(47.0)</td></tr><tr><td>Kaempfrol 3-xylosylglucoside</td><td>C<sub>18</sub>H<sub>14</sub>O<sub>5</sub></td><td>FLV</td><td>[M + H] +</td><td>581.1525</td><td>341.2486(1.6), 287.0557(49.5), 153.0195(2.8), 131.0512(2.3)</td></tr><tr><td>11'-Carboxy-o-toophenol</td><td>C<sub>18</sub>H<sub>14</sub>O<sub>5</sub></td><td>TPHE</td><td>[M + H] +</td><td>419.3222</td><td>177.1023 (0.5)</td></tr><tr><td>β-toophenol</td><td>C<sub>18</sub>H<sub>14</sub>O<sub>5</sub></td><td>TPHE</td><td>[2M - H - 2H<sub>2</sub>O] +</td><td>831.2267</td><td>417.6959(60.2)</td></tr><tr><td>Anatoogenin</td><td>C<sub>18</sub>H<sub>14</sub>O<sub>5</sub></td><td>GC</td><td>[M - NH<sub>4</sub>]</td><td>604.2051</td><td>325.0734(4.0), 299.0568(7.3), 165.0197(0.5)</td></tr><tr><td>Rhamnizin 3'-rutinoside</td><td>C<sub>18</sub>H<sub>14</sub>O<sub>6</sub></td><td>FLV</td><td>[M + Na<sup>+</sup> - H]</td><td>683.1485</td><td>303.0514 (23.3)</td></tr><tr><td>Mycacin V</td><td>C<sub>18</sub>H<sub>14</sub>O<sub>13</sub></td><td>FLV</td><td>[M + ACN] +</td><td>640.2098</td><td>151.0407 (5.4)</td></tr><tr><td>Bromonic acid</td><td>C<sub>18</sub>H<sub>14</sub>O<sub>13</sub></td><td>CBN</td><td>[M + ACN] +</td><td>518.5645</td><td>358.1972(0.5), 342.2304(1.5), 230.2464(1.8), 222.1338(0.3), 196.1846(0.3)</td></tr><tr><td>3,7-Dihydroxy-25-methoxycurbita-5,23-dien-19-α</td><td>C<sub>18</sub>H<sub>14</sub>O<sub>13</sub></td><td>STR</td><td>[M + Cl]</td><td>521.3404</td><td>485.7227(2.1)</td></tr><tr><td>Capsanoside I</td><td>C<sub>18</sub>H<sub>14</sub>O<sub>14</sub></td><td>TER</td><td>[M + Na<sup>+</sup>]</td><td>683.3291(4.4), 365.1088(1.6), 365.0929(1.6), 271.2444(7.5)</td><td>683.3291(4.4), 365.1088(1.6), 365.0929(1.6), 271.2444(7.5)</td></tr></tbody></table>
**3. Discussion**

The global metabolic comparison between the tissues and seeds of *C. frutescens* showed several feature differences between the pericarp, placenta, and seed. The Level 1 and 2 confidence metabolite annotations allowed us to assign a putative identification to these ions. Around 30% of metabolites were shared between all three parts. Compounds related to the primary metabolism showed few significant differences, they included amino acid related compounds, fatty acids, and phospholipids. As shown in the Venn diagram (Figure 5) and Table S1, placenta and pericarp have the biggest compound class diversity. Significantly, the seeds presented a higher number of putative identifications, and these were primarily saponins, terpenes, and fatty acids.

Pericarp compound classes were mainly composed of glycosylated compounds and terpenoids. Complementary to these findings, Materska demonstrated that chili pepper pericarp is abundant in glycosylated compounds [30]. Likewise, terpenoids were distributed in the whole fruit but pericarp showed a slightly higher proportion of them. These compounds are highly abundant in spices and herbs and give a wide range on the aroma and flavor spectrum [1]. Similarly, terpenoids have been described as showing antibiotic properties [41] and have been used in fragrances [42].

Placental tissue showed a large number of previously reported compounds with bioactivity, mainly capsaicin- and capsinoids-related compounds. In addition, alkaloids and tocopherols were present, a fact that is in agreement with current literature [28,30]. Found to be abundant in this fruit compartment were 6-O-acetylaustroinulin (terpenoid) and Myrciacitrin V (flavonoid) and they have not been reported in the *Capsicum* fruit.

The compounds found in chili seeds were predominantly fatty acids (3,16-Dihydroxypalmitate, Sterculynic acid) and saponins (Capsicoside A, Eleutheroside L and Tragopogonsaponin F) where they function as reserve nutrients for embryo development and propagation [43]. Moreover, seeds showed the presence of terpenes (Ursolic acid 3-[glucosyl-(1->4)-xyloside]) which are known to function as a natural promoter of predation and, as a consequence, a seed disperser [44]. Ritota et al. (2010) reported an abundance of fatty acids in sweet pepper species by nuclear magnetic resonance spectroscopy, in which polyunsaturated fatty acids were easily detected and pre-identified [6].

Our results were consistent with previously reported findings regarding the large diversity of secondary metabolites in fruits of *Capsicum* species and the non-targeted metabolomics profiling of Solanaceae [10,26,39,45–48]. Furthermore, new compounds, such as Myrciacitrin V, Feruloyl-β-sitosterol, 6-O-acetylaustroinulin and others, were putatively annotated as statistically significant in specific fruit parts.

Capsaicin, Dihydrocapsaicin, and capsaicinoids derivatives mainly accumulate in the placenta, as previously reported [27,49]. This class of compounds represents the most described and abundant metabolite in this genus and are predominantly known as being responsible for the pungency. Different bioactivity assays have been developed, demonstrating properties of capsaicinoids over different cell lines and metabolism, including as an analgesic and for weight-loss [22,50]. Large abundance of capsaicinoids in the chili fruit placenta was proposed by Tewksbury and Nabhan (2001), who suggest that capsaicin selectively discourages vertebrate predators (capsaicin has been found to repel or poison mammals) without deterring more effective and important seed dispersers, such as birds. [51].

A variety of new compounds in *Capsicum* genus, also reported in different species, were detected in pericarp, including isopimaric acid [41], which is a terpenoid with bioactive properties. Additionally, other compounds such as Pedalitin [52], Xi-8-acetyonyldihydrosanguinarine [53], Pratenol B [54], Uralenneoside [55] were detected in pericarp and have been previously reported as bioactive compounds. Quercetin 3-(6"-malonyl-glucoside) is an anthocyanin-related compound that has not been reported in pepper fruit, but this compound class is well known to be localized mainly in pericarp [56,57], due to its involvement as a protection system against solar damage in plants and to attract potential pollinators [29].

New putative compounds in placental tissue, such as Lycopodane [58], 2,4-Pentadiynylbenzene [59], Myrciacitrin V [60], Cinncassiol C [61], and 6-O-acetylaustroinulin [62]
have been reported as bioactive compounds, supporting the nutraceutical properties of chili pepper against metabolic disorders.

The existence of terpenes in seeds may result in different aromas that have been shown to firstly attract birds to mature fruits during the day [63] and secondly, to promote the dispersal of seeds. This function supports the ecological relationship between birds and chili pepper fruits, attracting the most beneficial vertebrate predators [51].

In summary, the non-targeted LC-MS metabolomics method that was developed in this study is shown to be a powerful tool for the putative identification of tissue-specific secondary metabolites at the red mature stage of chili pepper fruit. The use of databases available online gave rise to a faster comprehensive elucidation of global characteristics of a complex matrix than more traditional phytochemical studies. Nutraceutical, aroma, flavor, and new compounds that have not been reported before were putatively identified and related to pericarp, placenta or seeds of *C. frutescens*. As presented here, some of these compounds have been reported with bioactivity properties, supporting empirical properties of pepper fruit that have been known for centuries. The procedure developed here will be utilized for further studies in our laboratory, including to enable the exploration of comparisons between wild cultivars of chili pepper fruit with their cultivated counterparts and for the further understanding of secondary metabolism in this crop. We recommend that complementary analysis should be carried out to confirm structural elucidation. In addition, compound isolation and bioactivity properties should be considered in future studies.

# 4. Materials and Methods

## 4.1. Plant Material and Dissection of Tissues and Seed

Seeds of Tabasco pepper (*C. frutescens* L.) were treated with 3% hypochlorite solution. Plants were grown in optimum conditions (30-32 °C), at greenhouse facilities between June and September of 2016. Fruits from different plants were collected at 60 DPA (red ripe stage), washed with deionized water and immediately frozen with liquid nitrogen and stored at -80 °C until dissection and analysis.

Five biological replicates (plants) were considered for the experiment and three fruits per plant were collected. Each fruit was first placed into dry ice to facilitate hand dissection into pericarp, placenta, and seed using a sterile scalpel. All fruit parts were ground using a ball mill (Retsch MM301) under cold conditions and applying liquid nitrogen.

