Unrestricted Mass Spectrometric Data Analysis for Identification

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Unrestricted mass spectrometric data analysis for identification, localization and quantification of oxidative protein modifications Martin Rykær, Birte Svensson, Michael J. Davies, and Per Hägglund J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.7b00330 • Publication Date (Web): 18 Sep 2017 Downloaded from http://pubs.acs.org on September 20, 2017

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Journal of Proteome Research

Unrestricted mass spectrometric data analysis for identification, localization and quantification of oxidative protein modifications

Martin Rykær1, Birte Svensson1, Michael J. Davies2, Per Hägglund1* 1

Department of Biotechnology and Biomedicine, Technical University of Denmark, Søltofts Plads,

Building 221, DK 2800 Kgs. Lyngby, Denmark 2

Department of Biomedical Sciences, Panum Institute, University of Copenhagen, Blegdamsvej 3,

DK 2200 Copenhagen, Denmark *

Correspondence should be addressed to Per Hägglund ([email protected]; +46 708

978799) Abbreviations: 2,4-dinitrophenylhydrazine (DNPH), Mass Spectrometry (MS), Metal ionCatalyzed Oxidation (MCO), Peptide Spectrum Match (PSM), Post Translational Modification (PTM), Reactive Oxygen Species (ROS)

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Abstract Oxidation generates multiple diverse post-translational modifications resulting in changes in protein structure and function, associated with a wide range of diseases. Of these modifications, carbonylations have often been used as hallmarks of oxidative damage. However, accumulating evidence supports the hypothesis that other oxidation products may be quantitatively more important under physiological conditions. To address this issue we have developed a holistic mass spectrometry-based approach for simultaneous identification, localization and quantification of a broad range of oxidative modifications based on so-called ‘dependent peptides’. The strategy involves unrestricted database searches with rigorous filtering focusing on oxidative modifications. The approach was applied to bovine serum albumin and human serum proteins subjected to metal ion-catalyzed oxidation resulting in identification of more than sixty different types of oxidative modifications. The most common modification in the oxidized samples is hydroxylation, but carbonylation, decarboxylation and dihydroxylation, are also abundant. Carbonylation however showed the largest increase in abundance relative to non-oxidized control samples. Site specific localization of modified residues reveals several ‘oxidation hotspots’ showing high levels of modification occupancy, including specific histidine, tryptophan, methionine, glutamate and aspartate residues, even though the majority of the modifications occur at low occupancy levels on a diversity of side-chains.

Keywords: carbonylation; dependent peptides; LC-MS/MS; mass spectrometry; metal ioncatalyzed oxidation; post-translational modifications; protein oxidation; reactive oxygen species


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Introduction Cells continuously generate reactive oxidants, both radicals and spin-paired, commonly called reactive oxygen species (ROS), as by-products of aerobic metabolism and in response to various external and internal stimuli, e.g. inflammation induced by invading pathogens.1 Many of these oxidants are highly reactive and known to damage cellular components, with proteins being a primary target because of their high rate constants for reaction with oxidants and high abundance.2,3 Reaction of proteins with radicals such as hydroxyl (HO.) and peroxyl (ROO.), as well as spin paired oxidants (e.g. 1O2, H2O2, HOCl, ONOOH) results in a broad spectrum of changes including modification of protein side-chains, cleavage of the peptide backbone and formation of crosslinkages. These chemical modifications can cause altered physical (e.g. in mass, charge, structure, folding state) and functional properties (e.g. loss of enzyme activity or protein drug efficacy, interactions with receptors/binding partners) as well as the biological half-life of proteins.2,3 There is considerable evidence to support the hypothesis that oxidative protein modifications accumulate in mammalian tissues and are associated with deleterious effects on cell function and metabolism. Multiple studies have also reported protein oxidation to be important in the etiology of numerous human pathologies (e.g. Alzheimer’s and Parkinson’s diseases1,2,4). Consequently there is extensive interest in quantifying oxidative protein modifications and determining the nature, location, and consequences of these reactions. The nature of the oxidative modifications formed on protein side-chains is dependent on the character and reactivity of the various attacking ROS. For some oxidants (e.g. HO.) evidence has been reported for modification of many different side-chains yielding an array of products including aldehydes and ketones (generically named “protein carbonyls”), alcohols, hydroperoxides, hydroxylated aromatics, cross-linked species (e.g. dityrosine), oxy acids (from thiols, such as cysteine), disulfides (from cysteine), sulfoxides (of methionine) and multiple other reaction products.2,3 By contrast, less reactive oxidants (e.g. ROO.) yield a more limited range of products, extensive damage occurring for a smaller number of susceptible residues, e.g. cysteine, methionine, tryptophan, tyrosine and histidine.5 Quantification of protein carbonyls is commonly applied to assess the extent of irreversible protein oxidation, and represents an important marker of protein damage related to oxidative stress, disease and ageing.6–10 A widely-studied source of such carbonyls is metal ion-catalysed oxidation (MCO). The intermediates formed during MCO have not been unequivocally identified, but the reaction is 3 ACS Paragon Plus Environment


