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Enhanced approaches for identifying Amadori products: application to peanut allergens Katina L Johnson, Jason Williams, Soheila J. Maleki, Barry K. Hurlburt, Robert Elliot London, and Geoffrey A. Mueller J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.5b05492 • Publication Date (Web): 25 Jan 2016 Downloaded from http://pubs.acs.org on February 1, 2016

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Journal of Agricultural and Food Chemistry

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Enhanced approaches for identifying Amadori products: application to peanut

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allergens

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Katina L. Johnson1, Jason G. Williams1, Soheila J. Maleki3, Barry K. Hurlburt3, Robert E.

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London2, Geoffrey A. Mueller2

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1- Epigenetics & Stem Cell Biology Laboratory, National Institute of Environmental

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Health Sciences. 2- Genome Integrity & Structural Biology Laboratory, National Institute of

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Environmental Health Sciences 3- Agricultural Research Service, U.S. Department of Agriculture

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Corresponding author

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Geoffrey A. Mueller, Ph.D.

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NIEHS

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111 T.W. Alexander Dr MDMR01

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Research Triangle Park, NC 27709

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919-541-3872

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Fax 919-541-5707

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[email protected]

ACS Paragon Plus Environment

1


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Abstract (147/150 words)

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The dry roasting of peanuts is suggested to influence allergenic sensitization due to

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formation of advanced glycation end products (AGE) on peanut proteins. Identifying AGEs

25

is technically challenging. The AGEs of a peanut allergen were probed with nanoLC-ESI-MS

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and MS/MS analyses. Amadori product ions matched to expected peptides and yielded

27

fragments that included a loss of 3 waters and HCHO. Due to the paucity of b- and y-ions in

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the MS/MS spectrum, standard search algorithms do not perform well. Reactions with

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isotopically labeled sugars confirmed that the peptides contained Amadori products. An

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algorithm was developed based upon information content (Shannon entropy) and the loss

31

of water and HCHO. Results with test data show that the algorithm finds the correct

32

spectra with high precision, reducing the time needed to manually inspect data.

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Computational and technical improvements allowed better identification of the chemical

34

differences between modified and unmodified proteins.

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Key words

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Peanuts (Arachis hypogaea), Allergen, Advanced glycation end products, Amadori products,

38

Maillard reactions, Shannon Entropy

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List of abbreviations

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AGE: Advance glycation end products

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CML: Carboxymethyllysine

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CEL: Carboxyethyllysine

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LC: Liquid chromatography


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ESI: Electrospray ionization

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MS : Mass Spectrometry


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Introduction

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Allergies to peanuts are an increasing concern in public health. In the United States,

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1-2% of the population, or nearly 3 million people, are allergic to peanuts.1 The molecular

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basis for peanut allergy has been the subject of extensive commentary and research.2 One

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hypothesis suggests the common method of processing peanuts in the U.S., i.e. dry roasting,

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increases the allergenic nature of peanuts and tree nuts.3, 4 During dry roasting the

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proteins can be extensively modified with advanced glycation end products (AGE) and the

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connection to allergy has been supported by several in vitro studies suggesting that AGE

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modified proteins skew the immune response towards allergy.5-7 Recently these findings

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were corroborated in a murine study where dry roasting enhanced peanut allergic

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sensitization across mucosal and cutaneous routes.8 Since the AGE modifications appear to

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influence sensitization, knowledge of their chemical structures will be important for

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understanding the mechanism of action.

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Characterizing the AGE modifications on peanut allergens has proven challenging.

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Early studies utilized antibodies specific for certain types of AGEs.9 More recent studies

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have utilized mass spectrometry (MS) to specifically identify chemical modifications and

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the modified residues.10-12 This provided detailed atomic information, but several technical

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challenges make this a difficult process. First, the modifications on lysine and arginine

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residues prevent digestion of the allergens with commonly used trypsin-related proteases.

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Second, the proteins were difficult to extract and purify from roasted peanuts,13 and

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required extraction with urea,11 or multiple chromatography steps.12 Despite problems,

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some commonly modified peptides have been identified.10-12


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AGEs are created in a non-enzymatic mechanism known as the Maillard reaction.

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This process is accelerated by higher temperatures and dry roasting, which leads to an

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increased number of AGE modifications compared with boiling.14 The first step in

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formation of an AGE occurs when sugars react primarily with free amines forming a Schiff-

73

base.15 The modifications are most common on lysines and are less frequently observed on

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arginines, the N-terminus, and cysteines.16 The sugar modification then undergoes a

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further Amadori rearrangement to create a covalently modified protein. The ketone of the

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Amadori product, however, can initiate further decomposition resulting in a variety of

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different AGEs. The most common AGE created is a carboxymethyllysine, which is

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produced by additional adduct oxidation.

