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Article

# Network Analysis Provides Insight into Tomato Lipid Metabolism

Anastasiya Kuhalskaya <fnref n="1" />,<fnref n="2" />, Micha Wijesingha Ahchige <fnref n="1" />, Leonardo Perez de Souza <fnref n="1" />, José Vallarino <fnref n="1" />, Yariv Brotman <fnref n="1" />,<fnref n="2" /> and Saleh Alseekh <fnref n="1" />,<fnref n="3" />,*

Received: 14 February 2020; Accepted: 11 April 2020; Published: 14 April 2020

**Abstract:** Metabolic correlation networks have been used in several instances to obtain a deeper insight into the complexity of plant metabolism as a whole. In tomato (*Solanum lycopersicum*), metabolites have a major influence on taste and overall fruit quality traits. Previously a broad spectrum of metabolic and phenotypic traits has been described using a *Solanum pennellii* introgression-lines (ILs) population. To obtain insights into tomato fruit metabolism, we performed metabolic network analysis from existing data, covering a wide range of metabolic traits, including lipophilic and volatile compounds, for the first time. We provide a comprehensive fruit correlation network and show how primary, secondary, lipophilic, and volatile compounds connect to each other and how the individual metabolic classes are linked to yield-related phenotypic traits. Results revealed a high connectivity within and between different classes of lipophilic compounds, as well as between lipophilic and secondary metabolites. We focused on lipid metabolism and generated a gene-expression network with lipophilic metabolites to identify new putative lipid-related genes. Metabolite-transcript correlation analysis revealed key putative genes involved in lipid biosynthesis pathways. The overall results will help to deepen our understanding of tomato metabolism and provide candidate genes for transgenic approaches toward improving nutritional qualities in tomato.

**Keywords:** lipophilic compounds; lipid-related genes; lipid metabolism

## 1. Introduction

Plants produce a wide variety of biochemical compounds, starting at the central or primary metabolism which generates compounds absolutely vital for plant survival and continuing through the pathways of specialized or secondary metabolism [1-5]. Specialized metabolites display a tremendous diversity, are often specific to certain plant lineages, and play many different roles in adaptation to the environment [6]. Volatile organic compounds (VOCs) are often discussed as a subgroup of secondary metabolites, with their low molecular weight enabling movement across cell membranes and release into the surrounding environment [7]. Secondary metabolites can act as direct or indirect defense agents by deterring herbivores, fending off pathogens, and/or attracting predators or pollinators [8]. When it comes to human consumption of plants, secondary metabolites also fulfill an important role, since they can have beneficial health effects and some of them contribute to flavor [9,10].

<footnote n="1">¹ Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany; Kuhalskaya@mpimp-golm.mpg.de (A.K.); Wijesingha@mpimp-golm.mpg.de (M.W.A.); LPerez@mpimp-golm.mpg.de (L.P.d.S.); Vallarino@mpimp-golm.mpg.de (J.V.); brotman@mpimp-golm.mpg.de (Y.B.)</footnote>

<footnote n="2">² Department of Life Sciences, Ben Gurion University of the Negev, 84105 Beersheva, Israel</footnote>

<footnote n="3">³ Centre of Plant Systems Biology and Biotechnology, 4000 Plovdiv, Bulgaria</footnote>

<footnote>* Correspondence: Alseekh@mpimp-golm.mpg.de; Tel.: +49-(0)331-567-8211</footnote>
Since lipids may either be primary (e.g., glycerolipids, phospholipids) or secondary (derived from the isoprenoid pathway) metabolites, they are often discussed in the literature separately from other metabolites [11–13]. Lipids fulfill many different functions, ranging from carbon storage via cell-membrane components to signaling molecules [14].

The cultivated tomato (Solanum lycopersicum) has been widely used for metabolomic studies [15]. With production of over 150 million metric tonnes in 2017, tomato is the second most consumed vegetable in Europe after potato and the first by market value (www.fao.org/faostat/en/#home). The drought-tolerant green-fruited relative, Solanum pennellii, has been successfully hybridized with the cultivated tomato, and the offspring from that cross has been used to identify a wide range of phenotypic and metabolic quantitative trait loci (QTL) [16–18]. The importance of tomatoes for human diet, combined with the availability of genetic diversity from wild relative species, makes tomato an optimal model crop for studying different aspects of plant physiology [19]. In recent years, tomato metabolism has been intensively studied. Tomato's specialized metabolites in particular have received much attention, since many compounds are known to have positive health effects [20], serving among others as antioxidants. Many efforts have been undertaken to understand the genetic basis of their biosynthesis and to increase the production of these compounds in fruits [21–26].

However, due to its immense complexity and interconnection, it is a great challenge to understand plant metabolism as a whole. QTL mapping is a commonly used approach to dissect plant metabolism and identify genes in the corresponding pathways [27]. Although QTL mapping is useful for the elucidation of individual pathways, this approach is still not sufficient for dissecting metabolism in its entirety due to the latter's complexity [28]. Thus, metabolite correlation network analysis has been suggested as an additional method for elucidating novel connections in plant metabolism [29]. Several studies have used a network approach to display the correlation between metabolic compounds. Schauer et al. (2006) combined mQTL and network analysis to elucidate the relationship between metabolic and yield-related traits over two seasons of a tomato introgression-line (IL) population [30]. The study revealed a modular network with intra-modular connections of amino acids, sugars, and phenotypic traits, and highlighted the connections between metabolic and phenotypic traits. A similar approach was used to obtain several novel insights concerning the interrelation of seed primary [31] and fruit secondary metabolites with yield-associated traits [22]. Production of seed oils with desirable fatty-acid content raised the interest of oilseed breeders and biotechnologists as a result of the growing demand for vegetable oil [13,32–35]. Studies also focused on tomato seeds because their oil is an excellent source of important fatty acids involved in plant growth [36,37]. Following research has also been extended to other tissues, e.g., tomato leaves [31,38].

Despite the large-scale tomato cultivation and the number of studies related to the fruit's chemical composition and its quality, the majority of tomato metabolome research has focused on polar (non-lipid) primary and secondary compounds: sugars and their derivatives, amino acids, and organic acids. Traits such as aroma, color, and nutritional values, all deriving from secondary metabolites, have been extensively investigated, too [1,39]. However, lipid metabolism is still poorly studied in tomato, with several investigations focusing on cuticular waxes [40–43] and the characterization of genes involved in fatty-acid production [44].

In this study we performed a global correlation-based network analysis of metabolic traits, including lipids, volatiles, primary, and secondary metabolites from tomato fruit pericarp, and in addition plant phenotypic traits across a S. pennellii population in three field studies. This allowed us to expose the connectivity network between lipids and other branches of metabolism and key agronomical traits. We further generated a correlation network between the levels of lipophilic compounds and the expression of 1431 lipid-related genes in fruit and leaf. We were able to identify 123 potential lipid-related genes and obtained several novel insights concerning lipid metabolism.
## 2. Results

### 2.1. The Overall Metabolic Network in *S. pennellii* ILs

S. pennellii ILs have contributed over the years to the investigation of more than 2500 QTLs associated with plant morphology and to our understanding of the genetic regulation of primary, secondary metabolism, and lipids [31]. Here, in order to decipher the underlying regulatory organization of lipid metabolism together with other metabolic traits, we created a correlation-based network using already existing data of primary metabolites [45], secondary metabolites [22], lipophilic compounds [44], volatile compounds [10], and yield-related traits [30] across S. pennellii ILs in three independent seasons: 2001, 2003, and 2004 (see Section 4, Supplementary material Table S1). We correlated all metabolic and phenotypic traits against each other using Spearman's rank correlation coefficient. Figure 1 shows an overall view of a correlation-based network of all collected data in S. pennellii ILs. As expected, high correlations occur more often within the same group of metabolites or phenotypic traits than between different groups. The vast majority of overall correlations in the network were positive (84.4%) (Supplementary material Figure S1 and Table S2).

<figure><img src="image_90.png" /><figcaption>**Figure 1.** Overall metabolic network. Each node represents a metabolite or a whole plant phenotypic trait; edges connecting two nodes show an association between two traits. In total, the network is composed of 455 nodes and about 15,000 edges assembled into five large groups: lipophilic metabolites comprise of 171 nodes, primary and secondary metabolites have 89 and 147 nodes, respectively, phenotypic traits have 38 nodes, and the smallest group consists of 10 nodes and represents volatile compounds (Supplementary material Table S1).</figcaption></figure>

### 2.2. Lipophilic Compounds Expose Weak Correlation with Phenotypic Traits and Primary Metabolites

We initially focused on correlations between lipids and phenotypic traits, volatiles, primary, and secondary metabolites (see Section 4).

Few correlations between different lipid classes and yield-related traits such as brix and plant weight were identified (Figure 2a). Specifically, galactolipids were correlated negatively to plant weight. Our results showed correlations between phenotypic traits and other metabolic classes, with 46% of traits being connected to primary metabolites. Brix revealed more connections to primary
metabolites (primarily sugars) than to phenotypic traits. Brix reflects the total amount of soluble solids and therefore cannot be truly regarded as a phenotypic trait.

<figure><img src="image_91.png" /><figcaption>**Figure 2.** (a) Correlation between phenotypic traits and lipophilic compounds in *Solanum pennellii* introgression-lines (ILs). Yield-related traits showed three connection with several lipid classes such as phospho- and galactolipids. DAG—diacylglycerol; TAG—triacylglycerol; DGDG—digalactosyldiacylglycerol; MGDG—monogalactosyldiacylglycerol. (b) Correlation between phenotypic traits and volatiles in *S. pennellii* ILs. Various phenotypic traits are linked to each volatile class.</figcaption></figure>

The network showed negative and positive correlations among phenotypic traits and various volatile compounds (Figure 2b). Lipid-, amino-acid-, and carotenoid-derived volatiles showed 69.2%, 55%, and 90% positive correlations with several phenotypic traits, respectively. Lipid-derived volatile compounds (hexanal, trans-2-hexenal, cis-3-hexen-1-ol) showed some positive correlations with flower-, seed-, and yield-related traits. In addition, yield-related traits showed a high number of negative correlations with amino-acid-derived volatile isovaleronitrile. Two secondary metabolites (calystegine A₃ and calystegine B₂) showed several correlations with phenotypic traits (Supplementary material Table S2).

Our data show strong, mainly positive (92.1%) correlations within the class of primary metabolites. In agreement with previous studies, we observed a highly interconnected amino-acid module (Gly, Ile, Val, Thr, and Ser) [31]. However, primary metabolites such as galactinol and guanidine highly connected with specialized metabolites. In addition, two alkaloids (calystegine A3 and calystegine B2), belonging to the class of secondary metabolites, presented a high number of correlations with primary metabolites.

We observed seven links between primary metabolites and lipophilic compounds such as phospholipids, glycerolipids, and galactolipids.

Analysis of primary metabolites against volatile compounds indicated no significant correlations between amino-acid-derived volatiles like isovaleronitrile, benzaldehyde, 3-methylbutanal, and 2-phenylethanol and their upstream biosynthesis substrates such as leucine and phenylalanine. However, guanidine showed correlations with all amino-acid-derived volatiles. In total, amino-acid-derived volatiles revealed 80.8% positive correlations with primary metabolites. The majority of correlations between lipid-derived volatiles (trans-2-hexenal, hexanal, cis-3-hexen-1-ol) and carotenoid-derived volatiles (geranylacetone and b-ionone) with primary metabolites were positive.

## 2.3. Lipids Are Highly Correlated with Specialized Metabolites and Show Some Negative Correlations with Volatiles

Our results showed a high number of correlations between various lipophilic compounds and secondary metabolites, with 4588 correlations representing 30.8% of all edges shown in the network
(Supplementary Material Table S2). Our data introduced for the first time, a high correlation between specialized metabolites and lipophilic compounds. For example, phospholipids were correlated to almost all secondary-metabolite subclasses. Glycerolipids showed 1725 positive and 335 negative correlations with secondary metabolites. A high number of positive correlations (96%) was also found between galactolipids and secondary metabolites (Table 1, Supplementary material Figure S2).

<table><caption>**Table 1.** Percentage of positive correlations between lipids and secondary metabolites.</caption><thead><tr><th></th><th>MGDGs</th><th>DGDGs</th><th>Phospholipids</th><th>TAGs</th><th>DAGs</th></tr></thead><tbody><tr><td>Acyl sugars</td><td>100</td><td>96</td><td>50</td><td>78</td><td>100</td></tr><tr><td>Flavonoids</td><td>99</td><td>93</td><td>51</td><td>88</td><td>97</td></tr><tr><td>Hydroxycinnamate derivatives</td><td>93</td><td>94</td><td>48</td><td>82</td><td>95</td></tr><tr><td>N-containing compounds</td><td>93</td><td>92</td><td>49</td><td>78</td><td>90</td></tr><tr><td>Unspecified</td><td>100</td><td>99</td><td>50</td><td>85</td><td>100</td></tr><tr><td>Amino acid</td><td>100</td><td>100</td><td>45</td><td>71</td><td>100</td></tr><tr><td>Glycoalkaloids</td><td>100</td><td>100</td><td>50</td><td>78</td><td>100</td></tr><tr><td>Others (phenolics)</td><td>98</td><td>98</td><td>53</td><td>79</td><td>100</td></tr></tbody></table>

We identified 3249 correlations among various groups of secondary metabolites. Only 2% of all identified correlations were negative. Results indicated a high connection between and within different specialized-compound subclasses.

Correlation analysis revealed 10 positive and five negative interactions between lipid-derived volatiles and secondary metabolites. Correlations between amino-acid-derived volatiles and secondary metabolites showed an almost equal amount of positive and negative connections. Our observations revealed 58.7% (27 out of 46) of negative connections between carotenoid-derived volatiles and specialized metabolites (Figure 3a, Supplementary material Table S2). Additionally, a high number of negative correlations between triacylglycerols and volatiles was observed. Other lipid classes were connected to volatiles mostly positively (Figure 3b, Supplementary material Table S2).

<figure><img src="image_92.png" /><figcaption>**Figure 3.** (a) Connection between secondary metabolites and volatiles in *Solanum pennellii* ILs. Volatile compounds are linked to almost all classes of specialized metabolites. (b) Correlation between lipophilic compounds and volatiles in *Solanum pennellii* ILs. Lipid class of TAGs showed numerous negative connections with all types of volatile compounds. DAG—diacylglycerol; TAG—triacylglycerol; DGDG—digalactosyldiacylglycerol; MGDG—monogalactosyldiacylglycerol.</figcaption></figure>

Within all lipid classes we identified 5841 correlations, representing 39.2% of all edges shown in the network, with 1012 (17.3%) being negative. Lipids belonging to the same subclass showed
strong, mainly positive connections with other members of the same class (Table 2). For example, among different triacylglycerols (TAGs), out of 1229 identified correlations 1102 (89.7%) were positive. There was also a minor number of negative correlations within the monogalactosyldiacylglycerol (MGDG) and digalactosyldiacylglycerol (DGDG) subclasses. Out of all identified correlations within the phospholipids subclass, 78.1% were positive. Moreover, there are no negative correlations between diacylglycerols (DAGs) and DGDGs. The highest percentage of negative correlations was observed between phospholipids and TAGs (Table 2, Supplementary material Figure S3).

<table><caption>**Table 2.** Percentage of positive correlations between different lipid subclasses.</caption><thead><tr><th></th><th>MGDGs</th><th>DGDGs</th><th>Phospholipids</th><th>TAGs</th><th>DAGs</th></tr></thead><tbody><tr><td>MGDGs</td><td>99</td><td>99</td><td>75</td><td>96</td><td>90</td></tr><tr><td>DGDGs</td><td>99</td><td>99</td><td>70</td><td>87</td><td>100</td></tr><tr><td>Phospholipids</td><td>75</td><td>70</td><td>78</td><td>60</td><td>65</td></tr><tr><td>TAGs</td><td>96</td><td>87</td><td>60</td><td>90</td><td>85</td></tr><tr><td>DAGs</td><td>90</td><td>100</td><td>65</td><td>85</td><td>100</td></tr></tbody></table>

## 2.4. Fruit-Specific Lipid-Related Genes Show a Mainly Positive Pattern of Change with Lipophilic Compounds

For further exploration of lipid biosynthesis in tomato fruit pericarp, we correlated lipid profiles across 76 introgression lines [44] with multiple next-generation-sequencing gene-expression datasets from the same introgression lines. We used data consisting of 188 and 117 different lipophilic compounds from leaves and fruits of 74 ILs, respectively, and gene-expression data of lipid-related genes (1431) to perform the correlation analysis.

Using Spearman rank correlation coefficient, we identified 59 positive connections out of 173 in fruits and 33 positive correlations out of 73 in leaves. Figure 4 shows the significant correlation values between gene-expression levels and different lipid concentrations in fruits (Figure 4a) and leaves (Figure 4b).