## 4.2. Chemicals, Reagents, and Standards

All chemicals and reagents were purchased from AccesoLab S.A. de C.V. (Mexico, Mexico). Capsaicin and dihydrocapsaicin analytical standards, formic acid, methanol, acetonitrile were HPLC grade and purchased from Sigma-Aldrich (Mexico, Mexico).

## 4.3. Sample Extraction and UHPLC-MS Analysis

For metabolite extraction, the method employed was adapted from Matyash [64] as follows: Methanol, 1.5 mL, was added to 100 mg of sample in a test tube and vortexed for 1 min, then, 5 mL of diethyl ether was added. The mixture was incubated with gentle stirring for one hour at room temperature. Next, 1.5 mL of ultra-pure water (18 Ω, milli-Q system) was added and mixed vigorously for a further minute then kept at room temperature for 10 min to allow phase separation. After that, the sample was centrifuged at 1000 × g for 10 min. Aqueous and organic layers were recovered separately and vacuum dried (miVac®, Genevac) at 30 °C for 30 min and finally kept at -80 °C until further analysis.

Three quality control (QC) samples were prepared to account for instrument drift and system calibration during analysis in UHPLC-QTOF-HRMS; each QC sample was prepared by mixing homogenously all sample extracts into a new single vial, in both separated phases containing polar and non-polar compounds. QC samples were distributed at the beginning, middle, and end of the
injection run list. Analytical standards of capsaicin and dihydrocapsaicin were injected under the same conditions as samples. Extraction blanks were also considered during the experiment.

For LC-MS analysis, all samples (including QC, analytical standards and blank extraction) were resuspended in 1 mL of acetonitrile/ultra-pure water 50:50 (v/v) and filtered through a membrane of 0.2 µm (PTFE, Agilent Technologies, Santa Clara, USA). Samples were injected according to a randomized list order on an UPLC®(Acquity class I, Waters, Milford, CA, USA) coupled with an orthogonal QTOF (SYNAPT G1 HDMS, Waters, Milford, CA, USA) mass spectrometer. Chromatographic separation was achieved on a reversed phase CSH C18 column (2.1 mm × 150 mm, 1.7 µm, Waters, Milford, USA) maintained at 30 °C during chromatographic separation. Auto-sampling of 10 µL per sample was injected. Compounds were eluted using ultra-pure water with 0.1% (v/v) formic acid (solvent A) and acetonitrile with 0.1% (v/v) formic acid (solvent B) with a flow rate of 0.3 mL/min with the following gradient program: From 0.5 to 30 min, 1–75% B; 30 to 31 min, 75% B; 31 to 31.5 min, 75–100% B; 31.5 to 34.5, 100% B; 34.5 to 34.6, 100-1% B; 34.6 to 36 min, 1% B. The mass spectrometer mass range was set from 50 to 1500 Da. Both ionization modes were injected separated. For negative electrospray ionization (ESI) mode, the conditions were set as follows: Capillary voltage 2 kV; cone voltage 40 V; source temperature 150 °C; cone gas flow 20 L/h; desolvation temperature 350 °C; desolvation gas flow 600 L/h. For the positive ESI mode: Capillary voltage 3 kV; cone voltage 40 V; source temperature 130 °C; desolvation temperature 350 °C; desolvation gas flow 700 L/h. Leucine-Enkephalin (2 ng/mL) was infused as LockSpray reference internal mass calibrant at a flow rate of 5 µL/min and its signal was monitored every 10 s. The data format was collected in a continuum mode with a MS scan time of 1.5 s. In both the positive and negative ionization mode, data were acquired in MS<sup>E</sup> experiments; using Argon as the collision gas with a collision energy in the trap region of 6 eV (Function 1, low energy) and ranged from 20–40 eV (Function 2, high voltage).

## 4.4. Data Analysis

Raw data was imported to Progenesis QI for small molecules software (Non-Linear Dynamics, Waters, Milford, MA, USA) for automatic alignment, normalization, deconvolution, and compound pre-identification over all samples separating the aqueous and organic phases. The RT range was limited from 0.5 to 35 min for pre-identification method. Pre-identification was performed using ChemsPIDer Databases (PlantCyc, Plant Metabolic Network, KEGG, HMDB and ChEBI) and with an in-house database with a minimum match of 90% for precursor ions, MS/MS data and isotope distribution was included for increasing match score values. Statistics and graphics were performed using EZinfo 3.0 (Waters, Milford, MA, USA) and R (3.3.3v, Vienna, Austria) [65] software. Compounds were grouped according to their compound classes. The resulting data was first mean centered and scaled to Pareto and then submitted to a principal component analysis (PCA) using the first three components. Results were analyzed using one-way ANOVA and q-values were established using the false discovery rate (FDR < 0.01) to correct multiple comparisons by the Benjamini–Hochberg procedure [66].

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2218-1989/9/10/206/s1, Table S1: Putative identification.xlsx.

**Author Contributions:** Conceptualization, J.J.O.-O.; data curation, F.C.-H. and P.A.-G.; formal analysis, F.C.-H. and P.A.-G.; funding acquisition, J.J.O.-O.; methodology, F.C.-H. and P.A.-G.; supervision, O.M. and J.J.O.-O.; writing—original draft, F.C.-H.; writing—review and editing, O.M. and J.J.O.-O.

**Funding:** This work was supported by Consejo Nacional de Ciencia y Tecnología (CONACYT Mexico) for grant number 264354 and scholarship: 58787.

**Acknowledgments:** We thank Fernando Hernández-Godinez (Computational Biology Lab), María Esperanza Anaya-Gil (Metabolomics Lab), and Neftalí Ochoa-Alejo for their assistance, support, and suggestions during the experiment.

**Conflicts of Interest:** The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the result.
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Article

# Phytohormone and Transcriptomic Analysis Reveals Endogenous Cytokinins Affect Kiwifruit Growth under Restricted Carbon Supply

Simona Nardozza <fnref n="1" />*, Janine Cooney <fnref n="2" />, Helen L. Boldingh <fnref n="2" />, Katrin G. Hewitt <fnref n="2" />, Tania Trower <fnref n="2" />, Dan Jones <fnref n="1" />, Amali H. Thrimawithana <fnref n="1" />, Andrew C. Allan <fnref n="1" />,<fnref n="3" /> and Annette C. Richardson <fnref n="4" />

Received: 16 December 2019; Accepted: 2 January 2020; Published: 4 January 2020

**Abstract:** Following cell division, fruit growth is characterized by both expansion through increases in cell volume and biomass accumulation in cells. Fruit growth is limited by carbon starvation; however, the mechanism controlling fruit growth under restricted carbohydrate supply is poorly understood. In a previous study using red-fleshed kiwifruit, we showed that long-term carbon starvation had detrimental effects on carbohydrate, anthocyanin metabolism, and fruit growth. To elucidate the mechanisms underlying the reduction in fruit growth during kiwifruit development, we integrated phytohormone profiling with transcriptomic and developmental datasets for fruit under high or low carbohydrate supplies. Phytohormone profiling of the outer pericarp tissue of kiwifruit showed a 6-fold reduction in total cytokinin concentrations in carbon-starved fruit, whilst other hormones were less affected. Principal component analysis visualised that cytokinin composition was distinct between fruit at 16 weeks after mid bloom, based on their carbohydrate supply status. Cytokinin biosynthetic genes (IPT, CYP735A) were significantly downregulated under carbon starvation, in agreement with the metabolite data. Several genes that code for expansins, proteins involved in cell wall loosening, were also downregulated under carbon starvation. In contrast to other fleshy fruits, our results suggest that cytokinins not only promote cell division, but also drive fruit cell expansion and growth in kiwifruit.

**Keywords:** cytokinin; fruit expansion; kiwifruit; phytohormone

## 1. Introduction

*Actinidia* Lindl. spp. (kiwifruit) fruit growth and development is characterised by a rapid growth phase, where cell numbers in the ovary tissue rapidly increase by cell division (up to four weeks after full bloom [1] in the outer pericarp), and a cell expansion phase where cell volume increases and starch accumulates in cells [2]. Later in development, cell expansion and fruit growth slow down, with fruit progressing through maturity and ripening [3]. Carbohydrate supply manipulation of girdled canes (removal of a bark strip around the cane containing the phloem tissue) during *Actinidia chinensis* var. *deliciosa* 'Hayward' fruit development significantly increased fruit weight, whilst fruit weight was not affected in ungirdled canes due to the ability of kiwifruit to redistribute photosynthates within the

<footnote n="1">¹ The New Zealand Institute for Plant and Food Research Limited (PFR), 1142 Auckland, New Zealand; dan.jones@plantandfood.co.nz (D.J.); amali.thrimawithana@plantandfood.co.nz (A.H.T.); andrew.allan@plantandfood.co.nz (A.C.A.)</footnote>

<footnote n="2">² The New Zealand Institute for Plant and Food Research Limited (PFR), 3240 Hamilton, New Zealand; janine.cooney@plantandfood.co.nz (J.C.); helen.boldingh@plantandfood.co.nz (H.L.B.); kati.hewitt@plantandfood.co.nz (K.G.H.); tania.trower@plantandfood.co.nz (T.T.)</footnote>

<footnote n="3">³ School of Biological Sciences, University of Auckland, Private Bag 92019, 1142 Auckland, New Zealand</footnote>

<footnote n="4">⁴ The New Zealand Institute for Plant and Food Research Limited (PFR), 0294 Kerikeri, New Zealand; annette.richardson@plantandfood.co.nz</footnote>

<footnote>* Correspondence: simona.nardozza@plantandfood.co.nz</footnote>
vine [4]. In addition to ‘Hayward’, *A. chinensis* var. *chinensis* ‘Zes006’ fruit weight was also affected when carbohydrate supply was manipulated in girdled shoots [5–7].