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While manually examining MS/MS spectra of peanut extract for modifications, it

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was noticed that many spectra were observed that demonstrated significant neutral loss of

81

water, but very little to no b-ion and y-ion formation from the peptide backbone.12 It was

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previously noted that peptides modified by the Amadori product produce a common

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fragmentation pattern in MS/MS experiments in which ions that correspond primarily to

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neutral loss of 3 and 4 waters as well as an ion that corresponds to neutral loss of 3 waters

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and HCHO are observed.17 Very few b-ions and y-ions are generated from the

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fragmentation of these peptides making it difficult to identify the peptide.17 These spectra

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are commonly scored poorly by standard software packages as little information on the

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peptide sequence is obtained and even when b-ions and y-ions are present, dominant

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fragment ions (the neutral loss of 3 and 4 waters and the neutral loss of 3 waters and

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HCHO) are unassigned. However, a careful manual examination can sometimes find b- and

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y-series ions at low signal to noise levels that can then be employed for assignment of the


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peptide sequence. In this paper, the correspondence between the low information spectra

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and the glycated peptides was confirmed with isotopic labeling of the precursor sugars and

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recombinant peanut protein, an idea suggested by other glycation studies.18, 19 To target

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these low information spectra for glycation analysis, a new computational algorithm was

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designed using a Shannon entropy-based method.

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Materials and Methods

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Glycation of recombinant Ara h 1

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Purified recombinant Ara h 1 (0.5 mg/ml) (mature sequence - amino acid residues 85-626

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of UniProtKB/Swiss-Prot accession P43238.1 )

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55°C in the presence of 0.25 mol/L glucose, xylose, 1:1 12C glucose:13C glucose, or 1:1 12C

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xylose:13C xylose for 0 or 10 days. Trypsin was added at a 20:1 substrate to enzyme ratio

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and digestion was carried out at 37°C for 14 hrs. Reactions were stopped by freezing at -

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80oC for use in subsequent mass spectrometry analysis.12

12

was solubilized in PBS and incubated at

107 108

NanoLC-ESI-MS/MS

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NanoLC-ESI-MS/MS analyses were performed using an Agilent 1100 nanoLC system

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on-line with an Agilent XCT Ultra ion trap mass spectrometer with the Chip Cube Interface.

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20 µLs of the peptide digest were loaded onto an Agilent C18 chip (75 µm x 43 mm).

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Peptides were eluted by applying linear gradient from 5% acetonitrile, 0.1% formic acid to

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50% acetonitrile, 0.1% formic acid to the column over 45 minutes. The mass spectrometer

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was used in the positive ion mode, standard enhanced mode and included settings of a

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mass range from 200 to 2200 m/z, an ionization potential of 2.1 kV, an ICC smart target of

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200000 or 200 milliseconds of accumulation, and a 1.0 volt fragmentation amplitude.

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MS/MS data were acquired using a data dependent acquisition format with the 6 most

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abundant ions from each MS scan further interrogated by MS/MS. The automated

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switching for MS/MS required a threshold of 10000 counts.

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Generation of Peak Lists and Traditional Database Searching

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Peak lists were generated from the data obtained from the nanoLC-ESI-MS/MS

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analysis using the Data Extractor feature of the Spectrum Mill software from Agilent. The

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Data Extractor settings included limiting the data search to deconvoluted ions observed

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between 400 and 6000 Da and a retention time between 10 minutes and 50 minutes.

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Moreover, of the remaining MS/MS spectra, only spectra that contained sequence tag

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information greater than or equal to 1 residue were submitted for database searching. The

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resulting extracted data were then searched iteratively against the sequence of the

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recombinant Ara h 1 (gi:347447590) using the MS/MS Search function in the Spectrum Mill

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software. Search settings included a trypsin specificity with two missed cleavages allowed,

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a precursor ion mass tolerance of 2 Da, a product ion mass tolerance of 0.7 Da, variable

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methionine oxidation, and a minimum matched spectral intensity of 70%.

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matches were manually validated and the remaining unmatched spectra were then

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searched again against the rAra h 1 sequence using similar settings but allowing for lysine

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modification by either carboxymethyl-, carboxyethyl, or Amadori products.

Putative

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Test Data Set

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An LC-ESI-MS/MS test data set was generated using the tryptic digest of a 10 day

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incubation of rAra h 1 with 12C xylose. The 2929 MS/MS spectra were manually evaluated

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for fragmentation in the MS/MS that was low information and also suggested neutral loss

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of water(s) being the primary fragment ion(s). The resulting spectra were then manually

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matched to peptides of rAra h 1 modified by Amadori products or AGEs. The resulting 12


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spectra with confirmed Amadori products were then used for evaluation of the Shannon

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entropy-based algorithm.

145 146 147

NanoLC-ESI-MS/MS/MS NanoLC-ESI-MS/MS/MS analyses were performed using an Agilent 1200 nanoLC

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system on-line with an Agilent 6340 ion trap mass spectrometer with the Chip Cube

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Interface using the same LC and MS/MS conditions described above. Ions that

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demonstrated a neutral loss of 27, 18, or 13.5 m/z, from the precursor in the MS/MS

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spectrum were further analyzed in an automated way by MS/MS/MS experiments.

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Computational Algorithm

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Shannon Entropy is a measure of the information content of a message or data set.

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= − ∑

Eq [1]

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H is the Shannon Entropy, so named because it resembles the Boltzman equation for

158

thermodynamic entropy. P can be any data. In this case is the intensity at an m/z ratio of x.

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The equation sums the product of the P(x) times the log(P(x)) over all i points in the mass

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spectrum. The base of logarithm can be any number, in this case b=10. In data not shown,

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there was very little discriminatory gain to utilizing different bases. For each mass

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spectrum analyzed here, the ion peak lists were generated using the Data Extractor

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function of Spectrum Mill MS Proteomics Workbench (Agilent) and binned into vectors of

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length i, with a cumulative intensity in each bin. The intensities for each vector were

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normalized prior to calculating H.


