Targeted Metabolomics Analysis and Identification of Biomarkers for

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Targeted metabolomics analysis and identification of biomarkers for predicting browning of fresh-cut lettuce Carlos J. García, Maria I. Gil, and Francisco A. Tomas-Barberan J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.9b01539 • Publication Date (Web): 01 May 2019 Downloaded from http://pubs.acs.org on May 2, 2019

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Journal of Agricultural and Food Chemistry

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Targeted metabolomics analysis and identification of biomarkers for

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predicting browning of fresh-cut lettuce

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Carlos J. García, María I. Gil, Francisco A. Tomás-Barberán*

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Research Group on Quality, Safety, and Bioactivity of Plant Foods. CEBAS (CSIC), P.O.

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Box 164, Espinardo, Murcia 30100, Spain

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1 ACS Paragon Plus Environment


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ABSTRACT: The metabolism of phenolic compounds is a key factor in the development of

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wound-induced enzymatic browning of fresh-cut lettuce. In the present study, the lettuce midribs

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discriminant metabolites, selected in a previous untargeted metabolomics study, were thoroughly

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identified. Our results showed that their basal contents correlated with browning developed after

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5 days of storage. 5-trans-chlorogenic acid and 5-cis-chlorogenic acids were positively correlated

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with browning, while sinapaldehyde and its 4--D-glucoside and 4-(6’ malonyl) -D-glucoside

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conjugates were negatively correlated. Using targeted metabolomics, the metabolites were

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analyzed in lettuce heads with different degrees of development and different browning

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susceptibility, and these biomarkers were confirmed. Despite the large variability in the browning

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process of lettuce, the chlorogenic acids/sinapaldehyde derivatives ratio showed a linear

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correlation (r2 0.79) with the fresh-cut lettuce browning developed in 24 Romaine lettuce

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cultivars, validating the relevance of these biomarkers. These results show that the analysis of the

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basal content of these metabolites could be used in lettuce breeding programs to select cultivars

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that are more appropriate for the fresh-cut industry.

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KEYWORDS: UPLC-ESI-QTOF-MS, metabolomics, enzymatic browning, quality,

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phenylpropanoid metabolism, chlorogenic acid, sinapaldehyde.

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INTRODUCTION

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Lettuce (Lactuca sativa) is one of the most cultivated and consumed leafy crop around

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the world. The consumption of lettuce has increased even more due to the

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commercialization as fresh-cut ready-to-eat salads. After cutting, enzymatic browning

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that occurs on the lettuce cut edges reduces the shelf-life and consumer acceptance.1,2

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When the lettuce tissue is cut, the constitutive enzyme polyphenol oxidase (PPO)3 and

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the phenolic substrates come in contact resulting in browning.4,5 It has been pointed out

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recently6 that the great variability in lettuce discoloration, including edge browning, can

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be due to many factors such as the interplay among the phenylpropanoid pathway, the

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accumulation of the phenolic compounds produced, the activity of PPO and its reactions

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with the o-diphenols as well as the o-quinones produced. Thus, the browning process of

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fresh-cut lettuce is a very complex pathway that is triggered by wound-response signal

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metabolites.5,7,8 Among the signal metabolites, the oxylipins 9-hydroperoxy-12,13-

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epoxy-10-octadecenoic acid and 11,12,13-trihydroxy-9-octadecenoic acid, have been

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shown to enhance the phenylpropanoid pathway and the biosynthesis of the enzymatic

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browning substrates.9 After cutting, the synthesis and accumulation of specific phenolic

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compounds are induced although the basal levels of these compounds are very low or

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often not detectable.5 Tissue wounding induces the biosynthesis of caffeoylquinic acids

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and related compounds, as well as methoxy-cinnamic acid derivatives. These metabolites

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are either involved in the formation of brown polymers or the wound repairing via

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lignification.10 Previous untargeted metabolomics studies with UPLC-ESI-QTOF-MS

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have been used to explore the lettuce metabolome11-13 and explain the metabolic wound

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response of lettuce after cutting.9

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One of the main interests of the lettuce breeding and processing companies is to

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select cultivars which are appropriate for fresh-cut, meaning low browning potential.14 3 ACS Paragon Plus Environment


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Therefore, the identification of metabolic biomarkers that can predict the browning

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development after cutting is of great relevance.

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Previous untargeted metabolomics studies 9,16 have pointed out some metabolites

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that could be used as biomarkers of the tissue browning susceptibility after cutting in

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different lettuce cultivars. Among the entities detected some of the most relevant ones

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were phenolic metabolites of the group of the phenylpropanoids, although they were only

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tentatively identified (level 2).17 Some of them correlated with browning development

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after five days of storage while others were negatively correlated.16

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The most relevant basal metabolites present in harvested lettuce that correlated

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positively with browning development were tentatively identified as caffeoyl-quinic

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derivatives, while other caffeic acid conjugates such as caffeoyl tartaric and dicaffeoyl

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tartaric were not discriminant regarding browning development.16 Among those

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metabolites that were negatively correlated with browning development some methoxy

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hydroxycinnamic acid derivatives, with formula, masses, and fragments as those of

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ferulic acid methyl ester previously described in lettuce,9,11,13 were particularly relevant,

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although they were not identified. Other entities that when analyzed at time 0 (basal

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levels) were negatively correlated with browning development after 5 days storage were

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tentatively identified as galloyl hexose, dihydroxybenzoic acid hexose, syringic acid

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hexose, quercetin 3-6” acetyl glucoside, and 2-O-p-hydroxy benzoyl-6-galloyl-glucoside,

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although they were not as strong in the negative correlation as the ferulic acid methyl

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ester pointed out above.16 In this previous study, it was suggested that a ratio between the

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basal levels of some metabolites such as chlorogenic acid/ferulic derivatives detected at

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harvest could be a valuable tool to predict browning development after cutting.