<figure><img src="image_93.png" /><figcaption>**Figure 4.** Heatmap of correlation values between lipid-related genes expression and lipid levels in tomato (a) fruits and (b) leaves. The number of lipid-related genes is higher in the fruit dataset compared to the leaf dataset (81 and 42, respectively). In tomato fruit, lipid-related genes linked mostly to TAGs, while in tomato leaf predominantly to phospho- and galactolipids. DAG—diacylglycerol; TAG—triacylglycerol; DGDG—digalactosyldiacylglycerol; MGDG—monogalactosyldiacylglycerol; SQDG—sulfoquinovosyldiacylglycerol.</figcaption></figure>
Our analysis identified 123 potential lipid-related genes in both fruit and leaf datasets (Supplementary material Table S3). In agreement with previous results [44], we observed that lipase (*Solyc12g055730*) showed a significant high correlation with different levels of TAGs (Figure 4a).

Our fruit data showed that the expression of the gene for 1-acyl-sn-glycerol-3-phosphate acyltransferase (GPAT) (*Solyc11g065890*) is positively correlated with 18 unsaturated TAGs, four of which showing significant mQTLs in IL 11-2 and IL 11-3 [44]. Further, expression analysis revealed that in IL 11-2 this gene is expressed 1.5 times lower compared to M82, and that in IL 11-3 expression of the gene is 0.69 times higher than in M82. Moreover, various TAGs display correlations with lipid-related genes putatively annotated as phospholipase D (*Solyc01g103910*), non-specific lipid transfer protein (*Solyc10g075150*), acyl-ACP thioesterase (*Solyc12g006930*), and lipoxygenase (*Solyc03g122340*), whereas another lipoxygenase (*Solyc01g099210*) showed five positive correlations with various phospholipids. We also observed connections of galactolipids to several lipid-related genes (Supplementary material Table S3).

Finally, we identified strong correlations between lipophilic compounds and the expression of a class III lipase (*Solyc09g091050*) located on chromosome 9. The gene was negatively correlated with DGDG 36:4 (–0.46). Our previous QTL data showed a significant mQTL for galactolipids and phospholipids in this region. The QTL was mapped to a narrow overlapping region of IL 9-3, IL 9-3-1, and IL 9-3-2. The levels of DAGs, DGDGs and MGDGs, and TAGs were significantly decreased in IL 9-3, IL 9-3-1, and IL 9-3-2 compared to M82. Furthermore, gene expression was 4.6-fold higher in cultivated tomato compared to introgression lines harboring the gene (Figure 5).

<figure><img src="image_94.png" /><figcaption>**Figure 5.** Transcript level of class III lipase (*Solyc09g091050*) in red ripe fruits of M82, *Solanum pennellii*, IL 9-3, IL 9-3-1, IL 9-3-2. Expression of the gene in wild tomato species *Solanum pennellii* as well as in ILs carrying the same allele version is lower in comparison to cultivated tomato variety M82. Asterisks indicate significant differences (* *p* < 0.05).</figcaption></figure>

To provide additional support for the observed results, we performed genomic sequence analysis of the promoter region of the lipase (*Solyc09g091050*) and compared the promoter sequences of *S. lycopersicum* cv. M82 and *S. pennellii* by genome alignment [17]. Results showed several small deletions and nucleotide substitutions in the promotor region, while the coding region showed 99% similarity between M82 and *S. pennellii*.

Additionally, we investigated several significant correlations between different lipid classes and other lipid-related genes in tomato fruits (Supplementary material Table S3).

## 2.5. Leaf-Specific Lipid-Related Genes Show Many Negative Correlations with Lipophilic Compounds

Similar to the above, we extended our analysis and combined leaf lipid profiling of ILs [44] with transcriptomic data from the same lines [46,47]. Using Spearman rank correlation coefficient, we identified 45.2% (33 out of 73) and 54.8% (40 out of 73) positive and negative correlations, respectively (Figure 4a). Our results show correlations between lipid transfer protein (*Solyc03g079880*),
3-ketoacyl CoA thiolase 1 (*Solyc09g061840*), and glycerophosphoryl diester phosphodiesterase (*Solyc11g045040*) with different DAGs and phospholipids. Phospholipids additionally were linked to phospholipid-translocating flippase (*Solyc01g011100*), diacylglycerol kinase (*Solyc01g096500*), and lipid transfer proteins (*Solyc03g119210* and *Solyc10g075070*). Moreover, one transfer protein, *Solyc03g119210*, was correlated with galactolipids (DGDG 32:3 and SQDG 32:1). Many other significant correlations between different lipid classes and lipid-related gene candidates were identified (Supplementary material Table S3).

The cholesterol acyltransferase gene (*Solyc05g050710*), located on chromosome 5 (IL 5-3), predicted to take part in lipid catabolism, exhibited significant positive correlation with PC 32:0 (0.4). In the same region, several significant mQTLs for different lipid classes were identified [44]. In addition, the *Solyc05g050710* expression is almost 20-fold higher in *S. pennellii* compared to the cultivated variety (M82) (Figure 6a). Comparison of the coding sequence between *S. pennellii* and M82 revealed 99% identity. However, there are many differences in the promoter region, including a 17-bp deletion in *S. pennellii* compared to the cultivated variety, and additionally a 47-bp deletion in M82 compared to the allele derived from the wild species. This may account for the difference in the expression levels of *Solyc05g050710* between M82 and its wild relative *S. pennellii* (Figure 6b).

<figure><img src="image_95.png" /><figcaption>**Figure 6.** (a) Transcript levels of the cholesterol acyltransferase gene (*Solyc05g050710*) in red ripe fruits of M82, *Solanum pennellii*, IL5-3. Expression of the gene in wild tomato species *Solanum pennellii*, similar to IL 5-3 expressing the same allele version, is higher compared to cultivated tomato variety M82. (b) Comparison between the promoter regions of allelic versions of *Solyc05g050710* derived from cultivated tomato variety M82 and wild tomato *Solanum pennellii*. Deletions of 47 and 17 bp can be found in the promoter sequence of *Solanum lycorepsicum* and *Solanum pennellii* compared to the respective other. Asterisks indicate significant differences (* *p* < 0.001).</figcaption></figure>

**3. Discussion**

**3.1. Network Analysis: Correlation between Metabolic and Phenotypic Traits**

Numerous efforts have been made to identify and characterize metabolic quantitative trait loci (mQTLs) in tomato, with a focus on primary and secondary metabolites [10,22,30,44,48,49]. In addition, QTLs for volatile organic compounds from tomato fruit [24,50–52] and acyl sugars in tomato leaf trichomes have been defined [53,54], with further studies focusing on natural variation [54–58] and cuticle composition [59]. A series of genome-wide association studies (GWAS) contributed to assessing the effects of domestication and crop improvement on the fruit metabolome [15,46,47,59–62]. Despite all these studies, relatively few investigations have hitherto aimed at understanding the genetic basis of lipid composition in tomato fruit, with few studies mainly focused on cuticular lipids [17,59,63,64].

Here, we investigated correlations between different classes of metabolites and phenotypic traits in fruits, combined with expression analysis of lipid-related genes in tomato fruits and leaves. The generated network partially validates previously discovered correlations and presents new ones. For example, a QTL for brix (*Brix9-2-5*) deriving from green-fruited tomato species increases glucose
and fructose contents in cultivated tomato fruits [65]. We identified in our network significant positive correlations of brix with different sugars (Supplementary material Table S2). Additionally, we observed strong correlations between amino acids (Gly, Ile, Val, Thr, and Ser), with an average *r*-value of 0.78 and small percentage of negative interactions of correlations within the class of secondary metabolites, in agreement with previous studies [22,31,65].

Large classes of lipophilic compounds, similarly as specialized metabolites, display a considerable diversity across different plant species, e.g., isoprenoid-derived compounds are considered "secondary" metabolites produced in a cell-specific manner and are not directly involved in cell growth and development [13]. Our results showed a high number of significant correlations (30.8% of all) between lipophilic and specialized compounds. The number of significant correlations between lipids and other metabolic traits is smaller.

Several yield-related traits showed correlations to galactolipids and to one phospholipid. Tomato fruit as an organ, unlike maize cobs, for example, does not store much lipids. Unlike seeds, tomato fruit and leaf cells do not accumulate high amounts of storage lipids. Lipophilic compounds in tomato fruits and leaves participating mainly in signaling, membrane structure, and development [66–69].

Furthermore, lipids, similarly to secondary metabolites, were less correlated with phenotypic traits (Figure 1). It has been shown experimentally that the variability in secondary metabolites does not impact morphological and yield-related traits [21,70,71]. Therefore, the ability to change lipid composition or levels in tomato fruits would be a valuable tool for improving fruit quality and flavor. For example, the composition of fatty acids can be significantly changed without altering the overall plant morphology [72]. Nevertheless, versatility of lipophilic compounds might indirectly affect traits like shelf-life, if cuticle lipids are changed [73]. Until now, the influence of lipophilic compounds on overall plant phenotype remains unclear.

The connection between secondary metabolites and lipids seems to be more direct since changes in one compound class can have an effect on the other. Experimental evidence has already highlighted the close connection between secondary metabolites and cuticle lipids [42], however, no experimental validation exists of the relationship between those two metabolic classes in fruit pericarp.

In our data, we identified several negative correlations between lipid-derived volatiles and various TAGs. The observations here confirmed previous finding in tomato fruit describing linkage between decreasing levels of TAGs and simultaneously increasing levels of volatiles originating from lipids [44]. In tomato fruit some volatiles are linked to overall liking and flavor intensity [25].

In our network, we observed strong correlations within the lipid class. Lipophilic compounds correlate mostly positively between each other. For example, most of the correlations between galactolipids and glycerolipids are positive. However, phospholipids showed numerous negative correlations with all other classes. These results indicate that phospholipids are functionally the most distinct lipid class.

## 3.2. Network Analysis: Combining Metabolite Profiling and Expression for Gene Discovery

Plant lipid metabolism is constantly under investigation. In the well-studied model plant *A. thaliana* only around 40% of the 700 genes putatively annotated as lipid related are functionally characterized. In other plant species like tomato, that number is much smaller [74].

Recently we applied a quantitative genetic strategy to a *S. pennellii* IL population and mapped more than 160 various lipid species belonging to 10 different classes, with a total of 1528 and 428 mQTLs in fruit and leaf, respectively [44]. Here, we combined lipid profiling in leaf and fruit tissues across 76 ILs with gene expression analysis in order to identify genes involved in lipid biosynthesis.

To validate our approach, we checked whether our data confirmed previously proven correlations. We identified connections between a class III triacylglycerol lipase (*Solyc12g055730*) and various TAGs (TAG 48:2, TAG 58:0, TAG 48:3). It has been suggested that the enzyme catalyzes TAGs for further volatiles biosynthesis [44].
Our results highlighted several other lipid-related candidate genes in fruits and leaves (Figure 4). In tomato fruit, for example, the eQTL of lipase (*Solyc09g091050*) and mQTL of DGDGs and phospholipids confirmed high correlation between the gene and DGDG 36:4 (−0.46). Further validation of the gene's function is required.

Furthermore, we identified a high number of lipid-related genes that correlated positively with TAGs. For example, the gene putatively annotated as acyl-ACP thioesterase (*Solyc12g006930*) correlates positively with nine TAGs with an average *r*-value of 0.43. The enzyme is essential in the process of chain termination during de novo fatty-acid synthesis [75]. Another example is the 1-acyl-sn-glycerol-3-phosphate acyltransferase gene (GPAT) (*Solyc11g065890*), which correlated with 18 unsaturated TAGs. The *A. thaliana* ortholog (*At3g57650*) was shown to be involved in phospholipid and TAG biosynthesis [76,77]. In tomato, GPAT catalyzes acylation at the *sn*-1 position of glycerol-3-phosphate to produce lysophosphatidic acid (LPA) with subsequent TAG synthesis [78]. For the same gene we found a correlation between level of expression from leaf dataset and MGDG 32:6. Differences in connections depending on the tissue type could suggest altered function of the same gene in fruits and leaves.

Interestingly, our data revealed mainly positive correlations between expression of lipid-related genes and levels of lipophilic compounds in tomato fruit, whereas in tomato leaves these were mostly negative (Supplementary material Table S3). This may suggest that the genes involved in biosynthesis and regulation of lipid metabolism are generally different between fruits and leaves. These results are supported by a higher number of identified mQTLs in fruit compared to leaves [44]. This may indicate that lipid-related genes were less affected in leaves than in fruits in the context of the domestication process [79]. This could further mean that tomato fruits as sink tissues, which are dependent on carbon supply from source tissues, might need a tighter regulation of lipid production, compared to tissues like leaves where carbon is assimilated, making a flux to lipid metabolism "shorter" and more flexible [80].

In our leaf dataset, a high number of lipid-related genes were found to be correlated mainly with phospholipids and galactolipids compared to other subclasses. Galactolipids, for instance, represent the most abundant lipid class in thylakoid membranes, organelles specifically in leaves [81]. For instance, in our study we identified correlations between phospholipids and phospholipid-translocating flippase (*Solyc01g011100*), diacylglycerol kinase (*Solyc01g096500*), and lipid transfer proteins (*Solyc03g119210*, *Solyc10g075070*). Moreover, *Solyc03g119210* correlates with galactolipids. Another lipid-related gene candidate expressed in tomato leaves—3-ketoacyl CoA thiolase 1 (*Solyc09g061840*)—exposes correlations with DAGs and phospholipids [82]. The gene ortholog in *A. thaliana* was suggested to be involved in fatty-acid beta-oxidation [83]. Several other lipid-related genes such as particle serine esterase (*Solyc04g077180*) [84], cyclopropane-fatty-acyl-phospholipid synthase (*Solyc04g056450*) [85], acyl-CoA-binding protein (*Solyc08g075690*) [86], and long-chain fatty alcohol dehydrogenase (*Solyc09g090350*) [87] showed correlations with various phospholipids, galactolipids, and glycerolipids (Supplementary material Table S3).

It has been observed that lipid metabolism could be genetically regulated on intra-class and inter-class levels [44]. We have identified several examples of genes following the pattern of intra-class level regulation such as GPAT (*Solyc11g065890*), which correlates with 18 TAGs. In contrast, the pattern of inter-class regulation was followed by lipid transfer protein gene (*Solyc03g079880*) or 3-ketoacyl CoA thiolase 1 (*Solyc09g061840*), which contributes to regulation of two different lipid subclasses simultaneously.

In this study, we evaluated metabolic trait correlations and performed global analysis of trait associations across a *S. pennellii* IL population. This is by no means the first time that network analysis has been used for evaluation of the relationships between traits in wide genetic populations, with many previous examples in Arabidopsis, potato, and maize [88–90]. Besides, the analysis has been applied for a range of different traits and tissue types in tomato populations [30,31,45]. However, here we included for the first time three major different lipid classes and revealed several insights concerning
the interrelation of traits from yield-associated traits, primary, and secondary metabolism, volatiles with lipids.

Our network using correlation between gene expression and metabolite levels combined with DNA sequence analysis highlighted several candidate genes putatively involved in lipid biosynthesis or regulation. The presented results complement previous studies regarding metabolic traits in a *S. pennellii* IL population [10,22,30,44,45] and can be used for expanding the knowledge of lipid metabolism in tomato.

# 4. Materials and Methods

## 4.1. Plant Material

Data used in this study were based on *S. pennellii* ILs. The *S. pennellii* IL population was created by replacement of marker-defined genetic regions of the wild species *S. pennellii* with homologous fragments of the cultivated tomato *S. lycopersicum* (M82), representing whole wild-genome coverage of *S. pennellii* [16].

We used already available data for primary and secondary metabolites, lipids, and phenotypic traits. The data were obtained using a population grown in the Western Galilee Experimental Station in Akko, Israel, in a completely randomized design with one plant per m². The field was irrigated with 320 m³ of water per 1000 m² of field area throughout the season. The harvest of fruit was done when 80%–100% of tomatoes were red [16]. All the data were obtained from peeled-off fruit pericarp. Primary and secondary metabolites were available for three independent seasons: 2001, 2003, and 2004 [30,45]. Lipids data were available for seasons 2001 and 2003 [44]. Yield-related traits were available for seasons 2001 and 2004 [30], and flower-, seed- and fruit-related phenotypic traits—for season 2004 [30,91].

The data for volatile compounds were obtained from an *S. pennellii* IL population [10]. All lines were grown in randomized, replicated plots in three different sites (Gainesville, Citra, and Live Oak, Florida) over the seasons of 2002 to 2004. Volatile data we focused on in our study were consistently available only for season 2003. We also used available data for volatiles of interest for season 2004. Plants were grown using standard commercial practices in raised plastic mulched beds. Fruits from all plants for each line were combined and analyzed as they reached the red ripe stage [10].

Transcriptomic data across *S. pennellii* ILs RNA-seq of 1431 lipid-related genes from young leaf [47] and fruit peeled-off pericarp were obtained under http://ted.bti.cornell.edu [46]. We selected all expressed lipid-related genes across the tomato genome (based on GO). We extracted 647 and 786 lipid-related genes from leaf and fruit datasets, respectively.

