Detection Of Oxidized Polypeptides

  *US09134318B2*
  US009134318B2                                 
(12)United States Patent(10)Patent No.: US 9,134,318 B2
 Madian et al. (45) Date of Patent:Sep.  15, 2015

(54)Detection of oxidized polypeptides 
    
(75)Inventors: Ashraf G. Madian,  West Lafayette, IN (US); 
  Fred E. Regnier,  West Lafayette, IN (US) 
(73)Assignee:Purdue Research Foundation,  West Lafayette, IN (US), Type: US Company 
(*)Notice: Subject to any disclaimer, the term of this patent is extended or adjusted under 35 U.S.C. 154(b) by 0 days. 
(21)Appl. No.: 13/515,102 
(22)PCT Filed:Dec.  10, 2010 
(86)PCT No.: PCT/US2010/059828 
 § 371 (c)(1), (2), (4) Date: Aug.  27, 2012  
(87)PCT Pub. No.:WO20/11/072197 
 PCT Pub. Date:Jun.  16, 2011 
(65)Prior Publication Data 
 US 2012/0309040 A1 Dec.  6, 2012 
 Related U.S. Patent Documents 
(60)Provisional application No. 61/285,830, filed on Dec.  11, 2009.
 
Jan.  1, 2013 G 01 N 33 6848 F I Sep.  15, 2015 US B H C Jan.  1, 2013 G 01 N 33 6842 L I Sep.  15, 2015 US B H C
(51)Int. Cl. C12Q 001/37 (20060101); G01N 033/68 (20060101)

 
(56)References Cited
 
 U.S. PATENT DOCUMENTS
 6,864,099  B2  3/2005    Regnier     
 6,872,575  B2  3/2005    Regnier     
 7,449,170  B2  11/2008    Regnier et al.     
 2002//0037532  A1*3/2002    Regnier et al. 435/7.1
 2003//0129769  A1  7/2003    Regnier     
 2007//0087448  A1*4/2007    Nelsestuen 436/173
 2008//0145863  A1  6/2008    Regnier     
 2008//0299542  A1  12/2008    Loscalzo     
 2009//0148952  A1  6/2009    Regnier et al.     
 2009//0226884  A1  9/2009    Tsujimoto et al.     

 
 FOREIGN PATENT DOCUMENTS 
 
       WO       WO 01/86306       A2                11/2001      
       WO       WO 01/86306       A3                3/2003      
       WO       WO 03/027682       A2                4/2003      
       WO       WO 20/03/027682       A3                2/2004      
       WO       WO 20/06/039456       A1                4/2006      
       WO       WO 20/09/134439       A2                11/2009      
       WO       WO 20/09/134439       A3                3/2010      

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  Yeh et al., “No effect of cigarette smoking dose on oxidized plasma proteins,” Environ. Res., Feb. 2008, 106(2): 219-225. Epub Nov. 9, 2007.
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     * cited by examiner
 
     Primary Examiner —Satyendra Singh
     Art Unit — 1657
     Exemplary claim number — 1
 
(74)Attorney, Agent, or Firm — Brown Rudnick LLP; Adam M. Schoen

(57)

Abstract

A diagnostic method for determining the absence or presence of a disease is provided. The method generally includes assaying the amount and/or types of oxidized peptides in a sample from a subject, and comparing these to the amount and types of reference oxidized polypeptides. The method may include the use of stable isotope label, affinity selection, to identify and quantify changes in oxidized peptides or oxidized proteins associated with diseases such as type II diabetes mellitus, breast cancer, and Parkinson's disease, to monitor a patient's response to a therapeutic agent (e.g., an antioxidant), and/or to monitor disease recurrence.
10 Claims, 35 Drawing Sheets, and 39 Figures


CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application is a §371 National Phase entry of International Application No. PCT/US2010/059828, filed Dec. 10, 2010, which claims the benefit of U.S. Provisional Patent Application Ser. No. 61/285,830, filed Dec. 11, 2009, each of which is incorporated by reference herein in its entirety.

GOVERNMENT FUNDING

[0002] This invention was made with government support under Grant No. 1U24CA126480-01, awarded by the National Institutes of Health, and under Grant No. 5R01AG025362-02, awarded by the National Institutes of Health. The Government has certain rights in the invention.