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After sorting the values of H for the spectra with the lowest values, the second step

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of the algorithm was to check for the ion pattern typical of the Amadori products, namely

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that the maximum abundance ion had a neutral loss of a water and a loss of HCHO. All of

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the ions in the top 10% of intensities were checked for two corresponding mass peaks at

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the 2+, 3+, or 4+ states for 3H2O and HCHO, with a mass tolerance of 2 Da. Both the

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Shannon entropy and ion pattern based filtering steps are evaluated below.


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Results

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Amadori products identified from 12C:13C glucose treated samples

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The use of stable isotopes has routinely been used in conjunction with mass

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spectrometry for quantitation of metabolites and more recently proteins. In fact, a stable

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isotope dilution strategy has recently been employed to quantitate Amadori compounds in

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foods.20 In addition to aiding with quantitation, mixtures of isotopes can also be used to

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create mass signatures so that post-translational or chemical modifications can be more

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readily detected as was done by Priego-Capote, et al. in efforts to identify AGEs.18 In efforts

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to identify previously uncharacterized Amadori products or AGE modifications,

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recombinant Ara h 1 was treated with a 1:1 mixture of 12C:13C glucose and subjected to

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digestion followed by nanoLC-MS and MS/MS. Modification by glucose and subsequent

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rearrangement to the Amadori product and/or decay to an AGE results in peptides that are

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readily observed as a dyad of ions with one ion arising from the 12C glucose and a sister ion

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arising from the 13C glucose. These dyads are easily recognized when manually

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interrogating individual MS spectra. The mass difference between the pair of ions should

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be 1 Da for each carbon incorporated as had been demonstrated by Stefanowicz et al and

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Priego-Capote, et al .18, 19 Figure 1A shows an MS spectrum of the dyad (m/z 1122.7 and

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m/z 1125.6) where a peptide is modified by a six-carbon product. The ion at m/z 1122.7

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corresponds to the species that contains all 12C and the ion at m/z 1125.6 corresponds to

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the species that contains all 13C. Both species are doubly charged, hence the m/z difference

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of 3 for a six-carbon modification. In this example, the dyad corresponds to the tryptic


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peptide containing residues 452-471 of our construct) of Ara h 1 modified by an Amadori

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product at lysine 460. Figure 1B shows example data characteristic of a low information

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MS/MS spectrum similar to those previously observed in studies of in vitro glycated human

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serum and model peptides.17, 21, 22 A few characteristics to note are the scarcity of fragment

199

ions with those few that are observed being of high abundance and corresponding to the

200

neutral loss of waters and the neutral loss of 3 waters and HCHO from the precursor ion.

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This behavior matches well with pervious observations21 and the scheme suggested by

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Frovol et al. 22 However, looking on an intensity scale a factor of ten larger, Figure 1C

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demonstrates that the MS/MS spectrum does contain low abundance b- and y-ions that can

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be manually interpreted. This suggested that further targeted manual analyses of these

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characteristic spectra may be useful in identifying the modified peptides and the sites of

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Amadori product formation.

207 208

Design of Shannon Entropy Algorithm

209 210

A single nanoLC-MS/MS analysis of in vitro glycated recombinant Ara h 1 yielded

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2929 individual MS/MS spectra. These spectra were triaged using the Data Extractor

212

routine from the Agilent Spectrum Mill MS Proteomics Workbench, resulting in 706 spectra

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meeting the criterion outlined in the methods section. Among this set of 706 MS/MS

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spectra, twelve spectra with data similar to that shown in figure 1A were manually

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confirmed as containing Amadori products. Figure 2 shows a histogram of the calculated

216

Shannon entropy (H), for the test set, using a bin size of 100. Panels B, C, and D show three

217

examples of the normalized and binned MS data for which the H values are 1.6, 5.3, and 7.1


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respectively. Qualitatively, this confirmed that a low value of the Shannon entropy would

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be able to identify the type of spectra containing the typical Amadori product

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fragmentation pattern seen in figure 1A.

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The goal of the search algorithm was to significantly reduce the number of MS/MS

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or MS3 spectra that needed to be manually interrogated in efforts to identify b- and y- ions

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that may lead to peptide identification and localization of the site of the Amadori product

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modification, but at the same time not miss any of these rare modifications. Hence, the

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parameters were optimized to include a high false positive rate. For example, the optimal

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size of the number of bins is a tradeoff between m/z resolution and increased noise. The

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Shannon entropy was calculated for different bin sizes, and the data were assessed for

228

whether or not the known correct twelve spectra, landed in the bottom Y% of the H values.

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Figure 3 shows that at small binning values the resolution of the data is inadequate to use H

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to spot the twelve spectra, while at binning values >100 noise begins to dominate the

231

calculation and H is again ineffective at identifying the correct spectra. A bin size of 50 or

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100 were equivalent in maximally reducing the number of spectra that needed to be

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searched, by 93%. To be conservative in future studies, the top 90% of H values were

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eliminated from further examination using a bin size of 50.