## 4.2. Correlation Analysis

All metabolite and transcript values used for correlation analysis correspond to the standard scores of the log transformed data. Spearman correlation matrices were calculated in R (R Development Core Team, 2010) using the *cor* function of the *stats* package (https://www.r documentation.org/packages/stats). For the trait/trait correlations we used a critical *p*-value of 0.05, since it is a commonly used threshold for statistical analysis. Correlation *p*-values were obtained by performing a permutation test based on Spearman correlations using the *perm.cor.test* function of *jmuOutlier* package (https://www.r documentation.org/packages/jmuOutlier /versions/2.2/topics/perm.cor.test), set for 20,000 permutations. To select the most meaningful correlations for the network analysis, arbitrary cut-offs were set to an absolute correlation coefficient higher than 0.3 and 0.4 for trait/trait and trait/transcript networks, respectively. We set up the cut-off for trait/trait correlations so that known correlations would be incorporated in our network. Approved significant correlation between brix and sugars were shown before [65]. In our network the average correlation between brix and sugars were 0.378. Additionally, we used a relatively relaxed correlation coefficient threshold of 0.3, because we were integrating data from different platforms. For trait/transcript cut-off 0.3 we reported in total 1537
correlations with lipid classes. To get more insight on some of these correlations we decided to use a stronger cut-off of correlation coefficient (≥0.40).

Depending on the measurement or dataset, we had different amounts of replicates for the different traits in the introgression lines. For GC-MS replicate number was between 1 and 11, for LC-MS between 1 and 12, for lipids between 3 and 4, for phenotypic traits between 1 and 12 and for volatiles always 1. The number of replicates for M82 wild type was usually much higher.

The metabolite/metabolite network plot was produced using Cytoscape version 3.6.1 with nodes representing different metabolites and phenotypic traits and edges representing pairwise correlation above the set threshold. All metabolites and transcripts exhibiting at least one pairwise correlation above the metabolite/transcript correlation network cut-off were selected to be represented in the heatmap produced using the *plothmap* (https://CRAN.R-project.org/package=plothmap) package in R.

### 4.3. Promoter Analysis

Promoter analysis of *Solyc05g050710* and *Solyc09g091050* was performed on the accessions *S. lycopersicum* (M82) and *S. pennellii* (LA0716). Alignment of the promoter region of *Solyc05g050710* and *Solyc09g091050* was done with CLUSTALW (http://www.genome.jp/tools/clustalw/).

### 4.4. Trait Classes Used for Correlations

Phenotypic traits were divided to flower traits (anther length, anther width, anther length/width ratio, ovary length, ovary width, ovary length/width ratio, style length, style width, style length/width ratio), seed traits (seed length, seed width, seed length/width ratio, seed weight, seed number per fruit, seed weight per fruit, seed number per plant, seed weight per plant, seed number per fruit unit, inflorescence, flowers per inflorescence, flowers per plant), fruit-related (fruit length, fruit width, fruit length/width ratio, fruit pericarp thickness, fruit length/pericarp thickness ratio, fruit width/pericarp thickness ratio, fruit locule number) and yield-related ((brix (BX), brix yield (BY), plant weight (PW), total yield (TY), harvest index (HI), biomass (BM), fruit number (FN), red fruit weight (RED), earliness (EA), mean fruit weight (FW)) subgroups. Specialized metabolites were divided to flavonoids, glycoalkaloids, phenolics, N-containing compounds, hydroxycinnamate derivatives, acyl sugars, polyamines, and unspecified compounds subgroups, lipophilic compounds to trialkylglycerols, diacylglycerols, phospholipids, digalactoyldiacylglycerols, monogalactoyldiacylglycerols subgroups, and volatile compounds to carotenoid-, lipid-, and amino acid-derived subgroups. Volatile compounds were divided to carotenoid-, lipid-, and amino acid-derived subgroups.

**Supplementary Materials:** The following are available online http://www.mdpi.com/2218-1989/10/4/152/s1; Figure S1: Percentage of positive and negative correlations in overall metabolic network, Figure S2: Percentage of positive and negative correlations between lipophilic compounds and secondary metabolites, Figure S3: Percentage of positive and negative correlations within the class of lipophilic compounds. Table S1: classes and subclasses of metabolic and phenotypic traits used for the network analysis, Table S2: All identified correlations between metabolic and phenotypic traits used for the network analysis. Table S3: correlation between lipid-related genes in fruits and leaves.

**Author Contributions:** Conceptualization, S.A.; Y.B.; methodology, L.P.d.S., A.K., M.W.A.; formal analysis, A.K.M., W.A.; writing—original draft preparation, S.A., A.K., M.W.A.; writing—review and editing, S.A., Y.B., L.P.d.S., J.V., A.K., M.W.A.; visualization, A.K., M.W.A.; supervision, S.A., Y.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was in part supported by the PlantaSYST project by the European Union's Horizon 2020 research and innovation programme (SGA-CSA No 664621 and No 739582 under FPA No. 664620), and ISRAEL SCIENCE FOUNDATION (grant No. 859/19).

**Acknowledgments:** We thank Dr. Hezi Tenenboim for his kind editorial assistance.

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

# Metabolomics to Exploit the Primed Immune System of Tomato Fruit

Estrella Luna<fnref n="1" />, Amélie Flandin <fnref n="2" />,<fnref n="3" />, Cédric Cassan <fnref n="2" />,<fnref n="3" />, Sylvain Prigent <fnref n="2" />,<fnref n="3" />, Chloé Chevanne <fnref n="2" />, Camélia Feyrouse Kadiri <fnref n="2" />, Yves Gibon <fnref n="2" />,<fnref n="3" /> and Pierre Pétriacq <fnref n="2" />,<fnref n="3" />,*

Received: 14 February 2020; Accepted: 4 March 2020; Published: 6 March 2020

**Abstract:** Tomato is a major crop suffering substantial yield losses from diseases, as fruit decay at a postharvest level can claim up to 50% of the total production worldwide. Due to the environmental risks of fungicides, there is an increasing interest in exploiting plant immunity through priming, which is an adaptive strategy that improves plant defensive capacity by stimulating induced mechanisms. Broad-spectrum defence priming can be triggered by the compound β-aminobutyric acid (BABA). In tomato plants, BABA induces resistance against various fungal and bacterial pathogens and different methods of application result in durable protection. Here, we demonstrate that the treatment of tomato plants with BABA resulted in a durable induced resistance in tomato fruit against *Botrytis cinerea*, *Phytophthora infestans* and *Pseudomonas syringae*. Targeted and untargeted metabolomics were used to investigate the metabolic regulations that underpin the priming of tomato fruit against pathogenic microbes that present different infection strategies. Metabolomic analyses revealed major changes after BABA treatment and after inoculation. Remarkably, primed responses seemed specific to the type of infection, rather than showing a common fingerprint of BABA-induced priming. Furthermore, top-down modelling from the detected metabolic markers allowed for the accurate prediction of the measured resistance to fruit pathogens and demonstrated that soluble sugars are essential to predict resistance to fruit pathogens. Altogether, our results demonstrate that metabolomics is particularly insightful for a better understanding of defence priming in fruit. Further experiments are underway in order to identify key metabolites that mediate broad-spectrum BABA-induced priming in tomato fruit.

**Keywords:** tomato; metabolomics; biochemical phenotyping; priming; BABA; *Botrytis cinerea*; *Phytophthora infestans*; *Pseudomonas syringae*

## 1. Introduction

The increase in world food demand and the indiscriminate use of chemical fertilisation highlight the need to adopt sustainable crop production strategies. Given the major threat of phytopathogenic microbes to food production [1] and ecosystem stability worldwide [2], novel practices are needed to combat these threats. Tomatoes are a highly consumed fruit that represent the eleventh largest commodity, with nearly 183 million tons produced in 4 million hectares in 2017 [3]. Crop yields are strongly affected by filamentous and bacterial pathogens, including the fungus *Botrytis cinerea*, the oomycete *Phytophthora infestans* and the bacterium *Pseudomonas syringae*. These pathogens can claim the complete loss of the crop within days of exposure [4–6]. Currently, strategies of control against these biological threats are based on the use of chemical pesticides applied at a pre-harvest stage.

<footnote n="1">¹ School of Biosciences, Uni. Birmingham, Birmingham B15 2TT, UK; e.lunadiez@bham.ac.uk</footnote>

<footnote n="2">² UMR BFP, University Bordeaux, INRAE, 33882 Villenave d'Ornon, France; amelie.flandin@inrae.fr (A.F.); cedric.cassan@inrae.fr (C.C.); sylvain.prigent@inrae.fr (S.P.); chloe.chevanne@etu.u-bordeaux.fr (C.C.); camelia.kadiri@etu.u-bordeaux.fr (C.F.K.); yves.gibon@inrae.fr (Y.G.)</footnote>

<footnote n="3">³ Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 33140 Villenave d'Ornon, France</footnote>

<footnote>* Correspondence: pierre.petriacq@inrae.fr</footnote>
However, up to 50% of tomato losses occur at a post-harvest stage [7] due to, among many reasons, the inability to use chemicals at this stage due to residue toxicity. Therefore, new methods of disease control are needed in order to control infections by pathogenic microbes. Exploiting the plant immune system can represent an effective strategy to provide sustainable disease protection [8,9].

Plants are able to defend themselves against pathogens thanks to their innate immune system [10]. In addition, plants are able to sensitise their immune system to protect themselves against biotic threats. This is known as the priming of defence, which is commonly referred as the adaptive part of the plant immune system [11]. Priming occurs after the perception of stimuli that lead to an enhanced responsiveness of defence mechanisms upon subsequent attack [12]. Among stimuli, the chemical agent β-aminobutyric acid (BABA) has been widely studied for its capacity to result in broad-spectrum-induced resistance (IR) in a broad range of plant species [13]. BABA is a non-protein amino acid that has been demonstrated to be a plant product [14]. The work done in the model plant *Arabidopsis* (A.) *thaliana* revealed that this outstanding performance is due to the priming activity of multiple signalling pathways [13]. BABA primes salicylic acid (SA)-dependent defences and the deposition of callose, which result in effective protection against biotrophic and necrotrophic pathogens, respectively [15,16]. Importantly, it has been reported that, in many plant species, treatments with BABA result in a stress phenotype that manifests as changes in plant development (e.g., growth, yield, seed production) [17–19]. The discovery of the molecular receptor of BABA in *A. thaliana* sheds light into the reasons behind the stress response associated with this chemical: BABA binds to an aspartyl-tRNA synthetase and blocks the enzyme, consequently triggering the accumulation of its canonical substrate, uncharged tRNA, which leads to the activation of the stress response associated with amino acid imbalance in the plant [20]. Moreover, high concentrations of BABA or in specific plant species such as potatoes, can also lead to stress, as the chemical directly activates defence mechanisms, which is a costly trade-off in terms of energy resources to the plant [19,21].

In the tomato system, BABA is known to be able to induce resistance against at least 10 different pests and pathogens, including *B. cinerea*, *P. infestans* and *P. syringae* [13]. Similarly to what has been described in *A. thaliana*, priming of SA-dependent mechanisms has been reported [13]. However, it is likely that further priming mechanisms are responsible for its capacity to induce broad-spectrum resistance. Moreover, studies have demonstrated that priming of BABA is long-lasting [22]. For instance, it has been reported that, after the treatment of tomato seedlings, BABA-IR against *B. cinerea* is maintained for weeks in leaves [18]. Moreover, analysis of the resistance phenotypes in tomato fruit demonstrated that BABA-IR reaches the fruiting stages, as tomatoes from plants that had been treated with BABA at the seedling stage were more resistant to *B. cinerea* than the control [23].

Treatments of plants with BABA did not impact yield or fruit size but resulted in delayed fruit production and ripening [23]. In fruit, durable induced resistance has been linked to the accumulation of specific metabolites. For example, it was reported that, after treatment of seedlings with BABA, there was an accumulation of metabolites associated with alkaloid, terpenoid or jasmonate pathways [23]. It was therefore speculated that these metabolites could be responsible for the enhanced resistance, and therefore could mark the priming fingerprint in tomato fruit [24]. Importantly, however, the metabolites responsible for expression of priming of defence mechanisms after infection still remain unknown. Here, we aimed to determine the metabolic shifts that underpin the BABA priming of immune responses against pathogens of a different nature that infect fruit, including necrotrophic and biotrophic microbes. A combination of targeted biochemical phenotyping for major plant compounds involved in central metabolism and untargeted metabolomics could unveil discriminant metabolic biomarkers that respond to BABA priming and inoculations. To the best of our knowledge, this is the first metabolomic study with multiple fruit pathosystems in relation to the priming of immune responses.
## 2. Results

### 2.1. Effect of BABA on Broad-Spectrum Resistance in Fruit

We determined whether the treatment of tomato plants with BABA resulted in a durable induced resistance in tomato fruit against the fungal necrotrophic pathogen *B. cinerea* (*Bot*), the biotrophic oomycete *P. infestans* (*Phy*) and the hemibiotrophic bacteria *P. syringae* pv. *tomato* (*Pst*). Box-plots showing sample datapoints and mean of the scored symptoms (*n* = 11) revealed that BABA-treated plants produced fruit that were statistically more resistant to *Bot*, *Phy* and *Pst* (*P* < 0.01) than the controls (Figure 1). This indicates that BABA is able to induce resistance in tomato plants for a durable fruit protection against pathogens that have different infection strategies.

<figure><img src="image_99.png" /><figcaption>**Figure 1.** β-aminobutyric acid (BABA) primes tomato fruit for a durable disease resistance against three different pathogenic microbes. (A) photographs showing tomato fruit 2 days after infection with *Botrytis cinerea* (*Bot*, left), *Phytophthora infestans* (*Phy*, middle) or *Pseudomonas syringae* pv. *tomato* (*Pst*, right). (B) box-plots of disease symptoms from fruit that originated from plants treated with water or BABA (500 µM) then inoculated with water as a mock control or with *Bot* (left), *Phy* (middle) and *Pst* (right). Symptoms were scored 2 days after inoculation from 11 biologically replicated fruit (n = 11). Asterisks indicate statistically significant differences between water- and BABA-treated plants (t-test, *P* < 0.01).</figcaption></figure>

### 2.2. Effect of BABA on Fruit Yield and Development

We investigated whether the treatment of tomato seedlings with BABA could impact fruit development and yield. As described previously [23], no differences were found in fruit size (Figure 2A), but delayed fruit production (Figure 2B) and fruit ripening (Figure 2C,D) were reported. However, it was observed that, at 13 weeks of growth, the proportion of green fruit was statistically significantly higher in BABA-treated plants (40%) compared to control plants (33%) (Figure 2D), indicating that fruit production did not slowdown in BABA-treated plants after ripening processes had begun (Figure 2D). This might suggest a positive trade-off for BABA treatment on fruit development.
<figure><img src="image_100.png" /><figcaption>**Figure 2.** Effect of BABA on fruit yield and development. (A) fruit size (cm) from plants treated with water (blue) or 500 µM BABA (yellow). NS: not statistically significant (t-test, *P* > 0.05). (B) number of green fruit produced from water (blue)- or BABA (yellow)-treated plants after weeks of growth. Asterisks indicate statistically significant differences (t-test, *P* > 0.05). (C) number of ripped fruit produced from water (blue)- or BABA (yellow)-treated plants after weeks of growth. Asterisks indicate statistically significant differences (t-test, *P* < 0.05). (D) Proportion of fruit at different stages of fruit ripening in control and BABA-treated plants, expressed as percentage of occurrence per treatment at different weeks of growth. Asterisks indicate statistically significant differences between distributions at specific timepoints (Chi-square test, *P* < 0.05).</figcaption></figure>

2.3. *Global Metabolomics after BABA Treatment and After Inoculation*

In order to substantiate the disease resistance after BABA treatment (Figure 1), we investigated fruit
metabolism from ethanol extracts of freeze-dried tomato pericarps (n = 4) using i) targeted biochemical
profiling of several major compounds involved in the central metabolism [25,26], and ii) untargeted
metabolomics of semi-polar metabolites, including specialised compounds, via ultra-high-performance
liquid chromatography coupled to electrospray ionisation orbitrap high-resolution mass spectrometry
(thereafter referred to as LCMS). For this, the first, second and third fruit developed in the plants
were used for pathoassays with *B. cinerea* (*Bot*), *P. infestans* (*Phy*) and *P. syringae* (*Pst*), respectively.
Unbiased processing of LCMS data, followed by filtering of the most reliable variables, generated
6887 metabolomic features (see Materials and Methods). A global overview of metabolic profiles was
visualised by an unsupervised multivariate statistical method, Principal Component Analysis (PCA),
for all combinations of priming and pathosystems (Figure 3).
<figure><img src="image_101.png" /><figcaption>**Figure 3.** Global metabolomic changes after BABA treatment and after pathogen inoculation. Principle component analysis (PCA) score plots (n = 4) of 6887 LCMS-based metabolomics features (A) and 11 major compounds analysed by targeted biochemical phenotyping (B). Maximal variance explained by each PC is given in brackets.</figcaption></figure>

Firstly, for the 6887 metabolomic signals (Figure 3A), PCA explained 35% of the maximal variance of the dataset and resulted in a clear differentiation of the water and BABA treatments, thus suggesting a greater impact on metabolomic profiles for direct BABA application as compared to other conditions. This was confirmed by a univariate statistical method through a two-factor ANOVA (*P* < 0.05), which
quantitatively resulted in more statistically significant markers for the BABA factor (3052; 44%) than for the inoculation factor (2309; 33%) or the interaction (401; 6%) (Table 1).

