Targeted Metabolomics Analysis and Identification of Biomarkers for

Article Cite This: J. Agric. Food Chem. 2019, 67, 5908−5917

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Targeted Metabolomics Analysis and Identification of Biomarkers for Predicting Browning of Fresh-Cut Lettuce Carlos J. García, María I. Gil, and Francisco A. Tomaś -Barberań *

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Research Group on Quality, Safety, and Bioactivity of Plant Foods, Centro de Edafología y Biología Aplicada del Segura (CEBAS)−Consejo Superior de Investigaciones Científicas (CSIC), Post Office Box 164, Espinardo, Murcia 30100, Spain ABSTRACT: The metabolism of phenolic compounds is a key factor in the development of wound-induced enzymatic browning of fresh-cut lettuce. In the present study, the lettuce midribs discriminant metabolites, selected in a previous untargeted metabolomics study, were thoroughly identified. Our results showed that their basal contents correlated with browning developed after 5 days of storage. 5-trans-Chlorogenic acid and 5-cis-chlorogenic acid were positively correlated with browning, while sinapaldehyde and its 4-β-D-glucoside and 4-(6′-malonyl)-β-D-glucoside conjugates were negatively correlated. Using targeted metabolomics, the metabolites were analyzed in lettuce heads with different degrees of development and different browning susceptibility and these biomarkers were confirmed. Despite the large variability in the browning process of lettuce, the chlorogenic acids/sinapaldehyde derivatives ratio showed a linear correlation (r2 = 0.79) with the fresh-cut lettuce browning developed in 24 Romaine lettuce cultivars, validating the relevance of these biomarkers. These results show that the analysis of the basal content of these metabolites could be used in lettuce breeding programs to select cultivars that are more appropriate for the fresh-cut industry. KEYWORDS: UPLC−ESI−QTOF−MS, metabolomics, enzymatic browning, quality, phenylpropanoid metabolism, chlorogenic acid, sinapaldehyde

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lettuce metabolome11−13 and explain the metabolic wound response of lettuce after cutting.9 One of the main interests of the lettuce breeding and processing companies is to select cultivars that are appropriate for fresh-cut, meaning low browning potential.14 Therefore, the identification of metabolic biomarkers that can predict the browning development after cutting is of great relevance. Previous untargeted metabolomics studies9,16 have pointed out some metabolites that could be used as biomarkers of the tissue browning susceptibility after cutting in different lettuce cultivars. Among the entities detected, some of the most relevant entities were phenolic metabolites of the group of phenylpropanoids, although they were only tentatively identified (level 2).17 Some of them correlated with browning development after 5 days of storage, while others were negatively correlated.16 The most relevant basal metabolites present in harvested lettuce that correlated positively with browning development were tentatively identified as caffeoylquinic derivatives, while other caffeic acid conjugates, such as caffeoyltartaric and dicaffeoyltartaric, were not discriminant regarding browning development.16 Among those metabolites that were negatively correlated with browning development, some methoxyhydroxycinnamic acid derivatives, with formula, masses, and fragments as those of ferulic acid methyl ester previously described in lettuce,9,11,13 were particularly relevant, although they were not identified. Other entities that, when analyzed at time 0