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This study aimed at the identification of phenolic biomarkers of lettuce browning

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and their validation as early forecasting for the selection of Romaine lettuce cultivars

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better suited for industrial processing in plant breeding programmes.

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MATERIALS AND METHODS

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Chemical reagents. Methanol, acetonitrile and formic acid 0.1% v/v in water

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were from J. T Baker (Deventer, The Netherlands) and formic acid from Panreac

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(Barcelona, Spain). Authentic standards: Ferulic acid methyl ester, ferulic acid, isoferulic

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acid, sinapic acid, caffeic acid, p-coumaric acid, trans-sinapaldehyde, and 5-trans-

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caffeoylquinic acid were purchased from Sigma Aldrich (St. Louis, MO, USA). 5-cis-

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caffeoylquinic acid was obtained from the trans-authentic standard by thermal treatment

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at 30 ºC for 6 h exposed to daylight.15

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Enzymatic hydrolysis with -D-glucosidase. The lettuce extract (600 L) was

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added to 200 L of 0.2 M acetate buffer pH 4.5 and 1 mg of -D-glucosidase from Sigma

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Aldrich (St. Louis, MO, USA) and incubated at 37 ºC for 24 h. The resulting products

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were analyzed by UPLC-QTOF MS under the conditions reported below to evaluate the

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conversion of the potential -D-glucosides to the corresponding aglycone.

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Plant material and processing. Romaine lettuce was selected as one of the

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lettuce types most prone to browning development. Two cultivars, low browning (LB)

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and high browning (HB), were selected from Enza Zaden experimental field in Torre

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Pacheco (Murcia, Spain). The names of the cultivars are not mentioned as they are not

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relevant for this study. All lettuce cultivars were grown using standard lettuce production

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practices. For the study on the different development stages, the two cultivars LB and HB

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were examined. Heads were harvested before reaching the commercial development 5 ACS Paragon Plus Environment


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(development stage 1) and after reaching the full commercial development two weeks

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later (development stage 2) (Figure 1).18, 19 For the validation study, 24 lettuce cultivars

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grown in the same field under the same agricultural practices, were harvested at

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commercial maturity stage.16 Head weight, head length, compacity and number of leaves

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were measured to characterize the development stage. Lettuce heads were transported 30

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min by car to the lab and stored for 24 h at 7 ºC and 80 % relative humidity. Lettuce heads

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were processed as described previously16 with minor modifications. Four lettuce heads

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were used per cultivar as independent replicates. Ten leaves per head from the middle

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part of the head were processed, and midribs were carefully excised (1.5 cm). For the

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metabolomics analyses, samples of 30 g of midribs were directly frozen in liquid nitrogen

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and stored at -80 ºC. Midribs were then freeze-dried and milled into powder using a

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blender (Oster professional BPST02-B).

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Browning development analysis. For measuring the differences in the browning

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susceptibility between the cultivars, digital image analysis was carried out.16, 20 Twenty

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midrib pieces were placed together with the cut edge facing up, and the image was

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captured using a digital camera (Nikon D7100). Four images, one per replicate, were

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captured and saved as JPEG files. Images were analyzed using the software ImageJ

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version 1.48v (NIH Image, National Institute of Health, Bethesda, USA). The total cut

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edge area was considered as the area of study. The RGB color model was used to identify

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the browned pixels in the range of hue (20-35), saturation (40-195) and brightness (0-

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225).12, 20 Differences in color as hue between time 0 (just after cutting) and after five

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days of storage were calculated ( Hue).

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Targeted metabolomics analysis. Midribs were extracted as described

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previously21 with minor modifications. Freeze-dried and powdered samples (0.3 g) were

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mixed with 10 mL of methanol: water (80:20; v:v). The extraction mixture was sonicated


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for 30 min at room temperature, centrifuged at 10.000 × g for 15 min and filtered directly

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(0.22 µm PVDF filter) before the analysis by UPLC-ESI-QTOF-MS. Chemicals and

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reagents were previously described.9 UPLC-ESI-QTOF-MS analyses were carried out

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using an Agilent 1290 Infinity LC system coupled to the 6550 Accurate-Mass QTOF

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(Agilent Technologies, Waldbronn, Germany) with an electrospray interface (Jet Stream

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Technology). Chromatographic and mass spectrometry conditions were described

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previously.16 Samples (3 μL) were injected in a reverse phase Poroshell 120 EC-C18

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column (100 mm, 2.7 mm) (Agilent Technologies, Waldbronn, Germany). The column

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temperature was set at 30 ºC, and the flow rate at 0.4 mL/min. The mobile phases were