BACKGROUND

[0003] Oxygen-containing free radicals including, for example, hydrogen peroxide, singlet oxygen, peroxynitrite, and superoxide can occur in cells as part of the normal metabolism of nutrients. These reactive oxygen species can cause oxidative damage to sensitive cellular components. Their potential to damage cells is controlled in part by antioxidants and enzymes such as catalase, selenium-dependent glutathione, superoxide dismutase, and thioredoxin hyperoxidase, which are involved in either destroying the reactive oxygen species or repairing oxidative damage (Butterfield et al., Amino Acids, 2003, 25(3-4):419-425). Thioreductases also can be involved in repair of oxidative damage by converting disulfides, formed during an episode of oxidative stress, to thiols. When redox signaling and control systems fail or are disrupted, reactive oxygen species can accumulate, causing oxidative stress (Jones, D. P., Antioxid Redox Signal, 2006, 8(9-10):1865-1879).
[0004] Oxidative damage to DNA, RNA, and/or proteins can threaten the survival of a biological system (Butterfield et al., Amino Acids, 2003, 25(3-4):419-425). High concentrations of reactive oxygen species have been implicated in varied conditions such as, for example, Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis (ALS), atherosclerosis, diabetes mellitus, chronic renal failure, chronic lung disease, cancer, and many inflammatory diseases mellitus, chronic renal failure, chronic lung disease, cancer, and many inflammatory diseases (Haulica et al., Rom J Physiol, 2002, 37(1-4):15-27; Butterfield, D., Brain Res, 2004, 1000 (1-2):1-7; Dalle-Donne et al., Trends in Mol. Med., 2003, 9(4):169-176; Levine et al., Free Radic Biol Med, 2002, 32(9):790-796). One way that a protein may be irreversibly oxidized is though carbonylation (Dalle-Donne et al., Clin Chim Acta, 2003, 329(1-2):23-38).
[0005] The introduction of carbonyl groups into proteins can occur by i) cleavage of an amino acid side chain, ii) scission of the protein backbone, addition of lipid oxidation products, or iv) oxidation at a glycation site. Under severe oxidative stress, multiple carbonyl groups can be formed on a single protein.
[0006] There are many ways that carbonyl groups may be directly introduced into a protein. One way is through oxidation of a side chain of a proline, arginine, lysine, or threonine amino acid residue (Amici et al., J Biol Chem, 1989, 264(6):3341-3346). Metal-catalyzed oxidation can produce unique products such as glutamate semialdehyde or aminoadipic semialdehyde (Requena et al., PNAS, 2001, 98(1):69-74). Carbonyl groups also can be directly introduced into proteins by cleavage of the protein backbone. Another way that carbonyl groups can be directly introduced into proteins is via α-amidation or diamide pathways. Still another way is by oxidation of glutamyl or aspartyl amino acid side chains, in which case the product generated is N-acylated with a pyruvyl group (Stadtman et al., Amino Acids, 2003, 25(3-4):207-218).
[0007] There are also many ways that carbonyl groups may be indirectly introduced into a protein. One way is by addition of a carbonyl-containing side chain functional group such as, for example, 4-hydroxy-2-noneal, 2-propenal, or malondialdehyde at a cysteine, histidine, or lysine amino acid residue (Redox Proteomics, Dalle-Donne, I., Scalone, A. and Butterfield, D. eds., John Wiley & Sons, Inc., Hoboken, N.J., 2006, pp. 487-525). A second route by which carbonyl groups may be indirectly introduced into a protein is oxidation of advanced glycation end (AGE) products. This route is initiated by non-enzymatic addition of glucose to lysine residues to form a Schiff base that undergoes Amadori rearrangement. Subsequent oxidation of these glycated proteins results in the formation of carbonylated proteins (Dalle-Donne et al., Clin Chim Acta, 2003, 329 (1-2):23-38).

SUMMARY OF THE INVENTION

[0008] In one aspect, the invention provides a method for monitoring the health of a subject. Generally, the method can includes comparing a plurality of test peptides in a sample obtained from the subject, each test peptide having a detectable oxidation state, with a plurality of reference peptides, each reference peptide having a detectable oxidation state; and detecting a difference in oxidation state between at least one test peptide and the oxidation state of a corresponding reference peptide, wherein the difference in oxidation state is indicative of the health status of the subject.
[0009] In another aspect, the invention provides a method for monitoring the health of a subject. Generally, the method can include comparing the value of a parameter associated with the oxidation state of at least one test peptide obtained from the subject with a reference value for the parameter; and detecting a difference in the value of a parameter associated with the oxidation state of the test peptide with the reference value for the parameter, wherein the difference is indicative of the health status of the subject.
[0010] In another aspect, the invention provides a method for monitoring the health of a subject. Generally, the method can include obtaining, receiving, providing, or preparing a test oxidized peptidic profile from a subject; obtaining, receiving, providing, or preparing a reference oxidized peptidic profile; comparing the test oxidized peptidic profile with the reference oxidized peptidic profile; and detecting a difference between the test oxidized peptidic profile and the reference oxidized peptidic profile, where a difference between the test oxidized peptidic profile and the reference oxidized peptidic profile is indicative of the health status of the subject.
[0011] In another aspect, the invention provides a method for monitoring the health of a subject. Generally, the method can include obtaining, receiving, or providing a sample comprising a plurality of peptides obtained from a subject; and detecting or quantifying at least one oxidized peptide in the sample, wherein the oxidized peptide comprises at least one marker of oxidative stress selected from: 2-amino-3-oxo-butanoic acid; 2-amino-3-oxo-butanoic acid; a hydroxylation; glutamate semialdehyde; sulfonic acid; sulfinic acid; sulfenic acid; formylkynurenin; kynurenin; hydroxykynurenin; 2,4,5,6,7 hydroxylation of tryptophan; oxolactone; 4-hydroxy glutamate; conversion of histidine to asparagine; conversion of histidine to aspartate; 2-oxo-histidine, aminoadipic semialdehyde; an Amadori adduct; a 3-deoxyglucosone adduct; a glyoxal adduct; a methylglyoxal adduct; conversion of proline to pyroglutamic acid; conversion of proline to pyrrolidinone; a 4-hydroxynonenal (4-HNE) adduct; a malondialdehyde adduct; and any other oxidative modification.
[0012] The above summary of the present invention is not intended to describe each disclosed embodiment or every implementation of the present invention. The description that follows more particularly exemplifies illustrative embodiments. In several places throughout the application, guidance is provided through lists of examples, which examples can be used in various combinations. In each instance, the recited list serves only as a representative group and should not be interpreted as an exclusive list.