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thought to involve Fenton-chemistry where a ferrous ion (Fe2+) coordinated to the protein reacts with H2O2 and generates HO. in situ, in turn reacting with nearby amino acid residues.11 Side-chain modification is believed to be a major reaction pathway and many of these carbonyls appear to arise from modification of proline, arginine, lysine, and threonine.12 Carbonyls can also be generated on proteins either via secondary reactions of sugar and lipid-derived species with arginine, lysine, cysteine, and histidine residues,13 backbone fragmentation reactions,14,15 or fragmentation reactions of protein side-chains releasing low-molecular-mass fragments.16 The quantitative significance of each of these different processes to the overall carbonyl yield, and the significance of protein carbonyls compared to other modifications in determining the overall effects of oxidants on proteins has not yet been determined. Recent studies indicated that many more types of protein carbonyls can be generated than initially suggested.6,12 Moreover, other oxidation products, may be quantitatively more important at the low oxidant fluxes likely to be present in vivo.17 These various uncertainties motivate the introduction of novel approaches to determine the nature, location and quantification of oxidative modifications on proteins. Protein oxidation is often estimated by carbonyl quantification10 based on nucleophilic substitution by reagents such as 2,4-dinitrophenylhydrazine (DNPH). The resulting hydrazones are detected spectrophotometrically, or by use of antibodies (e.g. the Oxy-blot method) or mass spectrometry. However, the specificity of these approaches has been questioned,18 and non-carbonyl oxidation products are not detected. Thus erroneous conclusions may be arrived at with regard to the quantitative importance of the generated carbonyls. In LC-MS carbonyl derivatization by biotin hydrazide and Girard’s reagent have been used combined with protein and peptide-based enrichment using avidin or ion exchange chromatography.6,8,19 An advantage of these techniques is that the position of modifications can be determined. However this approach is limited by the level of carbonyl reactivity of the biotin derivative and relative quantification of the degree of oxidation of specific position(s) is not feasible because non-modified proteins or peptides are removed in the enrichment process. Although protein oxidation is gaining increasing attention within the proteomics field comprehensive analysis of the types of modifications and their locations suffers by challenges in the data analysis. Most mass spectrometry database search workflows involving PTMs are limited to a single, or a few modifications of one or a few selected amino acids. This strategy is justified if only specific modifications are targeted but a potential drawback is that information about PTMs not


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included in the database search is lost and the corresponding modified peptides may be falsely identified as different peptides with no modification.20 Moreover, the database search space is expanded exponentially with the number of included variable modifications, making analysis time as well as the number of false positive identifications increase dramatically. Finally, missassignment to the wrong amino acid residue can occur when only certain types of variable modifications are included. For instance, a mass shift of 15.99 Da may be assigned to hydroxylysine if this is the only variable modification specified in a database search even though an associated tandem mass spectrum clearly demonstrates oxidation of another type of amino acid. Different algorithms have been developed to tackle issues associated with data analyses involving multiple modifications.21–24 For example, Savitski et al22,25 introduced the concept of ”dependent peptides” which are modified peptides identified in an unrestricted database search against a library of previously identified non-modified “base peptides”. This process reduces the extreme search space expansion that normally would accompany a database search with many targeted variable modifications. In contrast to original program where “dependent peptides” were defined by simple peak correlation between modified and non-modified peptides, the feature is now integrated into MaxQuant26,27 which uses statistical analysis for validation of modified peptides.28 We here utilize the concept of “dependent peptides” for unbiased, comprehensive analysis of oxidative modifications in proteins exposed to a model MCO system containing high levels of iron and ascorbic acid, as described by Levine et al.29 Using a simple setup without enrichment or chemical derivatization we successfully identified, localized and quantified a wide range of oxidative modifications both in a model protein and in complex samples.

Experimental procedures Sample preparation and proteolytic digestion MCO was performed according to Maisonneuve et al30 with slight modifications. Briefly, 4 technical replicates of either BSA or serum from a 43 year old healthy caucasian male (0.5 mg/mL protein in 50 mM Hepes pH 7.4, 10 mM KCl, 10 mM MgCl2) was incubated with 100 µM FeCl3.6 H2O and 25 mM ascorbic acid in a final volume of 100 µL. Samples were oxidized at 37 °C for 0, 0.5 h or 12 h and the reaction was terminated by addition of 4 µL 25 mM EDTA, followed by buffer exchange to 50 mM ammonium bicarbonate (ABC), 8 M urea pH 7.8 by repeatedly (4x) 5 ACS Paragon Plus Environment