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acidified water (0.1 % formic acid) (Phase A) and acidified ACN (0.1 % formic acid)

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(Phase B). The linear gradient of phase-B was from 1 to 18 % in 10 min, from 18 to 38

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% within 10–16 min and from 38 to 95 % within 16–22 min. The same analyses were

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also repeated for some samples, using solvents without formic acid for identification

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purposes. The MS system was operated in the negative (for all the samples) and positive

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ion modes (in some samples for metabolite identification) with the mass range set at m/z

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100–1100 in full scan resolution mode. The fragmentor voltage was established at 100 V

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and acquisition rate at 1.5 spectra/s. Targeted MS/MS analyses were performed to

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complete the metabolite identification and confirm the presence of the tentative

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biomarkers. MS/MS product ion spectral parameters were m/z range of 50-750 using a

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retention time rate of 1 spectrum/s. A targeted list of m/z was inspected in the specific Rt

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of the ions using an isolation width of 4 m/z. Collision energies of 10, 15, 20 and 30 eV

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were used.

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RESULTS

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Identification of biomarkers. To characterize and identify the basal lettuce tissue

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metabolites that correlated positively and negatively with the browning development,

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authentic standards of 5-trans-caffeoylquinic acid (trans-chlorogenic acid) (calculated

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[M-H]- m/z 353.0878), ferulic acid methyl ester (calculated [M-H]- m/z 207.0663), and

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trans-sinapaldehyde (calculated [M-H]- m/z 207.0663), as well as caffeic, ferulic,

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isoferulic, p-coumaric, and sinapic acid were used.

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In the UPLC chromatograms four peaks with the [M-H]- of caffeoylquinic

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derivatives with m/z 353.0878 were detected (Figure 2). The main peak at 7.9 min

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coincided with an authentic standard of trans-5-caffeoylquinic acid (trans chlorogenic

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acid). The MS-MS fragments also coincided with those reported for this metabolite.22 The

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isomeric metabolites 1 and 3, showed chromatographic behavior and MS fragments

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consistent with 3-caffeoylquinic acid (trans-neochlorogenic acid) and 4-caffeoylquinic

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acid (trans-criptochlorogenic acid) respectively although they were minor metabolites in

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the lettuce extracts analyzed.22 These were also identified with surrogate standards23 using

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green coffee beans extracts.5 Peak 4 was the second relevant chlorogenic acid isomer

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present in the extract, and the retention time (Rt) and the MS fragments were consistent

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with a cis isomer of chlorogenic acid.24 This metabolite was obtained from the authentic

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standard of trans chlorogenic acid by thermal treatment under daylight to enhance the

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conversion of the trans to the cis isomer.15 The MS fragments (Table 1) also supported

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this identification in agreement with previously published results.22 These results show

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that the main basal metabolite that was positively correlated with the browning developed

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(Hue) after five days of storage was trans 5-caffeoylquinic, although the cis isomer also

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correlated in the same way.


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Regarding the previously reported ‘ferulic acid methyl ester’ identified by their

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MS and fragments, and also supported by previous reports of its presence in lettuce,11-13

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its identification resulted in being incorrect, as the metabolite previously identified

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tentatively in lettuce (Rt 13.5 min) had a different Rt than that of an authentic standard of

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ferulic acid methyl ester (16.8 min). Therefore, other metabolites consistent with the same

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molecular formula (C11H12O4) were considered. The more sensible one was

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sinapaldehyde that although it had not previously been reported in lettuce, a related

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metabolite, syringin (sinapylalcohol glucoside) had been reported in Iceberg lettuce,25

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and its biosynthetic precursor, 5-hydroxy-coniferaldehyde was tentatively identified in

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Romaine lettuce.9 The sinapaldehyde authentic standard showed MS-MS fragments very

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similar to those of ferulic acid methyl ester, although they had different relative intensities

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(% of base peak) and differed in one fragment. Ferulic acid methyl ester showed a

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fragment at m/z 133.0294 (C7H5O2), while sinapaldehyde showed a fragment at m/z

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121.4650 (C7H5O3) that was not present in the MS-MS analysis of ferulic acid methyl

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ester (Figure 3). Therefore, in the previous studies, it was possible the incorrect

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assignment of this metabolite based only on the MS-MS fragments without confirming

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its nature by chromatographic comparisons with an authentic standard. In the samples

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analyzed, only one minor peak 7 coincided chromatographically with sinapaldehyde at

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Rt 13.5 min when the ion chromatogram at 207.0663 was extracted, while two other main

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peaks, 5 (Rt 9.58 min) and 6 (Rt 11.4 min) (Figure 2), also produced the same mass as a

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fragment. These peaks were studied in more detail, as the peak 5 coincided with the

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metabolite that negatively correlated with browning in a previous study.16 These two

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metabolites showed a chromatographic behavior that indicated that they were more polar

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than sinapaldehyde, and probably conjugated derivatives of this metabolite as the m/z

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207.0663 with the same fragments as those of the sinapaldehyde standard were detected



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in both metabolites (Table 1). MS-MS experiments in negative and positive modes were

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carried out to identify these metabolites as authentic standards of sinapaldehyde