INTRODUCTION Lettuce (Lactuca sativa) is one of the most cultivated and consumed leafy crops around the world. The consumption of lettuce has increased even more as a result of the commercialization of fresh-cut ready-to-eat salads. After cutting, enzymatic browning that occurs on the lettuce cut edges reduces the shelf life and consumer acceptance.1,2 When the lettuce tissue is cut, the constitutive enzyme polyphenol oxidase (PPO)3 and the phenolic substrates come in contact, resulting in browning.4,5 It has been pointed out recently6 that the great variability in lettuce discoloration, including edge browning, can be due to many factors, such as the interplay among the phenylpropanoid pathway, the accumulation of the phenolic compounds produced, and the activity of PPO and its reactions with o-diphenols as well as o-quinones produced. Thus, the browning process of fresh-cut lettuce is a very complex pathway that is triggered by wound-response signal metabolites.5,7,8 Among the signal metabolites, the oxylipins, 9hydroperoxy-12,13-epoxy-10-octadecenoic acid and 11,12,13trihydroxy-9-octadecenoic acid, have been shown to enhance the phenylpropanoid pathway and the biosynthesis of the enzymatic browning substrates.9 After cutting, the synthesis and accumulation of specific phenolic compounds are induced, although the basal levels of these compounds are very low or often not detectable.5 Tissue wounding induces the biosynthesis of caffeoylquinic acids and related compounds as well as methoxycinnamic acid derivatives. These metabolites are either involved in the formation of brown polymers or the wound repairing via lignification.10 Previous untargeted metabolomics studies with ultra-performance liquid chromatography electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC−ESI−QTOF−MS) have been used to explore the © 2019 American Chemical Society

Received: Revised: Accepted: Published: 5908

March 8, 2019 April 29, 2019 May 1, 2019 May 1, 2019 DOI: 10.1021/acs.jafc.9b01539 J. Agric. Food Chem. 2019, 67, 5908−5917

Article

Journal of Agricultural and Food Chemistry

development 2 weeks later (development stage 2) (Figure 1).18,19 For the validation study, 24 lettuce cultivars grown in the same field under the same agricultural practices were harvested at the commercial maturity stage.16 Head weight, head length, compacity, and number of leaves were measured to characterize the development stage. Lettuce heads were transported 30 min by car to the lab and stored for 24 h at 7 °C and 80% relative humidity. Lettuce heads were processed as described previously,16 with minor modifications. Four lettuce heads were used per cultivar as independent replicates. A total of 10 leaves per head from the middle part of the head were processed, and midribs were carefully excised (1.5 cm). For the metabolomics analyses, samples of 30 g of midribs were directly frozen in liquid nitrogen and stored at −80 °C. Midribs were then freeze-dried and milled into powder using a blender (Oster Professional BPST02-B). Browning Development Analysis. To measure the differences in the browning susceptibility between the cultivars, digital image analysis was carried out.16,20 A total of 20 midrib pieces were placed together with the cut edge facing up, and the image was captured using a digital camera (Nikon D7100). Four images, one per replicate, were captured and saved as JPEG files. Images were analyzed using the software ImageJ, version 1.48v (NIH Image, National Institute of Health, Bethesda, MD, U.S.A.). The total cut edge area was considered as the area of study. The RGB color model was used to identify the browned pixels in the range of hue (20−35), saturation (40−195), and brightness (0−225).12,20 Differences in color as hue between time 0 (just after cutting) and after 5 days of storage were calculated (Δhue). Targeted Metabolomics Analysis. Midribs were extracted as described previously,21 with minor modifications. Freeze-dried and powdered samples (0.3 g) were mixed with 10 mL of methanol/water (80:20; v/v). The extraction mixture was sonicated for 30 min at room temperature, centrifuged at 10000g for 15 min, and filtered directly [0.22 μm polyvinylidene fluoride (PVDF) filter] before the analysis by UPLC−ESI−QTOF−MS. Chemicals and reagents were previously described.9 UPLC−ESI−QTOF−MS analyses were carried out using an Agilent 1290 Infinity LC system coupled to the 6550 Accurate-Mass QTOF (Agilent Technologies, Waldbronn, Germany) with an electrospray interface (Jet Stream Technology). Chromatographic and mass spectrometry conditions were described previously.16 Samples (3 μL) were injected in a reverse-phase Poroshell 120 EC-C18 column (100 mm, 2.7 mm, Agilent Technologies, Waldbronn, Germany). The column temperature was set at 30 °C, and the flow rate was 0.4 mL/min. The mobile phases were acidified water (0.1% formic acid) (phase A) and acidified acetonitrile (ACN) (0.1% formic acid) (phase B). The linear gradient of phase B was from 1 to 18% in 10 min, from 18 to 38% within 10−16 min, and from 38 to 95% within 16−22 min. The same analyses were also repeated for some samples, using solvents without formic acid for identification purposes. The MS system was operated in the negative (for all of the samples) and positive (in some samples for metabolite identification) ion modes with the mass range set at m/z 100−1100 in full-scan resolution mode. The fragmentor voltage was established at 100 V, and acquisition rate was 1.5 spectra/s. Targeted MS/MS analyses were performed to complete the metabolite identification and confirm the presence of the tentative biomarkers. Tandem mass spectrometry (MS/MS) product ion spectral parameters were m/z range of 50−750 using a retention time rate of 1 spectrum/s. A targeted list of m/z was inspected in the specific Rt of the ions using an isolation width of m/z 4. Collision energies of 10, 15, 20, and 30 eV were used.