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spinning 500 µL down to ~50 µL (Amicon 3K spin filters; Millipore). Following addition of 1 µL 0.25 M DTT samples were incubated 30 min, added 2 µL 0.25 M iodoacetamide and incubated 30 min in the dark. The reaction mixture was diluted 1:10 with 50 mM ABC pH 7.8, added Endoproteinase Glu_C (Roche) to a 1:50 Glu_C:protein ratio, digested at 29°C for 16 h followed by acidification with 0.5% TFA (final concentration), and subjected to solid phase extraction on C18 stage tips as previously described.31 LC-MS Proteolytic digests (1 µg) were analyzed on a Q-Exactive Orbitrap (Thermo Fisher Scientific) coupled to an EASY-nLC 1000 liquid chromatograph (Thermo Fischer Scientific). Peptides were loaded onto custom-made packed nanoLC columns (C18, 100Å, 1.9 µm particle size; Dr. Maisch GmbH; 75 µm ID, 15 cm Picofrit emitter; New Objectives) and eluted by a 70 min gradient at a flow rate of 250 nL/min. MS spectra were acquired with resolution of 70,000, automatic gain control (AGC) value of 1e6, maximum injection time (IT) of 20 ms and scan range from 350 to 1700 m/z. MS/MS spectra were acquired in data-dependent mode (Top 10 method) with resolution of 35,000, AGC value of 6e3, and maximum IT of 60 ms. Data analysis and statistical evaluation Raw files were converted to .mgf files and database searches were performed against a fasta file containing the Human or Bovine proteome (Uniprot 02-05-2013) with MaxQuant v 1.5.126,27 using the following parameters; enzyme: Glu_C (D/E), 3 missed cleavages; fixed modification: carbamidomethyl (cysteine); variable modifications: oxidation (methionine) and acetylation (Nterminus), 1% peptide level FDR, MS mass tolerance: 20 and 4.5 ppm (first and main search, respectively), MS/MS mass tolerance: 20 ppm (first and main serach), “dependent peptide” bin size: 0.0065 Da. Raw data and MaxQuant output files are deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD006582. The output data (allPeptides.txt and peptides.txt) was filtered using an in house VBA/Python script that gathers the relevant data, and correlates mass shifts between “dependent peptides” and “base peptides” to a list of 91 known oxidative modifications described in the literature6,8,32–36 (Table 1). If the mass of the experimental “dependent peptide” matches the corresponding “base peptide” with a mass shift for a known oxidative modification within +/− 10 ppm, and the mass shift is assigned to an amino acid known to host oxidative modifications with this mass shift (Table 1) at > 30% positional


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probability, the “dependent peptide” is accepted. The modification occupancy at a particular primary structure position was calculated using an in-house script by dividing the sum of signal intensities (area-under-the-curve) of all PSMs containing the modified position with the sum of the intensity of all PSMs containing the position (non-modified and modified). An example of a quantification output file is displayed in Table S1. The relative abundance of a particular mass shift correlated to a particular type of amino acid was calculated as an average of the particular mass shift occupancy for all the relevant primary structure positions and normalized to 100%. Only peptides identified in at least 3 technical replicates were accepted.

Results and discussion BSA as a model BSA exposed to MCO was used as a model system to develop a mass spectrometric approach for analysis of oxidative modifications. Briefly, BSA oxidized with a combination of FeCl3 and ascorbic acid for 0 h (control), 0.5 h and 12 h was digested by Glu C followed by mass spectrometric analysis. Glu C was used instead of trypsin due to the expected high prevalence of modifications on arginine and lysine. The data was processed for peptide identification using the “dependent peptide” feature in MaxQuant yielding sequence coverages of 85−94% (Figure S1). The analysis yielded a similar number of PSMs for non-modified “base peptides” in all samples, while the corresponding number for identified modified “dependent peptides” increased with reaction time in oxidized samples (Table S2). The mass shifts in the “dependent peptides” span from −800 to +2000 Da with the majority of PSMs (52% for 12 h oxidized samples) displaying mass shifts within the (−80) − (+80) Da range (Figure 1A). Comparison of oxidized and nonoxidized control samples reveals an increase in the abundance of PSMs corresponding to peptides with known oxidative modifications (Figure 1A-C). The complexity of the data was reduced by filtering and focusing on mass shifts associated with known oxidative modifications of particular amino acids (Table 1), facilitating more detailed analysis (Figure 1D). For example, mass shifts for modifications

introduced

in

the

sample

preparation

process37,38

such

as

unspecific

carbamidomethylation (+57.02 Da) and carbamylation (+43.00 Da) are removed (Figure 1B,C). Even though the filtration step reduces the number of peptides dramatically, there are however still