BRIEF DESCRIPTION OF THE FIGURES

[0013] FIG. 1. Schematic illustration of the strategy used for the identification of oxidized proteins and their oxidation sites in normal human plasma. Samples were examined individually to facilitate protein identification. Those with oxidative modifications were then enriched by pooling the samples.
[0014] FIG. 2. Chromatogram from avidin affinity chromatography of a normal human plasma sample. A sample of the plasma (15 mg of total protein) was applied directly to a 4.6 mm×100 mm column packed with Agarose to which avidin had been immobilized. The column was eluted initially with 0.15 M phosphate buffered saline, (pH 7.4) at 0.5 mL/min for 120 minutes then switched to a mobile phase containing 0.1M glycine/HCl (pH 2.5) for an additional 40 minutes at the same flow rate. Absorbance was monitored at 280 nm.
[0015] FIG. 3. Relative numbers of proteins identified by MALDI only, ESI only, and using both MALDI and ESI ionizing methods.
[0016] FIG. 4. Comparison between the number of oxidized proteins identified in the four donors' plasma using ESI-MS, MALDI-MS, or both.
[0017] FIG. 5. Annotation of proteins identified according to their tissues of origin. Proteins were searched in the human protein atlas database and their degree of expression (low, medium, strong) was plotted against tissue of origin.
[0018] FIG. 6. Total number of modifications identified and the corresponding oxidative modifications.
[0019] FIG. 7. Schematic illustration for the strategy for the identification and quantitation of oxidized proteins in the plasma of breast cancer patients compared to their controls. The samples were purified individually using avidin. Each pair of purified samples from breast cancer patient and their controls were then labeled with iTRAQ™ and identified and quantified using AB 4800 MALDI/TOF/TOF.
[0020] FIG. 8. Schematic illustration for the strategy for the detection of the oxidation sites of immunoglobulins and non-immunoglobulins.
[0021] FIG. 9. Plasma 8-isoprostane levels were significantly higher in breast cancer patients compared to their controls. Concentrations of the samples were expressed as averages with standard errors of control independent samples (n=6) and cancer patient samples (n=6). Comparison of control and cancer patients was made using the Student t-test. A p-value of 0.02 on a 2-tailed test was obtained which was considered statistically significant.
[0022] FIG. 10. A DAVID Gene Ontology (GO) analysis by molecular function of proteins that changed more than 50% in the plasma of BC patients compared to their controls.
[0023] FIG. 11. DAVID gene ontology (GO) by biological process of proteins that changed more than 50% in the plasma of BC patients compared to their controls.
[0024] FIG. 12. A network analysis of transcription factors showing positive regulation of biological processes. Arrows with solid lines indicate activation, arrows with dashed lines indicate inhibition, and canonical pathways (either activating or inhibiting) are highlighted. This network is statistically significant to a P-Value of 3.47e−42.
[0025] FIG. 13. Cellular location of the proteins that changed more than 50% in the plasma of breast cancer patients compared to their controls.
[0026] FIG. 14. Disease distribution of the proteins that changed more than 50% in the plasma of breast cancer patients compared to their controls. GeneGo™ histogram is ordered based on the most statistically significant diseases. Each bar within the numbered groupings represents a single donor.
[0027] FIG. 15. Gene ontology by processes for the proteins that changed more than 50%. The GeneGo™ histogram is ordered based on the most statistically significant processes. Each bar within the numbered groupings represents a single donor.
[0028] FIG. 16. The structures of the carbonylation products detected in this study. R refers to a polypeptide sequence. Glutamic semialdehyde is the oxidation product of proline and arginine. Aminoadipic semialdehyde is the oxidation product of lysine. 2-Amino-3-ketobutyric acid is the oxidation product of threonine. All other oxidation adducts are formed by the addition of glycation and advanced glycation end products (AGEs) or Advanced lipid peroxidation end products (ALEs) to the lysine residues.