<table><caption>**Table 1.** Univariate statistical analysis of the metabolomic features and major compounds.</caption><thead><tr><th>Two factors ANOVA<br>(P < 0.05)</th><th>BABA<br>(Water vs. BABA)</th><th>Inoculation<br>(Mock vs. Bot/Phy/Pst)</th><th>BABA × Inoculation</th></tr></thead><tbody><tr><td>Metabolomic<br>features</td><td>Total 6887</td><td>3052</td><td>2309</td><td>401</td></tr><tr><td>Major compounds</td><td>Total 11</td><td>0</td><td>4</td><td>0</td></tr></tbody></table>

Secondly, we tested 10 major compounds involved in central metabolism (sucrose, fructose, glucose, starch, fructose-6-P, glucose-6-P, glutamate, malate, fumarate and total proteins), as well as total polyphenols. PCA explained 83% of the maximal variance in the dataset and resulted in a separation of fruit by developmental characteristics (i.e., the first and second fruit versus the third fruit) rather than by pathosystems (Figure 3B). Hence, this multivariate differentiation indicates that the profiles of primary metabolites mostly respond to the developmental stage of the fruit, which supports the idea that central metabolism is tuned to fruit growth [27–29]. Complementarily, two-factor ANOVA (P < 0.05) only generated significant markers for the inoculation factor (4; 36%), including sucrose, fructose, glutamate and fumarate (Table 1 and Figure S1). Interestingly, such markers dropped upon Bot and Phy infections, while they were not drastically affected by Pst infection (Figure S1). Besides, fructose pools remained low across all treatments within the Pst pathoassay (i.e., third fruit). Altogether, this indicates that fruit infection affects the pools of central metabolites and those changes depend on the pathosystem.

Furthermore, a partial segregation of pathogen inoculations was observed on the PCA score plots obtained for each pathosystem from a dataset combining LCMS and targeted analyses (Figure 4). This was further exemplified by a supervised Partial Least Square Discriminant Analysis (PLS-DA) allowing a better differentiation of pathosystems and priming treatments (Figure S2). Two-factor ANOVA (P < 0.05) for each pathosystem not only confirmed that the BABA factor quantitatively outweighed the inoculation factor and the interaction, but also showed that all these factors were substantial (Table 2). Hence, this indicates that microbial challenges elicit distinct metabolic profiles. Overall, these results reveal metabolic shifts in fruit upon BABA priming and after pathogen inoculation, notably towards semi-polar biochemicals potentially involved in plant stress mitigation (i.e., specialised metabolites).

<table><caption>**Table 2.** Univariate statistical analysis for each pathosystem.</caption><thead><tr><th rowspan="2">Two Factors ANOVA (P < 0.05)</th><th>BABA</th><th>Inoculation</th><th rowspan="2">BABA × Inoculation</th></tr><tr><th>Water vs. BABA</th><th>Mock vs. Pathogen</th></tr></thead><tbody><tr><td><em>Botrytis cinerea</em></td><td>Total 6998</td><td>2297</td><td>724</td><td>482</td></tr><tr><td><em>Phytophthora infestans</em></td><td>Total 6998</td><td>1728</td><td>805</td><td>674</td></tr><tr><td><em>Pseudomonas syringae</em></td><td>Total 6998</td><td>2097</td><td>322</td><td>313</td></tr></tbody></table>
<figure><img src="image_102.png" /><figcaption>**Figure 4.** Partial segregation of pathogen inoculations for each fruit pathosystem. PCA score plots (n = 4) of 6898 features (6887 electrospray ionisation orbitrap high-resolution mass spectrometry (LCMS) variables + 11 major compounds) showing metabolomics overview between the three different pathosystems. Maximal variance explained by each PC are given in brackets.</figcaption></figure>
## 2.4. Primed Responses to Specific Pathogenic Microbes

To gain more insight into BABA priming upon different fruit infections, we next performed quantitative binary comparisons of metabolic markers for each pathosystem by comparing water-treated, mock-inoculated fruit versus i) BABA-treated, mock-inoculated fruit, ii) water-treated, pathogen-inoculated fruit, and iii) BABA-treated, pathogen-inoculated fruit. The resulting statistically significant metabolic markers (t-test, *P* < 0.01) were used to construct Venn diagrams showing common and specific markers (Figure 5A). Very few overlaps were observed between BABA (red) and pathogen (blue) conditions (2, 2 and 0 for *Bot*, *Phy* and *Pst*, respectively) and between pathogen and BABA priming (green) conditions (7, 4 and 4 for *Bot*, *Phy* and *Pst*, respectively). Instead, several markers overlapped between BABA treatment and BABA priming (118, 12 and 158 for *Bot*, *Phy*, and *Pst* respectively), and most markers were found either for BABA treatment (*Phy* and *Pst*) or for BABA priming (*Bot*) (Figure 5A). This suggests that BABA results largely in the accumulation of metabolites that could be used during the expression of priming. In addition, metabolic markers that specifically responded to BABA priming in the different fruit pathosystems were compared through a Venn diagram in order to reveal the common metabolic signatures of BABA priming against the three different pathogens (Figure 5B). Strikingly, no common markers were found upon the three infections, although very few markers were observed between *Pst* and *Bot* (10), *Pst* and *Phy* (3), and *Bot* and *Phy* (1). Hence, the primed responses are likely tailored to the encountered pathogenic microbes.

<figure><img src="image_103.png" /><figcaption>**Figure 5.** The primed responses are tailored to the encountered pathogenic microbes. (A) Venn diagrams showing quantitative binary comparisons were performed for each pathosystem (t-test, *n* = 4, *P* < 0.01) between water-treated, mock-inoculated fruit versus BABA-treated, mock-inoculated fruit (BABA, red); water-treated, pathogen-inoculated fruit (pathogen, blue); BABA-treated, pathogen-inoculated fruit (priming, green). (B) Venn diagrams showing the resulting priming clusters for each fruit pathosystem.</figcaption></figure>

## 2.5. Putative Annotation of Metabolic Markers

We then conducted a tentative annotation of the 14 metabolic markers that were common to *Pst* and *Bot* (10), *Pst* and *Phy* (3), and *Bot* and *Phy* (1) based on their detected *m/z* by high-resolution orbitrap-MS (Table 3). A Kruskal–Wallis test with correction for false rate discovery (Benjamini–Hochberg, *P* < 0.05) confirmed that these 14 metabolic markers showed statistically significant variations that were
visualised by bar charts (Figure S3). Putative prediction of compounds and pathways indicated several markers that belonged to the plant defence metabolism, including stress hormones and flavonoids. Interestingly, fungal pathogens (*Bot*, *Phy*) were associated with the induction of the putative marker jasmonoyl–isoleucine. Besides this, (hemi)biotrophic microbes (*Pst* and *Phy*) triggered the accumulation of putative salicylic derivatives and flavonoids (Figure S3). The *Pst*-related primed response further correlated with the depletion of a putative cytokinin. Hence, our results suggest that BABA priming against three different fruit pathogens rely on the induction of pathways involved in the defence hormonal metabolism. Further analytical studies are required to confirm the putative annotation of these priming markers.

## 2.6. Modelling of Resistance to Multiple Fruit Pathogens

Using a predictive biology approach based on generalised linear models [30], we aimed to determine whether resistance to fruit pathogens could be predicted by the detected metabolic markers (Figure 6). Based on those models (Figure 6A), good correlations were observed between measured and predicted values (mean = 0.87), and were statistically different from correlations based on randomly generated resistance (t-test, *P* < 2.2 × 10<sup>-16</sup>), which indicated the robustness of the predictions. Furthermore, according to the occurrence of metabolic markers in the models (Figure 6B), fructose appeared to be the best positively correlated predictor (appearing in 99% of the models), as well as sucrose, to a lesser extent (appearing in 41% of the models) (Table S1). Most predictors (32 out of 34) also showed a high statistical significance from a Kruskal–Wallis test with correction for false rate discovery (Benjamini–Hochberg, *P* < 0.05, Table S1). This corroborates the outcome from the two-way ANOVA method (Tables 1 and 2, and Figure S1). Hence, this indicates that soluble sugars involved in the central metabolism are essential to predict resistance to fruit pathogens. The analysis of such compounds is therefore critical for studies involving fruit–pathogen interactions. In addition to sugars, other metabolic predictors appearing in more than 25% of the models showed positive (19 markers) and negative correlations (15 markers) (Figure 6B and Table S1). Further analytical studies are required to annotate and/or identify such markers. Nonetheless, a tentative annotation of the top 15 predictors based on their detected *m/z* by high-resolution orbitrap-MS is presented in Table S1. Unsurprisingly, the resulting putative metabolites belonged to defence pathways (i.e., phenolics, flavonoids, terpenes, amino acid conjugates) and lipids. This suggests that immune perception and signalling seem pivotal in predicting resistance to fruit pathogens.
<table><caption>Table 3. Putative annotation of the primed response markers.</caption><thead><tr><th>Primed Response</th><th>Detected m/z (Da) <sup>1</sup></th><th>RT (min) <sup>1</sup></th><th>P Value <sup>2</sup></th><th>ESI Mode</th><th>Putative Adduct</th><th>Predicted m/z</th><th>Δppm</th><th>Putative Compound</th><th>Predicted Formula</th><th>Putative Pathway</th></tr></thead><tbody><tr><td>Bot and Pliy</td><td>346.1975</td><td>4.1</td><td>5.3 × 10<sup>-3</sup></td><td>+</td><td>[M+Na]+</td><td>323.2097</td><td>4</td><td>Jasmonoyl-isoleucine</td><td>C18H29NO4</td><td>Jasmonates</td></tr><tr><td rowspan="6">Bot and St</td><td>450.1119</td><td>7.5</td><td>6.7 × 10<sup>-3</sup></td><td>-</td><td>[M+FI]-</td><td>431.1206</td><td>15</td><td>Ribosylzeatin phosphate</td><td>C15H22N5O8P</td><td>Cytokinines</td></tr><tr><td>289.0896</td><td>2.5</td><td>5.6 × 10<sup>-3</sup></td><td>+</td><td>[M+H-2H<sub>2</sub>O]+</td><td>324.0998</td><td>8</td><td>5,6-Dimethoxy-[2*,2',7,8]furanoflavonone</td><td>C19H16O5</td><td>Flavonoids</td></tr><tr><td>380.1489</td><td>3.6</td><td>5.3 × 10<sup>-3</sup></td><td>+</td><td>[M+ACN+H]+</td><td>338.1154</td><td>0</td><td>3,5,7-Trihydroxy-6-prenylflavone</td><td>C20H18O5</td><td>Flavonoids</td></tr><tr><td>451.1238</td><td>7.4</td><td>8.4 × 10<sup>-3</sup></td><td>+</td><td>[M+H]+</td><td>450.1162</td><td>0</td><td>3,4,2*,4,6'-Pentahydroxychalcone</td><td>C21H22O11</td><td>Flavonoids</td></tr><tr><td>437.2213</td><td>7.1</td><td>3.8 × 10<sup>-2</sup></td><td>+</td><td>[M+ACN+Na]+</td><td>373.1889</td><td>37</td><td>Jasmonoyl-tyrosine</td><td>C21H27NO5</td><td>Jasmonates</td></tr><tr><td>535.3117</td><td>9.1</td><td>1.6 × 10<sup>-2</sup></td><td>-</td><td>[M-H]-</td><td>536.3114</td><td>14</td><td>Phosphoglycerolipid (20:2(11Z,14Z))/0(0)</td><td>C26H49O9P</td><td>Lipids</td></tr><tr><td rowspan="3">Pst</td><td>268.2271</td><td>5.4</td><td>2.5 × 10<sup>-2</sup></td><td>+</td><td>[M+NH4]+</td><td>250.1933</td><td>0</td><td>C16:3n-6,9,12</td><td>C16H26O2</td><td>Lipids</td></tr><tr><td>652.4048</td><td>6.2</td><td>3.7 × 10<sup>-2</sup></td><td>+</td><td>[M+NH4]+</td><td>634.3870</td><td>24</td><td>3-trans-p-Coumaroyl-rotundic acid</td><td>C39H54O7</td><td>Phenolics</td></tr><tr><td>272.0644</td><td>6.0</td><td>1.4 × 10<sup>-2</sup></td><td>-</td><td>[M+FI]-</td><td>253.0586</td><td>27</td><td>Salicyloyl-aspartic acid</td><td>C11H11NO6</td><td>Salicylic derivatives</td></tr><tr><td>154.0216</td><td>6.0</td><td>1.2 × 10<sup>-2</sup></td><td>+</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Unknown</td></tr><tr><td rowspan="3">Pst and Pliy</td><td>479.1402</td><td>4.3</td><td>4.2 × 10<sup>-2</sup></td><td>-</td><td>[M+Na-2H]-</td><td>458.1577</td><td>16</td><td>7-Hydroxy-5,4'-dimethoxy-8-methylisoflavone</td><td>C24H26O9</td><td>Flavonoids</td></tr><tr><td>525.1456</td><td>4.2</td><td>2.6 × 10<sup>-2</sup></td><td>+</td><td>[M+NH4]+</td><td>507.1139</td><td>3</td><td>Delphinidin 3-(acetyl)glucoside</td><td>C23H23O13</td><td>Flavonoids</td></tr><tr><td>452.1954</td><td>4.0</td><td>8.2 × 10<sup>-3</sup></td><td>-</td><td>[M+CH3COO]-</td><td>393.1940</td><td>27</td><td>Diphenhydramine salicylate</td><td>C24H27NO4</td><td>Salicylic derivatives</td></tr></tbody></table>

<footnote n="1">1. metabolomic parameters detected by LCMS. 2. P indicating the statistical significance from a Kruskal–Wallis test followed by correction for false discovery rate using the Benjamini–Hochberg method.</footnote>
<figure><img src="image_104.png" /><figcaption>**Figure 6.** Prediction of biotic resistance from metabolic markers. (A) correlation between predicted and measured resistance based on generalised linear models. (B) occurrence (%) of the metabolic markers in the models that showed a positive or negative correlation with the resistance to fruit pathogens. Details of untargeted markers are presented in Table S1.</figcaption></figure>

**3. Discussion**

In the present study, we evaluated the metabolic composition of tomato fruit in relation to the BABA-priming of young tomato plants and the infection of three different pathogens at the fruit stage. To the best of our current knowledge, this is the first metabolomic study on three different fruit pathosystems interacting with BABA priming.

Firstly, untargeted metabolomic profiling indicated a great impact of BABA treatment on metabolic profiles (Figure 3A and Table 1). Hence, the treatment of young tomato plants with BABA metabolically primes fruit tissues, and this stimulation was likely more critical than it was for the pathogen inoculations. This might result from the hormonal nature of BABA, which deeply affects plant metabolism, or from stress-related responses that are activated by the chemical itself, as it has been reported previously for high concentrations of BABA or in another *Solanum* species (i.e., potato) [21].
Secondly, targeted analyses of compounds involved in central metabolism demonstrated that the primary metabolic pools responded to the pathosystem inoculations, which reflected the developmental stage of the fruit, as exemplified by the multivariate distinction between the first/second fruit and the third fruit (Figure 3B). In complement, fructose, sucrose, fumarate and glutamate showed statistically significant variations upon inoculation (Table 1). Since fruit of slightly different ages harbour different profiles of primary compounds, we could assume that central metabolism is tuned to fruit growth, more specifically soluble sugars, amino and organic acids. This agrees with previous phenotyping and modelling studies on tomato that demonstrate metabolic shifts in carbon metabolism in the growing fruit [25,28,29,31]. Furthermore, it has been recently confirmed through transcriptomics and proteomics that the developing fruit not only undergoes metabolic shifts in central pathways, but also redox metabolism, such as for pyridine nucleotides that are detrimental to energy homeostasis [27,32,33]. However, major questions remain regarding the nature and dynamics of shifts in central metabolism upon pathogen inoculation. It is reasonable to expect that further investigations involving a more comprehensive view of fruit primary metabolism and how microbial challenges dynamically affect such pathways might significantly improve our understanding of the relationships between central metabolism and fruit-pathogen interactions. In turn, this should provide novel strategies to obtain fruit of better quality and stress resilience [32].

Upon pathogen challenge, while BABA is effective in leaf tissue, very little is known about its contribution in fruit. According to our fruit pathoassays (Figure 1), the treatment of tomato seedlings with BABA resulted in a broad-spectrum resistance against microbes that have different infection strategies, including necrotrophic or biotrophic, and fungal, oomycete or bacterial pathogens. Further, the primed responses are tailored to the encountered pathogen, as exemplified by the little overlap between the different primed states of the three pathosystems (Figure 5). This implies that the induced resistance state is very specific, which strongly suggests that BABA primes multiple signalling pathways through which such different microbes are resisted in the fruit. Among those metabolic responses, hormonal regulations appear detrimental to BABA-induced immunity [23,24,34]. Accordingly, putative annotation of metabolomic markers indicates that hormone conjugates, including salicylic and jasmonic derivatives, and other defence compounds (i.e., flavonoids), are induced upon infection and BABA treatment (Table 3). Given the diverse set of immune responses that the fruit deploys against different microbial stresses, our study highlights the adaptability of priming as a "stimulus-dependent plasticity of response traits" [35]. For this reason, the exact underlying molecular mechanisms of priming are difficult to describe precisely and their description requires further research [36].