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4−5 times more PSMs for “dependent peptides” than for “base peptides” in oxidized samples (Table S2). Altogether 67 different types of oxidative modifications were identified in BSA (peptides listed in Table S3). A significant fraction (15.4%) of the filtered PSMs from samples exposed to MCO for 12 h displayed mass shifts of +13.98 Da (+O −2H) predominantly corresponding to carbonyl containing modifications, while 46 and 10% of the PSMs showed mass shifts of +15.99 Da (+O) / +31.99 Da (+2O) which are mainly associated with mono- and dihydroxyl formation, respectively, on either aliphatic or aromatic side chains (Figure 1C). Another abundant mass shift (7% of the filtered PSMs) is +58.00 Da (+2H+2C+2O) corresponding to carboxymethylation of lysine (Table 1). Mass shifts related to

previously reported MCO markers of −1.03 Da (lysine-derived

aminoadipic semialdehyde), −2.01 Da (threonine-derived 2-amino-3-ketobutyric acid), and −43.05 Da (arginine-derived glutamic semialdehyde) are however surprisingly underrepresented.12 The number of PSMs displaying mass shifts of +13.98, +15.99, +31.99 and +58.00 Da increased as a function of MCO exposure time, thus increasing the confidence that these spectra are indeed associated with oxidation (Figure 2). In particular the number of PSMs with +13.98 Da mass shifts increase drastically upon MCO (from 4.1% to 15.4%), consistent with the use of carbonyl groups as markers of oxidative damage. In addition, MCO results in an increase in spectra with mass shifts of −30.01 Da (−2H, −1C, −1O; decarboxylation of glutamate or aspartate with formation of the corresponding carbonyl), −22.0 Da (−2H, −2C, −2N, +2O) and −23.0 Da (−H, −2C, −1N, +O), the latter two corresponding to oxidative conversion of histidine to aspartic acid and asparagine, respectively. The mass spectra displaying +13.98 and +15.99 Da mass shifts were further analyzed to evaluate the abundance of these modifications on different types of amino acid residues based on the relative signal intensities as outlined in the Experimental procedures. As shown in Figure 3A the highest abundance of +13.98 Da mass shifts was associated with the aliphatic residues valine and leucine. However, despite the large extent of modification of these residues, tryptophan was the most prominent target with regard to the formation of species with a +13.98 Da mass shift when the data was normalized to the amino acid frequency in BSA (Figure 3B). These tryptophan-derived modifications are likely to be indole ring-derived 2-oxoindoles (oxolactones) and are associated with a high extent of modification (high modification-occupancy) of the relatively small number of tryptophan residues present in BSA, whereas corresponding mass shifts detected for leucine, valine,


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isoleucine, and alanine occur with low extent of modification (low modification-occupancy) in the comparatively larger number of these aliphatic side-chains in BSA. In comparison, the +15.99 Da mass shifts were detected in a wider range of amino acids (Figure 4). Methionine and tryptophan are major targets, but a significant level of histidine oxidation (2-oxo histidine formation) was also observed. It should however be emphasized that methionine oxidation is readily induced in vitro during sample handling, and it is therefore difficult to make definite conclusions regarding the role of this modification in relation to MCO. Carbonylation of proline, yielding γ-glutamic semialdehyde via a ring opening reaction, and lysine to α-amino adipic semialdehyde, also result in +15.99 Da mass shifts.39,40 These products, however, were not among the most abundant targets associated with this mass shift (Figure 4). The occupancy of oxidative modifications was compared for different positions in the primary structure of BSA (Figure S2). In general, the distribution of modifications is uneven, showing a few residues with high modification occupancy and a large number of sites with low occupancy. The most highly modified residue after 0.5 h MCO is D278 that stands out as an “oxidation hotspot” with an abundant −30.01 Da mass shift consistent with side-chain decarboxylation. Thus this position seems to be particularly prone to modification by the MCO system possibly due to its proximity to a metal ion binding site or a particularly high reactivity of this residue. H33 also displayed a high overall occupancy of oxidative modifications with spectral mass shifts of +15.99 Da (corresponding to 2-oxo histidine), −22.0 Da (H→D) and −23.0 Da (H→N) as displayed in Figure S3. Notably the oxidation prone H33 and D278 are located in close proximity in a pocket on the surface of BSA (Figure 5). As both of these residues are potential ligands for metal ions such as iron, these data are consistent with metal ion association, and subsequent oxidant formation within this pocket on the surface of BSA. Moreover, these and other “oxidation hotspots” contain one or a few highly modified residues surrounded by low occupancy positions, indicating either i) that the oxidative event(s) generate radicals that react in a highly specific manner in close proximity to the metal ion with a rapidly decreasing extent of damage with increasing distance from this site, or ii) the occurrence of cascade reactions, e.g. involving peroxyl radicals and peroxides; cf data reviewed in2, arising from the initially-damaged protein side-chain, with damage therefore localized to the local area. These processes may explain why some hydrophobic and partially buried side chains are modified. It is also feasible that the protein is partially denatured or unfolded after prolonged oxidation. While the overall extent of oxidation increased with time of exposure to the MCO system, certain residues such as D278 showed a lower extent of modification at 12 h than at 0.5 h. It 9 ACS Paragon Plus Environment


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is plausible that further oxidation or backbone cleavage occurs at such sites on prolonged exposure to the MCO system, giving products that are not detected, thus lowering the apparent modification occupancy in the 12 h oxidized samples.