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conjugates were not available. The MS of metabolite 5 indicated that this was consistent

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with a formic acid adduct of the metabolite C17H10O9 ([M-H+formic acid]- m/z 415.1246

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in the negative mode; [M-H-46]- gave m/z 369, the calculated [M-H]- for sinapaldehyde-

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4-hexoside) was consistent with sinapaldehyde 4-O hexoside which appeared as a formic

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acid adduct in the MS. The MS-MS analysis in the negative mode yielded the fragments

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for the sinapaldehyde authentic standard (Table 1), and therefore confirmed the

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occurrence of a sinapaldehyde hexoside. The analysis in the positive mode (Figure 4),

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confirmed the nature of metabolite 5 with a [M+H]+ ion at m/z 393.1156 consistent with

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a sodium adduct of sinapaldehyde hexoside, and a fragment of the sodium adduct of the

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sinapaldehyde aglycone (m/z 231.0629) (Table 2). Metabolite 5 was converted to

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sinapaldehyde by enzymatic hydrolysis with -D-glucosidase, and therefore confirmed

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that this was sinapaldehyde 4-O--D-glucoside. The assignation of glucose as the

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hexoside is also supported by the occurrence of glucosyltransferases in Arabidopsis

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capable of glucosylating coniferaldehyde and sinapaldehyde. 26

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The chromatographic behavior of metabolite 6 indicated that this was a less polar

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derivative of sinapaldehyde than the corresponding glucoside 5 (an increase in Rt close

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to 2 minutes). The mass and fragments of the aglycone also coincided with those of

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sinapaldehyde (m/z 207.0658) in the negative mode suggesting that this was a different

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conjugate of 7. The MS in the negative mode showed that the [M-H]- peak was consistent

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with a double formic acid and Na adduct of C20H12O12 (m/z 523.1101). In the positive

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mode, the spectrum was clarifying with a sodium adduct at m/z 479.1151 as the base peak,

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an [M+H-CO2]+ fragment consistent with the loss of the final COO- from a dicarboxylic

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acid (m/z 435.1277), and a fragment at m/z 393.1141 that was coincident with the base 10 ACS Paragon Plus Environment

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peak of metabolite 5 identified as a sodium adduct of sinapaldehyde hexoside (Figure 4)

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which involved the loss of an 86 m.u. fragment consistent with the loss of a malonyl

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residue + 2H. Then, an additional loss of the hexosyl residue (loss of 162) to give a

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fragment at m/z 231.0619 consistent with a sodium adduct of the synapaldehyde aglycone

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was observed. The chromatographic behavior was also consistent with a more lipophilic

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metabolite than the hexoside (5) as the esterification with malonic acid decreased the

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hydrophilic character as it has been previously reported for quercetin (malonyl-

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glucosides) reported in lettuce.17, 27, 28 This was also confirmed when the metabolites were

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analyzed in UPLC without adding formic acid as a modifier. The removal of the formic

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acid did not affect the Rt of both sinapaldehyde (7) and its 4-glucoside (5), although it

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decreased the Rt of 6 by half a minute (rt 10.9 min), showing that the increase in pH of

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the mobile phase after the removal of formic acid increased the ionization of the free

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carboxyl of the malonic acid residue, and therefore increased its polarity and decreased

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its rt. This type of acylation is consistent with the lettuce biochemical activity as 6-

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malonyl-glucosides of other phenolic compounds have already been reported in lettuce in

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which quercetin-3-glucosides are present together with the 6”-malonated conjugates13, 21,

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28, 29

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pigmented lettuce cultivars.27, 28 Therefore, we tentatively identify metabolite 6 as the

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6’malonyl ester of sinapaldehyde 4--D-glucoside.

as well as the anthocyanin cyanidin-3-O-6”-malonyl-glucoside present in red

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Targeted metabolomics analysis to confirm that the selected biomarkers

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predict browning. Once the biomarkers related to browning were identified, other

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related metabolites were also examined in the UPLC-Q-TOF analyses following a

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targeted metabolomics approach. The metabolites that were negatively correlated with

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the browning development were also associated with precursors of the lignification

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process. Thus, other phenolic metabolites involved in lignin formation were also studied 11 ACS Paragon Plus Environment


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at basal levels, immediately after tissue cutting, to predict browning. Thus, ferulic acid,

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coniferyl alcohol, coniferin (coniferyl alcohol 4-glucoside), dihydro coniferylalcohol

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glucoside, syringing, and 5-hydroxy-coniferaldehyde were searched by targeted

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metabolomics (Table 1). However, they were not detected in any of the samples studied.