Whilst BABA treatment in many plant species results in a stress phenotype that manifests through developmental alterations (e.g., growth, yield, seed production) [17–19], we found no differences upon BABA application in fruit size, but observed delayed fruit production or fruit ripening (Figure 2), as described previously [23]. However, after the number of ripened fruit had equalised between both treatments, BABA-treated plants continued producing fruit at a much faster rate than the water-treated plants (Figure 2). Seemingly, through its induction of immune responses, BABA thus provides a positive fitness element for tomato plants. This trade-off might emerge, in part, from the stimulation of various signalling pathways, more specifically the ones that link to the central metabolism, such as amino acids or carbohydrates [34]. As a result, BABA-treated plants would perform particularly well. This agrees with what we know about plant perception of BABA in *A. thaliana*. The binding of BABA by an aspartyl-tRNA synthetase blocks the enzyme, consequently triggering the accumulation of its canonical substrate, uncharged tRNA, which leads to changes in amino acid pools in the plant, therefore affecting primary metabolism [20]. Subsequent signalling modulations might result from an alteration in amino acid precursors (e.g., ethylene, auxin) that would alter fruit production [37].

Despite its economic importance, the molecular mechanisms underlying the pathogenicity of *B. cinerea*, *P. infestans* and *P. syringae* are poorly understood in fruit. From a computational systems biology perspective, the study of plant-pathogen interactions involved structural and comparative genomics, transcriptomics, and protein-protein interactions [38]. Further, high-resolution metabolome
data and sufficient datapoints over time are essential to calculate metabolite coefficients and thus predict metabolic fluxes [39]. Recently, genome-scale metabolic models of *Solanum* species (i.e., potato, tomato) and *Phy* have been integrated to simulate the metabolic fluxes that occur during infection [40,41]. These studies yield insights into the molecular aspects of photosynthesis suppression by *Phy* via the flux of carboxylation to oxygenation reactions, or the nutrient intakes by *Phy* during different phases of the infection cycle. Interestingly, stage-specific profiles embedded in the joint metabolism of the host and pathogen could potentially be refined by integrating the high-resolution metabolome data of tomato infection [41]. Such elegant works involve leaf tissues. Here, we show that fruit metabolomics and modelling can assist in addressing fruit–pathogen interactions. Using top-down modelling based on the construction of generalised linear models [30], we demonstrate that metabolomics data can be used to accurately predict the measured resistance to various fruit pathogens (Figure 6A). Besides, through the evaluation of the occurrence of best predictors, our data indicate that soluble sugars, more specifically fructose [42], and defence metabolites are pivotal to predict the resistance to fruit pathogens (Table S1). Clearly, a more global systems biology approach based on a higher level of variation in the conditions (e.g., multiple genotypes or priming treatments, various growth stages of the fruit, several infection points) will shed some light on the underlying mechanisms of fruit–pathogen interactions.

Overall, our study validates the value of metabolomics and modelling approaches in the field of phytopathological investigations. This work provides a great perspective for the structural elucidation of the key metabolites involved in broad-spectrum BABA-induced priming in tomato fruit. Although it was only possible to tentatively annotate metabolic biomarkers on the basis of detected HR-accurate *m/z* (Table 3 and Table S1), our analytical and statistical approach can be further optimised for, e.g., metabolite identification through structural elucidation by NMR or targeted MS/MS analyses. A combination of LCMS with purification steps (e.g., SPE cartridge, fractionation) could prove useful for de novo identification.

# 4. Materials and Methods

## 4.1. *Tomato Cultivation*

Tomato (*Solanum lycopersicum*) Micro-Tom was used for all experiments described in this publication. Seeds were incubated for 4 days in wet paper at 28 °C to promote homogeneous germination. Germinates were then planted in individual 80 mL pots containing M3 soil. Plants were grown in a controlled-environment greenhouse chamber with 16h of light, at 26 °C, and 8 h of darkness at 21 °C, and 200 µM.m⁻¹.s⁻¹ light intensity. Experiments were performed from November 2017 until May 2018 in the United Kingdom.

## 4.2. *Biochemicals, Reagents and Treatments*

All solvents and reagents used in this study were of analytical or MS grades. B-aminobutyric acid (BABA) was obtained from Sigma-Aldrich (A4420-7). Treatments with BABA were performed entirely as described in [23]. Briefly, 2 week-old tomato seedlings were soil-drenched with 8 mL per pot of either water or 5 mM BABA solution, to generate a final concentration of 0.5 mm in the soil. One week post-treatment, roots were carefully washed under running tap water and then plants were transplanted into individual 2.2 L pots containing untreated M3 soil. Plants were allowed to grow for between 9 and 12 weeks until the fruit turned red, at which point they were harvested and infected with the different pathogens.

## 4.3. *Fitness Parameters of Tomato Fruit*

Growth and yield were assessed entirely as described in [23]. Assessment of fruit ripening was done as described in [29], by classifying fruit in different levels of maturity by colour.
4.4. *Pathogens and Inoculations*

Cultivations of *Botrytis cinerea* strain R16 [43], *Phytophthora infestans* 88,069 [44] and *Pseudomonas syringae* pv. *tomato DC3000* (*Pst DC3000*) [45] were done as described in the corresponding publications. For inoculations with *B. cinerea*, the first fruit were used. Inoculations were performed entirely as described in [23]. For infections with *P. infestans*, the second fruit were used. Inoculations were performed by placing 10 µl drops of a spore concentration of 5 × 10⁴ spores/mL onto the needle-wounded tip of the tomato fruit. After infection, fruit were kept at 20 °C in the dark. For *P. syringae* infections, the third fruit were used. Inoculations were done by spraying bacteria onto the fruit in a concentration of 10⁸ cells/mL in 10 mM MgSO₄ and 0.05% (v/v) Silwet L-77. Infected fruit were kept in the dark at 25 °C. Mock inoculations were performed by following the exact same protocols but without pathogens in the solutions. Fruit were 56 days post-anthesis (dpa), 63 dpa and 70 dpa for the first, second and third fruit, respectively.

Scoring of *B. cinerea* symptoms were performed entirely as described in [23]. Scoring of *P. infestans* disease was done by classifying lesions into different categories of fruit colonization: Class 0; healthy, Class I; necrosis associated with the lesion, Class II; necrosis and mycelium associated with the lesion, Class III; necrosis and mycelium spread in the fruit. Scoring of *Pst DC3000* disease was done by classifying lesions into different categories of fruit damage: Class 0; healthy, Class I; turgent but cracking fruit, Class II; cracked fruit, Class III; fruit tissue collapse. Disease severity rates were calculated from the nominal lesion categories of four fruit per plant (n = 11), as described in [46]. Statistical analysis of disease phenotypes was performed as described in [23].

4.5. *Metabolite Extraction*

For metabolome analysis, the first, second and third fruit developed by plants were used for pathoassays with *B. cinerea* (*Bot*), *P. infestans* (*Phy*) and *P. syringae* (*Pst*), respectively. Experiments on each type of fruit were separated by one week. Infections were performed as described above when the corresponding fruit were fully ripened. Two days after inoculation with the different pathogens, fresh pericarps were rapidly collected into 2 mL-microtubes, then flash-frozen in liquid nitrogen and freeze-dried for 72 h (Pilote Compact, SARL CRYOTEC, Saint-Gély-du-Fesc, France). Fine grinding of dried material was subsequently performed using a ball mixer for 2 min at 30 Hz (Retsch Mill MM400, fisher scientific, Bordeaux, France) after adding two metal beads (Beads inox AISI 400C 5 mm, CIMAP, Caen, France) to each tube (Micro-tube, 2 mL PP, Sarstedt, Germany). Ten milligrams of each replicated sample were weighed into 1.1 mL-micronic tubes (MP32033L, Micronic, Lelystad, Netherlands), randomised onto a 96-micronic rack (MPW51001BC6, Micronic, Lelystad, Netherlands) then capped using a robotised capper-decapper (Decapper 193000/00, Hamilton, Bienne, Switzerland). Each rack also contained an empty tube corresponding to the extraction blank. The resulting micronics were then stored at -80 °C. Extraction of metabolites was conducted on four biologically replicated pericarp samples (n = 4) using a robotised extraction method developed at Bordeaux Metabolome Facility (https://metabolome.cgfb.u-bordeaux.fr/en, Villenave d'Ornon, France). The robot was a bespoken piece of equipment that allowed for pipetting solvents, mixing, cooling and centrifuging racks of micronics. After decapping the micronics, the extraction began by adding 300 µL of solvent A containing 80% ethanol and 0.1% formic acid (v/v) with 250 µg/mL methyl vanillate as the internal standard. Racks were agitated on the robot (30 sec, 500 rpm) then placed for 15 min into a sonicator containing ice-cold water (Elmasonic S300, Elma, Singen, Germany). Racks were put back on the robot and centrifuged (5 min, 1350 g). The first round of extraction stopped by pipetting 300 µL of the resulting supernatant into new 1.1 mL-micronic tubes. A second round of extraction was performed with 300 µl of solvent A, and the resulting pellet was finally washed with solvent B (50% ethanol (v/v)). The micronics-containing supernatants were kept for filtration and the micronics with the pellets were kept for further starch and total protein analysis.

Filtration was also robotised (Microlab STARlet, Hamilton, Bienne, Switzerland) and allowed for the transfer of the supernatants onto a filtration 96-well sterile clear plate (MSGVS2210, 0.22 µM,
Hydrophil. Low Protein Binding Durapore, Millipore, Molsheim, France) according to the supplier's instruction. Filtrates were subsequently collected into a new micronic tube. Finally, quality control (QC) samples were prepared by robotically pipetting 15 µL of each sample into a single tube that was mixed afterwards (Microlab STARlet, Hamilton, Bienne, Switzerland). Each rack was supplemented with a micronic tube containing the QC mix. The QC sample was replicated six times along the project run.

## 4.6. Targeted Biochemical Phenotyping

Targeted analyses of sucrose, fructose, glucose, starch, fructose-6-P, glucose-6-P, glutamate, malate, fumarate, total soluble proteins and total polyphenols were conducted on the HiTMe plateau at *Bordeaux Metabolome Facility*. Measurements were based on coupled enzyme assays as described previously [25,26], except for total soluble proteins that were evaluated via Bradford assay [47], and total phenols that were measured colorimetrically using a redox reaction with Folin–Ciocalteu reagent and gallic acid as the standard [48].

## 4.7. Untargeted Metabolic Profiling

Untargeted metabolic profiling by UHPLC-LTQ-Orbitrap mass spectrometry (LCMS) was performed using an Ultimate 3000 ultra-high-pressure liquid chromatography (UHPLC) system coupled to an LTQ-Orbitrap Elite mass spectrometer interfaced with an electrospray (ESI) ionisation source (ThermoScientific, Bremen, Germany). The system was controlled by Thermo XCalibur v.3.063 software. Chromatographic separation was achieved at a flow rate of 350 µL/min using a GEMINI UHPLC C18 column (150 × 2 mm, 3 µm, Le Pecq, Phenomenex, France) coupled to a C18 SecurityGuard GEMINI pre-column (4 × 2 mm, 3 µm, Le Pecq, Phenomenex, France). The column was maintained at 35 °C and the injection volume was 5 µL. The mobile phase consisted of solvent A (0.05 % (v/v) formic acid in water) and solvent B (acetonitrile) with the following gradient: 0–0.5 min 3% B, 0.5–1 min 3% B, 1–9 min 50% B, 9–13 min 100% B, 13–14 min 100% B, 14–14.5 min 3% B, 14.5–18 min 3% B. Ionisation of samples was performed in both negative and positive mode with the following parameters: ESI⁻ (Heater temp: 300 °C, Sheath Gas Flow Rate: 45 (arb), Aux Gas Flow Rate: 15 (arb), Sweep Gas Flow Rate: 10 (arb), I Spray Voltage: 2.5 kV, Capillary Temp: 300 °C, S-Lens RF Level: 60%), and ESI⁺ (Heater temp: 300 °C, Sheath Gas Flow Rate: 60 (arb), Aux Gas Flow Rate: 20 (arb), Sweep Gas Flow Rate: 10 (arb), I Spray Voltage: 3.2 kV, Capillary Temp: 300 °C, S-Lens RF Level: 55%). MS full scan detection of ions was operated by FTMS (50–1500 Da) at a resolution of 240,000. Prior to analyses, the LTQ-Orbitrap was calibrated by infusing a solution of the calibration dependent of the ionisation mode (Pierce© ESI Negative Ion Calibration Solution (ref: 88324); Pierce LTQ Velos ESI Positive Ion Calibration solution (ref: 88323). The injection sequence started with three blank extracts, then three QC samples, then one blank extract, and each group of samples was subsequently injected, followed by a blank extract. Another two QC samples were injected throughout the analysis. In total, six QC samples and 16 blank extracts were injected to correct for mass spectrometer signal drift, and to filter out variables detected in blanks, respectively.

## 4.8. Processing and Statistical Analysis of Metabolomic Datasets

Processing of raw LCMS data using XCMS in R (v 3.6.1) [49] yielded 10,875 detected RT-m/z pairs for ESI⁺ and 5,796 for ESI⁻. After data-cleaning (blank check, ΔRT < 60 s, Δm/z < 0.015 Da, CV QC < 30%), 6887 variables were retained for further chemiometrics. Both untargeted and targeted metabolomic data were first normalised by median normalisation, cube-root transformation and Pareto scaling using MetaboAnalyst v.3 [50] before applying multivariate and univariate statistical analyses [51]. The normalised dataset is available as Supplemental Material 1. PCA and PLS-DA were performed with MetaboAnalyst v.3 providing satisfactory validation parameters of the multivariate models (R² > 0.87 and Q² > 0.35). PC coordinates for metabolomic features that are responsible for PC1 and PC2 are presented in Supplemental Material 2. Univariate statistical methods were performed using MeV v.4.9.0. [52] at *P* < 0.05 for two-factor ANOVA and *P* < 0.01 for binary comparisons by *t*-tests. In
addition, MarVis v 1 was used to confirm the statistically significant variation on the priming markers through a Kruskal–Wallis test at *P* < 0.05 with correction for false discovery rate [53,54]. Putative annotation of such markers was performed by screening the detected exact *m/z* against multiple online databases, including METLIN chemical database (https://metlin.scripps.edu/) [55] and KNApSAcK (http://kanaya.naist.jp/KNApSAcK/) [56]. The resulting predicted pathways were checked using the PubChem database (https://pubchem.ncbi.nlm.nih.gov/).

## 4.9. Top-down Modelling Approach

Generalised linear models were constructed in R (v 3.6.1) using the *glmnet* package (v 3.0-2) [30] in order to identify potential links between detected metabolic markers and resistance to the fruit pathogens. Those models were used to predict resistance values based on the detected metabolic markers. Cross-validation was applied by randomly dividing the datasets into two parts: 80 % of the individuals were used to construct the models and 20 % to check for the quality of the prediction. The quality of the models was assessed based on the mean square error between real and predicted values. To cope with this randomisation, 500 models were constructed for each measurement. Generalised linear models contain a penalisation value, allowing less informative variables to be discarded as this value increases (1000 values were tested for each of the 500 models), hence variables occurring the most in the models can be seen as the most stable predictors of resistance to biotic challenges. Given the high number of metabolic variables and the relatively small set of plants, 500 randomly generated resistance datasets were created to estimate the chances of predicting random values. A Student's *t*-test was used to compare the quality of predictions of real and random resistances.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2218-1989/10/3/96/s1, Figure S1. Inoculation-responsive central metabolites. Major compounds involved in central metabolism statistically responded to the inoculation factor of a two-factor ANOVA (P given into brackets). Bar charts indicate means of normalised intensities of four independent bioreplicates (n = 4; ± SEM). Bot: *Botrytis cinerea*, *Phy: Phytophthora infestans*, *Pst*: *Pseudomonas syringae* pv. *tomato*, B: BABA-treated plants, W: water-treated plants, M: mock-inoculated fruit, P: pathogen-inoculated fruit. Figure S2. Partial least square discriminant analysis for each pathosystem. PLS-DA score plots (n = 4) of 6898 features (6887 LCMS variables + 11 major compounds) between the three different pathosystems. Validation parameters of the PLS model are given in red for each plot. Figure S3. Metabolic markers for BABA primed responses against fruit pathogens. LCMS significant markers that overlap between Bot and Phy (A), Bot and Pst (B) and Phy and Pst (C) in response to BABA priming and after infection (see Table 3). Markers are labelled according to their high-resolution detected *m/z*. Bar charts indicate means of normalised intensities (n = 4; ± SEM). See Figure S1 for sample labels. Table S1. Putative annotation of the top 15 predictors. Supplemental Material 1: metabolomic parameters detected y LCMS. 2: P indicating the statistical significance from a Kruskal–Wallis test followed by correction for false discovery rate using the Benjamini–Hochberg method. Supplemental Material 1. Normalised metabolomics dataset combining 6887 *m/z* features and 11 major compounds. Supplemental Material 2. Coordinates for Principal Component Analyses ranking the important metabolomic variables.