[0029] FIG. 17. Schematic overview of the strategy used to quantitate oxidative modifications of DJ-1. Wild-type DJ-1 and M26I were untreated or exposed to a 10- or 500-fold molar excess of H2O2. The proteins were digested with trypsin, and the tryptic peptides were labeled by trypsin-catalyzed 16O/18O labeling. The 16O- and 18O-labeled peptides were mixed and analyzed by nanoscale LC/MS/MS.
[0030] FIG. 18. MS/MS analysis of peptide 99KGLIAAICAGPTALLABEIGFGSK122 (SEQ ID NO:1) in wild-type DJ-1 oxidized with a 10-fold molar excess of H2O2. (Top) MS/MS spectrum with all of the y and b ions assigned. The y and b ion map is superimposed on the peptide sequence. (Bottom) C106 is oxidized to cysteine sulfinic acid (“2O” form) according to the Mascot identification. The difference between the masses of the b8 and b7 ions equals the mass of cysteine sulfinic acid.
[0031] FIG. 19. MS/MS analysis of peptide 100GLIAAICAGPTALLAHEIGFGSK122 (SEQ ID NO:2) in wild-type DJ-1 oxidized with a 500-fold molar excess of H2O2. (Top) MS/MS spectrum with all of the y and b ions assigned. The y and b ion map is superimposed on the peptide sequence. (Bottom) C106 is oxidized to cysteic acid (“30” form) according to the Mascot identification. The difference between the masses of the b7 and b6 ions equals the mass of cysteic acid.
[0032] FIG. 20. MS/MS analysis of peptide 49DVVICPDASLEDAKK63 (SEQ ID NO:3) in the M26I mutant DJ-1 protein oxidized with 500-fold molar excess of H2O2. (Top). MS/MS spectrum with all of the y and b ions assigned. The y and b ion map is superimposed on the peptide sequence. (Bottom) C53 is oxidized to cysteic acid (‘3O’ form) according to the Mascot identification. The difference between the masses of the b5 and b4 ions equals the mass of cysteic acid.
[0033] FIG. 21. MS/MS analysis of peptide 13GAEEMETVIPVDVMR27 (SEQ ID NO:4) in wild-type DJ-1 oxidized with a 500-fold molar excess of H2O2. (Top) MS/MS spectrum with all of the y and b ions assigned. The y and b ion map is superimposed on the peptide sequence. (Bottom) Both methionine residues (M17 and M26) are oxidized to methionine sulfoxide according to the Mascot identification. The difference between the masses of y2 (after the neutral loss of one CH3SOH group, mass=63.998 Da) and y1 equals 83 Da. The difference between the masses of y11 (after the neutral loss of two CH3SOH groups, total mass=127.996 Da) and y10 (after the neutral loss of one CH3SOH group, mass=63.998 Da) equals 83.06 Da. Addition of the neutral loss mass to the 83 Da value obtained for y2-y1 and y11-y10 yields the mass of methionine sulfoxide.
[0034] FIG. 22 MS/MS analysis of peptide 131DKMMNGGMYTYSENKVEK148 (SEQ ID NO:5) in wild-type DJ-1 oxidized with a 500-fold molar excess of H2O2. (Top) MS/MS spectrum with all of the y and b ions assigned. The y and b ion map is superimposed on the peptide sequence. (Bottom) M133 is oxidized to methionine sulfoxide according to the Mascot identification. The difference between the masses of b3 (after the neutral loss of one CH3SOH group, mass=63.998 Da) and b2 equals 83.054 Da. The sum of this value and the neutral loss mass equals the mass of methionine sulfoxide.
[0035] FIG. 23. MS/MS analysis of peptide 131DKMIMNGGMYTYSENRVEK148 (SEQ ID NO:5) in wild-type DJ-1 oxidized with a 500-fold molar excess of H2O2. (Top) MS/MS spectrum with all of the y and b ions assigned. The y and b ion map is superimposed on the peptide sequence. (Bottom) M134 is oxidized to methionine sulfoxide according to the Mascot identification. The difference between the masses of b4 (after the neutral loss of one CH3SOH group, mass=63.998 Da) and b3 equals 83.033 Da. The sum of this value and the neutral loss mass equals the mass of methionine sulfoxide.
[0036] FIG. 24. Relative quantitation of DJ-1 modifications following exposure to different degrees of oxidative stress. DJ2O/UT: fold intensity change for WT DJ-1 oxidized by a 10-fold molar excess of H2O2 relative to untreated WT DJ-1. DJ30/UT: fold change for WT DJ-1 oxidized by a 500-fold molar excess of H2O2 relative to untreated WT DJ-1. M26120/UT: fold change for M26I oxidized by a 10-fold molar excess of H2O2 relative to untreated M26I. M26130/UT: fold change for M26I oxidized by a 500-fold molar excess of H2O2 relative to untreated M26I. Mean±SEM, N=3.
[0037] FIG. 25. 2D-PAGE showing the decreased propensity of the C106 of the DJ-1 M26I mutant to oxidize to the 2O form.