The dynamic interplay of phytohormones regulates the growth and development of fleshy fruit [8]. Cell division is primarily driven by cytokinins, whilst auxins and gibberellins are major players in cell expansion [9,10]. Abscisic acid increases as the fruit approaches maturity and together with ethylene is responsible for fruit ripening [11]. Studies on endogenous hormone profiles during kiwifruit development are limited, with most of the studies focused on the effects of exogenous hormone applications on fruit growth [12,13]. In kiwifruit, increases in endogenous cytokinin concentrations have been associated with cell division [14], fruit maturity, and ripening [14,15]. It has also been shown that exogenous cytokinin applications to developing kiwifruit after cell division had ceased increased both cell and fruit expansions [12]. In plants, cell expansion is the result of turgor pressure stress caused by an increase in cell osmotic potential and cell wall relaxation mediated by low-pH activated expansin proteins [16]. Expansin genes have been reported to be amongst genes induced by cytokinin [17], but their role in cytokinin-driven growth has not been elucidated.

Sugar and hormone signalling networks have been extensively studied in vegetative tissues (reviewed by Ljung et al. [18]) and to a lesser extent in reproductive tissues [19]. Cytokinins, in particular, have been implicated in the control of source-sink relationships of carbon compounds [20], and a role for them in controlling the availability of sugars in sink tissues has been proposed [21,22]. In Arabidopsis, high quantities of photosynthetically generated sugars have an effect on plant growth and on de novo cytokinin biosynthesis through the upregulation of two genes coding for key enzymes catalysing active cytokinin precursors synthesis: isopentenyltransferase (AtIPT3) and cytokinin hydroxylase (CYP735A2) [23]. Arabidopsis type-B response regulators, part of the MYB transcription factor family, mediate cytokinin transcriptional regulation [24]. A meta-analysis of published transcriptomic data identified a number of primary metabolic genes that are regulated by cytokinins including trehalose 6-phosphate pathway genes [17] and the *bZIP11* transcription factor. These genes are involved in the growth regulatory network including the sucrose non-fermenting-1 related protein kinase 1, which controls carbohydrate metabolism [25].

Kiwifruit carbon starvation mainly occurs when the leaf-to-fruit ratio on vines is low, but it can also be caused by environmental factors such as high temperature or low light during critical fruit growth phases reducing the photosynthetic carbon supply to fruit. In this study, we hypothesized that fruit growth under restricted carbohydrate supply was controlled at the phytohormonal level. To test this, we integrated phytohormone profiles with transcriptomic and developmental datasets for fruit grown under high or low carbohydrate supplies. Of all the phytohormones, cytokinin concentration positively correlated with fruit weight and the carbohydrate supply treatment. Cytokinin biosynthetic genes were also significantly downregulated under carbon starvation. As several expansin genes were also significantly downregulated under carbon starvation, we propose that cytokinin signalling affects fruit weight via regulation of cell expansion through expansins.

## 2. Results

### 2.1. *Fruit Weight and Phytohormone Correlations*

Carbon starvation during fruit development significantly restricted kiwifruit fruit growth by 16 weeks after mid bloom (WAMB; Figure 1a). Low carbohydrate supply fruit (carbon starved) were 25% smaller than untreated control fruit from ungirdled shoots, and 45% smaller than high carbohydrate supply fruit.

Phytohormones from the following classes were quantified in kiwifruit tissues: cytokinins, gibberellins, auxins, jasmonates, salicylates, and abscisates.

To ascertain if a particular phytohormone class was correlated with fruit weight, Pearson correlation analysis was performed. Phytohormones were measured from the fruit outer pericarp as this is the major fruit tissue, representing 60% of fruit tissue proportion [26,27]. Fruit weight was positively and
highly correlated with total cytokinin concentration (r = 0.91, *p* < 0.0001, Figure 1b). Fruit weight was also negatively and significantly correlated with total gibberellins (r = -0.61, *p* < 0.001, Figure 1c), positively and significantly correlated with abscisic acid (r = 0.65, *p* < 0.0001, Figure 1d), and negatively and significantly correlated with jasmonic acid (r = -0.55, *p* < 0.001, Figure 1e). Fresh weight was not significantly correlated with other phytohormones such as salicylic acid and auxin (data not shown).

<figure><img src="image_63.png" /></figure>

**Figure 1.** Fruit weight of kiwifruit (a) at three key stages of development: 8, 12, and 16 weeks after mid bloom (WAMB). Untreated control (black bar; vine standard), low carbohydrate supply (blue bar), and high carbohydrate supply (red bar). Values are averages ± SEM (n = 3 or 4). Statistical analysis by linear mixed effects model with type 3 sums of squares Kenward-Roger's method; different letters mean a statistical difference, adjusted for multiple comparison by Tukey's correction (*p* < 0.05). Pearson correlation analysis of between fruit weight and (b) total cytokinins, (c) total gibberellins, (d) abscisic acid, and (e) jasmonic acid concentration in fruit outer pericarp. *n* = 33 for cytokinins and *n* = 35 for the other phytohormones. FW, fresh weight.
## 2.2. Carbon Starvation Reduced Cytokinin Concentration in Fruit Outer Pericarp

The following cytokinin were quantified in kiwifruit outer pericarp tissue and were above the limit of detection (LOD): *trans*-zeatin (tZ), isopentenyl adenine (iP), *cis*-zeatin (cZ), dihydrozeatin riboside (DZR), isopentenyl adenine riboside (iPR), *trans*-zeatin riboside (tZR), *cis*-zeatin riboside (cZR), *trans*-zeatin-O-glucoside (tZROG), isopentenyl adenine-9-glucoside (iP9G), and *trans*-zeatin-9-glucoside (tZ9G) (Figure 2).

<figure><img src="image_64.png" /><figcaption>**Figure 2.** Cytokinin concentrations in the outer pericarp of developing kiwifruit: (a) iP, isopentenyl adenine; (b) iPR, isopentenyl adenine riboside; (c) tZ, *trans*-zeatin; (d) tZR, *trans*-zeatin riboside; (e) tZROG, *trans*-zeatin-O-glucoside; (f) DZR, dihydrozeatin riboside; (g) cZ, *cis*-zeatin; (h) cZR, *cis*-zeatin riboside. Untreated control (black bar; vine standard), low carbohydrate supply (blue bar), and high carbohydrate supply (red bar). Values are averages ± SEM (n = 3 or 4). Statistical analysis by linear mixed effects model with type 3 sums of squares Kenward-Roger's method; different letters mean a statistical difference adjusted for multiple comparison by Tukey's correction (*p* < 0.05). Fruit age is in weeks after mid bloom (WAMB). FW, fresh weight.</figcaption></figure>

tZ and iP had similar concentrations and represented the major active cytokinins in developing kiwifruit, followed by cZ with a concentration that was one order of magnitude lower (Figure 2a,c,g). Dihydrozeatin (DZ) concentration was below the detection level. In untreated control fruit,
the proportional concentration of active cytokinins to the total cytokinin pool decreased during development from 10% at 8 WAMB to 6% at 16 WAMB (Table S1). Cytokinin ribosides iPR and tZR were the major cytokinins (Figure 2b,d). Other ribosides DZR and cZR were also detected, but their concentrations were one order of magnitude lower than those of iPR and tZR (Figure 2f,h). In untreated control fruit, the proportional concentration of cytokinin ribosides increased during development from 50% at 8 WAMB to 90% at 16 WAMB (Table S1), and this pattern was shared with the other two treatments. For the reversible O-glycosylated conjugates, only tZROG was above the detection level and its proportional concentration decreased in untreated control fruit from 10% at 8 WAMB to 2% at 16 WAMB (Table S1). Irreversibly N-glycosylated cytokinins (iP9G and tZ9G) also decreased in untreated control fruit during development, from 25% at 8 WAMB to 2% at 16 WAMB (Table S1). By 16 WAMB, carbon starvation significantly reduced the majority of the active cytokinins and their metabolic or glycosylated intermediates (Figure 2). iP and its riboside iPR were significantly lower in carbon-starved fruit (low carbohydrate supply; Figure 2a,b). Carbon starvation had a similar effect on tZ, tZR, and tZROG, and their concentrations were significantly lower in carbon-starved fruit than in high carbohydrate supply fruit (Figure 2c,d,e). DZR concentration was also significantly lowered by carbon starvation (Figure 2f). cZ was not affected by carbon starvation, but cZR concentrations were significantly higher in high carbohydrate supply fruit than in carbon-starved fruit and the untreated control (Figure 2g,h).