**Author Contributions:** Conceptualization, E.L. and P.P.; Data curation, E.L., A.F., C.C. (Cédric Cassan) and P.P.; Formal analysis, E.L., A.F., C.C. (Cédric Cassan), C.C. (Chloé Chevanne), C.F.K., S.P. and P.P.; Funding acquisition, E.L., Y.G. and P.P.; Investigation, E.L., A.F., C.C. (Cédric Cassan), C.C. (Chloé Chevanne), C.F.K., S.P. and P.P.; Methodology, E.L., A.F., C.C. (Cédric Cassan), S.P. and P.P.; Project administration, E.L. and P.P.; Resources, E.L., S.P., Y.G. and P.P.; Software, S.P.; Supervision, E.L. and P.P.; Validation, E.L., S.P., Y.G. and P.P.; Visualization, E.L., S.P. and P.P.; Writing—original draft, E.L. and P.P.; Writing—review & editing, E.L., S.P., Y.G. and P.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** The authors are grateful for financial support from MetaboHUB (ANR-11-INBS-0010) and PHENOME (ANR-11-INBS-0012) projects to INRAE, and for the BBSRC Future Leader Fellowship BB/P00556X/1 and BB/P00556X/2 to EL.

**Acknowledgments:** The authors thank Sam Wilkinson for his comments on the project. *P. infestans* isolate was provided by Steve Whisson (The James Hutton Institute).

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

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# Identification of Bioactive Phytochemicals in Mulberries

Gilda D'Urso ¹, Jurriaan J. Mes ², Paola Montoro ¹, Robert D. Hall ³,⁴ and Ric C.H. de Vos ³,\*

¹ Department of Pharmacy, University of Salerno, 84084 Fisciano SA, Italy; gidurso@unisa.it (G.D.); pmontoro@unisa.it (P.M.)

² Business Unit Fresh Food and Chains, Wageningen Food & Biobased Research, Wageningen University and Research, 6708 WG Wageningen, The Netherlands; jurriaan.mes@wur.nl

³ Business Unit Bioscience, Wageningen Plant Research, Wageningen University and Research, 6708 PB Wageningen, The Netherlands; robert.hall@wur.nl

⁴ Laboratory of Plant Physiology, Wageningen University and Research, 6708 PB Wageningen, The Netherlands

Received: 29 November 2019; Accepted: 18 December 2019; Published: 20 December 2019

**Abstract:** Mulberries are consumed either freshly or as processed fruits and are traditionally used to tackle several diseases, especially type II diabetes. Here, we investigated the metabolite compositions of ripe fruits of both white (*Morus alba*) and black (*Morus nigra*) mulberries, using reversed-phase HPLC coupled to high resolution mass spectrometry (LC-MS), and related these to their in vitro antioxidant and α-glucosidase inhibitory activities. Based on accurate masses, fragmentation data, UV/Vis light absorbance spectra and retention times, 35 metabolites, mainly comprising phenolic compounds and amino sugar acids, were identified. While the antioxidant activity was highest in *M. nigra*, the α-glucosidase inhibitory activities were similar between species. Both bioactivities were mostly resistant to in vitro gastrointestinal digestion. To identify the bioactive compounds, we combined LC-MS with 96-well-format fractionation followed by testing the individual fractions for α-glucosidase inhibition, while compounds responsible for the antioxidant activity were identified using HPLC with an online antioxidant detection system. We thus determined iminosugars and phenolic compounds in both *M. alba* and *M. nigra*, and anthocyanins in *M. nigra* as being the key α-glucosidase inhibitors, while anthocyanins in *M. nigra* and both phenylpropanoids and flavonols in *M. alba* were identified as key antioxidants in their ripe berries.

**Keywords:** mulberry; high resolution mass spectrometry; antioxidant activity; in vitro gastrointestinal digestion; α-glucosidase inhibitory activity

## 1. Introduction

Mulberry belongs to the genus *Morus*, plant family Moraceae, which comprises 24 different species and one subspecies, with at least 100 varieties [1]. The most commonly known *Morus* species are *Morus alba* (white mulberry), *Morus nigra* (black mulberry) and *Morus rubra* (red mulberry.) [2]. They are deciduous trees originating from China and Japan, and have spread into America and Europe for silkworm breeding. Globally, the main use of mulberry trees is to produce leaves as feed for cultivating silkworms, but in various regions, they are also much appreciated for their fruit, which can be consumed both fresh and as an ingredient in processed food products [3].

<footnote>\* Correspondence: ric.devos@wur.nl; Tel.: +31-317480841</footnote>
Mulberry species have been used in Traditional Chinese Medicines (TCM) for the treatment of
several diseases, especially diabetes mellitus type II; they contain specific molecules, i.e., iminosugars
or iminocyclitols, which are low molecular weight carbohydrates in which the endocyclic oxygen
atom has been replaced by a nitrogen atom. These compounds are known to be able to inhibit the
enzyme α-glucosidase which is present in the brush border of the human intestine [4–6]. Inhibition of
α-glucosidase leads to a decreased rate of glucose absorption thus resulting in a lower postprandial
blood glucose level. Inhibitors of α-glucosidase can prevent the development of diabetes in individuals
with impaired glucose tolerance and/or impaired fasting of blood glucose [7].

Mulberries are also a good nutritional source of a variety of phenolic compounds, like flavonols and phenolic acids, as well as coloured anthocyanins in the case of black and red mulberry fruits [8–10]. Phenolic compounds are the subject of increasing scientific interest; they are natural antioxidants in plant-derived foods and food products and their intake is frequently related to human health. Many of the bioactivities ascribed to mulberries, such as antioxidant action, hypolipidemic effect and macrophage activating effect, have also been linked to their phenolic compound composition [11–14].

The present study aimed to determine differences in chemical composition between the ripe fruits of *M. alba* and *M. nigra*, which are the major species growing in Italy, by metabolomic analysis and to identify the bioactive compounds responsible for their antioxidant and α-glucosidase inhibitory bioactivities. To this purpose, we used I) HPLC-PDA with an online, post-column antioxidant detection system, and II) HPLC-PDA-HR Orbitrap FTMS with on-line fractionation into 96-wells plates followed by off line in vitro α-glucosidase inhibitory activity testing of the contents of the individual wells. A series of compounds present in mulberry fruits as well as major bioactives were further characterized using MSn fragmentation. In addition, we subjected mulberries to an in vitro gastrointestinal digestion system in order to investigate potential effects of digestion on the observed bioactivities upon consumption of these berries.

## 2. Results

### 2.1. Identification of Phytochemicals in Morus using LC-PDA-Orbitrap FTMS

To identify the metabolites present in white and black mulberry fruits, HPLC-PDA-Orbitrap
FTMS analysis was performed on their aqueous methanol extracts, thus generating both an LC-PDA
and an LC-MS profile per extract (Figures S1 and S2, respectively). Based on the exact mass of their
molecular [M+H]$^+$ ion masses, their MSn fragmentation patterns and their UV/Vis absorbance spectra,
we putatively identified 35 compounds in the fruits (Table 1).
<table><caption>**Table 1.** Metabolites manually identified in *Morus alba* and *Morus nigra* using accurate mass LC-MS⁹ in positive ESI mode. L. I.: level of identification according to the Metabolomics Society Initiative [15].</caption><thead><tr><th>N°</th><th>RT</th><th>Accurate Mass</th><th>Molecular Ion [M+H]<sup>+</sup></th><th>Molecular Formula</th><th>Putative ID</th><th>Fragment Ions [M+H]<sup>+</sup></th><th>L. I.</th><th>References</th></tr></thead><tbody><tr><td>1</td><td>1.58</td><td>163.0844</td><td>164.0923</td><td>C<sub>6</sub>H<sub>13</sub>NO<sub>4</sub></td><td>1-deoxynojirimycin</td><td>-</td><td>3</td><td>[16]</td></tr><tr><td>2</td><td>1.70</td><td>289.2253</td><td>290.2332</td><td>C<sub>15</sub>H<sub>31</sub>NO<sub>4</sub></td><td>n-nonl-deoxynojirimycin</td><td>206.8857/122.9243</td><td>3</td><td>[16]</td></tr><tr><td>3</td><td>2.14</td><td>147.0895</td><td>148.0974</td><td>C<sub>6</sub>H<sub>13</sub>NO<sub>3</sub></td><td>fagomine</td><td>-</td><td>3</td><td>[16]</td></tr><tr><td>4</td><td>2.19</td><td>181.0738</td><td>182.0817</td><td>C<sub>3</sub>H<sub>11</sub>NO<sub>3</sub></td><td>2-formyl-1H-pyrrole-1-butanoic acid</td><td>165.0544/136.0755</td><td>3</td><td>[11]</td></tr><tr><td>5</td><td>8.16</td><td>354.0951</td><td>355.1024</td><td>C<sub>6</sub>H<sub>18</sub>O<sub>9</sub></td><td>caffeoylquinic acid isomer I</td><td>163.0386</td><td>2</td><td>[17]</td></tr><tr><td>6</td><td>9.30</td><td>449.1084</td><td>449.1084</td><td>C<sub>21</sub>H<sub>21</sub>O<sub>11</sub><sup>+</sup></td><td>cyanidin hexoside</td><td>287.0546</td><td>2</td><td>[17]</td></tr><tr><td>7</td><td>9.35</td><td>507.3043</td><td>508.3122</td><td>C<sub>24</sub>H<sub>48</sub>NO<sub>10</sub></td><td>morusimic acid E</td><td>346.2587/284.2579</td><td>3</td><td>[18]</td></tr><tr><td>8</td><td>10.21</td><td>595.1662</td><td>595.1662</td><td>C<sub>27</sub>H<sub>30</sub>O<sub>15</sub><sup>+</sup></td><td>cyanidin hexose deoxyhexose</td><td>449.1058/287.0546</td><td>2</td><td>[17]</td></tr><tr><td>9</td><td>10.93</td><td>433.1135</td><td>433.1135</td><td>C<sub>21</sub>H<sub>21</sub>O<sub>10</sub></td><td>pelargonidin hexoside</td><td>271.0596</td><td>2</td><td>[17]</td></tr><tr><td>10</td><td>11.52</td><td>354.0951</td><td>355.1024</td><td>C<sub>16</sub>H<sub>18</sub>O<sub>9</sub></td><td>caffeoylquinic acid isomer II</td><td>163.0386</td><td>2</td><td>[17]</td></tr><tr><td>11</td><td>11.92</td><td>579.1714</td><td>579.1714</td><td>C<sub>27</sub>H<sub>31</sub>O<sub>14</sub><sup>+</sup></td><td>pelargonidin hexose deoxyhexose</td><td>433.1115/271.0596</td><td>2</td><td>[17]</td></tr><tr><td>12</td><td>12.24</td><td>626.1483</td><td>627.1542</td><td>C<sub>27</sub>H<sub>30</sub>O<sub>17</sub></td><td>quercetin hexose hexose</td><td>465.1023/303.0489</td><td>2</td><td>[17]</td></tr><tr><td>13</td><td>12.38</td><td>354.0951</td><td>355.1024</td><td>C<sub>6</sub>H<sub>18</sub>O<sub>9</sub></td><td>caffeoylquinic acid isomer III</td><td>163.0386</td><td>2</td><td>[17]</td></tr><tr><td>14</td><td>12.42</td><td>772.2062</td><td>773.2135</td><td>C<sub>33</sub>H<sub>40</sub>O<sub>21</sub></td><td>quercetin-3-O-rutinoside-7-O-glucoside</td><td>303.0496/465.0995/611.1576</td><td>1</td><td>[19]</td></tr><tr><td>15</td><td>13.25</td><td>466.1111</td><td>467.1190</td><td>C<sub>21</sub>H<sub>22</sub>O<sub>12</sub></td><td>dihydroquercetin hexoside/taxifolin hexoside</td><td>449.1069/305.0650</td><td>3</td><td>[20]</td></tr><tr><td>16</td><td>14.41</td><td>712.1487</td><td>713.1544</td><td>C<sub>30</sub>H<sub>32</sub>O<sub>20</sub></td><td>quercetin hexoside malonyl hexoside</td><td>551.1015/463.1021/303.0496</td><td>2</td><td>[21]</td></tr><tr><td>17</td><td>14.71</td><td>756.2112</td><td>757.2192</td><td>C<sub>33</sub>H<sub>40</sub>O<sub>21</sub></td><td>kaempferol-3-O-rutinoside-7-O-glucoside</td><td>611.1576/449.1065/287.547</td><td>1</td><td>[19]</td></tr><tr><td>18</td><td>15.35</td><td>386.1940</td><td>387.2020</td><td>C<sub>19</sub>H<sub>30</sub>O<sub>8</sub></td><td>roseoside</td><td>370.1118/208.0599</td><td>3</td><td>[22]</td></tr><tr><td>19</td><td>16.37</td><td>772.2062</td><td>773.2135</td><td>C<sub>33</sub>H<sub>40</sub>O<sub>21</sub></td><td>quercetin-hexose-deoxyhexose</td><td>303.0496/465.0995/611.1576</td><td>3</td><td>[19]</td></tr><tr><td>20</td><td>16.52</td><td>450.1162</td><td>451.1235</td><td>C<sub>21</sub>H<sub>22</sub>O<sub>11</sub></td><td>dihydrokaempferol-hexoside</td><td>287.0545/449.1065/535.1076</td><td>2</td><td>[23]</td></tr><tr><td>21</td><td>17.02</td><td>696.1517</td><td>697.1597</td><td>C<sub>30</sub>H<sub>32</sub>O<sub>19</sub></td><td>kaempferol hexoside malonyl hexoside</td><td>303.0496/465.0995/611.1576</td><td>2</td><td>[24]</td></tr><tr><td>22</td><td>17.22</td><td>772.2062</td><td>773.2135</td><td>C<sub>34</sub>H<sub>40</sub>O<sub>31</sub></td><td>quercetin-hexose-deoxyhexose</td><td>611.1576/449.1065/287.0547</td><td>2</td><td>[19]</td></tr><tr><td>23</td><td>18.28</td><td>756.2112</td><td>757.2192</td><td>C<sub>33</sub>H<sub>40</sub>O<sub>20</sub></td><td>kaempferol hexose deoxyhexose</td><td>611.1576/449.1065/287.0547</td><td>2</td><td>[19]</td></tr><tr><td>24</td><td>18.63</td><td>756.2112</td><td>757.2192</td><td>C<sub>33</sub>H<sub>40</sub>O<sub>20</sub></td><td>kaempferol hexose deoxyhexose</td><td>330.2640</td><td>3</td><td>[18]</td></tr><tr><td>25</td><td>19.59</td><td>491.3094</td><td>492.3173</td><td>C<sub>24</sub>H<sub>46</sub>O<sub>9</sub>N</td><td>morusimic acid C</td><td>303.0495/465.1019</td><td>1</td><td>[10]</td></tr><tr><td>26</td><td>21.39</td><td>610.1534</td><td>611.1606</td><td>C<sub>27</sub>H<sub>30</sub>O<sub>16</sub></td><td>quercetin-3-O-rutinoside</td><td>312.2529/268.2630/250.2525</td><td>3</td><td>[18]</td></tr><tr><td>27</td><td>22.06</td><td>329.2566</td><td>330.2645</td><td>C<sub>18</sub>H<sub>35</sub>NO<sub>4</sub></td><td>morusimic acid B</td><td>303.0498</td><td>2</td><td>[17]</td></tr><tr><td>28</td><td>22.42</td><td>464.0954</td><td>465.1027</td><td>C<sub>21</sub>H<sub>20</sub>O<sub>12</sub></td><td>quercetin-hexoside</td><td>449.1066/287.0546</td><td>1</td><td>[17]</td></tr><tr><td>29</td><td>24.39</td><td>594.1584</td><td>595.1664</td><td>C<sub>27</sub>H<sub>30</sub>O<sub>15</sub></td><td>kaempferol-3-O-rutinoside</td><td>303.0499</td><td>2</td><td>[26]</td></tr><tr><td>30</td><td>24.88</td><td>550.0958</td><td>551.1038</td><td>C<sub>24</sub>H<sub>22</sub>O<sub>15</sub></td><td>quercetin-malonylhexoside</td><td>163.0387</td><td>2</td><td>[27]</td></tr><tr><td>31</td><td>25.10</td><td>516.1268</td><td>517.1341</td><td>C<sub>25</sub>H<sub>24</sub>O<sub>12</sub></td><td>dicaffeoylquinic acid I</td><td>287.0546</td><td>2</td><td>[17]</td></tr><tr><td>32</td><td>25.59</td><td>448.1006</td><td>449.1078</td><td>C<sub>27</sub>H<sub>20</sub>O<sub>11</sub></td><td>kaempferol-hexoside</td><td>325.0913/163.0387</td><td>2</td><td>[27]</td></tr><tr><td>33</td><td>25.66</td><td>516.1268</td><td>517.1341</td><td>C<sub>25</sub>H<sub>24</sub>O<sub>11</sub></td><td>dicaffeoylquinic acid II</td><td>325.0913/163.0387</td><td>2</td><td>[27]</td></tr><tr><td>34</td><td>27.83</td><td>516.1268</td><td>517.1341</td><td>C<sub>25</sub>H<sub>24</sub>O<sub>12</sub></td><td>dicaffeoylquinic acid III</td><td>325.0913/163.0387</td><td>2</td><td>[27]</td></tr><tr><td>35</td><td>28.53</td><td>534.1009</td><td>535.1088</td><td>C<sub>24</sub>H<sub>22</sub>O<sub>14</sub></td><td>kaempferol malonyl hexoside</td><td>287.0546</td><td>2</td><td>[28]</td></tr></tbody></table>
In the fruits of *M. nigra*, four anthocyanins with a characteristic absorbance maximum at around 500–520 nm were identified. Compound **6**, cyanidin hexoside, showed a pseudomolecular ion at *m/z* 449.1084, corresponding to the molecular formula C21H21O11⁺ that, upon fragmentation, gave one principal product ion at *m/z* 287.0546; Compound **8**, cyanidin hexose-deoxyhexose, showed a pseudomolecular ion at *m/z* 595.1662, corresponding to the molecular formula C27H31O15⁺, that fragmented into two principal product ions at 449.1058 (C21H21O11⁺: loss of deoxyhexose) and 287.0546 (loss of hexose+deoxyhexose); Compound **9**, pelargonidin hexoside, showed a pseudomolecular ion at *m/z* 433.1135, corresponding to the molecular formula C21H21O10⁺, and gave one principal product ion at 271.0596; Compound **11**, pelargonidin hexose-deoxyhexose showed a pseudomolecular ion at *m/z* 579.1714 corresponding to molecular formula C27H31O14⁺ that, upon fragmentation, gave two principal product ions of *m/z* 433.1115 and 271.0596. Thus, anthocyanins detected represented both pelargonidin and cyanidin conjugated with one, two or three C6-sugars. These anthocyanin compounds have previously been identified in *Morus alba* fruits [17] and are responsible for the dark color of black mulberry fruits. These anthocyanins were present in berries of *M. nigra* and were not detectable in those of *M. alba* (Supplemental Table S1), as was expected from their differential colors.

