Identification of Bioactive Phytochemicals in Mulberries

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.


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].
Metabolites 2020, 10, 7 2 of 16 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][5][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][9][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][12][13][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 MS n 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.

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 MS n fragmentation patterns and their UV/Vis absorbance spectra, we putatively identified 35 compounds in the fruits (Table 1).  [28] 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 C 21 [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 C 6 -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 dihydrokaempferolhexoside in raspberry [23].
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-1Hpyrrole-1-butanoic acid, previously reported in M. alba fruits by Kim et al. [11].

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).

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). The M. nigra fruit showed an IC50 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 IC50 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 (  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 (

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.

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 N 2 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).   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).

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]. 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). 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).   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.

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) (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. 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.

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

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.

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.

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 n 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.

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.

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 IC 50 determination. Dose−response curves and IC 50 values were obtained by use of GraphPad Prism (version 6.00.283). The assay was performed with 3 replicates.

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 N 2 flow, and then tested for α-glucosidase inhibitory activity as described above (performed in 3 replicates).

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.