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Nevertheless, a previous study has shown that 5-hydroxy-coniferaldehyde (a biosynthetic

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precursor of sinapaldehyde) and coniferylalcocohol glucoside (coniferin) were induced

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after cutting and increased their content during the 5 days of storage of the tissues.9

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To validate the potential use of the basal content (immediately after cutting) of

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chlorogenic acids and sinapaldehyde derivatives as biomarkers to predict the

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development of browning after cutting, the ratio chlorogenic acids/sinapaldehyde and

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conjugates was examined. For this study, two lettuce cultivars with very different

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browning susceptibility after five days of storage were studied. As shown by measuring

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delta Hue (-Hue) which represent the difference in color between samples after five days

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of storage and just after cutting (Figure 1). The two lettuce cultivars with low and high

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browning susceptibility (LB and HB) were harvested at two different development stages

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which differed only in two weeks. Lettuce heads harvested in a less developed stage were

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less susceptible to browning development than those that were harvested at a more

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developed stage, particularly for the HB cultivar. The results confirmed that the LB

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cultivar showed higher levels of sinapaldehyde and its conjugates, while the HB cultivar

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had higher basal levels of chlorogenic acids (Figure 5). In the same way, heads harvested

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at an earlier development stage in the LB cultivar had a lower amount of basal chlorogenic

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and a higher amount of sinapaldehyde derivatives which was consistent with its lower

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browning development after cutting and storage. The differences observed between both

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biomarker metabolites were also consistent in the HB cultivar, in which results showed a


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higher browning susceptibility for this cultivar even at the lower development stage

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(Figure 1).

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Validation of the selected biomarkers in 24 lettuce cultivars with different

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browning susceptibility. Our objective was to confirm the use of these biomarkers to

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predict lettuce browning. For this purpose, the selected biomarkers were examined by the

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targeted metabolomics approach in 24 cultivars that showed different browning

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susceptibility evaluated as Hue between day 0 and day 5 after cutting and storage. To

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get insight if the application of these biomarkers satisfied this objective, the ratio

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chlorogenic acids/sinapaldehyde and conjugates was measured in samples at time 0

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(immediately after harvest and processed). Both chlorogenic acids (5-trans-caffeoyl

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quinic and 5-cis-caffeoyl quinic) and the three sinapaldehyde derivatives [trans-

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sinapaldehyde

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(6’malonyl)glucoside (6)] were detected and quantified in 24 lettuce cultivars. The ratio

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for each cultivar was then plotted against the -Hue values recorded as shown in Figure

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6. A linear regression model was then calculated with an r2 of 0.793. These results show

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the potential of these biomarkers, and particularly the ratio between them to predict the

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degree of browning development in fresh-cut Romaine lettuce.

(7);

synapaldehyde

4-glucoside

(5),

and

sinapaldehyde

4-

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DISCUSSION

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The phenolic biomarkers identified, particularly the groups of chlorogenic acids

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and sinapaldehyde derivatives have shown a positive and negative correlation,

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respectively, with browning development. This means that the basal levels of these

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biomarkers present in the lettuce tissue indicate that the lettuce cultivar has already a

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biosynthetic machinery ready for the immediate synthesis of caffeoyl quinic derivatives, 13 ACS Paragon Plus Environment


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that are substrates of the enzyme PPO and therefore would lead to browning development,

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or to the sinapaldehyde derivatives, precursors for lignin biosynthesis, that are not

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substrates of PPO and therefore do not lead to browning development (Figure 7).30 In

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fact, both groups of metabolites, although originally coming from the same phenolic

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metabolism pathway that involves the activation of the enzyme PAL (phenylalanine

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ammonia lyase), later split into two different metabolic branches leading to either PPO

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substrates, or lignin precursors, therefore enabling two different and complementary

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events that are associated with wound healing as they are the tissue enzymatic browning

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and the wound repairing through lignification.30 This means that if the phenolic

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metabolism of a given cultivar is more predisposed to the biosynthesis of PPO substrates,

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then the tissue will develop browning in a faster way than if the phenolic metabolism is

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more prone to the biosynthesis of lignin formation precursors. Therefore, the basal ratio

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between the chlorogenic acid isomers and the sinapaldehyde derivatives in lettuce tissues

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could be a plausible biomarker of the phenolic metabolism status and therefore of the

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potential browning development. This is also stressed by the fact that trans-

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cinnamaldehyde, and other related aldehydes such as 2-methoxy-cinnamaldehyde, are

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strong inhibitors of the enzyme PAL, while the related trans-cinnamic acid and trans-

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cinnamyl alcohol, and aliphatic aldehydes do not show this effect.31 Therefore, in

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addition to the direction of the phenylpropanoid metabolism towards the synthesis of

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lignin precursors, the potential inhibitory effect of sinapaldehyde and related compounds

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on the PAL that regulates the biosynthesis of phenylpropanoids can also be relevant

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factors, decreasing the susceptibility to develop browning in the corresponding cultivars.

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Forecasting the browning development in a specific lettuce cultivar after the fresh-

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cut process and storage is a very complex process as many different factors (genetic,

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developmental stage as well as environmental conditions) impact the browning 14 ACS Paragon Plus Environment

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susceptibility and the final quality loss.16 Large variability in browning susceptibility

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among individual lettuce heads of the same cultivar has already been reported.16

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Therefore, the use of the chlorogenic acids/sinapaldehyde and derivatives ratio to predict

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browning development in lettuce, that follows a linear regression with r2 close to 0.8,

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suggests that this ratio could be used as a relevant biomarker to forecast browning

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development. Further studies are guaranteed to evaluate the application of these

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biomarkers in lettuce breeding programmes, that will lead to new lettuce cultivars more

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appropriate for the fresh-cut industry.