MCO of human serum In order to study the abundance of oxidative modifications in a more complex system, human serum was subjected to MCO, followed by GluC digestion, mass spectrometric analysis and data processing with the “dependent peptides” feature in MaxQuant (Table S4). Altogether 65 different types of oxidative modifications in 18 serum proteins were identified (Table S5). The overall distribution of mass shifts associated with oxidative modifications in the identified “dependent peptides” from human serum proteins exposed to MCO for 12 h was similar to the corresponding profile of BSA, with 11% of identified PSMs corresponding to carbonyl containing modifications and 49, 12 and 7% of PSMs stemming from 15.99 Da, 31.98, and 58.00 Da mass shifts respectively (Figure 6). A majority of the modified peptides from serum proteins were derived from human serum albumin (HSA) and a more detailed analysis focused on this protein which showed 90−97% sequence coverage (Figure S4). Altogether, 164 and 145 modified residues were reproducibly identified in BSA and HSA, respectively. A high degree of overlap was observed for high occupancy modification sites in these protein which show 76.1% sequence identity. Also the overall trend of high occupancy amino acids surrounded by low-occupancy amino acids persisted in the primary structure of HSA (Figure S5). Similarly to BSA, decarboxylation of an acidic amino acid was the most abundant modification observed at an individual position after 0.5 h MCO, namely E268 in HSA, comparable to D278 in BSA. Both E268 and D278 are close to the conserved, and highly modified, histidine residue (H33) present in both proteins (Figure 5). The distance between α carbons of H33 and D278 (BSA) and H33 and E268 (HSA) in the crystal structure of the proteins, is 9.4 Å and 16.3 Å respectively. Thus a similar rationale for the high degree of modification of these residues can be postulated for HSA, as proposed for BSA (see above).


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Conclusion and perspectives To date many proteomics studies on oxidative modifications have focused on analysis of carbonyl groups targeted by detection and/or enrichment at the protein or peptide level. However, a major limitation with this type of workflow is that non-carbonyl oxidation products are overlooked and moreover it is in general not feasible to quantify the occupancy of the sites of the carbonyl groups because they are often subject to chemical derivatization and enrichment. It is demonstrated in the present study that unrestricted mass spectrometry data analysis based on the “dependent peptides” concept enables reproducible in-depth characterization, localization and quantification of a variety of different oxidative modifications in a single protein model (BSA) as well as in a highly abundant protein in a complex sample, represented by HSA in human serum. Such in-depth analysis based on data-dependent acquisition, however, is very challenging for low abundant proteins in complex samples due to limitations in dynamic range which results in a bias in the analysis towards highly abundant modifications. In these cases pre-fractionation or targeted enrichment of protein(s) of interest is recommended. As an alternative an isolated protein could be exposed to e.g. MCO to boost abundance of modified peptides and generate a library for targeted analysis of complex samples. It must be emphasized that the MCO system used here does not represent physiologically relevant conditions. In order to mimic oxidative challenges in a biological system shorter time intervals and/or lower concentrations of reactants should be applied. It should also be mentioned that information about certain labile oxidative modifications may be lost during sample handling. For example most reversible cysteine modifications are removed by chemical reduction and alkylation prior to proteolytic digestion. In addition to allowing unrestricted PTM analysis a significant advantage of the concept is that the number of false positive peptide identifications is reduced.22,25,41 However, there are two important limitations with the “dependent peptide” approach to point out. Firstly, only sub-stoichiometric modifications are detected due to the requirement of a non-modified “base peptide”. Secondly, only singly modified peptides are considered when “dependent peptides” are matched to “base peptides”. Thus peptides carrying two or more modified residues are either discarded or missannotated as a peptide with a modification mass equivalent to the sum of the multiple modifications. However, by focusing the analysis on a set of known oxidative modifications the risk of missassigning multiply modified peptides is minimized, although it cannot, for example, be ruled out that some +31.99 Da mass shifts assigned as di-oxidation of a single residue actually represent



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mono-oxidation of two proximal residues. Further development of algorithms for non-restricted analysis of mass spectrometric data will however probably improve the specific and sensitivity for site-specific assignments of protein oxidation products and other PTMs.

Acknowledgement The authors would like to thank Anne Blicher and Lene Holberg Blicher for their technical assistance with LC-MS/MS analysis. Kim Henriksen (Nordic Bioscience A/S) is acknowledged for providing human serum samples. The Q-Exactive Orbitrap mass spectrometers used in this study were granted by the Danish Council for Independent Research | Natural Sciences (grant number 11106246) and the Velux Foundation. The Danish Research Foundation, Nordic Bioscience A/S and Technical University of Denmark are acknowledged for a joint PhD scholarship to MR. MJD is supported by a grant from the Novo Nordisk Foundation (grant number NNF13OC0004294).