We further employed principal component analysis to assess the effects of the treatment on cytokinin composition. It is shown in Figure 3 that 59%, 57%, and 76% of variation can be explained by principal component 1 (PC1) at 8, 12, and 16 WAMB, respectively. An effect was first observed at 12 WAMB, with the cytokinin composition of high carbohydrate supply treatment fruit being distinct from that of the untreated control fruit and low carbohydrate supply treatment. At 16 WAMB, the data clustered into three distinct cohorts, of which high and low carbohydrate supply treated fruit showed the highest divergence in cytokinin composition. Dimension 1 was strongly and positively driven by all the measured cytokinins, with the exception of cZ, which mainly drove dimension 2 (Figure S1).

2.3. *Carbon Starvation Had Lesser Effects on Other Phytohormones*

Gibberellins GA1, GA19, and GA20 were present in the outer pericarp of kiwifruit in concentrations above the limit of detection (Figure 4a–c). Active gibberellin GA1 concentrations were 4-fold lower than concentrations of its precursor GA19. GA19 was the most abundant gibberellin, and its concentration decreased 2-fold during fruit development. GA20 was present at 4- to 8-fold lower concentrations than GA19, and its concentrations were stable during development. Under carbon starvation, GA19 concentrations decreased less during fruit development than in the other treatments (Figure 4b), and at 16 WAMB, it was about 3-fold higher than in high carbohydrate supply fruit (*p* < 0.0001).

Indole-3-acetic acid (IAA) was the only auxin detected by our system and its concentration slightly increased during fruit development (Figure 4d). Carbon starvation had no effect on IAA concentrations.

The concentration of the stress hormone abscisic acid (ABA) was stable during fruit development, although by 16 WAMB, its concentration in high carbohydrate supply fruit was significantly higher than in fruit from the other treatments (Figure 4e). This suggests that high carbohydrate supply fruit was the main driver of the positive correlation between fruit weight and ABA observed in Figure 1d (data not show). Jasmonic acid (JA) concentration decreased during fruit development and at 8 WAMB, its concentration was significantly higher in high carbohydrate supply fruit than in fruit from the other treatments (Figure 4f). JA bioactive isoleucine conjugate (JA-Ile) was below the detection level.
<figure><img src="image_65.png" /><figcaption>**Figure 3.** Principal component analysis (PCA) based on cytokinin concentrations in the outer pericarp of developing kiwifruit: (a) 8 weeks after mid bloom (WAMB); (b) 12 WAMB; (c) 16 WAMB. Black, untreated control (vine standard); blue, low carbohydrate supply (carbon starvation); red, high carbohydrate supply. PC, principal component. Confidence ellipses: *p* < 0.05.</figcaption></figure>
<figure><img src="image_66.png" /><figcaption>**Figure 4.** Gibberellins, auxin, abscisic acid, and jasmonic acid concentrations in the outer pericarp of developing kiwifruit: (a) GA1, gibberellin A1; (b) GA19, gibberellin A19; (c) GA20, gibberellin A20; (d) IAA, indole-3-acetic acid; (e) ABA, abscisic acid; (f) JA, jasmonic acid. Untreated control (black bar; vine standard), low carbohydrate supply (carbon starvation; blue bar), and high carbohydrate supply (red bar). Values are averages ± SEM (n = 4). Statistical analysis by linear mixed effects model with type 3 sums of squares Kenward–Roger’s method; different letters mean a statistical difference, adjusted for multiple comparison by Tukey’s correction (*p* < 0.05). Fruit age is in weeks after mid bloom (WAMB). FW, fresh weight.</figcaption></figure>

## 2.4. Carbon Starvation Downregulates Cytokinin Biosynthetic Genes

The cytokinin biosynthetic and catabolic pathway was constructed (Figure 5) using published information [28,29]. Gene models that code for the enzymes in the cytokinin pathway were identified by gene mining the kiwifruit genome [30] on the basis of previously published kiwifruit data [15] and homologues from other species [31,32], marked as blue in the chart (Figure 5). Where genes coding for a particular enzyme were unknown in Arabidopsis or were not found for kiwifruit, the enzymes were marked in grey.
<figure><img src="image_67.png" /><figcaption>**Figure 5.** Cytokinin biosynthetic and catabolic pathways. Enzymes in blue are known and have been identified in kiwifruit; enzymes in grey are unknown and/or not identified in kiwifruit. Modified from [28,29]. βGLU, β-glucosidase; CK, cytokinin; CK-N-GT, cytokinin N-glucosyltransferase; CKX, cytokinin dehydrogenase; CYP735A, cytokinin hydroxylase; cZ, cis-zeatin; cZR, cZ riboside; cZRM(D/T)P, cZ nucleotides; DMAPP, dimethylallyl pyrophosphate; DZ, dihydrozeatin; DZR, DZ, riboside; DZRM(D/T)P, DZ nucleotides; HMGR, 3-hydroxy-3-methylglutaryl-coenzyme A reductase; iP, N⁶-(Δ²-isopentenyl)adenine; iPR, iP riboside; iPRM(D/T)P, iP nucleotides; IPT, adenosine phosphate-isopentenyltransferase; MEP, methylerythritol phosphate pathway; LOG, LONELY GUY (cytokinin riboside 5'-monophosphate phosphoribohydrolase); MVA, mevalonate pathway; tRNA, transfer RNA; tZ, trans-zeatin; tZR, tZ riboside; tZRM(D/T)P, tZ nucleotides; ZOG, zeatin O-glucosyltransferase.</figcaption></figure>

The expression of a number of genes involved in cytokinin biosynthesis was reduced under carbon starvation. Five genes of the 3-hydroxy-3-methylglutaryl-coenzyme A reductase 1 family were expressed in kiwifruit and the *HMGR1.1* gene was significantly downregulated under carbon starvation (Figure 6). We identified nine adenylate isopentenyltransferase (*IPT*) genes: three *IPT1*, one *IPT2*, two *IPT3*, and three *IPT5*. Of these, *IPT1.2*, *IPT1.3*, and *IPT5.1* were significantly downregulated under carbon starvation from 12 WAMB. Of the four cytokinin hydroxylase 1 genes (*CYP735A1*) expressed, *CYP735A1.1* was significantly downregulated under carbon starvation from 12 WAMB.

Expression of cytokinin catabolic genes were also affected by carbon starvation, although to a lesser extent than the biosynthetic genes, with an overall upregulation of the genes involved in reversible conjugation (zeatin *O*-glucosyltransferase, ZOG; β-glucosidase, βGLU) or irreversible cleavage (cytokinin hydrogenase, CKX) (Figure 7). The main cytokinin dehydrogenase (CKX5.3) was only significantly upregulated at 12 WAMB.

## 2.5. *Carbon Starvation Effects on the Genes in the Multistep Phosphorelay (MSP) Cytokinin Signalling*

The genes involved in the first two steps of the multistep phosphorelay (MSP) cytokinin signalling were upregulated by carbon starvation (Figure S2). Histidine kinases *AHK2.1*, *AHK3.5*, *AHK4.2*, and *AHK4.3* were significantly upregulated at 16 WAMB (2-, 1.8-, 1.6-, and 1.6-fold, respectively). The histidine-containing phosphotransfer protein *AHP1.7* was 5-fold upregulated under carbon starvation. Four of the 12 A-type Arabidopsis Response Regulators (type-A ARR) genes identified in kiwifruit, which are negative regulators of cytokinin signalling, were downregulated under carbon starvation whilst four of the 19 type-B ARRs genes, which act as positive regulators of cytokinin signalling, were upregulated by this treatment (Figure S3). *ARR12.5* was 1.5-fold upregulated by carbon starvation from 12 WAMB.
<figure><img src="image_68.png" /><figcaption>**Figure 6.** Heat-map of kiwifruit cytokinin biosynthetic genes. DESeq comparison of low carbohydrate supply (carbon starvation) versus high carbohydrate supply treatments. Differentially expressed genes were identified based on the DESeq analysis from Nardozza et al. [6]. For each gene, the expression level (base mean) and the log2 fold-change are presented. Fruit age is in weeks after mid bloom (WAMB). Bold figures mean differences are significant for adjusted *p* < 0.05 (DESeq analysis).</figcaption></figure>
<figure><img src="image_69.png" /><figcaption>**Figure 7.** Heat-map of kiwifruit cytokinin catabolic genes. DESeq comparison of low carbohydrate supply (carbon starvation) versus high carbohydrate supply treatments. Differentially expressed genes were identified based on the DESeq analysis from Nardozza et al. [6]. For each gene, the expression level (base mean) and the log2 fold-change are presented. Fruit age is in weeks after mid bloom (WAMB). Bold figures mean differences are significant for adjusted *p* < 0.05 (DESeq analysis).</figcaption></figure>
## 2.6. Carbon Starvation Results in Downregulation of Expansin Genes

Expansins are a class of cell wall proteins able to drive non-enzymatic pH-dependent cell wall relaxation by partnering with H(+)-ATPases proton pumps [16,33]. Of the expansin genes identified in kiwifruit [30], the expansin A class genes were the most affected by carbon starvation at 16 WAMB. EXP2-7 and EXP24 were significantly downregulated under carbon starvation (Figure 8). Highly expressed EXP3, EXP5, and EXP6 were downregulated by 3.5-, 4.5-, and 9-fold, respectively. In addition, plasma membrane H(+)-ATPase genes were also downregulated (Figure S4).