[0038] FIG. 26. Urinary isoprostanes (ng/24 hours) measured at baseline, 3, and 6 weeks in diabetic (DM) and control (lean) rats. The difference in isoprostane concentration between diabetic and controls at 3 and 6 weeks was significant to the level of p<0.05.
[0039] FIG. 27. A schematic illustration of the strategy used for the identification, quantification, and characterization of carbonylated proteins and their oxidation sites in the plasma of diabetic and lean Zucker rats. For protein identification and quantification, the samples were run individually, digested and then labeled with the iTRAQ™ reagent. Characterization and quantification of oxidative post-translational modifications was achieved using pooled samples.
[0040] FIG. 28. Avidin affinity chromatogram of a Zucker diabetic rat plasma sample overlayed on that of a lean rat plasma sample. Plasma samples (each of 5 mg total proteins content) were applied directly to a 4.6×100 mm column packed with UltraLink Biosupport™ to which avidin had been immobilized. The column was eluted initially with 0.15 M phosphate buffered saline, (pH 7.4) at 0.5 mL/min for 120 min then switched to a mobile phase containing 0.1M dimethylglycine/HCl (pH 2.5) for an additional 40 min at the same flow rate. Absorbance was monitored at 280 nm. Based on absorbance-based quantification an average of 1% of the total protein from five lean rat plasma samples was captured by avidin affinity chromatography (SD=0.0014). The corresponding amount captured from five diabetic rat plasma samples was 1.7% (SD=0.46).
[0041] FIG. 29. A heat map of the quantitation of the oxidized proteins and their oxidation sites. The first column shows the proteins detected. The first element of this map is the fold change of these proteins in the diabetic rat plasma versus their lean controls as quantitated by iTRAQ™ labeling strategy. The second element of this map is shown the fold change of the oxidation sites in the diabetic rat plasma versus their lean controls.
[0042] FIG. 30. Disease pathways that were identified by GeneGo™ analyses using oxidized protein data from these studies.
[0043] FIG. 31. Relative quantification of the carbonylated peptide KVADALAK (SEQ ID NO: 6) using selective reaction monitoring (SRM). During the analytical work flow, the parent protein beating this HNE modified peptide was biotinylated with biotin hydrazide and after avidin affinity selection of the parent this biotinylated peptide was released by trypsin digestion. Quantification was based on two CID transitions, the fragment ion at m/z=915.5 (y5) and m/z=599.4 (y2−NH3). As shown in the Figure, the levels of these two fragments were elevated 20-fold in the diabetic rat plasma pooled sample compared to their control lean rat plasma pooled sample.
[0044] FIG. 32. A heat map of types of oxidation sites detected in this study. The first column shows the proteins detected. These oxidation sites can be either a product of: direct oxidation, Advanced Lipidperoxidation End products (ALE) adducts or Advanced Glycation End products (AGE) adducts. Methylglyoxal and glyoxal can be considered AGE or ALE.
[0045] FIG. 33. Structures of carbonylation products detected in this study. R refers to the sequence of polypeptides, aa refers to lysine, histidine or cysteine that can form Michael adducts with 4-HNE.
[0046] FIG. 34. Structures of green tea polyphenols.
[0047] FIG. 35. Schematic illustration for the strategy used for the identification, quantitation and characterization of carbonylated proteins and their oxidation sites in the plasma of green tea fed diabetic and their control diabetic rats. Five samples from each of the green tea fed diabetic and their control diabetic rats were pooled together and the oxidation sites were characterized using LC-ESI-MS/MS and quantitated using selective reaction monitoring (SRM).
[0048] FIG. 36. Relative quantitation of carbonylated peptides using selective reaction monitoring (SRM). The peptide was quantitated based on two transitions, 915.5 (y5) and 599.4 (y2-NH3). As shown in the figure, the levels of these two transitions were significantly reduced (0.04%) in the green tea fed diabetic rat plasma pooled sample compared to their control diabetic rat plasma pooled sample.
[0049] FIG. 37. A diagram comparing three approaches for the identification of carbonylated proteins and their carbonylation sites.
[0050] FIG. 38. A general scheme for the different routes of protein carbonylation.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

