Evolution of Browning in Apple during CA Storage: A Proteomics Approach.

K. Buts, D. Hatoum, M. Hertog and B. Nicolai S. Carpentier Department of Biosystems Department of BiosystemsMeBioS, KU Leuven Plantenbiotechniek, KU LeuvenWillem de Croylaan 42 - bus 2428 Willem de Croylaan 42 - bus 24553001 Heverlee, Belgium 3001 Heverlee, Belgium

Keywords: Malus x domestica, Braeburn browning disorder, high throughput proteomics, identification, quantification

AbstractDuring long term storage of apple, physiological disorders may occur. One

major group of internal disorders is characterized by flesh browning. The susceptibility to flesh browning is cultivar, batch and season dependent and is caused by a combination of pre and postharvest factors. This can result in considerable economic losses with incidence levels up to 40%. Braeburn and Kanzi are commercial cultivars in Belgium that are prone to browning. An important influencing factor is the controlled atmosphere storage of apple. The main objective of this experiment is to investigate how the proteome changes during storage. Apples were picked and samples were taken immediately after harvest and after two weeks, two months and four months of storage under brown inducing conditions. Proteins were extracted using a phenol extraction and quantified with a modified Bradford procedure. For each moment in storage the four least brown apples were sampled and after tryptic digestion, analysed using tandem mass spectrometry. Those measurements were run in 2 modes, one suitable for identification of peptides and their corresponding proteins, the other one for quantitative analysis of the samples. To increase the identification rate of the quantitative runs, a tool was developed to link database search results with the less qualitative spectra of the quantitative analysis.

INTRODUCTIONIn order to deliver high quality apples to the consumer whole year round, fruit is

stored under controlled atmosphere (CA) conditions. Low O2, slightly elevated CO2 partial pressure, and low temperature are used to reduce the respiration rate of the fruit in storage. However, it is possible that the stored fruit suffer from various physiological disorders caused by pre- and postharvest factors. For example bitter pit (Faust and Shear, 1968), watercore (Marlow and Loescher, 1984; Watkins et al., 1993; Harker et al., 1999), superficial scald (Bain and Mercer, 1963; Emongor et al., 1994) and browning (Rabus and Streif, 2000; Elgar et al., 1998; Elgar et al., 1999) are disorders occurring frequently in apple. Braeburn is a cultivar which is highly susceptible to the CA induced browning disease, called Braeburn browning disorder (BBD). It is characterized by flesh browning and the formation of lens-shaped cavities, developing in the inner apple tissue. The sensitivity to BBD varies from year to year since it depends on seasonal and orchard factors, maturity at harvest and postharvest

storage factors. It can lead to considerable economic losses, with incidence levels up to 40%. The final aim of this research is to unravel which proteins are at the basis of this disorder, which protein changes develop during BBD and which protein/peptide might feature as a biomarker to identify sensitive batches in an early stage. In this manuscript focus is on the general protein changes during storage under brown inducing conditions. To study these changes, a gel and label free proteomics workflow was used.

MATERIALS AND METHODSApples (Malus x domestica ‘Braeburn’) were picked in the orchard of the

Experimental Garden for Pome and Stone Fruits (pcfruit) in Sint-Truiden, Belgium, on 28/10/2010, their optimal harvest date as determined by the Flanders Centre of Postharvest Technology (VCBT). Cortex tissue samples were taken from the in- and outside of the fruit (Fig. 1). Samples were taken immediately after harvest, and after 14, 33 and 128 days of CA storage. To make sure browning would develop, brown inducing storage conditions were applied without delay: optimal O2 level (2.5 %), elevated CO2 level (3.7 %) and elevated temperature (4 °C). For each moment in storage the four least brown apples were sampled and stored at -80 °C until further use.

Frozen tissue samples were grounded in the presence of liquid nitrogen and proteins were extracted according to the phenol extraction method of Carpentier et al. (2005) with slight modifications. 300 mg of grounded tissue was suspended in 850 μl of ice-cold extraction buffer (1 M Tris-HCl pH 8.5, 0.5 M EDTA, 0.1 M KCl, 6.5 mM DDT, 1 mM PMSF, 0.7 M sucrose) and vortexed for 30 s. 850 μl of ice-cold Tris buffered phenol (pH 8.0) was added and vortexed for 10 min at 4 °C. After 3 min of centrifugation (8000 rpm, 4 °C) the phenolic phase was collected and re-extracted by adding 850 μl of extraction buffer and vortexed for 30 s. The mixture was centrifugated again for 3 min (8000 rpm, 4 °C), the phenolic phase collected and left overnight for precipitation with 5 volumes of 100 mM ammonium acetate in methanol at -20 °C. The samples were centrifuged for 60 min at 13000 rpm at 4 °C, after removal of the supernatant the pellet was rinsed with cold acetone / 0.2 % DDT and incubated for 1 h at -20 °C. Samples were rinsed a second time with cold acetone / 0.2 % DDT and centrifuged for 30 min (13000 rpm, 4 °C). The pellet was briefly air-dried and resuspended in 100 μl buffer (8 M ureum, 5 mM DTT).