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AUTHOR INFORMATION

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Corresponding author

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*Telephone: +34-968-396200, ext. 6334. Fax: +34-968-396213. E-mail: [email protected]

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ORCID

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María I Gil: 0000-0003-4340-7727

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Francisco A. Tomás-Barberán: 0000-0002-0790-1739

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Funding

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The present study was funded by MINECO (Project AGL2013- 48529-R), CSIC (Grant

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201870E014), and Fundación Séneca Región de Murcia (19900/GERM/15). Carlos García is

349

holder of a Ph.D. fellowship grant (BES-2014-069233).

350 351

Notes

352

The authors declare no competing financial interest.

353 354

ACKNOWLEDGMENTS

355

Authors are grateful to Enza Zaden S.A. for providing the plant material.



356

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357

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processed lettuces stored in air or controlled atmosphere with emphasis on romaine and iceberg

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(12) Garcia, C.J., Garcia-Villalba, R., Garrido, Y., Gil, M.I., Tomas-Barberan, F.A.

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F.A.; Luo, L.; Huang, D. A novel integrated non-targeted metabolomics analysis reveals

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significant metabolite variations between different lettuce (Lactuca sativa L.) cultivars. Hort. Res.

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of different storage conditions for the varietal screening of lettuce for fresh-cut performance.

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hybrid

quadrupole/ion

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(17) Sumner, L.W.; Amberg, A.; Barrett, D.; Beale, M.H.; Beger, R.; Daykin, C.A.; Fan,

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R.; Kopka, J.; Lane, A.N.; Lindon, J.C.; Marriott, P.; Nicholls, A.W.; Reily, M.D.;, Thaden, J.J.;

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Viant, M.R. Proposed minimum reporting standards for chemical analysis. Metabolomics, 2007,

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Characterization of polyphenols and antioxidant properties of five lettuce varieties and escarole.

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Food Chem. 2008, 108, 1028-1038.

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N.E. Analysis of chlorogenic acids and related cinnamic acid derivatives from Nicotiana tabacum

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dissociation method. Chem. Central J. 2014, 8, 66.

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(26) Lim, E.K.; Jackson, R.G.; Bowles, D.J. Identification and characterisation of

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Arabidopsis glucosyltransferases capable of glucosylating coniferyl aldehyde and sinapyl

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(27) Marin, A.; Ferreres, F.; Barberá, G.G.; Gil, M.I. Weather variability influences color

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and phenolic content of pigmented baby leaf lettuces throughout the season. J. Agric. Food Chem.,

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(28) Ferreres, F.; Gil, M.I.; Castañer, M.; Tomas-Barberan, F.A. Phenolic metabolites in

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red pigmented lettuce (Lactuca sativa). Changes with minimal processing and cold storage. J.

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(29) Zhou, W.; Chen, Y.; Xu, H.; Liang, X.; Hu, Y.; Jin, C.; Lu, L.; Lin, X. Short-term

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nitrate limitation prior to harvest improves phenolic compounds accumulation in hydroponic-

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cultivated lettuce (Lactuca sativa L.) without reducing shoot fresh weight. J. Agric. Food Chem.

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2018, 66, 10353-10361.

443 444

(30) Vanholme, R.; Demedts, B.; Morreel, K.; Ralph, J.; Boerjon, W. Lignin biosynthesis and structure. Plant Physiol. 2010, 153, 895-905.

445

(31) Fujita, N., Tanaka, E., Murata, M., 2006. Cinnamaldehyde inhibits phenylalanine

446

ammonia-lyase and enzymatic browning of cut lettuce. Biosci. Biotechnol. Biochem. 2006, 70,

447

672-676.

448



449

Figure Captions

450

Figure 1. Δ HUE values of low browning (LB) and high browning (HB) susceptibility cultivars

451

at development stages 1 (harvested two weeks before commercial maturity) and 2 (harvested at

452

commercial maturity). Error bars are standard deviations (n=4).

453

Figure 2. Chromatographic separation of caffeoylquinic acid isomers (A) and sinapaldehyde

454

derivatives (B). UPLC-MS-QTOF analyses of lettuce samples in the negative mode. (A)

455

Ion extracted chromatogram at m/z 353.0878: (1) 3-trans-caffeoylquinic acid; (2) 5-trans-

456

caffeoylquinic acid; (3) 4-trans-caffeoylquinic acid; (4) 5-cis-caffeoylquinic acid. (B) Ion

457

extracted chromatogram at m/z 207.0663: (5) trans sinapaldehyde 4--D-glucoside; (6)

458

trans sinapaldehyde 4-(6’ malonyl)--D-glucoside; (7) trans sinapaldehyde.

459

Figure 3. Sinapaldehyde (7) (A) and ferulic acid methyl ester (12) (B) UPLC-MS-QTOF

460

fragments (m/z 207.0663) in the negative mode and at 20 eV.

461

Figure 4. UPLC-MS-QTOF MS/MS fragments in the positive mode of sinapaldehyde 4--D-

462

glucoside (A) and sinapaldehyde 4-(6’-malonyl)--D-glucoside (B).

463

Figure 5. Chlorogenic acid isomers (A) (trans- plus cis-5-caffeoylquinic acids), and

464

sinapaldehyde and conjugates (B), in the selected Romaine lettuce cultivars with low browning

465

(LB) and high browning (HB) susceptibility at development stages 1 (harvested two weeks before

466

commercial maturity) (Light green bar) and 2 (harvested at commercial maturity) (Dark green

467

bar). Error bars are standard deviations (n=4).