(basal levels), were negatively correlated with browning development after 5 days of storage were tentatively identified as galloyl hexose, dihydroxybenzoic acid hexose, syringic acid hexose, quercetin 3-(6″-acetylglucoside), and 2-O-p-hydroxybenzoyl-6-galloylglucoside, although they were not as strong in the negative correlation as the ferulic acid methyl ester, pointed out above.16 In this previous study, it was suggested that a ratio between the basal levels of some metabolites, such as chlorogenic acid/ferulic derivatives, detected at harvest could be a valuable tool to predict browning development after cutting. This study aimed at the identification of phenolic biomarkers of lettuce browning and their validation as early forecasting for the selection of Romaine lettuce cultivars better suited for industrial processing in plant breeding programs.

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MATERIALS AND METHODS

Chemical Reagents. Methanol, acetonitrile, and 0.1% (v/v) formic acid in water were from J.T.Baker (Deventer, Netherlands),

Figure 1. ΔHue values of LB and HB susceptibility cultivars at development stages 1 (harvested 2 weeks before commercial maturity) and 2 (harvested at commercial maturity). Error bars are standard deviations (n = 4). and formic acid was from Panreac (Barcelona, Spain). Authentic standards of ferulic acid methyl ester, ferulic acid, isoferulic acid, sinapic acid, caffeic acid, p-coumaric acid, trans-sinapaldehyde, and 5trans-caffeoylquinic acid were purchased from Sigma-Aldrich (St. Louis, MO, U.S.A.). 5-cis-Caffeoylquinic acid was obtained from the trans-authentic standard by thermal treatment at 30 °C for 6 h exposed to daylight.15 Enzymatic Hydrolysis with β-D-Glucosidase. The lettuce extract (600 μL) was added to 200 μL of 0.2 M acetate buffer at pH 4.5 and 1 mg of β-D-glucosidase from Sigma-Aldrich (St. Louis, MO, U.S.A.) and incubated at 37 °C for 24 h. The resulting products were analyzed by UPLC−QTOF−MS under the conditions reported below to evaluate the conversion of the potential β-D-glucosides to the corresponding aglycone. Plant Material and Processing. Romaine lettuce was selected as one of the lettuce types most prone to browning development. Two cultivars, low browning (LB) and high browning (HB), were selected from the Enza Zaden experimental field in Torre Pacheco (Murcia, Spain). The names of the cultivars are not mentioned because they are not relevant for this study. All lettuce cultivars were grown using standard lettuce production practices. For the study on the different development stages, the two cultivars LB and HB were examined. Heads were harvested before reaching the commercial development (development stage 1) and after reaching the full commercial

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RESULTS Identification of Biomarkers. To characterize and identify the basal lettuce tissue metabolites that correlated positively and negatively with the browning development, authentic standards of 5-trans-caffeoylquinic acid (transchlorogenic acid) (calculated [M − H]− m/z 353.0878), ferulic acid methyl ester (calculated [M − H]− m/z 207.0663), 5909