Protein concentration was determined with the Bradford method, using BSA as standard (Bradford, 1976). Iodoacetamide was added to the samples until a final concentration of 0.015 M and vortexed for 30 min in the dark. Samples were diluted 4 times with 100 mM ammonium bicarbonate. For protein digestion 0.2 µg/µL trypsine was added and incubated overnight at 37 °C. Samples were acidified with trifluoroacetic acid (0.5 % final concentration) and desalted using solid phase extraction. Columns (Supelco Inc, Bellefonte, PA, USA) were washed with 1 mL 95 % ACN and equilibrated with 1 mL 2 % ACN, 0.1 % TFA. Then the digested sample was added and washed with 1 mL 2 % ACN, 0.1 % TFA. Peptides were eluted with 1 mL 84 % ACN, 0.1 % TFA, after which solvents were evaporated using a speedvac and dissolved in 0.1 M ammonium formate.

For the mass spectrometry analysis two dimensional liquid chromatography tandem mass spectrometry (LC-MS/MS) was performed using the 2-dimensional nanoAcquity Ultra performance liquid chromatography (UPLC) system online coupled to a Synapt HDMS

QTOF MS instrument (Waters, Milford, MA, USA) as described by Vertommen et al. (2011).

For identification of the present peptides, obtained peak lists of data dependent (DDA) runs were searched against a homemade apple database using Proteinlynx Global Server (PLGS 2.5, Waters). The apple database was built out of the protein sequences released together with the apple genome, combined with all published apple proteins which were present in Swiss-Prot database.

Data independent analysis (DIA) runs were used for quantification of the peptides present in the apple extracts, using Progenesis LC-MS (Nonlinear Dynamics, UK). The ion intensities / peptide abundances, were measured as the sum of the peak areas within the isotope boundaries, found in the aligned runs of the different apple samples. To further improve the identification rate, an identification DDA database was constructed and linked to the DIA features. In a first phase, DIA and DDA features were aligned based on their mass to charge ratio (m/z) and retention time (RT); in a second phase, masses of fragmentation ions were compared for each of the linked features. To detect significantly differential peptides related to storage time, or sampling position, a multivariate statistical analysis was performed (The Unscrambler 10.3). After a principal component analysis (PCA), partial least square regression (PLS) was performed with a full cross validation. Using an iterative Jackknifing procedure, the significant features of the PLS-model were determined.

RESULTS AND DISCUSSIONThe final goal of this research project is to discover a biomarker for the development

of BBD in a very early stage. Therefore the 4 least brown apples of every moment in time were sampled to monitor the protein profile of non-brown apples, to be able to track changes before apoptosis appears in the tissue. We are in search for particular peptide/protein expression profiles, namely peptides which are low in abundance at harvest and have increased presence after storage, or are highly abundant at harvest but decrease during storage.

By linking DDA and DIA results, we were able to significantly increase the success rate of peptide identification, with a false discovery rate (FDR) of less than 1 % based on 3 or more linked fragments. The quantitative analysis in Progenesis revealed 98236 features with their normalized abundances. After linking the DIA dataset to the DDA database, an additional 6860 peptides were identified, together leading to an identification rate of 14 %. Five iterative PLS regression analysis resulted in 4564 features which were significantly different for storage time (p<0.05), of which 879 peptides were identified.

Figure 2 depicts several peptides with the specific increasing or decreasing expression profiles, table 1 lists the identified peptides with their corresponding relative abundances. The abundance of each peptide is expressed relative to the maximum abundance observed for that peptide. Note that the protein name is only given as additional information as peptide abundance is not necessarily proportional to protein abundance, which is mainly due to the protein inference problem (Nesvizhskii and Aebersold, 2005; Shi and Wu, 2009; Huang et al., 2012).