Supporting Information Table S1. Example of quantification output from in-house script showing modification occupancy of primary structure positions in BSA exposed to 12 h MCO (average of 4 technical replicates). Table S2. Number of peptide spectrum matches in four replicates of MCO oxidized and control BSA samples. Table S3. List of modified peptides identified from 12 h MCO treated BSA samples. Table S4. Number of peptide spectrum matches in four replicates of MCO oxidized and control serum samples. Table S5. List of modified peptides identified from 12 h MCO treated serum samples. Figure S1. Sequence coverage in BSA. Figure S2. Primary structure modification occupancy (% modified) in BSA exposed to MCO. Figure S3. Spectra of peptides with unmodified (A) and modified (B-D) versions of H33 in BSA.


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Figure S4. Sequence coverage in HSA. Figure S5. Primary structure modification occupancy (% modified) for HSA exposed to MCO.



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Table 1. List of oxidative modifications with associated compositions and mass shifts. Amino acid Modification/product

Composition

Mass (Da)

Reference

R C M D E P H H C H S T K W H A E I L P Q R S V W A C D E F H I K L M N P P R S T V W Y P W W C

−5H−1C−3N+1O −2H−1S −4H−1C−1S+1O −2H−1C−1O −2H−1C−1O −2H−1C−1O −1H−2C−1N+1O −2H−2C−2N+2O −1S+1O −2H−1C−2N+2O -2H −2H −3H−1N+1O −1C+1O −1H−1C−1N+2O -2H + O1 -2H + O1 -2H + O1 -2H + O1 −2H+1O -2H + O1 -2H + O1 -2H + O1 -2H + O1 −2H+1O +1O +1O +1O +1O +1O +1O +1O +1O +1O +1O +1O +1O +1O +1O +1O +1O +1O +1O +1O +1O+2H −1C+2O −4H+2O −1H+1N+1O

-43.05 -33.99 -32.01 -30.01 -30.01 -30.01 -23.02 -22.03 -15.98 -10.03 -2.02 -2.02 -1.03 3.99 4.98 13.98 13.98 13.98 13.98 13.98 13.98 13.98 13.98 13.98 13.98 15.99 15.99 15.99 15.99 15.99 15.99 15.99 15.99 15.99 15.99 15.99 15.99 15.99 15.99 15.99 15.99 15.99 15.99 15.99 18.01 19.99 27.96 28.99

Bachi et al. # Bachi et al. Bachi et al. # # Bachi et al. Bachi et al. # Bachi et al. # # Bachi et al. Bachi et al. # # Bachi et al. # Bachi et al. Bollineni et al. § # Bachi et al. Bachi et al. # Bachi et al. # # Bachi et al. Morgan et al. ¤ * Havelund et al. * Havelund et al. Havelund et al. * # Bachi et al. Havelund et al. * Bollineni et al. § % Van Buskirk et al. * Havelund et al. # Bachi et al. # Bachi et al. Bachi et al. # # Bachi et al. Bachi et al. # Bachi et al. # # Bachi et al. Bachi et al. # # Bachi et al. # Bachi et al. Bachi et al. # # Bachi et al. Bachi et al. # Bachi et al. # # Bachi et al. Bachi et al. # # Bachi et al. # Bachi et al. Bachi et al. # # Bachi et al. Morgan et al. ¤ Bachi et al. # # Bachi et al. Bachi et al. #

Glutamic semialdehyde Dehydroalanine Aspartate semialdehyde Decarboxylation Decarboxylation Pyrrolidinone Asparagine Aspartic acid Serine Aspartylurea 2-amino-3-ketopropionic acid 2-amino-3-ketobutyric acid Aminoadipic semialdehyde Kynurenine Formylaspargine Alanine carbonyl 4-oxo glutamatic acid Isoleucine carbonyl Leucine carbonyl Pyroglutamic acid 4-oxo glutamine Arginine carbonyl Aminomalonic acid Valine carbonyl Oxolactone Serine Sulfenic acid 3-hydroxyaspartic acid Hydroxyglutamic acid Hydroxyphenylalanine (tyrosine) 2-oxohistidine 4-hydroxyisoleucine or other isomers Hydroxylysine 3-hydroxyleucine or other isomers Methionine sulfoxide 3-hydroxyasparagine Glutamic semialdehyde Hydroxyproline Hydroxyarginine Hydroxyserine Hydroxythreonine Hydroxyvaline 2-, 4-, 5-, 6-, and 7-hydroxytryptophan Dihydroxyphenylalanine (DOPA) 5-hydroxy-2-aminovaleric acid derivative Hydroxykynurenine β-unsaturated-2,4-bis-tryptophandione S-nitrosylcysteine