DOI: 10.1021/acs.jafc.9b01539 J. Agric. Food Chem. 2019, 67, 5908−5917

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Figure 2. Chromatographic separation of (A) caffeoylquinic acid isomers and (B) sinapaldehyde derivatives. UPLC−QTOF−MS analyses of lettuce samples in the negative mode. (A) Ion extracted chromatogram at m/z 353.0878: (1) 3-trans-caffeoylquinic acid, (2) 5-trans-caffeoylquinic acid, (3) 4-trans-caffeoylquinic acid, and (4) 5-cis-caffeoylquinic acid. (B) Ion extracted chromatogram at m/z 207.0663: (5) trans-sinapaldehyde-4β-D-glucoside, (6) trans-sinapaldehyde-4-(6′-malonyl)-β-D-glucoside, and (7) trans-sinapaldehyde.

and trans-sinapaldehyde (calculated [M − H]− m/z 207.0663), as well as caffeic, ferulic, isoferulic, p-coumaric, and sinapic acids were used. In the UPLC chromatograms, four peaks with [M − H]− of caffeoylquinic derivatives with m/z 353.0878 were detected (Figure 2). The main peak at 7.9 min coincided with an authentic standard of trans-5-caffeoylquinic acid (transchlorogenic acid). The MS/MS fragments also coincided with those reported for this metabolite.22 The isomeric metabolites 1 and 3 showed chromatographic behavior and MS fragments consistent with 3-caffeoylquinic acid (transneochlorogenic acid) and 4-caffeoylquinic acid (trans-criptochlorogenic acid), respectively, although they were minor metabolites in the lettuce extracts analyzed.22 These were also identified with surrogate standards23 using green coffee bean extracts.5 Compound 4 was the second relevant chlorogenic acid isomer present in the extract, and the retention time (Rt) and the MS fragments were consistent with a cis isomer of chlorogenic acid.24 This metabolite was obtained from the authentic standard of trans-chlorogenic acid by thermal

treatment under daylight to enhance the conversion of the trans to cis isomer.15 The MS fragments (Table 1) also supported this identification, in agreement with previously published results.22 These results show that the main basal metabolite that was positively correlated with the browning developed (Δhue) after 5 days of storage was trans-5caffeoylquinic, although the cis isomer also correlated in the same way. With regard to the previously reported “ferulic acid methyl ester” identified by the MS and fragments and also supported by previous reports of its presence in lettuce,11−13 its identification resulted in being incorrect, because the metabolite previously identified tentatively in lettuce (Rt of 13.5 min) had a different Rt than that of an authentic standard of ferulic acid methyl ester (16.8 min). Therefore, other metabolites consistent with the same molecular formula (C11H12O4) were considered. The more sensible metabolite was sinapaldehyde. Although it had not previously been reported in lettuce, a related metabolite, syringin (sinapylalcohol glucoside), had been reported in Iceberg lettuce,25 and its 5910


5911

3-trans-caffeoylquinic acid (trans-neochlorogenic acid) 5-trans-caffeoylquinic acidb (trans-chlorogenic acid) 4-trans-caffeoylquinic acidb (trans-criptochlorogenic acid) 5-cis-caffeoylquinic acidb (cis-chlorogenic acid) sinapaldehyde glucosideb sinapaldehyde-4-(6′-malonyl)glucosideb trans-sinapaldehydeb sinapic acidd sinapyl alcohole ferulic acidd isoferulic acidd ferulic acid methyl esterd ferulic acid 7-O-glucosidee 5-hydroxyconiferaldehydee coniferyl alcohole coniferyl alcohol glucoside (coniferin)e dihydroconiferyl alcohol glucosidee syringine 7.9 8.5 9.4 9.6 11.4 13 12.1

3498 87120 86150 984908 N/D 44806 45738 44805 4156 64942 N/D 985480 64178 44377 64182 95893 64181 12.1 12.6 16.8

6.3

Rt (min)

95165

Metlin ID

C17H10O9 C20H12O12 C11H12O4 C11H12O5 C11H14O4 C10H10O4 C10H10O4 C11H12O4 C16H20O9 C10H10O4 C10H12O3 C16H22O8 C16H24O8 C17H24O9