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Next, the peak lists of the bottom 10% of H values (n=71) were examined for spectra

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that contained the characteristic fragmentation pattern in Figure 1A. These characteristics

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were: the maximum abundance ion had a neutral loss of 3 water molecules from a 2+, 3+,

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or 4+ precursor ion and a neutral loss of HCHO. Empirically the neutral loss of 3 waters

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was the most abundant fragment ion. This eliminated 34 more spectra from consideration,

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and retained 37 spectra that still contained the correct 12 spectra. Hence the false positive


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rate was 68%, but the combination of the two filtering procedures reduced the number of

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data sets that needed to be examined by 95%. As an aside if the Shannon entropy filter is

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not used, a filter based only on the loss of water and HCHO correctly retains the twelve

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spectra of interest but eliminates only 37% of the total spectra for manual interpretation.

245 246

Applying the Shannon Entropy and Ion Filter

247 248

The algorithm described above was applied to two additional NanoLC-ESI-MS/MS

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experiments containing 2948 MS/MS spectra and 3029 MS/MS spectra and candidate

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spectra were identified. Figure (amino acids 541-559 K556-Amadori), 4B (amino acids

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560-575 K572-Amadori), and 4C (amino acids 541-559 K547-Amadori and K556-Amadori)

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show example MS/MS data of several ions found to correspond to an Amadori product

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modification of recombinant, in vitro glycated peanut allergen, Ara h 1. These data further

254

suggested that MS3 of ions that arose from neutral loss of water in the MS/MS would be

255

useful for the unambiguous assignment of the candidate ions to tryptic peptides from Ara h

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1. Hence we employed a strategy similar to the one employed by Zhang, et al. where

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neutral loss triggered MS3 was used.17 Figure 5 shows the MS, MS/MS, and MS3 data that

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result from such an experimental approach. The data in panel 5A showing a dyad of ions

259

confirm that an isotopic mixture of the two sugars was used in creating the glycation. Panel

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5B shows the characteristic low information MS/MS and ion fragmentation pattern

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observed when performing MS/MS on many glycated peptides. Finally, figure 5C shows

262

the MS3 with the annotated b- and y- ions that were used to identify the modified peptide

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and glycation product. The sum of these data allowed us to unambiguously identify a


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tryptic peptide spanning the amino acid residues 541-556 with an Amadori product

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modification of K547. Table 1 lists the new Ara h 1 modifications that were identified using

266

some or all of this combined procedure. Thirteen new modifications were identified that

267

had not been previously characterized.


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Discussion

269 270

Using isotopic labeling, MS3, and computational strategies, we were able to

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formulate a workflow that enabled the rapid identification of Amadori product-modified

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peptides. In a test sample containing 2929 total MS/MS spectra, using only computational

273

approaches, we were able to reduce the number of spectra down to 37 for manual

274

interpretation. These 37 spectra contained all 12 spectra of the Amadori product-modified

275

peptides. Two of the modified peptides were observed as both 2+ and 3+ ions, but there

276

was no significant difference in the fragmentation patterns based upon which charge state

277

was selected. It should be pointed out, however, that we cannot make any generalizations

278

with regards to the charge state-dependence of the fragmentation of the Amadori products

279

due to this extremely small sampling. Nevertheless, this combination of approaches

280

facilitated the identification of 13 additional ions corresponding to peptides containing

281

modifications on the peanut allergen, Ara h 1, that we previously had not been able to

282

characterize.

283

The peptides in Table 1 show that some of the same peptides are modified with

284

different AGEs. This is theoretically possible as AGEs can be heterogeneous. It is

285

interesting to speculate that local protein structure and chemistry may have some

286

influence on the type of AGE modifications, but not enough data is available to fully assess

287

this. Some of the peptides in Table 1 were identified in other studies as AGE modified

288

peptides. 10-12 Whether or not these are ‘hot spots’ for AGE modifications is unknown.

289

Because the AGEs are still problematic to identify, it is difficult to conclude that other


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solvent accessible sites are not modified. It is important to not over-interpret the currently

291

small data set of modifications.

292

It is important to note that if glycation is suspected before mass spectrometric

293

analyses, additional techniques such as the neutral-loss directed MS3, multi-stage

294

activation, and electron transfer dissociation would be warranted and could lead to more

295

robust characterization of sites of glycation. The Shannon entropy algorithm detailed here

296

occupies a niche where it is likely best applied as a rescue technique to reanalyze data-

297

dependent-acquisition data sets that have already been collected. In other words, if no

298

modified peptides were found with standard software packages the data can be parsed

299

again with this algorithm to reduce the dataset to a manageable size (nearly a 99%

300

reduction in our test case) for manual MS/MS spectra interpretation.

301

This computational technique is simple and easy to generalize. The utility of the

302

isotopic labeling was only to confirm the initial suspicion that these low information

303

spectra were indeed the spectra that contained the modified peptides. This is not

304

necessary in future applications. Other applications may include the characterization of

305

glycosylated peptides where the MS/MS collisional energy is primarily absorbed by the

306

carbohydrate, which also reduces the number of b- and y- ions for peptide identification.