A series of flavonols with different substituents were present in both white and black mulberry fruits (12, 14–17, 19–24, 26, 28–30, 32, 35). All these compounds showed the characteristic flavonol absorbance peaks at around 260 nm (resulting from the A-ring) and at around 350 nm (due to B-ring) and producing daughter ions of *m/z* 303.0496 (quercetin) or 287.0547 (kaempferol); many of the compounds detected have not previously been described for mulberry fruits. For instance, compounds 14, 19, 22 show the same pseudomolecular ion [M+H]⁺ at *m/z* 773.2135 with the same fragments of *m/z* 303.0496, 465.0995 and 611.1565, corresponding to the loss of two hexose and one deoxyhexose moiety, but with different RTs. These compounds were thus identified as different isomers of quercetin-hexose-hexose-deoxyhexose. A similar fragmentation pattern has been reported for a quercetin-trisaccharide in tomato fruit [19]. Specifically, compound 14 was confirmed as quercetin-3-O-rutinoside-7-O-glucoside based on the retention time of the reference compound reported in tomato fruit database [19].

Compounds 17, 23, and 24 likewise showed a similar pseudomolecular ion [M+H]⁺ at *m/z* 757.2192 with the same fragments of *m/z* 287.0547, 449.1065 and 611.1576, corresponding to the loss of two hexoses and one deoxyhexose moiety, but with different RTs. These were thus identified as kaempferol-hexose-hexose-deoxyhexose isomers. The fragmentation pattern of these compounds agrees with known kaempferol glycosides in tomato [19]. Moreover, compound 17 was confirmed as kaempferol-3-O-rutinoside-7-O-glucoside based on the retention time of the reference compound reported in tomato fruit database [19].

Compound 16 showed a pseudomolecular ion at *m/z* 713.1544 that upon fragmentation, gave three principal product ions at *m/z* 303.0496, 463.1021 and 551.1015 corresponding to the loss of two hexose moieties and one malonyl moiety; this compound was thus tentatively identified as quercetin hexose-malonyl-hexoside. This fragmentation pattern is consistent with that reported for a quercetin malonyl glucoside in lettuce [21]. Compound 21 showed a pseudomolecular ion at *m/z* 697.1597 which gave three principal product ions at *m/z* 287.0545, 449.1065 and 535.1076, corresponding to the loss of two hexose and one malonyl moiety; this compound was identified as kaempferol hexose-malonyl-hexoside. This compound showed a similar fragmentation pattern as reported in Cycorium intibus [24]. Thus, similar flavonol conjugates consisting of both quercetin and kaempferol esterified with one to three C6-sugars, or one or two sugars with one malonyl group, were present in both white and black mulberries.

Compound 20 showed a pseudomolecular ion at *m/z* 451.1235 that gave one principal product ion at *m/z* 289.0703, corresponding to the loss of a hexose moiety; this compound was tentatively identified as dihydrokaempferol-hexoside. A similar fragmentation pattern was reported for dihydrokaempferol-hexoside in raspberry [23].
Three N-containing sugars, i.e., compounds (1) 1-deoxynojirimicin, (2) N-nonil deoxynojirimicin and (3) fagomine, were found in fruits of both mulberry species. These compounds have previously been reported for leaves of *M. alba* [16] and are well known for inhibiting the enzyme α-glucosidase and consequently, might contribute to an antihyperglycemic effect [4–6].

Four piperidine alkaloids, morusimic acids B, C and E (compounds 7, 25, 27) were also identified based on their exact molecular mass and fragmentation; these compounds have previously been reported in fruits of *M. alba* from Turkey [18].

Both *M. alba* and *M. nigra* fruits also contained caffeoylquinic acids monomers (5, 10, 13) as well as dimers (31, 33, 34). All three caffeoylquinic acid isomers (5, 10, 13) showed a pseudomolecular ion at 355.1024 that, upon fragmentation, gave the same daughter ion at m/z 163.0386, corresponding to the loss of their quinic acid moiety. These compounds have also been reported in *M. alba* fruits from Serbia [17]. The three dicaffeoylquinic acid isomers (31, 33, 34) showed a pseudomolecular ion at 517.1341 that produced the same MS/MS base peak at 163.0387. These compounds have previously been reported in leaves of *M. alba* [27].

Compound 4 showed a pseudomolecular ion at m/z 182.0817, that fragmented into two principal product ions at 165.0544 and 136.0755. It was putatively identified as the alkaloid 2-formyl-1H-pyrrole-1-butanoic acid, previously reported in *M. alba* fruits by Kim et al. [11].

## 2.2. Global Metabolome Differences between Morus Alba and Morus nigra Fruits

Ripe fruits of *Morus alba* and *Morus nigra* were collected from trees growing at various locations in the Campania Region (Italy) and subjected to untargeted LCMS-based metabolite profiling. Subsequent unbiased data processing generated a dataset with the relative intensities and in-source mass spectra of 361 putative metabolites in the samples (Supplemental Table S1; note that this metabolite list misses some of the manually identified compounds described in Table 1, indicating that one or more parameter settings in the untargeted data processing workflow appears suboptimal for these specific compounds). An unsupervised multivariate statistical method, Principal Components Analysis (PCA), was subsequently applied to the entire metabolite dataset resulting in a clear differentiation of *M. alba* and *M. nigra* fruit samples (Figure S3). Among the most significantly (*p* < 0.05) differing metabolites were anthocyanins (Supplemental Table S1), as was expected from the differential fruit colours of both species. In addition, it was possible to identify two other flavonoids only detectable in *M. nigra*, namely dihydroquercetin hexoside and dihydrokaempferol hexoside. In fact, an important step for the biosynthesis of anthocyanidins is the reduction of dihydroflavonols catalysed by the enzyme DFR (dihydroflavonol 4-reductase) converting dihydroquercetin and dihydrokaempferol into colorless leucoanthocyanidins, which are further converted by the enzyme anthocyanin synthase (ANS) into cyanidin and pelargonidin, respectively [29], thereby providing the fruit colour in *M. nigra*. Several flavonol conjugates, including quercetin glycosides 19 and 26 and kaempferol glycoside 29 (Table 1), were also significantly (*p* < 0.05) higher in *M. nigra* fruit, while the mono- and di-caffeoyl quinic acids (phenylpropanoids) were not differential between both fruit species (Supplemental Table S1). These data suggest that *M. nigra* fruits exhibit a higher activity of the general flavonoid pathway than *M. alba* fruit. The alkaloids identified did not significantly differ between the *M. nigra* and *M. alba* fruit samples analyzed (Supplemental Table S1).

## 2.3. α-Glucosidase Inhibitory Activity and Effect of In Vitro Gastrointestinal Digestion

The α-glucosidase inhibitory activity of the black and white mulberries was firstly evaluated using the crude aqueous-methanol extracts of the fresh fruits. The extraction solvent was evaporated by freeze-drying and the metabolites re-dissolved in MQ-water. These water extracts were then tested for inhibiting α-glucosidase enzyme activity, monitored through the increase in the pNP product, detected at 412 nm, using 96-wells plates kept at 30 °C; the α-glucosidase inhibitor acarbose was used as a positive control (Figure 1b). Both mulberry extracts showed a marked and similar α-glucosidase inhibitory activity as compared to the water blank (Figure 1a).
<figure><img src="image_108.png" /><figcaption>**Figure 1.** α-glucosidase inhibitory activity of mulberry methanol extracts. The Y axis represents the α-glucosidase activity (increase in 415 nm absorbance per minute) and the X axis the sample type tested. (a) inhibitory activity of water extracts of *Morus alba* and *Morus nigra* compared to the negative control (water). (b) enzyme activity inhibition by acarbose (positive control) at increasing concentrations (mM) in the assay. Data represent means and standard deviations (n = 3 assays).</figcaption></figure>

The *M. nigra* fruit showed an $IC_{50}$ value of 0.75 ± 0.004 mg/g DW (n = 3), while that for *M. alba* fruit was 0.93 ± 0.003 mg/g DW (n = 3). In comparison, the $IC_{50}$ value of acarbose was 13.83 ± 0.02 mg/g.

A simulated gastrointestinal digestion was then applied to estimate the effect of consumption and digestion on the α-glucosidase inhibitory activity of mulberry fruits (Figure 2). For both fruit types, the bioactivity measured in the original fruit extracts was partially lost upon this in vitro digestion (GI samples compared to MN and MA samples). Gastric digestion (PG) resulted in a slight decrease in bioactivity in *M. nigra* only. The control incubation consisting of water instead of fruit extract in the digestion test (DC samples) showed a slight inhibition of the α-glucosidase activity as compared to the negative control (NC of undigested fruits: water instead of both fruit extract and digestion enzymes and buffer): a decrease of 0.04 enzyme units. Taking this inhibiting effect of the digestion conditions on α-glucosidase into account, it was calculated that the simulated gastrointestinal digestion resulted in an overall reduction in α-glucosidase inhibitory activity of about 50% (a decrease of about 0.055 units from 0.13 in DC to 0.075–0.08 in GI samples, compared to a decrease of about 0.115, i.e., from 0.17 units in NC to about 0.055 in original MN and MA extracts).
<figure><img src="image_109.png" /><figcaption>**Figure 2.** α-glucosidase inhibitory activity after in vitro gastrointestinal digestion. Inhibition activity of original *Morus nigra* (MN) and *Morus alba* (MA) fruit extracts, and after their in vitro stomach (Post Gastric, PG) digestion and in vitro gastrointestinal (GI) digestion. DC: digestion control, representing the digestion process, including all enzymes, without plant material; NC: negative control (NC), representing only water. Data represent means values and standard deviations (n = 3 measurements).</figcaption></figure>

## 2.4. LCMS Combined with 96-Well Format Fractionation

In order to pinpoint those compounds in *Morus* fruits that are responsible for the observed α-glucosidase inhibitory activity, we subsequently used HPLC separation combined with both 96-well plate fractionation and Orbitrap FTMS detection. Injection, fractionation and FTMS analyses of the *M. alba* and *M. nigra* crude extracts, as used in the α-glucosidase inhibition assay, were performed in triplicate; a water blank was injected as a control. The fractionation plates were subsequently dried under a gentle N₂ flow at 30 °C, the dried well contents re-dissolved in water and tested for α-glucosidase inhibitory activity. Compounds present in bioactive wells were then further characterized from their corresponding UV/Vis spectra and FT-MS data.

The results of the α-glucosidase inhibitory activity of individual wells are shown in Figure 3A,B for *M. alba* and *M. nigra*, respectively. Based on the average enzyme activity measured in the wells of the water control sample, we set a threshold value at 0.17 absorbance units per minute, below which we considered a sample well to possess α-glucosidase inhibitory bioactivity.

The compounds identified in the active wells of both fruit types were the amino sugar acids **1–3** and the flavonoids **15–17**, **19–20** and **32**. Two anthocyanins, **6** and **9**, only present in *M. nigra* (see Table 1), also showed bioactivity. In addition, other fractions clearly showing α-glucosidase inhibitory were detected e.g., between 34.7 and 35.2 min in *M. alba*, although we have yet been unable to pinpoint and identify the specific bioactive compound(s) (Table 2).
<figure><img src="image_110.png" /><figcaption>**Figure 3.** α-glucosidase inhibitory activity of 96-well LC-MS fractions of (A) *M. alba* and (B) *M. nigra* extracts. The Y axis shows the enzyme activity and the X axis the retention time corresponding to the LC-MS fraction. The vertical line at an enzyme activity of 0.17 indicates the average value in the water control. The wells considered bioactive are the ones below an enzyme activity value of 0.15.</figcaption></figure>

<table><caption>**Table 2.** Retention time window of bioactive 96-well fractions and putatively corresponding compounds (numbers refer to Table 1) in *M. alba* and *M. nigra*. n.i. = not identified.</caption><thead><tr><th>M. alba Retention Time (min)</th><th>Bioactive Metabolite</th><th>M. nigra Retention Time (min)</th><th>Bioactive Metabolite</th></tr></thead><tbody><tr><td>1.6–2.08</td><td>1-2-3</td><td>1.8–2.27</td><td>1-2-3</td></tr><tr><td>4.88–5.35</td><td>n.i.</td><td>4.13–4.6</td><td>n.i.</td></tr><tr><td>12.82–13.28</td><td>15</td><td>8.33–8.8</td><td>6</td></tr><tr><td>13.75–14.23</td><td>16/17</td><td>10.2–10.67</td><td>9</td></tr><tr><td>19.82–20.3</td><td>25</td><td>10.2–10.67</td><td>9</td></tr><tr><td>22.62–23.08</td><td>28</td><td>12.53–13</td><td>15</td></tr><tr><td>23.55–24.02</td><td>29</td><td>13.4–13.93</td><td>16</td></tr><tr><td>24.95–25.42</td><td>32</td><td>14.4–14.87</td><td>17</td></tr><tr><td>31.02–31.5</td><td>n.i.</td><td>15.8–16.27</td><td>19</td></tr><tr><td>34.7–35.2</td><td>n.i.</td><td>16.27–16.73</td><td>20</td></tr><tr><td>35.22–35.68</td><td>n.i.</td><td>24.67–25.12</td><td>31/32</td></tr><tr><td>-</td><td></td><td>40.07–40.53</td><td>n.i.</td></tr></tbody></table>

## 2.5. Total Antioxidant Activity and HPLC with Online Antioxidant Detection

The total antioxidant activity of the mulberry fruits was compared between other fruits well known for their antioxidant activity: cultivated strawberry (*Fragaria × ananassa*) and wild strawberry (*Fragaria vesca*). This antioxidant assay (Table 3) indicated that the aqueous-methanol extract of *M. nigra* is slightly more active than that of *M. alba*; in fact the mulberry fruits showed about the same activity as strawberry, which is among the fruit species with the highest antioxidant capacity [30].
<table><caption>**Table 3.** Antioxidant capacity of *Morus alba* and *Morus nigra* fruits compared with strawberry fruits. Data represent average values ± standard deviation of three independent extractions. All antioxidant values are expressed as mg Trolox per g of fresh weight. TEAC: Trolox-equivalent antioxidant capacity.</caption><thead><tr><td>Extracts</td><td>TEAC mg/g FW</td></tr></thead><tbody><tr><td><i>Morus alba</i> (White mulberry)</td><td>39.40 ± 0.02</td></tr><tr><td><i>Morus nigra</i> (Black mulberry)</td><td>49.42 ± 0.01</td></tr><tr><td><i>Fragaria vesca</i> (Wild strawberry)</td><td>50.61 ± 0.01</td></tr><tr><td><i>Fragaria ananassa</i> (Strawberry)</td><td>51.31 ± 0.01</td></tr></tbody></table>

Subsequently, a HPLC-PDA system coupled to online ABTS⁺ cation radical reaction and detection [31] was used to determine the relative contribution of each individual component to the total antioxidant activity (Figure 4). Several antioxidant components could be identified by comparison of their retention times and absorption spectra with those of the LC-PDA-FTMS/MS analysis using the same chromatographic conditions. According to this online antioxidant assay, the key antioxidants in *M. nigra* corresponded to anthocyanins, in particular, compounds **6** and **9**. The other compounds responsible for antioxidant activity in both *M. alba* and *M. nigra* were caffeoylquinic acids, like compounds **10** and **13**, and flavonols like compounds **15**, **26**, **28**, **32** (see Table 1).