C16H18O9 [M [M [M [M [M [M [M [M [M [M [M [M [M [M

− − − − − − − − − − − − − −

H + FAc]− H + FA + Na]− H]− H]− H]− H]− H]− H]− H]− H]− H]− H]− H]− H]−

[M − H] 415.1246 523.1101 207.0663 223.0612 209.0819 193.0506 193.0506 207.0663 355.1035 193.0505 179.0714 341.1242 343.1398 371.1348

353.0878

353.0878

[M − H]−

C16H18O9 −

353.0878

[M − H]−

353.0878

C16H18O9

parent ion [M − H]

−

C16H18O9

molecular formula

134.038 134.0387 133.0294

207.0659 207.0658 177.0191 193.0131

191.0566

191.0561

191.0561

191.0557

m/z

192.0419

192.0414 361.1641 192.0437 164.0457

179.0353

173.0455

179.0349

179.0353

m/z

51

31 44 21 45

1

49

1

78

Ia

177.0192

177.019 275.0533 149.023 135.9135

161.0241

161.0237

161.023

m/z

18

5 20 39 35

3

4

1

I

149.0238

121.465 121.0287

135.0445

135.0442

m/z

secondary peaks I

4

13 53

27

55

105.0346

105.0342

m/z

38

9

I

I = peak intensity (% of base peak). bMetabolites were identified with standards (level 1 identification)16 and with MS/MS fragments and detected in the samples. cFA = formic acid adduct. Metabolites were searched with standards (level 1 identification)16 and MS/MS fragments but not detected. eMetabolites were not detected in the samples.

d

a

5 6 7 8 9 11 12 13 14 15 16 17 18 19

4

3

2

1

b

phenylpropanoids

base peak

Table 1. UPLC−QTOF−MS−MS Targeted Metabolomics Analysis of Phenolic Metabolites, with a Collision Energy of 20 eV

Journal of Agricultural and Food Chemistry Article


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sinapaldehyde and probably conjugated derivatives of this metabolite because m/z 207.0663 with the same fragments as those of the sinapaldehyde standard were detected in both metabolites (Table 1). MS/MS experiments in negative and positive modes were carried out to identify these metabolites because authentic standards of sinapaldehyde conjugates were not available. The MS of metabolite 5 indicated that this was consistent with a formic acid adduct of the metabolite C17H10O9 ([M − H + formic acid]− m/z 415.1246 in the negative mode; [M − H − 46]− gave m/z 369, the calculated [M − H]− for sinapaldehyde-4-hexoside), consistent with sinapaldehyde 4-O-hexoside, which appeared as a formic acid adduct in MS. The MS/MS analysis in the negative mode yielded the fragments for the sinapaldehyde authentic standard (Table 1) and, therefore, confirmed the occurrence of a sinapaldehyde hexoside. The analysis in the positive mode (Figure 4) confirmed the nature of metabolite 5 with a [M + H]+ ion at m/z 393.1156, consistent with a sodium adduct of sinapaldehyde hexoside and a fragment of the sodium adduct of the sinapaldehyde aglycone (m/z 231.0629) (Table 2). Metabolite 5 was converted to sinapaldehyde by enzymatic hydrolysis with β-D-glucosidase and, therefore, confirmed that this was sinapaldehyde-4-O-β-D-glucoside. The assignation of glucose as the hexoside is also supported by the occurrence of glucosyltransferases in Arabidopsis capable of glucosylating coniferaldehyde and sinapaldehyde.26 The chromatographic behavior of metabolite 6 indicated that this was a less polar derivative of sinapaldehyde than the corresponding glucoside 5 (an increase in Rt close to 2 min). The mass and fragments of the aglycone also coincided with those of sinapaldehyde (m/z 207.0658) in the negative mode, suggesting that this was a different conjugate of compound 7. The MS in the negative mode showed that the [M − H]− peak was consistent with a double formic acid and Na adduct of C20H12O12 (m/z 523.1101). In the positive mode, the spectrum was clarifying with a sodium adduct at m/z 479.1151 as the base peak, a [M + H − CO2]+ fragment consistent with the loss of the final COO− from dicarboxylic acid (m/z 435.1277), and a fragment at m/z 393.1141 that was coincident with the base peak of metabolite 5 identified as a sodium adduct of sinapaldehyde hexoside (Figure 4), which involved the loss of an 86 mu fragment, consistent with the loss of a malonyl residue + 2H. Then, an additional loss of the hexosyl residue (loss of 162) to give a fragment at m/z 231.0619, consistent with a sodium adduct of synapaldehyde aglycone, was observed. The chromatographic behavior was also consistent with a more lipophilic metabolite than hexoside (5) because the esterification with malonic acid decreased the hydrophilic character, as previously reported for quercetin (malonylglucosides) in lettuce.17,27,28 This was also confirmed when the metabolites were analyzed in UPLC without adding formic acid as a modifier. The removal of formic acid did not affect the Rt of both sinapaldehyde (7) and its 4-glucoside (5), although it decreased the Rt of compound 6 by half a minute (Rt of 10.9 min), showing that the increase in pH of the mobile phase after the removal of formic acid increased the ionization of free carboxyl of the malonic acid residue and, therefore, increased its polarity and decreased its Rt. This type of acylation is consistent with the lettuce biochemical activity because 6-malonylglucosides of other phenolic compounds have already been reported in lettuce, in which quercetin-3glucosides are present together with the 6″-malonated conjugates13,21,28,29 and the anthocyanin cyanidin-3-O-6″-

