Evolution of Browning in Apple during CA Storage: A Proteomics
Approach.
K. Buts, D. Hatoum, M. Hertog and B. Nicolai
Department of Biosystems
MeBioS, KU Leuven
Willem de Croylaan 42 - bus 2428
3001 Heverlee, Belgium
S. Carpentier
Department of Biosystems
Plantenbiotechniek, KU Leuven
Willem de Croylaan 42 - bus 2455
3001 Heverlee, Belgium
Keywords: Malus x domestica, Braeburn browning disorder, high throughput proteomics,
identification, quantification
Abstract
During 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.
INTRODUCTION
In 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 lensshaped 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 METHODS
Apples (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 DISCUSSION
The 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.
CONCLUSIONS
By 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.
ACKNOWLEDGEMENTS
IWT, 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
100
VQATDITCGPGHGISIGSLGEDGSEDHVSGV
FVNGAK
4.14
0.69
0.95
100
GLDDVQSEIHDLDWESTFFLR
3.46
1.11
5.37
100
0
0
0
100
LYNAFVLDADNLIPK
3.35
5.1
9.05
100
KINFGEGSTYSYVK
8.72
12.47
23.37
100
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
MSIASFYNPGNDAFISPAPAVLEK
TVDNDIPVIDKSFGFDTAVEEAQR
Polygalacturonide glycan
hydrolase
ACC oxidase
Major allergen mal d
NAGPEDLVATEAMLAR
100
79.79
44.18
14.02
Carbohydrate-binding-like
fold-chloroplast alphaglucan
GVSAYVDLMQDLIPEMKDGTVR
100
99.46
40.23
19.65
Early-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.