307

Additional health applications include the identification of glycation markers in patient

308

sera of type 2 diabetes, aging, and cardiovascular disease. 23, 24

309

In the future, the technique can be applied to peanut extract and other foods.25 In a

310

preliminary examination of peanut extracts the method appears applicable, however the

311

variety and mass degeneracy of AGEs is currently confounding further progress. Further

312

purification of the protein of interest may be required. Nevertheless, knowledge of the


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common fragmentation patterns and exact masses may be useful for MS detection of trace

314

amounts of peanut allergen in prepared food. This could improve the accuracy and safety

315

of food labeling as recently proposed.11 Additionally, identifying the sites of modification

316

and the chemical modifications may explain why dry roasting skews the immune response

317

towards allergy. In a study directly applicable to Ara h 1 studied here, AGE-modified Ara h

318

1 was demonstrated to influence the proliferation of Caco-2 cells, which are a model for

319

intestinal epithelia. This influence depended on the incubation time and temperature of

320

Ara h 1 with the sugars, indicating the possibility that specific AGE modifications may be

321

important for influencing the pro-inflammatory network.26 The technique developed here

322

will be applicable in characterizing the modifications that lead to the differential effect.

323 324 325 326

Acknowledgements

327

The authors thank Ms. Andrea Adams for technical assistance and Drs. Peter Thompson

328

and Jeffrey Kuhn for a critical reading of the manuscript.

329 330

Funding Sources

331

This research was supported in part by Research Project Number Z01- ES102885-01 to

332

REL, and Z01-ES102488-05 to JGW in the Intramural Research Program of the National

333

Institute of Environmental Health Sciences, National Institutes of Health.

334 335

The authors affirm that no conflicts of interest exist.


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Table 1 Sequence Numbers

Peptide Sequence

Predicted MW

m/z

Charge State

Observed MW

Delta Mass

Corresponding Modification†

214-228

IVQIEAK*PNTLVLPK

1662

912.7

2

1823.4

161.4

Amadori Product

285-307

VAK*ISMPVNTPGQFEDFFPASSR

2524.2

896.4

3

2686.2

162

Amadori Product

386-401

K*GSEEEGDITNPINLR

1770.9

967.7

2

1933.4

162.5

Amadori Product

402-421

EGEPDLSNNFGK*LFEVKPDK

2262.1

808.4

3

2422.2

160.1

Amadori Product

414-421

LFEVK*PDK

974.5

569.5

2

1137

162.5

Amadori Product

452-471

AMVIVVVNK*GTGNLELVAVR

2081.2

748.6

3

2242.8

161.6

Amadori Product

461-477

GTGNLELVAVRK*EQQQR

1925

661.7

3

1982.1

57.1

CML

461-477

GTGNLELVAVRK*EQQQR

1925

666.5

3

1996.5

71.5

CEL

461-477

GTGNLELVAVRK*EQQQR

1925

696.2

3

2085.6

160.6

Amadori Product

541-556

IFLAGDK*DNVIDQIEK

1817

990

2

1978

161

Amadori Product

541-559

IFLAGDKDNVIDQIEK*QAK

2144.2

769.9

3

2306.7

162.5

Amadori Product

541-559

IFLAGDK*DNVIDQIEK*QAK

2144.2

823.4

3

2467.2

323

2 x Amadori Product

557-575

QAK*DLAFPGSGEQVEKLIK

2057.1

706.2

3

2115.6

58.5

CML

560-575

DLAFPGSGEQVEK*LIK

1729.9

947

2

1892

162.1

Amadori Product

337 338 339

* Site of peptide modification

340

† CML: carboxy-methyl-lysine . CEL: Carboxy-ethyl-lysine. Amadori product: C6H10O5


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Figure Legends

342

Figure 1. Mass spectra of the tryptic peptide spanning amino acids 452-471from glycated

343

recombinant Ara h 1. Panel A shows the mass spectrum of the dyad of ions at m/z 1122.7

344

and m/z 1125.6 that correspond to amino acid residues 452-471 with K460 modified by

345

glycation that arises from the incubation of rAra h 1 with 1:1 12C glucose:13C glucose. Panel

346

B is the low information MS/MS spectrum of ion m/z 1122.7 with an inset showing a zoom

347

in from m/z 1070 to m/z 1120. The neutral losses of 3 waters and 3 waters plus HCHO are

348

characteristic for Amadori product modified peptides. Panel C is the 10X zoom of the

349

MS/MS of ion m/z 1122.7 which shows very low abundance b- and y- series ions.

350 351

Figure 2. Shannon Entropy of Binned MS Spectra. The Shannon entropy H was calculated

352

for the test set of spectra according to equation 1. A) Histogram of H for the test spectra.

353

B), C), and D) are examples of binned MS spectra (a compressed m/z scale) for visual

354

comparison of calculated H values.

355 356

Figure 3. Optimizing search strategy. The number of bins (compressed m/z) is plotted

357

against the bottom percent of H values that the known amadori product spectra occur. The

358

optimum number of bins (between 50 and 100) puts the known spectra among the fewest

359

other false positives, about 7%.

