Evaluation and identification of markers of postharvest damage in mushrooms (Agaricus bisporus) using a GC/MS metabolomic approach

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1 Evaluation and identification of markers of postharvest damage in mushrooms (Agaricus bisporus) using a GC/MS metabolomic approach Aoife O Gorman a, Catherine Barry-Ryan a, Jesus M. Frias a a School of Food Science & Environmental Health, Dublin Institute of Technology, Dublin1, Ireland.(aoife.ogorman@student.dit.ie) ABSTRACT The aim of this research was to use the GC/MS profiling method coupled with chemometric tools to profile mechanically damaged and undamaged mushrooms during storage and to identify specific metabolites that may be used as markers of damage. Mushrooms grown under controlled conditions were bruise damaged by vibration to simulate damage during normal transportation. Three damage levels were examined; undamaged, damaged for 2 minutes and damaged for 4 minutes and two time levels studied; day zero and day one after storage at 4 C. Applying this technique a library with over 1 mushroom specific metabolites were identified. Random forest classification models were applied to predict damage in mushrooms producing models with low error rates of >1%. Fatty s were identified as the most important group of metabolites for predicting damage in mushrooms. PLS models also yielded models with low error rates. With a view to exploring biosynthetic links between metabolites, a pairwise correlation analysis was performed for all polar and non-polar metabolites. The appearance of a high correlation between linoleic and pentadecanoic in the non-polar phase of damaged samples indicated the switching on of a metabolic pathway when a mushroom is damaged. Keywords: GC/MS; metabolic profiling; mushrooms; damage; chemometrics INTRODUCTION Mushrooms are one of the most important horticultural crops grown in Ireland with an export value exceeding 1 million in 28 [1]. Fresh mushrooms are highly perishable with short shelf-lives compared to most other vegetables. They are also highly sensitive to post harvest practices whose injuries on the cap can lead to colour discolouration. The major cause of quality loss that accounts for the reduction in market value of mushrooms is postharvest browning [2],[3] and [4]. Mechanical damage triggers the browning process within mushroom tissues changing the metabolic state of the mushroom. Recent studies have demonstrated a number of spectroscopic and imaging techniques as being important tools for the investigation of various mushroom quality related issues, such as mechanical damage [5], freeze-damage detection [6] and detection of mushroom diseases [7], however there is a need to investigate specific metabolites and metabolic pathways that may be related to browning in order to fully understand it. GC/MS is one of the most frequently used tools for profiling primary metabolites [8]. In recent times metabolomics has emerged as a field of increasing interest to food scientists [9], with applications ranging from profiling of plant species [1] to studying the effects of stress and so forth [11]. As a profiling technique GC/MS has been used to detect and discriminate fungal diseases in mango [12] and to study potatoes [13], tomatoes [14] and pears [15]. GC/MS coupled with chemometrics has not to the authors knowledge been used to detect damage in mushrooms; however it has been used as a tool in the food industry for similar uses e.g. metabolic profiling using GC/MS to profile metabolic changes in sound and brown pears using a PLS-DA multivariate statistical approach [15]. GC/MS profiling has also found a function in determining phytochemical diversity in tubers of potatoes [13]. The objective of this study was to use GC/MS to profile damaged and undamaged mushrooms, building a library of mushroom specific metabolites. The GC/MS data would then be coupled with chemometric methods to develop models to (a) predict damage in mushrooms and (b) attempt to identify specific metabolites as markers of damage. A secondary aim was to use the data to explore mushroom biochemistry, with a view to detecting any unexpected close linkages between metabolites by applying correlation analysis.