Nevertheless similar patterns in stress and ripening related proteins are reported in apple during storage. For example major allergen Mal d 1 is a pathogenesis-related protein

(PR-10) which can be expressed in response to exposure to pathogens, wounding or abiotic and biotic stress. It was suggested that ripening caused increasing levels of Mal d 1 after cold storage (Atkinson et al., 1996; Bolhaar et al., 2005; Matthes and Schmitz-Eiberger, 2009; Sancho et al., 2006; Hsieh et al., 1995). Also poly-1,4-α-D-galacturonide glycanohydrolase (PG), an enzyme involved in ripening processes by degrading polygalacturanon in cell walls, is correlated to storage potential of apple (Wakasa et al., 2006). Even though CA suppresses fruit ripening and hence ethylene production, increasing ACC oxidase was reported during long time storage of apple (Gorny and Kader, 1996; Bulens et al., 2012).

Low oxygen stress leads to an enhanced glycolysis, this to increase the substrate level for ATP production (Bailey-Serres et al., 2012). Also during cold storage of apple, an increased glycolysis is described (Duque et al., 1999). In this experiment, the peptide TVDNDIPVIDKSFGFDTAVEEAQR of the glycolysis enzyme phosphofructokinase, increases during storage. Lizada (1993) reported the drop of citrate synthase during ripening in mango and also during low oxygen stress, the first step of the Krebs Cycle is indicated as inhibited (Bailey-Serres et al., 2012). In this way, the cycle will be reorganized, going from oxoglutarate to succinate, leading to a downregulation of NADH production.

CONCLUSIONSBy combining DDA database search results with DIA data, twice as much peptides

were identified. Significant up and down regulated peptides were mostly coming from proteins involved in the central metabolism, or from stress related proteins. The presence of this significant changing peptides indicates that a quantitative shotgun proteomics experiment as described here, is useful for biomarker discovery.

ACKNOWLEDGEMENTSIWT, the Flemish government agency for Innovation by Science and Technology is

greatly acknowledged for the funding of IWT project 080527. We also want to thank Twan America and Jan Cordewener (PRI-WUR) for the mass spectrometry measurements of the apple extracts.

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Tables

Table 1. Overview of relative abundances of identified peptides with an increasing or decreasing expression profile in time. Abundances are relative as compared to the time point with the highest abundance (100%). Protein abundances do not necessarily correspond to peptide abundances, mainly due to protein inference.

Peptide sequence Relative abundance Protein

At harvest 14d 33d 128d

IKPQPPCGTYAPTAVTFNR 5.48 4.72 8.43 100Polygalacturonide glycan hydrolase

VQATDITCGPGHGISIGSLGEDGSEDHVSGVFVNGAK 4.14 0.69 0.95 100

GLDDVQSEIHDLDWESTFFLR 3.46 1.11 5.37 100 ACC oxidase

MSIASFYNPGNDAFISPAPAVLEK 0 0 0 100

LYNAFVLDADNLIPK 3.35 5.1 9.05 100 Major allergen mal d

KINFGEGSTYSYVK 8.72 12.47 23.37 100

TVDNDIPVIDKSFGFDTAVEEAQR 0 1.89 4.54 100 Phosphofructokinase

TFVGYESEFTSVLPPAR 3.93 5.44 9.47 100 De novo

TPAEDLKDLLTTGSVGAEALVYFFWLLSEVK 1.61 3.54 6.25 100 De novo

ISPIEVDAVLLSHPEVAQGVAFGVPDDK 15.92 28.46 70.31 100 AMP dependent kinase

SQIPLSQPESEAGGFLDPKTMATGQLFSR 14.53 33.67 90.91 100 ATP citrate lyase

YLSSVLFQDLRQEAENMQPVAVD 100 82.08 30.21 3.67 Cyanoalanine synthase

TTVPTLPEEIIAETEKVK 100 75.91 44.22 17.38 Amylase

TIQFVDWCPTGFK 100 70.66 71.72 46.27 Tubulin

NAGPEDLVATEAMLAR 100 79.79 44.18 14.02

Carbohydrate-binding-like fold-chloroplast alpha-glucan

GVSAYVDLMQDLIPEMKDGTVR 100 99.46 40.23 19.65Early-responsive to dehydration

ILYSSVVYPHNYGFIPR 100 55.06 42.39 12.28 Pyrophosphatase

GMTGLLWETSLLDPDEGIRFR 100 57.25 42.59 7.87 Citrate synthase

Figures

Fig. 1 In- and outside sampling positions of the apple cortex tissue.

Fig. 2 Expression profiles of significantly different peptides. Within the framework of biomarker discovery, most interest is exhibit for increasing or decreasing abundances.