Figure 3. (A) Sinapaldehyde (7) and (B) ferulic acid methyl ester (12) UPLC−QTOF−MS fragments (m/z 207.0663) in the negative mode and at 20 eV.

biosynthetic precursor, 5-hydroxyconiferaldehyde, was tentatively identified in Romaine lettuce.9 The sinapaldehyde authentic standard showed MS/MS fragments very similar to those of ferulic acid methyl ester, although they had different relative intensities (% of base peak) and differed in one fragment. Ferulic acid methyl ester showed a fragment at m/z 133.0294 (C7H5O2), while sinapaldehyde showed a fragment at m/z 121.4650 (C7H5O3) that was not present in the MS/ MS analysis of ferulic acid methyl ester (Figure 3). Therefore, in the previous studies, the incorrect assignment of this metabolite was possible based only on the MS/MS fragments without confirming its nature by chromatographic comparisons to an authentic standard. In the samples analyzed, only one minor compound 7 coincided chromatographically with sinapaldehyde at Rt of 13.5 min when the ion chromatogram at 207.0663 was extracted, while two other main compounds 5 (Rt of 9.58 min) and 6 (Rt of 11.4 min) (Figure 2) also produced the same mass as a fragment. These peaks were studied in more detail, because compound 5 coincided with the metabolite that negatively correlated with browning in a previous study.16 These two metabolites showed a chromatographic behavior that indicated that they were more polar than 5912


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Figure 4. UPLC−QTOF MS/MS fragments in the positive mode of (A) sinapaldehyde-4-β-D-glucoside and (B) sinapaldehyde-4-(6′-malonyl)-βD-glucoside.

malonylglucoside is present in red-pigmented lettuce cultivars.27,28 Therefore, we tentatively identify metabolite 6 as the 6′-malonyl ester of sinapaldehyde-4-β-D-glucoside. Targeted Metabolomics Analysis To Confirm That the Selected Biomarkers Predict Browning. Once the biomarkers related to browning were identified, other related metabolites were also examined in the UPLC−QTOF analyses, following a targeted metabolomics approach. The metabolites that were negatively correlated with the browning development were also associated with precursors of the lignification process. Thus, other phenolic metabolites involved in lignin formation were also studied at basal levels, immediately after tissue cutting, to predict browning. Thus, ferulic acid, coniferyl alcohol, coniferin (coniferyl alcohol 4-glucoside), dihydroconiferyl alcohol glucoside, syringin, and 5-hydroxyconiferaldehyde were searched by targeted metabolomics (Table 1).