360 361

Figure 4. Example mass spectra of several Amadori product modified tryptic peptides from

362

rAra h 1. Panel A is the MS/MS spectrum of the peptide containing amino acids 541-559

363

with K556 modified by an Amadori product. The inset is a zoom in from m/z 740 to m/z


20

Page 21 of 35


364

770 with ions corresponding to the losses of 1 H2O, 3H20, 4H20 and 3 H2O and HCHO

365

labeled. The sister ions at m/z 743.7, m/z 747.9, and m/z 753.8 correspond to the 13C

366

labeled Amadori product (m/z 771.8) that co-isolated during the MS/MS of 12C-ion: m/z

367

769.9. Panel B is the MS/MS spectrum of the peptide spanning amino acids 560-575 with

368

K572 modified by an Amadori product. The inset is a zoom in from m/z 900 to m/z 945

369

with ions corresponding to the losses of 1 H2O, 2H20, 3H20, 4H20 and 3 H2O and HCHO

370

labeled. Panel C is the MS/MS spectrum of the peptide containing amino acids 541-559

371

with both K547 and K556 modified by Amadori products. The inset is a zoom in from m/z

372

775 to m/z 820 with ions corresponding to multiple water losses and water and HCHO

373

losses labeled. This spectrum is extremely complicated because the fragmentation of two

374

Amadori products and the fact that all the neutral losses exist as ion dyads due to the co-

375

isolation of the 13C labeled Amadori product containing peptide with the natural abundance

376

(99% 12C) Amadori product labeled peptide when performing MS/MS.

377 378

Figure 5. Example of the MS, MS/MS, and MS3 data that result from a neutral loss triggered

379

MS3 experiment performed on rAra h1. The data in panel A again shows a mass spectrum

380

that demonstrates a dyad of ions corresponding to a tryptic peptide that is glycated as a

381

result of the incubation of rAra h 1 with 1:1 12C glucose:13C glucose . Panel B is the MS/MS

382

spectrum of ion m/z 990.5 showing the loss of multiple waters and HCHO with an inset

383

showing a zoom in from m/z 945 to m/z 990. Panel C shows the MS3 of the ion m/z 990.5 >

384

m/z 964.0 with the resulting b- and y- ions annotated that allow for the unambiguous

385

assignment of the Amadori modified tryptic peptide covering amino acid residues 541-559

386

with K547 as the site of modification.


21


387

Page 22 of 35

References

388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430

1. Liu, A. H.; Jaramillo, R.; Sicherer, S. H.; Wood, R. A.; Bock, S. A.; Burks, A. W.; Massing, M.; Cohn, R. D.; Zeldin, D. C., National prevalence and risk factors for food allergy and relationship to asthma: results from the National Health and Nutrition Examination Survey 2005-2006. J Allergy Clin Immunol 2010, 126, 798-806 e13. 2. Mueller, G. A.; Maleki, S. J.; Pedersen, L. C., The Molecular Basis of Peanut Allergy. Curr Allergy Asthm R 2014, 14. 3. Beyer, K. B.; Morrow, E.; Li, X. M.; Bardina, L.; Bannon, G. A.; Burks, A. W.; Sampson, H. A., Effects of cooking methods on peanut allergenicity. J Allergy Clin Immun 2001, 107, 1077-1081. 4. Iwan, M.; Vissers, Y. M.; Fiedorowicz, E.; Kostyra, H.; Kostyra, E.; Savelkoul, H. F. J.; Wichers, H. J., Impact of Maillard Reaction on Immunoreactivity and Allergenicity of the Hazelnut Allergen Cor a 11. J Agr Food Chem 2011, 59, 7163-7171. 5. Ilchmann, A.; Burgdorf, S.; Scheurer, S.; Waibler, Z.; Nagai, R.; Wellner, A.; Yamamoto, Y.; Yamamoto, H.; Henle, T.; Kurts, C.; Kalinke, U.; Vieths, S.; Toda, M., Glycation of a food allergen by the Maillard reaction enhances its T-cell immunogenicity: role of macrophage scavenger receptor class A type I and II. J Allergy Clin Immunol 2010, 125, 175-83 e1-11. 6. Buttari, B.; Profumo, E.; Capozzi, A.; Facchiano, F.; Saso, L.; Sorice, M.; Rigano, R., Advanced glycation end products of human beta(2) glycoprotein I modulate the maturation and function of DCs. Blood 2011, 117, 6152-6161. 7. Hilmenyuk, T.; Bellinghausen, I.; Heydenreich, B.; Ilchmann, A.; Toda, M.; Grabbe, S.; Saloga, J., Effects of glycation of the model food allergen ovalbumin on antigen uptake and presentation by human dendritic cells. Immunology 2010, 129, 437-45. 8. Moghaddam, A. E.; Hillson, W. R.; Noti, M.; Gartlan, K. H.; Johnson, S.; Thomas, B.; Artis, D.; Sattentau, Q. J., Dry roasting enhances peanut-induced allergic sensitization across mucosal and cutaneous routes in mice. J Allergy Clin Immun 2014, 134, 1453-1456. 9. Chung, S. Y.; Champagne, E. T., Association of end-product adducts with increased IgE binding of roasted peanuts. J Agr Food Chem 2001, 49, 3911-3916. 10. Chassaigne, H.; Norgaard, J. V.; van Hengel, A. J., Proteomics-based approach to detect and identify major allergens in processed peanuts by capillary LC-Q-TOF (MS/MS). J Agr Food Chem 2007, 55, 4461-4473. 11. Hebling, C. M.; McFarland, M. A.; Callahan, J. H.; Ross, M. M., Global Proteomic Screening of Protein Allergens and Advanced Glycation Endproducts in Thermally Processed Peanuts. J Agr Food Chem 2012. 12. Mueller, G. A.; Maleki, S. J.; Johnson, K.; Hurlburt, B. K.; Cheng, H.; Ruan, S.; Nesbit, J. B.; Pomes, A.; Edwards, L. L.; Schorzman, A.; Deterding, L. J.; Park, H.; Tomer, K. B.; London, R. E.; Williams, J. G., Indentification of Maillard reaction products on panut allergens that influence binding to the receptor for advanced glycation end products. Allergy 2013. 13. Schmitt, D. A.; Nesbit, J. B.; Hurlburt, B. K.; Cheng, H. P.; Maleki, S. J., Processing Can Alter the Properties of Peanut Extract Preparations. J Agr Food Chem 2010, 58, 1138-1143. 14. Uribarri, J.; Woodruff, S.; Goodman, S.; Cai, W. J.; Chen, X.; Pyzik, R.; Yong, A.; Striker, G. E.; Vlassara, H., Advanced Glycation End Products in Foods and a Practical Guide to Their Reduction in the Diet. J Am Diet Assoc 2010, 110, 911-916.