#

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


W W E F I K L M P P R V W W Y Y W Y M R R W F W Y C W F H,K R K W Y K Y R K K R R,K C,K,H Y C,K,H

Tryptophandione Dihydrodioxoindole Glutamate hydroperoxide Dihydroxyphenylalanine (DOPA) Isoleucine hydroperoxide Lysine hydroperoxide Leucine hydroperoxide Methionine sulfone Glutamic acid Proline hydroperoxide Arginine hydroperoxide Valine hydroperoxide N-formylkynurenine Dioxindolylalanine Trihydroxyphenylalanine (TOPA) Tyrosine hydroperoxide Tryptophan chlorination 3-chlorotyrosine Homocysteic acid Glyoxal-derived imidazolone Glyoxal-derived hydroimidazolone Hydroxy-bis-tryptophandione 2- or 4-nitrophenylalanine 6-nitrotryptophan and other isomers 3-nitrotyrosine Sulfonic acid Hydroxy-N-formylkynurenine Trihydroxyphenylalanine (TOPA) Acrolein-Michael adduct Malondialdehyde-derived N-propenalarginine Glyoxal-derived carboxymethyllysine Dihydroxy-N-formylkynurenine 3,5-dichlorotyrosine Methylglyoxal-derived carboxyethyllysine 3-bromotyrosine Methylglyoxal-derived argpyrimidine Acrolein derived FDP-lysine Crotonaldehyde-derived dimethyl-FDP-lysine 3-deoxyglucosone-derived imidazolone A Methylglyoxal-derived tetrahydropyrimidine ONE-Michael adduct 3,5-dibromotyrosine HNE-Michael adduct

−2H+2O −2H+2O +2O +2O +2O +2O +2O +2O +2O +2O +2O +2O +2O +2O +2O +2O −1H+1Cl −1H+1Cl −2H−1C+3O +2C+1O +2H+2C+1O −4H+3O −1H+1N+2O −1H+1N+2O −1H+1N+2O +3O +3O +3O +4H+3C+1O +4H+3C+1O +2H+2C+2O +4O −2H+2Cl +3H+3C+2O −1H+1Br +4H+6C+1O +6H+6C+1O +10H+8C+1O +8H+6C+4O +6H+7C+4O +11H+9C+2O −2H+2Br +11H+9C+2O

#

29.97 29.97 31.99 31.99 31.99 31.99 31.99 31.99 31.99 31.99 31.99 31.99 31.99 31.99 31.99 31.99 33.96 33.96 33.97 39.99 42.01 43.95 44.99 44.99 44.99 47.98 47.98 47.98 56.03 56.03 58.01 63.98 67.92 71.01 77.91 92.03 94.04 122.07 144.04 154.03 154.10 155.82 156.22

Bachi et al. # # Bachi et al. & Gebicki et al. # Bachi et al. & Gebicki et al. Gebicki et al. & Gebicki et al. & # Bachi et al. Bachi et al. # & Gebicki et al. & Gebicki et al. Morgan et al. ¤ # Bachi et al. Bachi et al. # Bachi et al. # ¤ Morgan et al. £ Fu et al. # Bachi et al. # Bachi et al. Bachi et al. # # Bachi et al. Bachi et al. # Bachi et al. # # Bachi et al. Bachi et al. # # Bachi et al. # Bachi et al. Bachi et al. # # Bachi et al. Bachi et al. # Bachi et al. # # Bachi et al. Bachi et al. # # Bachi et al. # Bachi et al. Bachi et al. # # Bachi et al. Bachi et al. # Bachi et al. # # Bachi et al. Bachi et al. # # Bachi et al. # Bachi et al.

Bachi, A.; Dalle-Donne, I.; Scaloni, A. Chem. Rev. 2013, 113 (1), 596–698. Bollineni, R. C.; Hoffmann, R.; Federova, M. Free Radic Biol Med. 2014 68, 186-195. ¤ Morgan P. E.; Pattison D. I.; Davies M. J. Free Radic Biol Med. 2012, 52(2), 328-339. * Havelund, J. F.; Wojdyla, K.; Davies, M. J.; Jensen, O. N.; Møller, I. M.; Rogowska-Wrzesinska, A. J. Proteomics 2017, 156, 40–51. % Van Buskirk, J. J.; Kirsch, W. M., Kleyer, D. L., Barkley, R. M., Koch, T. H. Proc Natl Acad Sci U S A 1984, 81(3), 722-725. & Gebicki S, Gebicki JM. Biochem J. 1993 F289 ( Pt 3), 743-749. £ Fu, X.; Wang, Y.; Kao, J.; Irwin, A.; d'Avignon, A.; Mecham, R. P.; Parks, W. C.; Heinecke, J. W. Biochemistry 2006, 45(12), 3961-3971. §