<figure><img src="image_70.png" /><figcaption>**Figure 8.** Heat-map of kiwifruit expansin genes. DESeq comparison of low carbohydrate supply (carbon starvation) versus high carbohydrate supply treatments. Differentially expressed genes were identified based on the DESeq analysis from Nardozza et al. [6]. For each gene, the expression level (base mean) and the log2 fold-change are presented. Fruit age is in weeks after mid bloom (WAMB). Bold figures mean differences are significant for adjusted *p* < 0.05 (DESeq analysis).</figcaption></figure>
**3. Discussion**

In kiwifruit, fruit growth increases with an increase in carbohydrate supply [4,5]. The fruit expansion phase in kiwifruit appears to differ from those of other fleshy fruits. Auxin and/or gibberellins drive fruit expansion in strawberry [34], apple [10], and tomato [35]. Our results showed that cytokinins are key phytohormones during fruit expansion in kiwifruit. Phytohormone profiling revealed that fruit weight differences obtained under contrasting carbohydrate supplies positively correlated with cytokinin concentrations in the fruit outer pericarp. Changes in gibberellin, jasmonic acid, and abscisic acid concentrations were mostly developmental in nature and not strongly affected by carbohydrate supply. Interestingly, gibberellin precursor GA19 increased under carbon starvation, however, this effect was not maintained downstream in the biosynthetic pathway, and both GA20 and active GA1 concentrations were unaffected, suggesting that the role of gibberellin was not significant in the kiwifruit cell expansion phase. Surprisingly, unconjugated auxin concentrations were not correlated with fruit growth and were also not affected by fruit carbohydrate supply. This is in contrast to observations in fleshy fruit types [10,34,35], suggesting that free auxin is not critical during the fruit expansion phase of kiwifruit. However, we cannot rule out that conjugated auxins, which were not measured in this study, may play a role.

Similar to Arabidopsis seedlings, where de novo cytokinin synthesis is induced by sugar, our data showed that in carbon-starved kiwifruit, cytokinin synthesis is repressed. This is via downregulation of the cytokinin biosynthetic genes *IPTs* and *CYP735As*, suggesting that sugar control over cytokinin production could apply to sink tissues. Signalling of carbon starvation in kiwifruit is mediated by the sucrose non-fermenting-1 related protein kinase 1 via trehalose 6-phosphate [6]. In Arabidopsis, it has been proposed that cytokinins have a role in regulating the genes of the trehalose 6-phosphate pathway [17]. A similar scenario may occur in kiwifruit outer pericarp tissue, where the observed reduction of trehalose 6-phosphate concentration in carbon-starved fruit [6] could be triggered by cytokinin signalling.

In maturing kiwifruit, cytokinin catabolism was not critical for controlling cytokinin concentrations [15], whilst in other plant species (e.g., cabbage, maize, and wheat) it has been suggested that *IPT* and *CKX* transcription levels are coupled [31,36,37] and *CKX* is a key enzyme for cytokinin homeostasis. Our results show that genes coding for cytokinin catabolic enzymes were only marginally affected by carbon supply treatments and cytokinin dehydrogenase was not associated with different cytokinin concentrations (Figure 2; Figure 7). These data suggest that, in kiwifruit, carbon starvation affects de novo synthesis rather than irreversible cleavage of cytokinins during the fruit expansion phase.

Whilst transcriptional data for cytokinin biosynthetic genes supported the phytohormone data, the regulation of the multistep phosphorelay cytokinin signalling was less clear. Under carbon starvation, we observed an upregulation of the genes involved in the multistep phosphorelay pathway (histidine kinase, histidine phosphotransferase, and type-B RRs), suggesting that they might not be controlled at the transcriptional level as transcription is triggered by decreased cytokinin concentrations. Multistep phosphorelay signalling is triggered by cytokinin binding, which leads to autophosphorylation of histidine kinase. The phosphate is then transferred to histidine phosphotransferase and the final acceptor type-B RR proteins, which positively regulate transcription in the nucleus (reviewed by Kieber and Schaller [38]). In contrast, type-A RR are negative feedback regulators in cytokinin signalling and are transcriptionally activated by type-B RR [39]. In kiwifruit outer pericarp, type-A RR were mostly downregulated when cytokinin concentrations were low. This is in agreement with findings from Bhargava et al. [40], where type-A response regulators were upregulated in response to exogenous cytokinin applications, suggesting an increase in endogenous cytokinins could lead to a similar response.

Expansins are proteins involved in the acidic relaxation of plant cell walls and drivers of short-term cell expansion [41]. Effects of cytokinins on expansin-mediated cell wall relaxation and cell expansion has been suggested [42]. A meta-analysis of cytokinin effects on Arabidopsis transcriptome
identified expansins as targets of cytokinin signalling [43]. Expansins drive acidic cell elongation in Arabidopsis roots, where α-expansinA genes and plasma membrane H(+)-ATPase are controlled by cytokinins via ARR1 [44]. In soybean, cytokinins regulate the expression of expansin and cell wall expansion [45]. The effects of cytokinins on expansin during cell expansion and fruit expansion have not been well characterised. A study of grape berries associated cytokinin concentrations with post-veraison cell expansion and berry growth [19]. Exogenous cytokinin application induced expansin expression in white sweet clover (Melilotus alba) [42]. In kiwifruit, under carbon starvation, we observed significant decreases in cytokinin concentrations, cytokinin biosynthetic genes transcription, a-expansin transcription, and fruit weight, suggesting a role for expansin in driving cell expansion and fruit expansion. Cytokinin concentrations were developmentally driven as the positive correlation between total cytokinin concentration and fruit weight held true also within each treatment, in agreement with Pilkington et al. [15]. Further experimentation will be required to clarify the mechanism and if, similar to Arabidopsis roots, the signalling may be mediated by a type-B RR [44].

In conclusion, our results suggest a new role for cytokinins in kiwifruit growth, where they contribute to stimulate cell expansion via a mechanism that could involve expansin protein. This is in contrast to other fleshy fruits where cytokinins are a key phytohormone class during cell division, and further supports the responsiveness of kiwifruit to exogenous cytokinin treatments following the cell division phase. We acknowledge a limitation of this study in discussing the functional role of cytokinin in kiwifruit as developmental phases were inferred from the literature [1,2], rather than defined from direct cell number and cell size observations. Overall, these findings contribute to expand the current knowledge on fruit weight determination in kiwifruit.

# 4. Materials and Methods

## 4.1. Plant Material

*Actinidia chinensis* (Planch.) var. *chinensis* 'Zes006' (red fleshed) kiwifruit was sampled as per the experiments described in Nardozza et al. [6]. The outer pericarp tissue from the following treatments was used: high carbohydrate supply (girdled shoot with leaf to fruit ratio of 4), low carbohydrate supply (girdled shoot with a leaf to fruit ratio of 1), and untreated control (ungirdled shoot with a leaf to fruit ratio of 1; vine standard [5]). Individual shoots represented the experimental unit. Girdles were applied to shoots at 4 WAMB and maintained open until the end of the experiment. Only samples collected at 8, 12, and 16 WAMB were considered to match the transcriptomic data. Fresh fruit weight data are given in Nardozza et al. [6]. Outer pericarp tissues were collected to include three to four biological replicates (due to lost samples in the field or during analysis; see details in Table S2), snap frozen in liquid nitrogen, and stored at -80 °C until further analysis.