468

Figure 6. Linear regression model of Romaine lettuce cultivars. N=24; Coefficients: b[0]= -

469

27.6194509856, b[1] =3.8792637023 r2 = 0.7930727837. Ratio: Σ chlorogenic acids / Σ

470

sinapaldehyde and conjugates (mg/g of fresh midribs).

471

Figure 7. Metabolic routes leading to PPO substrates (brown metabolites) or lignin biosynthesis

472

precursors (green metabolites) with an indication of the biomarkers identified and the key

473

enzymes. 20 ACS Paragon Plus Environment

Page 20 of 30

Page 21 of 30


474 475

Figure 1

Development stage 1 Development stage 2

20

 HUE

15

10

5

LB

HB

476 477



478

Page 22 of 30

Figure 2

A x10 5 -ESI EIC(353.0878) Scan Frag=100.0V ChlorogenicAcid.d 8.5 8 7.5 7 6.5 6

7.971

2

5.5 5 4.5 4 3.5 3 2.5 2 1.5

4

3

1

9.469

1 0.5 0

6.337 5.8

6

6.2

6.4

8.448 6.6

6.8

7

7.2

7.4

B

7.6 7.8 8 8.2 8.4 8.6 Counts vs. Acquisition Time (min)

8.8

9

9.2

9.4

9.6

9.8

10

10.2

10.4

x10 4 -ESI EIC(207.0663) Scan Frag=100.0V Sinapaldehyde.d 2.2

11.451

6

2 1.8

5

1.6 1.4

9.680

1.2 1

7

0.8 0.6 0.4

* 13.596

0.2 0

479

8

8.5

9

9.5

10

10.5

11 11.5 12 Counts vs. Acquisition Time (min)

480


12.5

13

13.5

14

14.5

15

Page 23 of 30

481


Figure 3

A

Sinapaldehyde 100

C9H5O4

Intensity (%)

80

60 -CH3

C10H8O4

-CO

40

C8H5O3

20 C7H5O -O

C7H5O3

-CH3

-CO

C11H11O 4

0 105.0342 121.4650 149.0230 177.0191 192.0437 207.0659

Fragments Fragmentm/z m/z

B

Ferulic acid methyl ester 100

C7H5O2

Intensity (%)

80

60

40

20

C10H8O4 C7H5O

-CO

C9H5O4

-O C8H5O3

-CH3

-CO

C11H11O 4

0 482

-CH3

105.0346 133.0294 149.0238 177.0192 192.0419 207.0663 Fragments m/z Fragment m/z 23 ACS Paragon Plus Environment


483

Page 24 of 30

Figure 4

484

A x10 3 +ESI Product Ion (rt: 9.725 min) Frag=120.0V [email protected] (393.1146[z=1] -> **) 393.1146.d H OH

OH

7.5 7

H3CO

OCH3

6.5

HO

393.1156

8

[Na+]

HO

H

O

[Na+]

H

H

OH

6

O

H H3CO

5.5

OCH3

5 4.5

H

4

O

3.5 H

140

160

180

200

240

260

280

320

340

380

400

420

440

460

488.2576

360

469.2690

339.2054

313.6735

300

327.1751

292.6390

231.0629

220

O

480

500

520

540

500

520

540 560 580 Counts vs. Mass-to

B x10 3 +ESI Product Ion (rt: 11.432 min) Frag=120.0V [email protected] (479.1146[z=1] -> **) 479.1146.d

O

HO HO

HO HO

H

OH

6 H3CO

5.5

[Na+]

OH

O

H

485

100

120

140

160

180

200

220

240

260

280

300

320

O

486


360

380

400

540.7798

422.7954

378.0868

340

404.8186

O

327.0681

312.0498

292.0743

271.0434

249.1626

240.1298

231.0619

213.0097

201.0500

185.0449

173.1286

138.0099

129.1017

107.0091

2

0

H

393.1141

2.5

1

OCH3

435.1277

4

0.5

O

OCH3

3.5 H

OH

H3CO

H3CO

3

O

H

H O

H

4.5

O

H H

OH

[Na+]

5

O

H

H

OCH3

479.1151

H OH

6.5

1.5

[Na+]

H O

7.5 7

560 580 Counts vs.