22

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15. Hodge, J. E., The Amadori Rearrangement. Adv Carbohyd Chem 1955, 10, 169-205. 16. Rabbani, N.; Thornalley, P. J., Glycation research in amino acids: a place to call home. Amino Acids 2012, 42, 1087-96. 17. Zhang, Q.; Petyuk, V. A.; Schepmoes, A. A.; Orton, D. J.; Monroe, M. E.; Yang, F.; Smith, R. D.; Metz, T. O., Analysis of non-enzymatically glycated peptides: neutral-loss-triggered MS3 versus multi-stage activation tandem mass spectrometry. Rapid Commun Mass Sp 2008, 22, 3027-3034. 18. Priego-Capote, F.; Scherl, A.; Muller, M.; Waridel, P.; Lisacek, F.; Sanchez, J. C., Glycation Isotopic Labeling with C-13-Reducing Sugars for Quantitative Analysis of Glycated Proteins in Human Plasma. Mol Cell Proteomics 2010, 9, 579-592. 19. Stefanowicz, P.; Kijewska, M.; Kluczyk, A.; Szewczuk, Z., Detection of glycation sites in proteins by high-resolution mass spectrometry combined with isotopic labeling. Anal Biochem 2010, 400, 237-243. 20. Meitinger, M.; Hartmann, S.; Schieberle, P., Development of Stable Isotope Dilution Assays for the Quantitation of Amadori Compounds in Foods. J Agr Food Chem 2014, 62, 5020-5027. 21. Lapolla, A.; Fedele, D.; Reitano, R.; Arico, N. C.; Seraglia, R.; Traldi, P.; Marotta, E.; Tonani, R., Enzymatic digestion and mass spectrometry in the study of advanced glycation end products/peptides. J Am Soc Mass Spectr 2004, 15, 496-509. 22. Frolov, A.; Hoffmann, P.; Hoffmann, R., Fragmentation behavior of glycated peptides derived from D-glucose, D-fructose and D-ribose in tandem mass spectrometry. J Mass Spectrom 2006, 41, 1459-1469. 23. Beisswenger, P. J., Methylglyoxal in diabetes: link to treatment, glycaemic control and biomarkers of complications. Biochemical Society Transactions 2014, 42, 450-456. 24. Simm, A., Protein glycation during aging and in cardiovascular disease. J Proteomics 2013, 92, 248-259. 25. Yu, T. Y.; Morton, J. D.; Clerens, S.; Dyer, J. M., Proteomic Investigation of Protein Profile Changes and Amino Acid Residue Level Modification in Cooked Lamb Meat: The Effect of Boiling. J Agr Food Chem 2015, 63, 9112-9123. 26. Teodorowicz, M.; Fiedorowicz, E.; Kostyra, H.; Wichers, H.; Kostyra, E., Effect of Maillard reaction on biochemical properties of peanut 7S globulin (Ara h 1) and its interaction with a human colon cancer cell line (Caco-2). Eur J Nutr 2013.


23


Page 24 of 35

1A

Intens. x10 6

1123.1

12C

2.0

Amadori product

1123.6

13C

1122.7

1126.1 1126.6

1125.6

1.5

Amadori product

1.0

1124.2

0.5

0.0

1115.0

1117.5

1120.0

1122.5

1127.1

1125.0

1127.5


1130.0

1132.5

1135.0

m/z

Page 25 of 35


1B

Intens. x105

(M+2H-‐3H2O)2+ (M+2H-‐4H2O)2+

6

Intens. x10 5

1096.2

(M+2H-‐3H2O)2+

6

4

(M+2H-‐3H2O-‐HCHO)2+

(M+2H-‐4H2O)2+

(M+2H-‐H2O)2+

1087.2

1113.6

1080.7 2

4

1071.7

1105.6

0 1070


2

1075

1080

1085

1090

1095

1100

1105

1110

1115

m/z

(M+2H-‐H2O)2+

0 200

400

600

800

1000

1200

1400


1600

1800

2000

m/z


Page 26 of 35

1C b5 b6 b7 C H O 6 10 5

A M V I V V V N K G T G N L E L V A V R y9 y8

y6 y5 y4 y3

10X

Intens. x10 4

y6

6

b5 y3

4

345.3

y4

444.4

2

b6 y5

y9

970.7

557.5 613.6

b7

y8

712.7

0

200

400

600

800

1000

1200

1400


1600

1800

2000

m/z

Page 27 of 35


Figure 2 200

1

A

0.5

Normalized Intensity

Number of Spectra

180 160 140

120 100 80 60 40

0

0

0.8 0.6 0.4 0.2 0

0

0.4 0.3

B C D

10

10

H=1.6 20

30

40

50

60

70

90

100

H=5.3 20

30

40

50

60

70

80

90

100

H=7.1

0.2

20

80

0.1

0

0

1

2

3

4

5

6

H (Shannon Entropy)