## 4.2. Phytohormones

### 4.2.1. Phytohormone Extraction and Fractionation

Frozen plant material was ground in liquid nitrogen to a fine powder using a mortar and pestle and stored at -80 °C for chemical analysis. To each sample (200 mg fresh weight), 1 mL chilled (4 °C) extraction solvent 80:20 CH3CN:H2O, labelled internal standard mix ([2H5] Z 10 ng, [2H5] (9R)Z 3 ng, [2H5] t-ZROG 30 ng, [2H5] (9G)Z 3 ng, [2H6] iP 0.5 ng, [2H6] (9R) iP 1 ng, [2H6] iP9G 3 ng, [2H5] t-ZOG 7 ng, [2H2] GA7 0.5 ng, [2H4] SA 1 ng, [2H6] ABA 2 ng, [2H5] JA 2 ng, [2H10] JA-Ile 2 ng, [13C6] IAA 10 ng; OlchemIm Ltd., Olomouc, Czech Republic), and 0.8 g stainless steel beads 0.9–2 mm (Next Advance Inc., Troy, NY, USA) were added. Samples were bead beaten for 5 min (Bullet Blender 24 Gold, Next Advance Inc., Troy, NY, USA) and then extracted overnight in the dark at 4 °C using an end-over-end rotator at 30 rotations/min. After centrifugation at 13,000× g for 5 min, the supernatant was transferred into a 96-well collection plate (Phenomenex, Torrance, CA, USA). The remaining pellet was re-extracted twice with 1 mL 80:20 CH3CN:H2O overnight, and combined with the first supernatant,
and evaporated to dryness using a CentriVap concentrator (Labcon, Petaluma, CA, USA). Samples were reconstituted in 1 mL 1 M formic acid (aq) and placed on an orbital shaker at room temperature for 1 h at 230 rpm (IKA Labortechnik, Staufen, Germany). To remove interfering compounds, the extract was passed through a SOLA SCX 96-well plate (10 mg/2 mL, Thermo Scientific) equilibrated with 4 mL acetonitrile and conditioned with 4 mL 1 M formic acid (aq). After conditioning, the reconstituted samples were loaded, washed with 3 mL of 1 M formic acid (aq), 3 mL of water, and the acidic plant hormones were eluted with 1.5 mL acetonitrile (Fraction A). The plate was then washed with 1 mL water, followed by 1.5 mL of 0.35 M ammonium hydroxide and the cytokinins were eluted with 0.5 mL of 0.35 M ammonium hydroxide in 60% acetonitrile (Fraction B). Each fraction was evaporated to dryness using a CentriVap concentrator (Labcon, Petaluma, CA, USA).

Prior to derivatisation with bromocholine bromide (BETA), Fraction A was further purified using a modification of a method described by Kojima et al. [46] to increase the sensitivity of analysis for gibberellins. Briefly, samples were reconstituted in 1 mL 80:20 CH3CN:H2O + 4% trimethylamine and applied to a Hypersep 96-well plate (Hypercarb 25 mg/1 mL, Thermo Scientific) equilibrated with 1 mL acetonitrile and conditioned with 1 mL water. The sample eluate was collected into a 96-well collection plate and a further 0.5 mL CH3CN + 4% trimethylamine was applied to the Hypersep plate and collected into the same collection plate. After evaporation, the samples were reconstituted with 160 µL CH3CN and to each sample 40 µL of derivatisation solution (6.25 M BETA in 86% acetonitrile) and 20 µL 4% trimethylamine in CH3CN was added. The mixed solution was incubated at 50 °C at 300 rpm for 24 h (Eppendorf, ThermoMixer C, Hamburg, Germany) and then evaporated to dryness.

### 4.2.2. Liquid-Chromatography Tandem Mass-Spectrometry (LC-MS/MS) Analysis

Liquid-Chromatography Tandem Mass-Spectrometry (LC-MS/MS) experiments were performed on a 5500 QTrap triple quadrupole/linear ion trap (QqLIT) mass spectrometer equipped with a TurboIon-Spray™ interface (AB Sciex, Concord, ON, Canada) coupled to an Ultimate 3000 UHPLC (Dionex, Sunnyvale, CA, USA).

### 4.2.3. Cytokinins

For cytokinins, dried samples from Fraction B were reconstituted in 200 µL 10:90 CH3OH:H2O + 1% acetic acid and filtered through a conditioned (200 µL CH3OH) 96-well 0.45 µm hydrophobic filter plate (Pall Filters, AcroPrep, Cortland, NY, USA) prior to mass spectrometric (MS) analysis. Cytokinins were separated on an Acquity UPLC BEH C18 1.7 µm 2.1 × 150 mm ID column (Waters, Wexford, Ireland) maintained at 70 °C. Solvents were (A) 15 mM ammonium formate adjusted to pH 4 with formic acid and (B) methanol with a flow rate of 350 µL min⁻¹. The initial mobile phase, 15% B was held for 6.5 min, then ramped linearly to 20% B at 9 min, then to 50% B at 12.5 min and 100% B at 14 min and holding at 100% B for 1 min before resetting to the original conditions. Injection size was 10 µL. MS data were acquired in the positive ion mode using a scheduled multiple reaction monitoring (MRM) method. The transitions monitored (Q1 and Q3) are listed in Table S3. Other operating parameters were as follows: ion spray voltage 4500 V; temperature 600 °C; curtain gas 45 psi; ion source gas 1 60 psi; ion source gas 2 60 psi; collision gas set to medium.

### 4.2.4. Acidic Phytohormones

For acidic phytohormones (gibberellins, auxins, jasmonates, salicylates and abscisates), Fraction A dried and derivatised samples were reconstituted in 100 µL 5% (CH3OH:CH3CN): 95% (5 mM ammonium formate adjusted to pH 3.7 with formic acid). An internal standard for each analyte was created by derivatising a mixed analytical standard with a deuterated analogue of bromocholine bromide ([²H₉]-BETA) using a modified method to that described by Sun et al. [47], as here detailed. To each sample, 100 µL of internal standard (5 ppb [²H₉]-BETA) was added. Samples were filtered through a 0.7-µm glass filter plate prior to LC-MS analysis. Acidic phytohormones were separated on an Acquity UPLC BEH C18 1.7 µm 2.1 × 150 mm ID column (Waters, Wexford, Ireland) maintained
at 40 °C. Solvents were (A) 5% ($CH_3OH:CH_3CN$):95% (5 mM ammonium formate adjusted to pH 3.7 with formic acid) and (B) 95% ($CH_3OH:CH_3CN$):5% (5 mM ammonium formate adjusted to pH 3.7 with formic acid) with a flow rate of 250 µL min⁻¹. The initial mobile phase, 0% B was held for 1 min before ramping linearly to 5.3% B at 2 min, 7.5% B at 5.5 min, 40% B at 11 min, and holding for 3.5 min before ramping to 100% B at 15.5 min and holding at 100% B until 21 min before resetting to the original conditions. Injection size was 2 µL. MS data were acquired in the positive mode using a scheduled MRM method. The transitions monitored (Q1 and Q3) are listed in Table S4. Transitions for compounds other than gibberellins were detuned from optimum to reduce their sensitivity to fit within the dynamic linear range of the instrument. Other operating parameters were as follows: ion spray voltage 4500 V; temperature 600 °C; curtain gas 45 psi; ion source gas 1 60 psi; ion source gas 2 60 psi; collision gas set to medium.

### 4.2.5. Phytohormone Identification and Quantification

Identification and quantification of all compounds were confirmed through the comparison of the acquired spectra with spectra from the authentic standards. All data were analysed and processed using Analyst version 1.6.2 and MultiQuant version 3.0 software packages. Concentrations were calculated for each compound in equivalence to their respective stable isotope as the internal standard.

### 4.3. Transcriptomic Data

The Red5 *A. chinensis* var. *chinensis* genome [30] was mined for the gene models involved in the cytokinin biosynthetic, catabolic, and signalling pathways, and for plasma membrane H(+)-ATPase genes. The expansin gene models list was sourced from Pilkington et al. [30]. Gene models were then searched in the differentially expressed gene (DEG) lists generated by Nardozza et al. [6] (Bioproject ID PRJNA593615) and heat maps were created to visualise the effect of the carbohydrate supply on gene transcription, with a focus on the two girdled treatments. DESeq output is shown in Figure S5.

### 4.4. Statistical Analysis

The effect of the carbohydrate supply and fruit age (factors) on fruit weight and phytohormone concentrations were analysed using a linear mixed effects model (LME, type 3 sums of squares Kenward-Roger's method) in R (version 3.5.1) [48]. The biological replicates were treated as random effects. When significant effects or interactions were present, the means were separated on the basis of all pairwise comparisons of least-squares means, adjusted for multiple comparison by Tukey's correction (letters assigned; confidence level 95%). Residual plots were inspected to check for the assumptions of normality and constant variance. Where appropriate, a log-transformation was used prior to analysis with the fitted means back-transformed onto the original scale. Pearson correlation analysis was performed to identify positive or negative correlations between fruit weight and phytohormone concentration. Principal component analysis for cytokinin hormones were also performed in R using the MixOmics libraries [49]. Data were plotted using package ggplot2 [50] with 95% confidence interval ellipses.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2218-1989/10/1/23/s1. Figure S1: Cytokinin biplot at 16 weeks after mid bloom; Figure S2: Heat-map for multistep phosphorelay (MSP) pathway kiwifruit genes: cytokinin receptors histidine kinases and positive regulators histidine-containing phosphotransfer proteins; Figure S3: Heat-map for multistep phosphorelay (MSP) pathway kiwifruit genes: negative regulators A-type Arabidopsis Response Regulators (ARR) and positive regulators B-type ARR; Figure S4: Heat-map of kiwifruit plasma membrane H(+)-ATPase genes; Table S1: Proportion of cytokinin types on total cytokinin concentrations in different treatments; Table S2: List of collected samples and biological replicates used for the experiments; Table S3: Multiple reaction monitoring (MRM) transitions used for cytokinin analysis; Table S4: Multiple reaction monitoring (MRM) transitions used for acidic phytohormone analysis; Table S5: DESeq pairwise comparison between low carbohydrate supply (LC) and high carbohydrate supply (HC) samples at 8, 12 and 16 weeks after mid bloom.