2 MATERIALS & METHODS Second flush mushrooms were grown at the Teagasc Research Centre Kinsealy (Dublin, Ireland), harvested damage-free. A set of 12 closed cap, defect free Agaricus bisporus strain A15 (Sylvan Spawn Ltd., Peterborough, United Kingdom) mushrooms were selected for this study and immediately transported by road to the testing laboratory. Special trays were designed to hold mushrooms by the stem using a metal grid to avoid contact between (a) mushrooms and (b) between the top of mushroom caps and the tray lid during transportation. A subset of 8 mushrooms was subjected to vibrational physical damage using a shaker table (Gyrotary G2, New Brunswick Scientific So., USA) set at 3 rpm; two damage levels were chosen for 2 minutes (D2) and 4 minutes (D4), which correspond to threshold quality and unacceptable L-values for mushrooms respectively [16]. The remaining 4 mushrooms were untreated and labelled as undamaged (UD). Samples were analysed on day zero and day one after storage at 4 C. Twenty mushrooms were selected for each day and damage level. Sample preparation involved the manual dissection of each mushroom into its three main tissue types (cap, gills and stipes) before freezing at -7 C in a cryogenic fridge (Polar 34V Cryogenic fridge, Angeelantoni Industrie spa, Massa Martana, Italy) until further processing. Extraction, fractionation and methoxyamination of carbonyl moieties, followed by derivatisation of protons with N-methyl-N-(trimethylsilyl)-trifluoracetamide (MSTFA) prior to GC/MS analysis was performed as described previously [17] with minor modifications. A total of 72 injections were taken (36 polar and 36 non-polar). Multivariate statistical analysis was performed on the GC/MS data using principal component analysis (PCA), random forest modelling (RF) and partial least squares discriminant analysis (PLS-DA). Correlation analyses were performed on the polar and non-polar metabolites separately for each damage level and mushroom age. Pair-wise correlation was performed on the response ratios of all metabolites. Two metabolites were considered to be highly correlated if the coefficient has a value of >.9 [17] & [13]. RESULTS & DISCUSSION During the analysis of the 72 chromatograms a library with 15 metabolites was built. Table 1 contains 62 metabolites from both polar and non-polar chromatograms, a number (44) of metabolites were not included in the table as they were only found in a very small percentage of chromatograms (<1%). In the non-polar chromatograms a number of fatty s and phenolic compounds were identified. Sugars, polyols and amino s were identified in polar phase chromatograms. Since authentic chemical standards were not run for every metabolite, the metabolite identity should be regarded as putative rather than exact for these metabolites (Table 1).

3 Hexadecanoic 3,4-dihydrobenzyl alcohol 1,3,8-trihydroxy-6- methylanthraquinone* phenol 2,4-bis(1,1- dimethylethyl)* 9,12- Octadecadienoic Eicosanoic Proline Citric Tyrosine Threonine D-glucitol Phthalic Table 1 Metabolites identified by GC/MS as components of Agaricus bisporus Fatty Acids Phenolics Amino Sugars & Organic Acids Others Acids Polyols Dodecanoic Benzoic Alanine D-mannose Acetic * Pyrimidne Tridecanoic 2-(4- Asparagine D-fructose* Gluconic Urea* methoxyphenyl)ethanol Tetradecanoic Diphenyl ether Glycine D-ribose* Saccharic Benzene Pentadecanoic 2,6-bis(1,1- dimethylethyl)-4- Aspartic Erythrose* Succinic* Silanamine chloro-phenol 8-phenyl-6-thiotheophylline α-dglucopyranosideα-dfructofuranosyl trans-9- Hexadecenoic Heptadecanoic Octadecanoic 4-phenyl-2- hydroxystilbene* Tryptophan D-ribo-hexitol Propanedioic Valine Inositol Quinaldic * Serine Glycerol 3-acetoxy-3- hydroxypropionic Glutamine* Pentanedioic * 3-octanol* Heneicosanoic * Tricosanoic * 11-Eicosanoic * 9,15- Octadecadienoic * Ricinoleic * Erucic cid* Docosanoic * Hexanoic * * Metabolites found in a low percentage of sample (>1% less than 15%): Metabolites verified by authentic reference compounds. Compounds identified with very high probabilities (similarity coefficient or reverse similarity coefficient >85%) are indicated in bold Detection of Damage 1. Principal Component Analysis (PCA) Samples were studied separately on the basis of their tissue type (cap, gills, stipe) and on their age (day zero/day one). The score plot for day zero caps is shown in Figure 1 for PC1 vs PC2; these first two principal components accounted for 5 and 4% respectively of the total variance in the GC/MS data set. Separation between some samples on the basis of damage is readily apparent for e.g. the majority of UD caps and D2 caps form clusters on the score plot. D4 samples were spread randomly throughout the score plot. A similar trend was seen for day one samples, indicating that metabolite levels are affected by damage. In the case of gill and stipe tissue, cluster for different damage levels were not clearly evident with overlapping of damage levels seen for both day zero and day one samples