7

8

0

0

10


20

30

40

50

60

70

80

90

Bin number (compressed m/z)

100


Figure 3

Known Amadori Products are in bo3om Y% of Shannon Entropy Values

35 30 25 20 15 10 5 0 1

10

100 Number of Bins


1000

Page 28 of 35

Page 29 of 35


4A C6H10O5 I F L A G D K D N V I D Q I E K Q A K

Intens. x10 6

(M+3H-‐3H2O)3+

1.00

0.8


0.75

(M+3H-‐H2O)3+ 763.8

(M+3H-‐4H2O)3+ 746.4

0.25

0.4

753.8

751.9

(M +3H-‐3H2 743.7 O-‐ 3+ HCHO)742.4

0.50

0.6

0.00 740

0.2

(M+3H-‐3H2O)3+

1.25

1.0

0.0

Intens. x10 6

745

750

755

760

765

m/z

(M+3H-‐H2O)3+

400

600

800


1000

1200

m/z


Page 30 of 35

4B C6H10O5 D L A F P G S G E Q V E K L I K x10 6

Intens.

(M+2H-‐H2O)2+

(M+2H-‐3H2O)2+

1.25

Intens. x10 5

1.00

938.1

(M+2H-‐H2O)2+

2

0.50



1

905.6

(M+2H-‐2H2O)2+

(M+2H-‐4H2O)2+

929.6

911.6

0.25

(M+2H-‐3H2O)2+

3

0.75

0.00

920.3

0 905

600

700

800

900

1000

910

1100


915

920

1200

925

930

1300

935

940

1400

m/z

m/z

Page 31 of 35


4C C6H10O5

C6H10O5

I F L A G D K D N V I D Q I E K Q A K 1.5

Intens.

(M+2H-‐4H2O)3+

x10 5

(M+2H-‐3H2O)3+

(M+2H-‐6H2O)3+

Intens. x10 5 2.0

(M+2H-‐2H2O)3+ 1.5

(M+2H-‐3H2O-‐HCOH)3+

1.0

(M+2H-‐H2O)3+

0.5

802.3

(M+2H-‐ 3+ (M+2H-‐ 2H2O) 3H2O)3+ 814.1 807.9

(M+2H-‐ 3H2O-‐ HCOH)3+

(M+2H-‐ H2O)3+ 817.8

796.0

774.6

0.5 769.2

(M+2H-‐ 4H2O)3+

788.2

778.9

1.0

(M+2H-‐5H2O-‐HCOH)3+

0.0

(M+2H-‐ 5H2O-‐ HCOH)3+

(M+2H-‐ 790.1 6H2O)3+

0.0 770

600

650

700

750

800

850


900

775

780

785

950

790

795

800

1000

805

810

815

1050

m/z

m/z


Page 32 of 35

5A

Intens. x10 6

MS Scan

991.0

6

12C

Amadori product

994.0

13C

Amadori product

990.5

4

994.5

2

0 980

985

990

995


1000

1005

1010m/z

Page 33 of 35


5B

Intens. x10 6

964.0

MS/MS (990.5)

(M+2H-3H2O)2+ Intens. x10 6 1.25

0.8

964.0

(M+2H-3H2O)2+

(M+2H-3H2O-HCHO)2+

0.6

1.00

(M+2H-H2O)2+

(M+2H-3H2 949.0

0.75

(M+2H-H2O)2+

(M+2H-4H2O)2+

0.50

0.4

O-HCHO)2+

(M+2H-2H2O)2+

955.0 0.25 958.5

0.2

969.5

0.00 945

950

981.5

972.5

955

960

965

970

975

980

985 m/z

0.0 200

400

600

800

1000

1200

1400


1600

1800

2000

m/z


Page 34 of 35

5C

b2 b3 b4

C6H10O5

I F L A G D K D N V I D Q I E K y8 y7 y6 y5 y4 Intens. x10 6

y6

MS3 (990.5>964.0)

745.5

1.5 833.4

y7

1.0

0.5

y5

b2

632.5

y4

261.3

b3

b4

y8

517.5

910.5

958.5

0.0 300

400

500

600

700


800

900

1000

1100

m/z

Page 35 of 35


TOC graphic % %%

Intens. x10 6

12C

Amadori product

%

1123.1

!=−

! !! !"#! (!(!! ))!

%

!

1123.6

%

2.0

13C

%

1122.7

%

Amadori product

%

1126.1 1126.6

%

1125.6

1.5

m/z%

%

%

1.0

%

1124.2

%

0.5

%

0.0

%

1115.0

%

1117.5

%

1120.0

%

1122.5

%

1127.1

%

1125.0

%

1127.5


%

1130.0

%

1132.5

%

1135.0

m/z

%

Enhanced Approaches for Identifying Amadori ... - ACS Publications

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