**Author Contributions:** Conceptualization, S.N., H.L.B., and A.C.R.; Methodology, J.C. and S.N.; Formal analysis, K.G.H., T.T., D.J., and A.H.T.; Data curation, S.N.; Writing—original draft preparation, S.N., J.C., and A.C.A.;
Writing—review and editing, A.C.A., A.C.R., S.N., and H.L.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by PFR's Premium Kiwifruit research programme, funded by the New Zealand Ministry of Business, Innovation, and Employment's Strategic Science Investment Fund.

**Acknowledgments:** The authors thank Catrin Guenther and Charlotte Voogd for their critical review of the manuscript, and Mark Wohlers for statistical advice.

**Conflicts of Interest:** The authors declare no conflicts of interest.

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<figure><img src="image_71.png" /></figure>

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
<figure><img src="image_72.png" /></figure>

Article

GC-MS Based Metabolite Profiling to Monitor
Ripening-Specific Metabolites in Pineapple
*(Ananas comosus)*

Muhammad Maulana Malikul Ikram <fnref n="1" />, Sobir Ridwani <fnref n="2" />, Sastia Prama Putri <fnref n="1" />,* and
Eiichiro Fukusaki <fnref n="1" />

Received: 10 February 2020; Accepted: 25 March 2020; Published: 31 March 2020

**Abstract:** Pineapple is one of the most cultivated tropical, non-climacteric fruits in the world due to its high market value and production volume. Since non-climacteric fruits do not ripen after harvest, the ripening stage at the time of harvest is an important factor that determines sensory quality and shelf life. The objective of this research was to investigate metabolite changes in the pineapple ripening process by metabolite profiling approach. Pineapple (Queen variety) samples from Indonesia were subjected to GC-MS analysis. A total of 56, 47, and 54 metabolites were annotated from the crown, flesh, and peel parts, respectively. From the principal component analysis (PCA) plot, separation of samples based on ripening stages from C0–C2 (early ripening stages) and C3–C4 (late ripening stages) was observed for flesh and peel parts, whereas no clear separation was seen for the crown part. Furthermore, orthogonal projection to latent structures (OPLS) analysis suggested metabolites that were associated with the ripening stages in flesh and peel parts of pineapple. This study indicated potentially important metabolites that are correlated to the ripening of pineapple that would provide a basis for further study on pineapple ripening process.

**Keywords:** pineapple; metabolomics; ripening; non-climacteric

**1. Introduction**

Pineapple (*Ananas comosus*) market value is approximately 14.7 billion USD with a production volume of around 25 million metric tons in the world [1]. Pineapple is categorized as a non-climacteric fruit. The major difference between climacteric and non-climacteric fruit is non-climacteric fruit produces low levels of ethylene and does not show any major peak in the respiration rate during the ripening process, whereas climacteric fruit depends on ethylene bursts during ripening [2,3]. In addition, ethylene treatment does not give any effect to non-climacteric fruit with the exception of degreening (removal of chlorophyll) [4]. Another distinct characteristic of non-climacteric fruit is the fruit will not continue its ripening process after harvest, thus making it important to be harvested in the right ripening stage to ensure proper quality [3]. The ripening stage of pineapple is divided into 5 stages, C0–C4, with the green-ripe fruit at C0 and the full-ripe fruit at C4 based on United Nations Economic Commission for Europe (UNECE) Standard for pineapple (FFV-49) as seen in Figure 1 [5]. This classification is based on the peel color of pineapple, in which C0 stage contains 0% yellow color, C1 stage contain 0%–25% yellow color, C2 stage contain 25%–50% yellow color, C3 stage contain 50%–75% yellow color, and C4 stage contain 75%–100% yellow color [5]. Pineapple is usually exported

<footnote n="1">¹ Department of Biotechnology, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan; malikul_ikram@bio.eng.osaka-u.ac.jp (M.M.M.I.); fukusaki@bio.eng.osaka-u.ac.jp (E.F.)</footnote>

<footnote n="2">² Center for Tropical Horticulture Studies, IPB University, Jl. Baranangsiang, Bogor 16144, Indonesia; ridwanisobir@gmail.com</footnote>

<footnote>* Correspondence: sastia_putri@bio.eng.osaka-u.ac.jp; Tel.: +81-6-6879-7416</footnote>
in the C1 stage [6], while the fully ripe fruit (C4 stage) is mainly for domestic consumption. The fruit of pineapple consists of the fusion of individual fruits. These individual fruits are developed from a single flower and the external of these fruits were protected with a hard-polygonal shield, commonly called as pineapple peel [7]. On top of pineapple fruit, there are leaves that can be used for vegetative reproduction of pineapple commonly called the pineapple crown. This crown part is commonly harvested along with the fruit harvest [7]. Crown and peel parts develop during pineapple fruit development. Therefore, to understand the pineapple ripening process comprehensively, analysis of pineapple peel and crown is needed in addition to flesh analysis. At present, there is limited information on the differences in metabolite composition of pineapple from different ripening stages [8]. Monitoring metabolites changes using tools such as metabolomics, a comprehensive study of metabolite, is a powerful tool for further understanding pineapple ripening process.

<figure><img src="image_73.png" /><figcaption>**Figure 1.** Pineapple sample from all ripening stages. From left to right: C0 until C4 stages. This classification is based on the peel color of pineapple, in which C0 stage contains 0% yellow color, C1 stage contain 0%–25% yellow color, C2 stage contain 25%–50% yellow color, C3 stage contain 50%–75% yellow color, and C4 stage contain 75%–100% yellow color [5].</figcaption></figure>

Recent studies about fruit ripening metabolomics mostly focused on climacteric fruits, such as banana, mango, capsicum, dates, avocado, peach, climacteric melon, and mangosteen [9–16]. On the other hand, only a few of non-climacteric ripening processed fruit had been elucidated using metabolomics approach, such as cherry, blackcurrant, blueberry, non-climacteric melon, and pineapple [15,17–21]. Previous metabolomics studies on non-climacteric fruit employed mass spectrometry-based instruments, such as gas chromatography-mass spectrometer (GC-MS) or liquid chromatography-mass spectrometer (LC-MS). Several studies performed a combination of headspace-solid phase microextraction (HS-SPME) with GC-MS to measure volatile compounds during the ripening process [15,20]. Reports on pineapple ripening have been focused in volatile and phenolic compounds using HS-SPME-GC-MS, high-performance liquid chromatography with diode array detection and electrospray ionization multiple-stage mass-spectrometry (HPLC-DAD-ESI-MSn), and electrospray ionization mass spectrometry (ESI(-)FT-ICR MS) [21–24]. The outcome from these previous reports suggested the changes in phenolic patterns, such as coumaroyl isocitrate and S-p-coumaryl, and volatile compounds, such as methyl 3-(methylthio)propanoate and δ-octalactone, along the pineapple ripening process.

As mentioned previously, previous studies on pineapple ripening were on the targeted analysis of volatile and phenolic compounds using the flesh part as a sample. To date, there is no study that analyzed different parts of pineapple including flesh, peel, and crown parts, and incorporating broad coverage of primary metabolites such as sugar, organic acid, amino acid, sugar alcohol, sugar acid, and amine compounds. In order to suggest metabolites that are associated with ripening, there are several different multivariate analyses that can be used. The most common multivariate analysis is principal component analysis (PCA) and orthogonal projections to latent structures (OPLS) regression analysis [25,26]. In this study, a metabolite profiling approach using GC-MS in combination with PCA and OPLS was conducted to monitor the changes of primary metabolites (sugar, organic acid, amino acid, etc.) during pineapple ripening process by analyzing pineapple fruit (crown, flesh, and peel) from different ripening stage (Figure S1). OPLS model was constructed using metabolites annotated by GC-MS as an explanatory variable and ripening stages as a response variable. The constructed model from flesh and peel samples indicated several potentially important metabolites that were correlated
with the pineapple ripening process. This study would be important to complement the knowledge
of the pineapple ripening process and could serve as a basis for post-harvest handling strategy in
pineapple industries.

## 2. Results

### 2.1. Optimization of Sample Preparation Methods in Pineapple Fruit

The first analysis was to compare two different sample preparation methods, namely food processor and freeze-drying. This analysis was conducted to optimize sample preparation of pineapple fruit in GC-MS analysis. A total of 47 metabolites belonging to various metabolite classes were annotated by GC-MS analysis in flesh samples (Table S1). These metabolites comprise of 26 metabolites belongs to sugars class, 12 metabolites belong to amino acids and amines, and 9 metabolites belong to organic acids. These annotated metabolites were subjected to PCA to clearly visualize the differences in metabolite levels in pineapple flesh prepared by food processing and freeze-drying methods.