<figure><img src="image_111.png" /><figcaption>**Figure 4.** Antioxidant activity. Overlay of representative antioxidant chromatograms of fruit of *Morus alba* (in blue) and *Morus nigra* (in black). Antioxidant profiles of fruit extracts were determined online, by a post column reaction with ABTS⁺ cation radicals after HPLC separation and PDA detection of compounds. The ABTS-radicals remaining after post-column reaction were recorded at 600 nm: negative peaks thus indicate antioxidant activity. The numbers refer to the main peaks identified (see Table 1): **6** cyanidin hexoside, **9** pelargonidin hexoside, **10** and **13** caffeoylquinic acid isomers, **15** dihydroquercetin hexoside, **26** quercetin hexose deoxyhexose, **28** quercetin hexoside, and **32** kaempferol hexoside.</figcaption></figure>

### 3. Discussion

In the present study, we compared ripe fruits of *Morus alba* and *Morus nigra* for their metabolite composition in relation to their potential relevant bioactivities upon consumption, i.e., α-glucosidase inhibiting and antioxidative activities. Using HPLC-PDA-Orbitrap FTMS analysis of aqueous-methanol extracts, we were able to detect a large series of compounds and identified a number of metabolites, previously reported for mulberry or other fruit species, as well as new compounds being present in either
or both *M. alba* and *M. nigra*. Fruit of both species exhibited a marked α-glucosidase inhibiting activity in vitro, an indication of their potential beneficial effect with regard to type II diabetes. Moreover, we showed that this α-glucosidase inhibiting activity was partially resistant to simulated gastric and intestinal digestion. Anthocyanins appear among the potential bioactive compounds in *M. nigra* fruit (Figure 3) and the general instability of anthocyanins at the alkaline conditions of gastrointestinal digestion [32] may at least partly explain the loss of α-glucosidase inhibitory activity in *M. nigra* fruits. When calculating the α-glucosidase inhibiting activity of mulberries in units of acarbose, a well known type II diabetes drug based on its α-glucosidase inhibiting activity (https://www.drugs.com/pro/precose.html), our data suggest that consumption of about 20–25 g of fresh mulberry fruit corresponds to 50 mg of acarbose, taking into account a 50% bioactivity loss upon digestion. It has been shown that an intake of 100 mg acarbose 3 times a day can significantly reduce type II diabetes risk factors [33]. Thus, a daily consumption of 100–150 g fresh mulberries may exert relevant pharmacological effects with regard to type II diabetes. Using analytical LC-based extract fractionation, it was possible to pinpoint three known iminosugar acids, i.e., [1-deoxynojirimycin (1), N-nonil-deoxynojirimycin (2) and fagomine (3)], and 7 phenolic compounds, including five flavonols [dihydroquercetin hexoside, (15) quercetin hexoside malonyl hexoside (16), kaempferol-3-O-rutinoside-7-O-glucoside (17), quercetin hexose (28) and kaempferol hexoside (32)] present in both *M. alba* and *M. nigra*, and 2 anthocyanins [cyanidin hexoside (6), pelargonidin hexoside (9)] only present in *M. nigra*, as the key α-glucosidase inhibitors in mulberry fruits. While compounds 1, 2, 3, 6, 28 and 32 have already been reported to exert this bioactivity [5,16,34,35], in our study, we were able to detect novel α-glucosidase inhibitory compounds in mulberries. A similar approach, using accurate mass LCMS coupled to 96-well fractionation and bioactivity testing, has recently been used to identify novel compounds in pepper fruits interacting with the human hot-taste receptor [36].

Although it was not yet possible to identify the novel α-glucosidase inhibitory compounds in mulberry fruits on the basis of the observed accurate mass only, this method can well be optimized and adapted for further structural characterization of these bioactives, e.g., by using so-called multistage mass spectrometry at high mass resolution [19], if needed, combined with NMR experiments. For the latter approach, the same bioactive wells from replicate plates may be pooled to get sufficient NMR signals for the de novo identification. Alternatively, bioactive extracts can be re-injected in a LC-MS-SPE set up to collect and concentrate individual LC-MS peaks upon repeated injections; the SPE cartridges containing the active compounds (based on their known accurate mass and LC-retention time) can then be subjected to NMR for structural elucidation [37].

In addition to the α-glucosidase inhibitory activity, the ABTS⁺-radical based total antioxidant assay indicated significant antioxidant activity present in the same mulberries, comparable to that of strawberries, which are among the fruit species with the highest antioxidant capacity [32]. The higher activity in *M. nigra* compared to *M. alba* fruits is likely due to the presence of anthocyanins, which both provide fruits with their dark color and contribute to antioxidant activity [38]. Indeed, using HPLC with online antioxidant detection [32], we were able to pinpoint anthocyanins as the main phenolic antioxidants in *M. nigra*, while both phenylpropanoids and flavonols were the key phenolic antioxidants in *M. alba*.

This work shows that it is well possible, using analytical scale techniques, to pinpoint the compounds that are key to the well described bioactivities of mulberry fruits, and to validate the value of metabolomics technologies in the phytochemical and bioactivity evaluation of functional foods. However, further studies towards, for example growth conditions, genotypic variation, fruit development and ripening are needed to obtain the best material for preparation of such functional foods with optimal composition of bioactive ingredients or for purification of the bioactive compounds.
# 4. Materials and Methods

## 4.1. Mulberry Materials

Fruits of *M. alba* and *M. nigra* were manually picked at ripe stage in May 2014 in different areas of the Campania region in Italy, in particular, the geographical locations Solofra (GPS coordinates latitude: 40.8291: longitude: 14.8456), Roccadaspide (GPS coordinates latitude: 40.4253: longitude: 15.1917), Fisciano (GPS coordinates latitude: 40.7728: longitude: 14.7994), San Sossio Baronia (GPS coordinates latitude: 410712: longitude: 15.2005). *Morus alba* fruits (MAF) were collected from 4 locations and coded as MAF-S (collected in Solofra) MAF-big (collected in San Sossio Baronia), MAF-wt (collected in San Sossio Baronia), MAF-R (collected in Roccadaspide); *M. nigra* fruits (MNF) were collected at five locations and coded as MNF-R (collected in Roccadaspide), MNF-U14 (collected in Fisciano), MNF-U13 (collected in Fisciano), MNF-S (collected in Solofra) and MNF-U13 (collected in Fisciano). All samples were botanically identified by Prof. V. De Feo (Department of Pharmacy University of Salerno) and compared with reference materials, then were freeze-dried before being transported to The Netherlands. They were then ground to a fine powder and stored at -80 °C until analysis.

For comparing antioxidant activities, fresh frozen mulberries were compared with fresh fruit of strawberry and wild strawberry collected in June 2014 in Campania (Italy).

## 4.2. Extract Preparation

The sample extracts used for LC-MS analysis were prepared essentially as described in De Vos et al. [31]: 30 mg of freeze dried samples were extracted with 1200 µL of 75% methanol in MQ water containing 0.1% of formic acid. Mixtures were then sonicated for 15 min, centrifuged at 12,500 g for 10 min and filtered over a 0.45 µm filter (Minisart SRP4, Biotech GmbH, Germany).

For α-glucosidase inhibitory activity testing, for both NanoMate fractionation and HPLC with online antioxidant analysis, 1 mL of supernatant was dried in a speedvac and taken up in 250 µL of water, sonicated and filtered through a 0.45 µm filter (Minisart SRP4, Biotech GmbH, Germany). These concentrated extracts were prepared in 3 independent replicates.

## 4.3. LC-PDA-Orbitrap FTMS Analysis

A metabolite analysis was performed using an HPLC (Waters Aquity) coupled to both a photodiode array detector (PDA; Waters) and an LTQ Ion trap-Orbitrap Fourier transformed Mass spectrometer (FTMS; Thermo) hybrid system. A Luna 3 µm C18 150 × 2 mm column (Phenomenex, USA) at 40 °C was used to separate the extracted metabolites, with MQ water with 0.1% formic acid (A) and acetonitrile with 0.1% formic acid (B) as solvents. A linear gradient from 5% to 35% B in 45 min, at a flow rate of 0.19 mL/min, was used [33]. In order to prevent possible order and batch effects in the LCMS analysis, all samples were analysed in a single series and in random order. Three quality control samples (QCs) prepared from a mix of all samples were included and equally distributed over the study samples, in order to check system stability and estimate overall technical variation.

Electrospray ionization (ESI) in positive mode was used to generate ions from eluting compounds. ESI source parameters were as follows: capillary voltage 43 V; tube lens voltage 120 V; capillary temperature 295 °C; Sheath and Auxiliary Gas flow at 40 and 3 (arbitrary units), respectively, Sweep gas 0 n, Spray voltage 5 V. MS spectra were acquired by full range acquisition covering *m/z* 104–1350 at a resolution of 60,000 FWHM.

## 4.4. LCMS data Processing and Multivariate Analysis

The raw LCMS data files were processed using Metalign software [34] for baseline correction, noise estimation, and ion-wise mass spectral alignment. MSClust software [35] was then used to assemble redundant mass signals derived from the same metabolite, including natural isotopes, adducts and in-source fragments, based on their corresponding retention times and relative abundance patterns across samples. This resulted in the relative intensities of 361 mass peak clusters, each representing a
(reconstructed) putative metabolite, present in at least two samples. These metabolite intensity data were then subjected to a multivariate analysis using GeneMaths XT software version 2.12 (Applied Maths, Belgium). Metabolite intensities were firstly log2-transformed and then mean-centred across samples.

Using multiple online databases, including KNApSAcK (http://kanaya.naist.jp/KNApSAcK/), Dictionary of Natural Products (http://dnp.chemnetbase.com), Metlin (https://metlin.scripps.edu/), HMD (http://www.hmdb.ca), in-house libraries based on standards, as well as the mass spectra information within the clustered mass peaks and from additional LC-MS<sup>n</sup> runs generating accurate mass spectral trees from the top 3 intensity ions every 30 s [19]. Selected metabolites (Table 1) were manually annotated as far as was possible using the mass data and the UV/Vis-absorbance spectral data available.

### 4.5. *In Vitro* Simulated Gastrointestinal Digestion

In vitro digestion was carried out on freeze-dried fruit samples of both *M. alba* and *M. nigra*, following the protocol described by McDougall et al. [36] with slight modification. Release of phytochemicals from fruit was checked by LC-MS at different stages of digestion, i.e., after gastric digestion (post gastric, PG) and gastrointestinal digestion (GI). Both PG and GI samples were stored at -80 °C until further analysis. During the process, three different controls were used: (1) plant material without digestion solutions and enzymes, diluted in water using the same ratio used for the samples coming from the digestion process (2) the solutions with all the ingredients for digestion but without plant material (DC: Digestion control), (3) plant material with all the ingredients for digestion but without active enzymes (the enzymes where added at the end of the digestion process, to the cold extract). Both α-glucosidase inhibitory activity and antioxidant activity were investigated for each of these PG and GI samples using the methods described below.

### 4.6. α-Glucosidase Inhibition Assay

The α-glucosidase assay uses the synthetic substrate p-nitrophenyl-α-D-glucopyranoside (pNPG), which is hydrolyzed by α-glucosidase to release p-nitrophenol (pNP), a color agent that can be monitored at 415 nm. Briefly, 10 µL of extract was combined with 40 µL of 100 mM phosphate buffer (pH 6.8) and 20 µL of α-glucosidase (0.6 units per mL buffer). After mixing and incubation for 5 min at 37 °C, 20 µL of a 20 mM pNPG solution in buffer was added to start the reaction. The reaction was monitored in time at 415 nm by a TECAN SpectraFluor microplate reader. Acarbose was used as a positive control, while water was used as a negative control for enzyme inhibition. The enzyme activities were evaluated as increase in the absorbance at 415 nm per minute and the percentage of enzyme inhibition was calculated. Three dilution series of extracts were used for IC₅₀ determination. Dose-response curves and IC₅₀ values were obtained by use of GraphPad Prism (version 6.00.283). The assay was performed with 3 replicates.

### 4.7. NanoMate LC-Fractionation of Extracts

The HPLC-PDA-FTMS system was adapted with a chip-based nano-electrospray ionization source/fractionation robot (NanoMate Triversa, Advion BioSciences) coupled between the PDA and the inlet of the Ion Trap/Orbitrap hybrid instrument [19]. In this system, the compounds separated and eluting from the analytical column firstly passed the PDA detector for determining their UV/Vis absorbance spectra and then the eluent was automatically split by a NanoMate LC-fraction collector/injection robot (Advion) into a nanoflow for chip-based ESI nanospray Orbitrap FTMS analysis and the rest for fractionation into microwells with a collection time of 28 sec per well. The sample injection volume was 5 µL. The gradient and flow conditions were the same as described above, with an additional 30 µL/min 100% isopropanol added into the LC flow via a T-junction between the PDA and the NanoMate, in order to improve the solvent composition for generating a stable nano-electrospray. The eluent flow was split by the NanoMate at a ratio of 219.5 µL/min to the fraction collector and 0.5 µL/min to the nano-electrospray source. LC-fractions were collected every 28.2 s (i.e., 100 µL
solvent) into 96-well plates (Twin tec, Eppendorf). After collection, the plates were dried at 30 °C under a gentle N₂ flow, and then tested for α-glucosidase inhibitory activity as described above (performed in 3 replicates).

## 4.8. Antioxidant Activity and HPLC Analysis with Online Antioxidant Detection

The total antioxidant capacity of fruits was analyzed using the ABTS⁺ radical scavenging method, essentially according to Capanoglu et al. [37] with slight modifications. The fruits were collected in June 2014 and the antioxidant activity was tested on basis of fresh weight. 0.5 g of samples (fresh fruit) were extracted in 1.5 mL of methanol (final MeOH concentration about 77%, taking into account a fruit water content of 95%) containing 0.05% of formic acid, sonicated for 15 min and centrifuged at 12,500 g for 15 min, filtered through 0.45 µm (Minisart SRP4, Biotech GmbH, Germany) and then 10 µL of extract was used to test the antioxidant activity. Trolox was used as a reference.

To determine total antioxidant capacity, 10 µL of sample extracts or standard solution was mixed with 90 µL of ABTS-radical working solution (pH 7.4) and after 40 s, the remaining ABTS⁺ radicals were measured at 415 nm using 96-well microplates (Nunc, Roskilde, Denmark) and an Infinite® M200 micro plate reader (Tecan, Gröding, Austria). The analyses were done using 3 replicates and the results were expressed in terms of mg Trolox Equivalent Antioxidant Capacity (TEAC) per g fruit FW. In addition, the contribution of individual antioxidants to the total antioxidant capacity of the crude mulberry extracts was determined using an HPLC-PDA system coupled to post-column on-line antioxidant detection [37,38]. For this, the extracts of *M. alba* and *M. nigra* fruits, also used for the LC-MS analysis, were analyzed using a W600 Waters HPLC system coupled to a Waters 996 PDA detector (240–600 nm) [37,38]. Eluted compounds were allowed to react online for 30 s at 40 °C in a buffered solution of ABTS⁺ cation radicals (pH 7.4). Then, the absorption of the remaining ABTS⁺ radicals was monitored at 412 nm by a second detector (Waters 2487, dual-wavelength UV–vis detector). Peak identification was done by comparing PDA-absorbance spectra and retention times of eluting peaks with data taken from the literature and annotations were confirmed by HPLC-FTMS and MS/MS analyses, as described above.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2218-1989/10/1/7/s1, Figure S1: Representative LC-MS chromatograms, recorded in ESI positive ionization mode, of *M. nigra* (upper trace) and *M. alba* (lower trace) fruit extracts; Y-axes are on the same scale (1.5\*E7; base peak intensity in ion counts/sec). Values above peaks indicate retention times (in minutes) and detected *m/z* value, Figure S2: Representative LC-PDA chromatograms of aqueous-methanol extracts from ripe fruits of *M. nigra* (A and B) and *M. alba* (C and D). Figures show absorbance at 520 nm (A and C) representing elution profile of anthocyanins, and at 355 nm (B and D) representing mainly flavonoids and phenylpropanoids, Values above peaks indicate retention times (in minutes). Note: intensity scales (Y-axes) are similar for all traces, Figure S3: 3 dimensional PCA plot of 5 *Morus alba* and 4 *M. nigra* fruit samples, harvested from trees spread over region Campania, Italy, based on their variation in 371 metabolites detected by the untargeted LCMS approach. The 3 quality control samples are technical replicates of a mix of samples. The X-axis (PC1) explains 33.2% of the total metabolites variation, the Y-axis (PC2) 18.6% and the Z-axis (PC3) 14.2%, Table S1: Relative intensity of all putative metabolite features (clusterID's) for each of the analyzed mulberry samples, Table S2: Description of column heads, Table S3: MSI Identification level.

**Author Contributions:** Conceptualization, G.D., P.M. and R.C.H.d.V.; methodology, G.D., J.J.M. and R.C.H.d.V.; software, G.D. and R.C.H.d.V.; formal analysis, G.D.; investigation, G.D., P.M. and R.C.H.d.V.; resources, P.M., J.J.M. and R.D.H.; data curation, G.D. and R.C.H.d.V.; writing—original draft preparation, G.D.; writing—review and editing, G.D., J.J.M., P.M., R.D.H. and R.C.H.d.V.; visualization G.D.'U and R.C.H.d.V.; supervision, P.M., J.J.M., and R.C.H.d.V.; project administration, G.D., P.M. and R.C.H.d.V. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** This research was carried out at the Bioscience department of Wageningen Plant Research of Wageningen UR. Special thanks to Harry Jonker and Bert Schipper for their technical help in HPLC-antioxidant and LC-PDA-FTMS analysis, and to Monic Tomassen (Fresh Food and Chains, Food & Biobased Research, Wageningen UR) for helping with the in vitro gastrointestinal digestions.

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