[0051] The invention described herein involves the detection of oxidized peptides in the plasma and are used to diagnose or monitor disease states in an individual. These oxidative modifications of peptides are referred to below as an oxidative stress induced post-translational modification (OSi˜PTM). The oxidized peptides being detected are not natively plasma peptides, but instead are peptides, or are derived from peptides, that may originate in cells found in non-blood tissues (e.g., organs or tumors) or are generated at sites of inflammation in the circulatory system.
[0052] In some cases, the oxidized peptides may be derived by digesting oxidized peptides selected from plasma. Sites of oxidation often may be more easily detected in digested peptide fragments than in an undigested parent peptide.
[0053] Peptide oxidation can be a marker for many different disease states. Peptides can contain multiple sites that were oxidized in, for example, an undigested parent peptide. Moreover, a single peptide may bear multiple types of oxidation. The site and type of oxidation may be disease-specific and, therefore, useful for diagnosis and/or monitoring of a disease. For example, the relative degree (i.e., the level) of oxidation at a particular site can vary with, and be used as a marker for, disease progression. The degree and type of oxidation at any particular site on a peptide may be independent of the oxidation status at other sites on the peptide or on other peptides derived from the same protein parent. Of all the potential oxidation sites on a peptide, only a small number may actually be oxidatively modified by carbonylation through oxidative stress. Carbonylation generally increases in biological systems with increasing oxidative stress, but not necessarily in direct proportion at any particular site.
[0054] Additionally, oxidative stress experienced by an organ can be observed by analyzing the oxidative status of organ-specific peptides through, for example, oxidized peptide fragments. Oxidation patterns can be compared between normal and stressed individuals in, for example, a diagnostic method. Alternatively, oxidation patterns can be compared from a single individual at different points in time in, for example, a method that monitors the progression of disease, the efficacy of treatment, or the recurrence of disease.
[0055] Oxidative stress itself and therapeutic agents that control oxidative stress can be monitored as well. Some types of carbonylation can be reduced by antioxidants of both natural and synthetic origin that have been administered to biological systems, the purpose being to protect cells against oxidative stress. In some cases, for example, antioxidant concentration can alter some types of protein oxidation and the resulting oxidative signatures. Thus, monitoring protein oxidization can be used to monitor the presence and efficacy of antioxidants.
[0056] Throughout this disclosure, each of the following terms shall have the indicated meaning.
[0057] “Corresponding oxidation site” refers to a site of peptide oxidation common among a plurality of individual molecules of the same peptide—i.e., molecules having the same amino acid sequence.
[0058] “Non-blood peptide” refers to a peptide present in the blood, plasma, or a blood product because the peptide is shed or released into the circulatory system as a result of oxidative stress-induced cell death—whether apoptosis, necrosis, or some other mechanism—as opposed to having a typical blood-related function such as, for example, nutrient transport, oxygen transport, endocrine function, immunological function, maintaining osmolarity, and the like. In contrast, “blood peptide” refers to a peptide modified by oxidative stress at some extracellular location, generally in the circulatory system.
[0059] “Normal,” in the context of an individual, refers to an individual who does not exhibit any symptoms or clinical signs of a particular disease. In the context of a sample, “normal” refers to sample obtained from an individual who does not exhibit any symptoms or clinical signs of a particular disease.
[0060] “Peptide” refers to a sequence of amino acid residues without regard to the length of the sequence. Therefore, the term “peptide” refers to any amino acid sequence having at least two amino acids and therefore includes a full-length protein, any fragment of a full-length protein, any amino acid sequence that possesses an addition, a deletion, and/or a substitution of one or more amino acid residues compared to a reference protein, and any synthetically-produced amino acid sequence that includes at least two amino acid residues.
[0061] “Protein” refers to a sequence of amino acid residues as natively expressed by a cell.
[0062] “Sign” or “clinical sign” refers to an objective physical finding relating to a particular disease capable of being found by one other than the patient.
[0063] “Symptom” refers to any subjective evidence of disease or of a patient's condition.
[0064] PTM refers to a post-translational modification.
[0065] OSi˜PTM refers to an oxidative stress induced post-translational modification.
[0066] DNPH refers to dinitrophenylhydrazine.
[0067] AGE refers to advanced glycation end products.
[0068] ETD refers to electron transfer dissociation.
[0069] ECD refers to electron capture dissociation.
[0070] CID refers to collision-induced dissociation.
[0071] ROS refers to reactive oxygen species.
[0072] OS refers to oxidative stress.