However, they were not detected in any of the samples studied. Nevertheless, a previous study has shown that 5-hydroxyconiferaldehyde (a biosynthetic precursor of sinapaldehyde) and coniferyl alcohol glucoside (coniferin) were induced after cutting and increased their content during the 5 days of storage of the tissues.9 To validate the potential use of the basal content (immediately after cutting) of chlorogenic acids and sinapaldehyde derivatives as biomarkers to predict the development of browning after cutting, the ratio of chlorogenic acids/sinapaldehyde and conjugates was examined. For this study, two lettuce cultivars with very different browning susceptibility after 5 days of storage were studied, as shown by measuring Δhue, which represents the difference in color between samples after 5 days of storage and just after cutting (Figure 1). The two lettuce cultivars with low and high 5913


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6 231.0619 17 271.0434

m/z I m/z I

20 8 185.0418 393.1141

m/z I

18 51 231.0629 435.1277

m/z m/z

393.1146 479.1151

I = peak intensity (% of base peak).

Figure 6. Linear regression model of Romaine lettuce cultivars. N = 24. Coefficients: b0 = −27.619 450 985 6, b1 = 3.879 263 702 3, and r2 = 0.793 072 783 7. Ratio: ∑chlorogenic acids/∑sinapaldehyde and conjugates (mg/g of fresh midribs).

browning susceptibility (LB and HB) were harvested at two different development stages, which differed only in 2 weeks.

a

parent ion

[M + H + Na]+ [M + H + Na]+ C17H10O9 C20H12O12

molecular formula

Figure 5. (A) Chlorogenic acid isomers (trans- plus cis-5caffeoylquinic acids) and (B) sinapaldehyde and conjugates in the selected Romaine lettuce cultivars with LB and HB susceptibility at development stages 1 (harvested 2 weeks before commercial maturity) (light green bar) and 2 (harvested at commercial maturity) (dark green bar). Error bars are standard deviations (n = 4).

9.6 11.4

Rt (min) sinapaldehyde derivatives

sinapaldehyde-4-β-D-glucoside sinapaldehyde-4-β-D-(6′-malonyl) glucoside 1 2

secondary peaks

a

base peak

Table 2. UPLC−QTOF−MS MS/MS Analysis of Metabolites in Positive Mode, with a Collision Energy of 20 eV

393.1156 479.1151

I


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Figure 7. Metabolic routes leading to PPO substrates (brown metabolites) or lignin biosynthesis precursors (green metabolites) with an indication of the biomarkers identified and the key enzymes.

Lettuce heads harvested in a less developed stage were less susceptible to browning development than those that were harvested at a more developed stage, particularly for the HB cultivar. The results confirmed that the LB cultivar showed higher levels of sinapaldehyde and its conjugates, while the HB cultivar had higher basal levels of chlorogenic acids (Figure 5). In the same way, heads harvested at an earlier development stage in the LB cultivar had a lower amount of basal chlorogenic acids and a higher amount of sinapaldehyde derivatives, which was consistent with its lower browning development after cutting and storage. The differences observed between both biomarker metabolites were also consistent in the HB cultivar, in which results showed a higher browning susceptibility for this cultivar, even at the lower development stage (Figure 1). Validation of the Selected Biomarkers in 24 Lettuce Cultivars with Different Browning Susceptibility. Our objective was to confirm the use of these biomarkers to predict lettuce browning. For this purpose, the selected biomarkers were examined by the targeted metabolomics approach in 24 cultivars that showed different browning susceptibility evaluated as Δhue between days 0 and 5 after cutting and storage. To obtain insight if the application of these biomarkers satisfied this objective, the ratio of chlorogenic acids/ sinapaldehyde and conjugates was measured in samples at time 0 (immediately after harvest and processed). Both chlorogenic acids (5-trans-caffeoylquinic and 5-cis-caffeoyl-

quinic) and the three sinapaldehyde derivatives [transsinapaldehyde (7), sinapaldehyde-4-glucoside (5), and sinapaldehyde-4-(6′-malonyl)glucoside (6)] were detected and quantified in 24 lettuce cultivars. The ratio for each cultivar was then plotted against the Δhue values recorded, as shown in Figure 6. A linear regression model was then calculated with a r2 of 0.793. These results show the potential of these biomarkers and, particularly, the ratio between them to predict the degree of browning development in fresh-cut Romaine lettuce.