420

440

460

480

554.3135

120

381.2115

100

361.1143

80

303.1738

0

151.0359

120.0822

110.0689

1 0.5

129.5778 138.0661

2 1.5

215.0332

185.0419

3 2.5

Page 25 of 30

487


Figure 5

A

Σ Chlorogenic isomers

mgr/gr fresh product

mg/g fresh product

8


6

4

2

0 LB

B

HB

Σ Sinapaldehyde and conjugates


mgr/gr fresh product

mg/g fresh product

0.04

0.03

0.02

0.01

0.00 LB

HB

488 489 25 ACS Paragon Plus Environment


490

Page 26 of 30

Figure 6

Chlorogenic acid isomers sinapaldehyde and conjugates

60

40

20

0

-20

6 491

8

10

12

14

∆ HUE

492 493


16

18

20

Page 27 of 30

495


Figure 7

496

Brown polymers Chlorogenic acid

PPO

HO COOH O

quinate O-hydroxycinnamoyl transferase

HO

O OH

OH

Lignin

OH

O CoA

coumarate CoA ligase

Caffeic acid

POX

OH

OH

OH

O

OCH3

HO

O

OH

caffeate O-methyl transferase

Sinapaldehyde

HO

cinnamic acid 4-hydroxylase O HO

PAL

OH

cinnamyl alcohol deshydrogenase

OH

OCH3

O OCH3

H

O

OH

OH HO

OCH3

caffeate O-methyl transferase OH

Ferulic acid

O

OCH3

ferulate 5-hydroxylase

OH

H

O

O

OH OH

HO

HO

OCH3 O

NH2

cinnamyl CoA reductase

OH

Phenylalanine

OCH3

OH

CoA

coumarate CoA ligase

497

OH OCH3

498



Page 28 of 30

Table 1. UPLC-QTOF-MS-MS targeted metabolomics analysis of phenolic metabolites. Collision energy 20eV. BasePeak Phenylpropanoids 1

acida

Metlin ID 95165

Rt (min) 6.3

Molecular Formula C16H18O9

Parent ion [M-H]-

3498

7.9

C16H18O9

87120

8.5

86150

m/z

m/z

I

353.0878

191.0557

179.0353

78

[M-H]-

353.0878

191.0561

179.0349

1

161.0230

1

C16H18O9

[M-H]-

353.0878

191.0561

173.0455

49

161.0237

4

9.4

C16H18O9

[M-H]-

353.0878

191.0566

179.0353

1

161.0241

3

984908 N/D

9.6 11.4

C17H10O9 C20H12O12

[M-H+FA][M-H+FA+Na]-

415.1246 523.1101

207.0659 207.0658

192.0414 361.1641

31 44

177.0190 275.0533

5 20

44806

13

C11H12O4

[M-H]-

207.0663

177.0191

192.0437

21

149.0230

12.1

C11H12O5

[M-H]-

223.0612

193.0131

164.0457

45

C11H14O4

[M-H]-

209.0819

192.0419

51

7

3-trans-caffeoylquinic (trans neochlorogenic acid) 5-trans-caffeoylquinic acida (trans chlorogenic acid) 4-trans-caffeoylquinic acida (trans criptochlorogenic acid) 5-cis-caffeoylquinic acida (cis-chlorogenic acid) Sinapaldehyde glucosidea Sinapaldehyde-4(6’malonyl)glucoside a trans-Sinapaldehydea

8

Sinapic acidb

45738

9

Sinapyl alcoholc

44805

11

Ferulic acidb

4156

12.1

C10H10O4

[M-H]-

193.0506

134.0380

12

Isoferulic acidb

64942

12.6

C10H10O4

[M-H]-

193.0506

134.0387

13

Ferulic acid methyl esterb

N/D

16.8

C11H12O4

[M-H]-

207.0663

133.0294

14

Ferulic acid 7-O-glucosidec

C16H20O9

[M-H]-

355.1035

15 16 17

5-hydroxyconiferaldehydec

64178 44377 64182

C10H10O4 C10H12O3 C16H22O8

[M-H][M-H][M-H]-

193.0505 179.0714 341.1242

95893

C16H24O8

[M-H]-

343.1398

64181

C17H24O9

[M-H]-

371.1348

2 3 4 5 6

18 19

Coniferyl alcoholc Coniferyl alcohol glucoside (coniferin)c Dihydroconiferyl alcohol glucosidec Syringinc

Secondary peaks

985480

a Metabolites

m/z

I

m/z

I

135.0442

55

135.0445

27

39

121.4650

13

135.9135

35

121.0287

53

177.0192

18

149.0238

4

m/z

105.0342

9

105.0346

38

identification with standards (level 1 identification)16, and with MS/MS fragments and detected in the samples. b Metabolites searched with standards (level 1 identification)16 and MS/MS fragments but not detected. c Metabolites not detected in the samples. (FA) formic acid adduct. (I) Peak intensity (% of base peak).


I

Page 29 of 30


Table 2. UPLC-MS-QTOF MS-MS analysis of metabolites in positive mode. Collision energy 20 eV. Base peak

1

2

Sinapaldehyde derivatives Sinapaldehyde 4--Dglucoside

Rt (min)

Molecular Formula

Parent ion

9.6

C17H10O9

[M+H+Na]+

393.1146

Sinapaldehyde-4-D-(6’malonyl) glucoside

11.4

C20H12O12

[M+H+Na]+

479.1151

Secondary peaks

m/z

m/z

393.1156

231.0629

18

185.0418

20

479.1151

435.1277

51

393.1141

8

(I) Peak intensity (% of base peak).


I

m/z

I

m/z

271.0434

I

17

m/z

I

231.0619

6


Page 30 of 30

TOC Graphic.

Brown polymers Chlorogenic acid

PPO

HO COOH O HO

O OH

OH OH

Lignin POX OH OCH3

OH OCH3

Sinapaldehyde

O OCH3

H

OH OCH3


Targeted Metabolomics Analysis and Identification of Biomarkers for

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