[0073] RPC refers to reversed-phase chromatography.
[0074] The term “and/or” means one or all of the listed elements or a combination of any two or more of the listed elements.
[0075] The terms “comprises” and variations thereof do not have a limiting meaning where these terms appear in the description and claims.
[0076] Unless otherwise specified, “a,” “an,” “the,” and “at least one” are used interchangeably and mean one or more than one.
[0077] Also herein, the recitations of numerical ranges by endpoints include all numbers subsumed within that range (e.g., 1 to 5 includes 1, 1.5, 2, 2.75, 3, 3.80, 4, 5, etc.).
[0078] Excessive oxidative stress leaves a carbonylation fingerprint on peptides in biological systems. Carbonylation is a post-translational modification that often leads to the loss of protein function and can be a component of multiple diseases. Protein carbonyl groups can be generated directly or indirectly. Direct carbonylation can occur by, e.g., amino acids oxidation or the α-amidation pathway. Indirect carbonylation can occur by, e.g, forming adducts with lipid peroxidation products or glycation and advanced glycation end products. Studies of oxidative stress have historically been complicated by the low concentration of oxidation products and wide array of routes by which proteins are carbonylated. Methods described herein, which generally can include new selection and enrichment techniques, coupled with advances in mass spectrometry, allow one to identify hundreds of new carbonylated protein products from a broad range of proteins located at many sites in biological systems.
[0079] Redox regulation is a subject of broad interest in regulatory biology. Oxidation and reduction of amino acid side chains in proteins is a normal part of redox regulation in cells where slight surges in reactive oxygen species are generally dealt with by oxidation of sulfhydryl groups to mixed disulfides. After an oxidative stress episode has passed, these disulfides are reduced back to sulfhydryls and the normal redox potential of the cell is restored. An array of enzymes has evolved in aerobic organisms specifically for repairing oxidative modifications produced in proteins during such events. Proteins that are too seriously damaged for repair are destroyed by proteasomes and lysosomes.
[0080] There are, however, cases where these coping mechanisms are exceeded. Excessive levels of reactive oxygen species from either the environment or aberrations in electron transport can produce such high levels of oxidative stress that large amounts of irreparably damaged proteins are generated. In the process, proteasomes and lysosomes themselves can be altered to the point that their ability to degrade proteins is compromised (Brownlee, M., Diabetes, 2005, 54(6):1615-1625). Under chronic oxidative stress, damaged proteins can accumulate to toxic levels, often causing cell death and, potentially, oxidative stress-related diseases (Stadtman et al., Ann. N. Y. Acad. Sci., 2000, 928:22-38; Davies et al. Neurology, 2006, 66(2, Suppl. 1):S93-S96). Pathological levels of oxidative stress have been implicated in a plethora of diseases ranging from diabetes mellitus (Brownlee, M., Diabetes, 2005, 54(6):1615-1625) and neurodegenerative diseases (Uttara et al., Curr. Neuropharmacol., 2009, 7(1): 65-74) to inflammatory diseases (Naito et al., Curr. Drug Targets: Inflammation Allergy, 2005, 4(4):511-515), atherosclerosis (Victor et al. Curr. Pharm. Des., 2009, 15(26):2988-3002), cancer (Tas et al. Med. Oncol., 2005, 22(1):11-15), and even aging (Stadtman et al., Ann. N. Y. Acad. Sci., 2000, 928:22-38; Perez et al. PNAS, 2009, 106(9):3059-3064). Although all of these diseases have been widely studied, understanding of the protein chemistry involved is relatively primitive. Until very recently protein carbonylation from pathological oxidative stress has been accessed with the dinitrophenylhydrazine (DNPH) colorimetric test for carbonyl groups. The proteins involved, potential changes in their structure and function, sites of oxidation, mechanisms of oxidation, repair or degradation, the long-term fate of oxidized proteins that precipitate in cells, and how oxidized proteins cause cell death are issues that need more study to understand how oxidative stress diseases threaten health.
[0081] Collectively, proteins can be oxidized in more than 35 ways (Table 1). All of these post-translational modifications occur in three basic ways that are distinguishable by mass spectrometry. One involves oxidative cleavages in either the protein backbone or amino acid side chains in which Pro, Arg, Lys, Thr, Glu or Asp residues are most likely to undergo oxidative cleavage. A second mechanism is by indirect addition of lipid oxidation products such as 4-hydroxy-2-noneal, 2-propenal, or malondialdehyde to proteins. Mass increases with this type of modification and is unique to the appended group. Finally, carbonyl groups may be generated in proteins by oxidation of what have come to be known as advance glycation end (AGE) products. AGE products are common in long-lived proteins such as hemoglobin, especially in the case where glucose levels and oxidative stress are elevated, as in diabetes mellitus. Structures of some carbonylated oxidation products are seen in Table 2. All of these forms of oxidation can be reflected independently in proteins of an individual at one time.
[0082] 
[00001] [TABLE-US-00001]
  TABLE 1
 