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DISCUSSION The phenolic biomarkers identified, particularly, the groups of chlorogenic acids and sinapaldehyde derivatives, have shown a positive and negative correlation, respectively, with browning development. This means that the basal levels of these biomarkers present in the lettuce tissue indicate that the lettuce cultivar has already a biosynthetic machinery ready for the immediate synthesis of caffeoylquinic derivatives that are substrates of the enzyme PPO and, therefore, would lead to browning development or to the sinapaldehyde derivatives, precursors for lignin biosynthesis, that are not substrates of PPO and, therefore, do not lead to browning development (Figure 7).30 In fact, both groups of metabolites, although originally coming from the same phenolic metabolism pathway that involves the activation of the enzyme phenylalanine ammonia lyase (PAL), later split into two different metabolic 5915



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branches, leading to either PPO substrates or lignin precursors, therefore enabling two different and complementary events that are associated with wound healing because they are the tissue enzymatic browning and the wound repairing through lignification.30 This means that, if the phenolic metabolism of a given cultivar is more predisposed to the biosynthesis of PPO substrates, then the tissue will develop browning in a faster way than if the phenolic metabolism is more prone to the biosynthesis of lignin formation precursors. Therefore, the basal ratio between the chlorogenic acid isomers and the sinapaldehyde derivatives in lettuce tissues could be a plausible biomarker of the phenolic metabolism status and, therefore, of the potential browning development. This is also stressed by the fact that trans-cinnamaldehyde and other related aldehydes, such as 2-methoxycinnamaldehyde, are strong inhibitors of the enzyme PAL, while the related trans-cinnamic acid and transcinnamyl alcohol and aliphatic aldehydes do not show this effect.31 Therefore, in addition to the direction of the phenylpropanoid metabolism toward the synthesis of lignin precursors, the potential inhibitory effect of sinapaldehyde and related compounds on PAL that regulates the biosynthesis of phenylpropanoids can also be relevant factors, decreasing the susceptibility to develop browning in the corresponding cultivars. Forecasting the browning development in a specific lettuce cultivar after the fresh-cut process and storage is a very complex process because many different factors (genetic and developmental stage as well as environmental conditions) impact the browning susceptibility and the final quality loss.16 Large variability in browning susceptibility among individual lettuce heads of the same cultivar has already been reported.16 Therefore, the use of the chlorogenic acids/sinapaldehyde and derivatives ratio to predict browning development in lettuce that follows a linear regression, with r2 close to 0.8, suggests that this ratio could be used as a relevant biomarker to forecast browning development. Further studies are guaranteed to evaluate the application of these biomarkers in lettuce breeding programs that will lead to new lettuce cultivars more appropriate for the fresh-cut industry.

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Article

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AUTHOR INFORMATION

Corresponding Author

*Telephone: +34-968-396200, ext. 6334. Fax: +34-968396213. E-mail: [email protected]. ORCID

María I. Gil: 0000-0003-4340-7727 Francisco A. Tomás-Barberán: 0000-0002-0790-1739 Funding

The present study was funded by Ministerio de Economiá y Empresa (MINECO, Project AGL2013-48529-R), Consejo ́ Superior de Investigaciones Cienti ficas (CSIC, Grant 201870E014), and Fundación Séneca Región de Murcia (19900/GERM/15). Carlos Garciá is holder of a Ph.D. fellowship grant (BES-2014-069233). Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors are grateful to Enza Zaden S.A. for providing the plant material. 5916


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Targeted Metabolomics Analysis and Identification of Biomarkers for

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