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Figure legends Figure 1. Number of identified “dependent peptides” PSMs with different mass shifts for BSA samples exposed to MCO for 12 h as described in Experimental procedures. Unfiltered data (insert: mass shifts in a wider span from −800 to +2400 Da) for samples exposed to MCO for 12 h (A) and an untreated control (B). C: Values for untreated control subtracted from 12 h MCO. D: Filtered data only including PSMs with mass shifts corresponding known oxidative modifications with matching amino acid targets (Table 1). All presented values of PSM counts are averages based on 4 technical replicates. Standard deviations were generally below 20%, and for the more abundant mass shifts (+13.98, +15.99, +31.99 and +58.0 Da) below 5%. Figure 2. Number of PSMs displaying mass shifts associated with oxidative modifications in BSA. Green, red and blue bars represent samples exposed to MCO for 0 h (control), 0.5 h and 12 h, respectively. All presented values of PSMs are averages based on 4 technical replicates (standard deviations are indicated by error bars). Figure 3. Relative abundance of 13.98 Da mass shifts in 12 h MCO samples correlated to different amino acid targets. A: The abundance of mass shifts calculated based on relative intensities as described in Experimental procedures and normalized to 100%. B: Same as in A but normalized to the frequency of the different amino acids in the BSA sequence. All presented values are averages based on 4 technical replicates. Figure 4. Relative abundance of 15.99 Da mass shifts in 12 h MCO samples correlated to different amino acid targets. A: The abundance of mass shifts were calculated based on relative signal intensities as described in Experimental procedures and normalized to 100%. B: Same as in (A) but normalized to the frequency of the different amino acids in the BSA sequence. All presented values are averages based on 4 technical replicates. Figure 5. Modified residues from the 12 h oxidized samples, mapped on the three-dimensional structures of BSA (A; PDB entry 3v03) and HSA (B; PDB entry 1ao6) presented from three different angles. Red: Residues modified to > 10% occupancy indicated with arrows; yellow: modified residues with < 10% occupancy; green: residues without significant detectable modifications. Figure 6. Number of identified PSMs with different mass shifts for human serum samples exposed to MCO for 12 h as described in Experimental procedures. Unfiltered data (insert: mass shifts in a wider span from −800 to +2400 Da) for samples exposed to MCO for 12 h (A) and an untreated control (B). C: Values for untreated control subtracted from 12 h MCO. Same as in (A) but with 20 ACS Paragon Plus Environment

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the number of corresponding PSMs in the control samples subtracted. D: Filtered data only including mass shifts corresponding to known oxidative modifications with matching amino acid targets (Table 1). All presented values of PSM counts are averages based on 4 technical replicates. Standard deviations were generally below 25%, and for the highly abundant mass shifts (+13.98 +15.99, -30.01, +31.98 and +58.0 Da) below 17%.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 1. Number of identified “dependent peptides” PSMs with different mass shifts for BSA samples exposed to MCO for 12 h as described in Experimental procedures. Unfiltered data (insert: mass shifts in a wider span from −800 to +2400 Da) for samples exposed to MCO for 12 h (A) and an untreated control (B). C: Values for untreated control subtracted from 12 h MCO. D: Filtered data only including PSMs with mass shifts corresponding known oxidative modifications with matching amino acid targets (Table 1). All presented values of PSM counts are averages based on 4 technical replicates. Standard deviations were generally below 20%, and for the more abundant mass shifts (+13.98, +15.99, +31.99 and +58.0 Da) below 5%.


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Figure 2. Number of PSMs displaying mass shifts associated with oxidative modifications in BSA. Green, red and blue bars represent samples exposed to MCO for 0 h (control), 0.5 h and 12 h, respectively. All presented values of PSMs are averages based on 4 technical replicates (standard deviations are indicated by error bars).



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 3. Relative abundance of 13.98 Da mass shifts in 12 h MCO samples correlated to different amino acid targets. A: The abundance of mass shifts calculated based on relative intensities as described in Experimental procedures and normalized to 100%. B: Same as in A but normalized to the frequency of the different amino acids in the BSA sequence. All presented values are averages based on 4 technical replicates.


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Figure 4. Relative abundance of 15.99 Da mass shifts in 12 h MCO samples correlated to different amino acid targets. A: The abundance of mass shifts were calculated based on relative signal intensities as described in Experimental procedures and normalized to 100%. B: Same as in (A) but normalized to the frequency of the different amino acids in the BSA sequence. All presented values are averages based on 4 technical replicates.



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Figure 5. Modified residues from the 12 h oxidized samples, mapped on the three-dimensional structures of BSA (A; PDB entry 3v03) and HSA (B; PDB entry 1ao6) presented from three different angles. Red: Residues modified to > 10% occupancy indicated with arrows; yellow: modified residues with < 10% occupancy; green: residues without significant detectable modifications.


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Figure 6. Number of identified PSMs with different mass shifts for human serum samples exposed to MCO for 12 h (A) and an untreated control (B). C: Unfiltered data. B. Same as in (A) but with the number of corresponding PSMs in the control samples subtracted. D: Filtered data only including mass shifts corresponding to known oxidative modifications with matching amino acid targets (Table 1). All presented values of PSM counts are averages based on 4 technical replicates. Standard deviations were generally below 25%, and for the highly abundant mass shifts (+13.98 +15.99, -30.01, +31.98 and +58.0 Da) below 17%. 27 ACS Paragon Plus Environment


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For TOC only


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Unrestricted Mass Spectrometric Data Analysis for Identification

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