4 pc pc2 Figure 1 PC1 vs PC2 score plots for cap tissue day zero samples 2. Random Forests (RF) A total of 7 RF models were built using the GC/MS data. The first model developed attempted to discriminate damaged from undamaged samples (using all the data) and to identify specific metabolites that could be used as possible markers of damage in mushrooms. The model tried to predict damage in mushrooms using all the metabolites identified by the GC/MS, a variable indicating the tissue from which the metabolite originated (cap, gill or stipe), and the age of the sample as explanatory variables, This resulted with a model with good classification with an out-of-bag (OOB) error rate of 11%, sensitivity 89% and specificity 92%. The variable of importance (VIP) plot for predicting damage indicated pentadecanoic, dam.rf linoleic, myo-inositol, benzoic and hexadecanoic as the five most important metabolites that could be used as markers for damage (Figure 2). Pentadecanoic Linoleic. Myo.inositol Benzoic..TMS Hexadecanoic...trimethylsilyl.ester Diphenyl.ether Methanone.diphenyl..phenylhydrazone Trimethylsilyl.ether.of.glucitol Ricinoleic...trimethylsiloxy..trimethylsilyl.ester Citric...trimethyl.ester Nonanoic..9..o.propylphenyl..methyl.ester Methyl di.tert.butyl.hydroxyphenyl.propionate Day Dodecanoic...methyl.ester Octadecanoic..methyl.ester Cyclohexadiene.1.4.dione.2.6.bis.1.1.dimethylethyl.. X9.Hexadecenoic...methyl.ester a.d.glucopyranoside Trimethylsilyl.ether.of.glycerol Benzene.1.3.bis.1.1.dimethylethyl.. Hexadecenenitrile Tissue Butanedioic...dimethyl.ester Heptadecanoic...methyl.ester Phthalic...bis.2.ethylhexyl.ester Pentadecanoic...methyl.ester Tridecanoic...12.methyl..methyl.ester Butanedioic. Eicosanoic..Methyl.ester Alanine Linoleic. Benzoic..TMS Myo.inositol Pentadecanoic Diphenyl.ether Nonanoic..9..o.propylphenyl..methyl.ester Citric...trimethyl.ester Dodecanoic...methyl.ester Trimethylsilyl.ether.of.glucitol Hexadecanoic...trimethylsilyl.ester Methanone.diphenyl..phenylhydrazone a.d.glucopyranoside Benzene.1.3.bis.1.1.dimethylethyl.. Trimethylsilyl.ether.of.glycerol Hexadecenenitrile Day Ricinoleic...trimethylsiloxy..trimethylsilyl.ester Octadecanoic..methyl.ester X9.Hexadecenoic...methyl.ester Methyl di.tert.butyl.hydroxyphenyl.propionate Cyclohexadiene.1.4.dione.2.6.bis.1.1.dimethylethyl.. Alanine Serine..bis..trimethylsilyl.. Phosphoric...tris.trimethylsilyl.ester Heptadecanoic...methyl.ester Pentadecanoic...methyl.ester Phthalic...bis.2.ethylhexyl.ester Benzene propanyl.3.ylidene.tris. X8.phenyl.6.thio.theophylline Tissue MeanDecreaseAccuracy Figure 2 VIP plot of metabolites that are important in the RF model for predicting damage

5 Linoleic [ppm] Models were also produced for each tissue type (cap, gills, stipe) and also for each mushroom age (day zero/day one). Cap and stipe tissue produced the models with the lowest error rates (>1%), with cap tissue having an OOB error rate of just 8.3%, indicating that cap tissue alone could be used to predict damage. VIP plots were produced for each RF model. It was observed that fatty s were the most important metabolite group for predicting damage in mushrooms with linoleic, pentadecanoic and hexadecanoic identified as important markers by a number of models. 3. Partial Least Squares-Discriminant Analysis (PLS-DA) PLS-DA models were developed in an attempt to differentiate between damage levels for each tissue type. The models had the ability to detect undamaged samples quite well particularly for cap tissue (Table 2). The RF model produced using cap tissue also performed well for the discrimination of damage (OOB 8.3%), highlighting the usefulness of this tissue alone for predicting damage in mushrooms using PLS-DA and RF models. Misclassification of samples was seen between D2 and D4 for all tissue types, however lower error rates were seen for cap tissue (training and testing models). Although the models did not perform well for discriminating between the damage levels (D2/D4) they did perform well for discriminating undamaged samples from damaged ones, making PLS-DA an important tool for detecting damage in mushrooms. Table 2 Performance statistics of PLS-DA models built using GC/MS data Damage Level #LV Tissue Sensitivity Specificity (Minutes) 4 Cap Stipes 92 a, 97 b 74 a, 69 b 76 a, 81 b 76 a, 81 b 78 a, 6 b 85 a, 82 b 2 4 Cap Stipes 4 3 Cap Stipes a Training set, b Testing set #LV: Number of latent variables 69 a, 71 b 56 a, 61 b 62 a, 54 b 65 a, 75 b 36 a, 54 b 45 a, 51 b 66 a, 72 b 79 a, 74 b 71 a, 67 b 8 a, 85 b 89 a, 81 b 75 a, 8 b Correlation of Metabolite Levels Interestingly high correlations were seen between pentadecanoic and linoleic (Figure 3) in damaged samples for both day zero and day one (non-polar phase metabolites). These two metabolites were not correlated in undamaged samples which suggests that a metabolic pathway (related to fatty s and possible membrane regeneration) becomes switched on when a mushroom becomes damaged. These two metabolites were also identified by a number of RF models as variables of importance for modelling damage in mushrooms D1 Cap D1 D1 Stalks 1 5 UD D2 D4 D Cap D D Stalks Pentadecanoic [ppm] Figure 3 Plots of response ratios for linoleic and pentadecanoic for each tissue type and day