Figure 2 shows the comparison between these two methods after analysis by GC-MS. From the score plot, flesh samples prepared by two different methods were clearly separated along the PC1 with a 95.2% variance. The loading plot showed that almost all metabolites were accumulated in the flesh samples prepared by freeze-dry method. Only sucrose was found to accumulate in samples prepared by food processor method. In the next analysis, we applied the freeze-drying method to analyze different parts of pineapple in GC-MS. A total of 54, 44, and 50 metabolites were annotated from crown, flesh, and peel, respectively (Table S2). PCA score plot from Figure 3 clustered pineapple fruit into three different parts (crown, flesh, and peel) based on the metabolite distributions. Amino acid and organic acid were found to be accumulated in crown part, while the peel and flesh part show accumulation of sugar and sugar-acid. Due to the separation for each part, this result becomes the basis to analyze three parts of pineapple separately.

<figure><img src="image_74.png" /><figcaption>**Figure 2.** PCA of comparison of different sample preparation methods: Food processor method and freeze-drying method. 47 annotated metabolites from GC-MS analysis were auto-scaled prior to PCA. (Left: Score plot between food processor and freeze dry samples; Legends represent the samples and colored as follow: food processor: light brown circle, freeze dry: red circle. Right: Loading plot shown almost all metabolites (except sucrose shown by light brown arrow) showed higher accumulation by freeze-dry method).</figcaption></figure>
<figure><img src="image_75.png" /><figcaption>**Figure 3.** PCA result from different parts of pineapple. 64 tentatively identified metabolites from GC-MS analysis were auto-scaled prior to PCA. Left: Score plot between crown, flesh, and peel part. Legends represent the sample and colored as follows: crown: green circle, flesh: yellow triangle, peel: red square. Right: Loading plot shown that crown part accumulates organic and amino acid, while flesh and peel accumulate sugar and sugar acid.</figcaption></figure>

## 2.2. GC-MS and Principal Component Analysis of Pineapple from Different Ripening Stages

Analysis of crown, flesh, and peel parts of pineapple from different ripening stages was conducted separately. Metabolite profiling approach using GC-MS instrument could detect 351 metabolite peaks in the crown part, 297 metabolite peaks in the flesh part, and 359 metabolite peaks in the peel part. Among those peaks, 85 peaks in the crown part, 74 peaks in the flesh part, and 73 peaks in the peel part were annotated using MSP Library containing RI and EI-MS from our laboratory experimental data. Metabolites from QC samples with RSD more than 20% were excluded from the analysis [27]. After exclusion of metabolites with RSD higher than 20%, the number of annotated metabolites in crown part were 56 metabolites, in the flesh part were 47 metabolites, and in the peel part were 54 metabolites. A complete list of these metabolites during ripening analysis is shown in Table S3.

Figure 4 shows the score plot from PCA for pineapple from different ripening stages (C0 to C4 stage) as observed in three different parts of pineapple. As seen in Figure 4b,c, flesh and peel part showed two distinct clusters along PC1. Less ripe samples (C0–C2) were clustered together and ripe fruit samples (C3 and C4) formed a separate cluster. This trend was explained by 63.9% and 53.3% variance in the flesh and peel part, respectively. However, this trend was not shown in the crown part of pineapple from all principal components. Loading plot in Figure 4b,c showed the metabolite accumulation in the less ripe and ripe samples for flesh and peel part, respectively. These metabolite intensities that used to create a score plot were normalized using an internal standard, ribitol. The internal standard was chosen because it is not present in pineapple samples and stable in a mixed solvent solution.
<figure><img src="image_76.png" /><figcaption>**Figure 4.** PCA result from flesh, crown and peel parts of pineapple from different ripening stages. Variables used for PCA were 56, 47, and 54 annotated metabolites by GC-MS from crown, flesh, and peel parts respectively. Data was auto scaled prior to PCA. (a) Score and loading plot from the crown part; (b) score and loading plot from the flesh part; (c) score and loading plot from the peel part. Legends represent the samples and colored as follows: brown: C0 stage, red: C1 stage, green: C2 stage, blue: C3 stage, black: C4 stage. Upper part show score plot; Bottom part show loading plot. Loading plot was colored based on metabolite classes: green: sugars; red: organic acids; yellow: amino acids and amines.</figcaption></figure>

## 2.3. Orthogonal Projection to Latent Structures of Pineapple Ripening Process

Orthogonal projection of latent structures (OPLS) regression analysis was conducted to identify metabolites that were highly influenced by the process related to the response variable [28]. In this study, two latent variables were used to construct the model using flesh and peel parts of pineapple. Crown part was not analyzed based on the previous result in PCA which indicated that crown part was not able to show any trend in ripening process. Response variables that were used to generate the model were ripening stages from C0 as 1, C1 as 2, C2 as 3, C3 as 4, and C4 as stages 5, while the explanatory variables were metabolites annotated by GC-MS analysis. Pineapple from C0 to C4 ripening stages harvested in April 2019 were used as a training set to generate the model (Figure 5). Model validation was conducted by leave-one-out cross-validation from each replicate.
<figure><img src="image_77.png" /><figcaption>**Figure 5.** Orthogonal projection to latent structures (OPLS) results from flesh and part of pineapple. Explanatory variables in flesh part are 47 metabolites, while in peel part are 54 metabolites. Response variable for both models is the ripening stages with numbered as follows: C0 stage as 1, C1 stage as 2, C2 stage as 3, C3 stage as 4, and C4 stage as 5. Value of R², Q², RMSEE, and RMSECV were used to evaluate the model. (a) OPLS model of flesh part; (b) OPLS model of peel part.</figcaption></figure>

The constructed OPLS regression model with R² of 0.888 and 0.931 for flesh and peel part, respectively, are shown in Figure 5. In the OPLS regression analysis, statistically important metabolites for the models were indicated by the score of variable important in projection (VIP). Metabolites with a VIP score of more than 1 considered important for the model [28] (Table S4). Contributing metabolites were chosen based on the five highest VIP scores. The metabolites were melezitose, inositol, xylonic acid, gluconic acid, and raffinose in the flesh model, whereas inositol, mannose, galactose, sucrose, and aspartic acid were the top five highest VIP metabolites in the peel. Among these highest VIP metabolites in both flesh and peel, melezitose, xylonic acid, gluconic acid, and sucrose have a positive correlation with the ripening stages, while inositol, raffinose, mannose, galactose, and aspartic acid showed a negative correlation with the ripening process (Figure 6).

### 3. Discussion

Metabolite profiling is known to be useful to analyze a large group of metabolites that belong to a specific class of compounds that reflects the dynamic response to physiological change or developmental stimuli [29,30]. In this study, a metabolite profiling approach using GC-MS was employed for the study of pineapple ripening process. GC-MS is suitable for metabolite profiling because it provides high sensitivity, reproducibility, and quantitation of a large number of metabolites with a single-step extraction [31,32]. Metabolite annotated in pineapple crown, peel and flesh were classified as sugars, amino acids, amines, organic acids, and other compounds. Sugars were found to be the most abundant in pineapple. This is in agreement with previous work that mentioned the high content of sugars was observed in pineapple flesh samples [21]. In this study, we conducted for the first time the analysis of peel and crown parts of pineapple in addition to flesh samples. Annotated metabolites from each part were subjected to PCA and OPLS analyses. Principal component analysis (PCA) is a multivariate data analysis that could show the variance among the samples using metabolites as the explanatory data [33]. Based on the PCA, pineapple ripening was clustered into two major phases namely C0-C2 stages (early ripening) and C3-C4 stages (late ripening). The trends above were observed only in flesh and peel samples, whereas there was no clear trend of ripening in crown part. It was previously suggested that crown photo-assimilation seems to be derived from its own photosynthesis, not from
the fruit [34]. It is also well known that pineapple maturation developed from the bottom, not from the top [35]. Therefore, crown part is considered to have no correlation with fruit ripening.

<figure><img src="image_78.png" /><figcaption>**Figure 6.** Bar graph of five highest variable important in projection (VIP) metabolites related to pineapple ripening process in (a) flesh and (b) peel part. The relative intensity of the five highest VIP metabolites was normalized by the internal standard. Vertical axis represents metabolites relative intensity and horizontal axis represents ripening stages. Significant differences (*p* < 0.05) are indicated with the different letters based on mean comparison Tukey's test.</figcaption></figure>

Further analysis to identify potentially important metabolites that are correlated with the ripening process was conducted using OPLS regression analysis. OPLS regression analysis is known to be more powerful to explain the relationship between the response variable and explanatory variable because it is a supervised multivariate analysis [28]. The OPLS regression model shown in Figure 5 has some parameters that could be used to evaluate the quality of the model itself. These parameters are R², Q², RMSEE, and RMSECV. R² is defined as the square of the correlation coefficient between observed and predicted value in a regression [36]. Q² is known to be a reliable parameter for model predictivity [16,36]. RMSEE or root mean square error of estimation and RMSECV or root mean square error of cross-validation are the values to evaluate accuracy, prediction, and model robustness [36,37].