6 CONCLUSION A library with over 1 metabolites was built. Chemometric tools were successfully applied to GC/MS data to predict damage in mushrooms. RF models produced models with OOB error rates of less than 1% and identified specific fatty s as important markers of damage. PLS-DA models were also able to predict damage in mushrooms in an acceptable manner. Correlation matrices produced using non-polar metabolites indicated that linoleic and pentadecanoic were highly correlated in damaged samples. These metabolites were also highlighted by RF models and could be used as possible markers of damage. ACKNOWLEDGMENTS We acknowledge financial support from the Irish Department of Agriculture and Food under the Food Institutional Research Measure (FIRM), supported through EU and national funds. REFERENCES [1] Bord Bia. 29. Factsheet on the Irish agricultural and food & drink sector. Retrieved January 211, from [2] Jolivet, S., Arpin, N., Wichers, H.J. & Pellon, G Agaricus bisporus browning: a review. Mycological Research, 12(12), [3] Eastwood, D. & Burton, K.S. 22. Mushrooms a matter of choice and spoiling oneself. Microbiology Today, 29, [4] Burton, K.S. 24. Cultural factors affecting mushroom quality cause and control of bruising. Mushroom Science, 16, [5] O Gorman, A., Downey, G., Gowen, A.A., Barry-Ryan, C. & Frias, J.M. 21. Use of Fourier transform infrared spectroscopy and chemometric data analysis to evaluate damage and age in mushrooms (Agaricus bisporus) grown in Ireland. Journal of Agricultural and Food Chemistry, 58(13), [6]Gowen, A.A., Taghizadeh, M. & O Donnell, C.P. 29. Identification of mushrooms subjected to freeze damage using Hyperspectral imaging. Journal of Food Engineering, 93(1), [7] Gaston, E., Frias, J.M., Cullen, P.J., O Donnell, C.P. & Gowen, A.A. 21. Visible-near infrared Hyperspectral imaging for the identification and discrimination of Brown blotch disease on mushrooms (Agaricus bisporus) caps. Journal of Near Infrared Spectroscopy, 18(5), [8] Fiehn, O. 28. Extending the breadth of metabolite profiling by gas chromatography coupled with mass spectrometry. TrAC Trends in Analytical Chemistry, 27(3), [9] Garcia-Canas, V., Simo, C., Leon, C. & Cifuentas, A. 21. Advances in Nutrigenomics research: Novel and future analytical approaches to investigate the biological activity of natural compounds and food functions. Journal of Pharmaceutical and Biomedical Analysis, 51(2), [1] Arbona, V., Iglesias, D.J., Talon, M. & Gomez-Cadenas, A. 29. Plant phenotype demarcation using nontargeted LC-MS and GC-MS metabolite profiling. Journal of Agricultural and Food Chemistry, 57(16), [11] Roessner-Tunali, U., Hegemann, B., Lytoychenko, A., Carrari, F., Bruedigam, C., Granot, D. & Fernie, A.R. 23. Metaboloic profiling of transgenic tomato plants overexpressing hexokinases reveals that the influence of hexose phosphorylation diminishes during fruit development. Plant physiology, 133(1), [12] Moalemiyan, M., Vikram, A. & Kushalappa, A.C. 27. Detection and discrimination of two fungal diseases of mango (cv. Keitt) fruits base don volatile metabolite profiles using GC/MS. Postharvest Biology and Technology, 45(1), [13] Dobson, G., Shepherd, T., Verrall, S.R., Conner, S., McNicol, J.W., Ramsey, G., Shepherd, L.V., Davies, H.V. & Stewart, D. 28. Phytochemical diversity in tubers of potato cultivars and landraces using GC/MS metabolomics approach. Journal of Agricultural and Food Chemistry, 56, [14] Schauer, N., Zamir, D. & Fernie, A.R. 25. Metabolic profiling of leaves and fruit of wild species tomato: a survey of the Solanum lycopersicum complex. Journal of Experimental Botany, 56(41), [15] Pedreschi, R., Franck, C., Lammertyn, J., Erban, A., Kopka, J. Hertog, M., Verlinden, B. & Nicolai, B. 29. Metabolic profiling of Conference pears under low oxygen stress. Postharvest Biology and Technology, 51, [16] Gormley, T.R. & O Sullivan, L Use of a simple reflectometer to test mushroom quality. The Mushroom Journal, 34, [17] Lisec, J., Schauer, N., Kopka, J., Willmitzer, L. & Fernie, A.R. 26. Gas chromatography mass spectrometry-based metabolite profiling in plants. Nature Protocols, 1, [18] Steuer, R., Kurths, J., Fiehn, O. & Weckwerth, W. 23. Observing the interpreting correlations in metabolomic networks. Bioinformatics, 19(8),

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