  Types of oxidative protein modifications
    Amino  
    acid   oxidative modification
   
    T   2-amino-3-oxo-butanoic acid
    Y   hydroxylation
    R   glutamic semialdehyde
    C   cysteic acid (sulfonic acid)
    C   sulfunic acid
    C   sulfenic acid
    W   formylkynurenin
    W   kynurenin
    W   hydroxykynurenin
    W   2,4,5,6,7 hydroxylation of tryptophan
    W   oxolactone
    H   4-hydroxy glutamate
    H   asparagine
    H   aspartate
    H   2-oxo-histidine
    D   hyroxylation
    M   oxidation (sulfoxide)
    M   sulfone
    L   hydroxy Leucine
    K   Aminoadipic-semialdehyde
    K   Amadori adduct
    K   3-deoxyglucosone adduct
    K   glyoxal adduct
    K   methylglyoxal adduct
    N   hyroxylation
    P   hyroxylation
    P   glutamic semialdehyde
    P   pyroglutamic
    P   pyrrolidinone
    F   hyroxylation
    F   dihydroxy phenylalanine
    K   hyroxylation
    C/H/K   hydroxynonenal (HNE)
      Michael adduct
    K   malondialdehyde
   
[0083] 
[00002] [TABLE-US-00002]
  TABLE 2
 
  Amino acids and corresponding carbonylation products.
  Amino Acid   Carbonylation product
 
[see pdf for image] [see pdf for image] [see pdf for image]
 
[see pdf for image] [see pdf for image]
 
[see pdf for image] [see pdf for image]
 
[see pdf for image] [see pdf for image]
 
[see pdf for image] [see pdf for image]
 
[see pdf for image] [see pdf for image]
 
[see pdf for image] [see pdf for image]
 
  P-Lys-NH2 [see pdf for image] [see pdf for image]
 
  P-His-IM-NH [see pdf for image]
 
  P-Cys-SH [see pdf for image]
 
[0084] One analytical problem involves recognizing oxidized proteins and differentiating between the various types of oxidative modifications. Methods described herein address this analytical problem and provide a platform for practical applications of such analyses.

Isolating Carbonylated Proteins

[0085] Biological fluids such as blood plasma contain thousands of proteins that vary 1010-fold or more in concentration, a small portion of which will be oxidatively modified. Current proteomics tools require much simpler mixtures and concentrations of 1 ng/mL or more for large-scale protein identification. As a means of dealing with these problems multiple methods have been described for selection and recognition of carbonylated proteins, all of which exploit the relatively unique property of carbonyl groups to form Schiff bases. Through derivatization of carbonyl groups with a reagent such as dinitrophenylhydrazine or biotin hydrazide (Table 3), affinity chromatography can be used to select oxidized proteins derivatized with these groups, greatly enriching them or their proteolytic fragments in the process.
[0086] 
[00003] [TABLE-US-00003]
  TABLE 3
 
  Reagents and corresponding enrichment chemistry
  Chemical Reagent   Enrichment chemistry
 
[see pdf for image]   The hydrazide group reacts with carbonyl groups on oxidized proteins forming hydrazones, which can be then isolated with DNPH-specific antibiodies.
 
[see pdf for image]   The hydrazide group reacts with carbonyl groups on oxidized proteins forming hydrazones, which can be then isolated with avidin.
 
[see pdf for image]   The hydrazide group reacts with the carbonyl groups on oxidized proteins forming hydrazones. The quaternary amine can be selected by strong cation exchange (SCX) at pH 6.0.
 
[see pdf for image]   The hydroxylamine group reacts with carbonyl groups on oxidized proteins forming oximes, which then can be isolated with avidin.
 
[see pdf for image]   The hydroxylamine group reacts with carbonyl groups on oxidized proteins forming oximes, which then can be isolated with avidin.
 
[see pdf for image]   Forms a reversible covalent ester with 1,2- and 1,3-diols in aqueous media that captures glycated peptides and proteins.
 

Dinitrophenylhydrazine
[0087] Derivatization of carbonyl groups with dinitrophenylhydrazine (DNPH) has been used for more than half a century as a qualitative analytical method in organic chemistry. With slight modification of this old method, DNPH derivatization was adapted to enhance the isolation, identification, and quantification of carbonylated proteins through selection of derivatized proteins with DNPH targeting antibodies. Tryptic digestion of protein mixtures derivatized in this manner and selected followed by reversed-phase chromatography coupled with tandem mass spectrometry (RPC-MS/MS) or ion exchange and reversed-phase chromatography coupled to tandem mass spectrometry (IEC/RPC-MS/MS) has proven successful in identification and quantification of carbonylated proteins.

Biotin Hydrazide

[0088] Biotin hydrazide (BHZ) and biocytin hydrazide have been used in a similar fashion. BHZ reacts readily with carbonyl groups, allowing carbonyl groups to be derivatized and the parent proteins to be selected with an immobilized avidin or streptavidin sorbent. Streptavidin and avidin bind biotin with comparable affinity Isolation and identification of carbonylated proteins through biotin derivatization has been achieved with a multiple step chromatographic process in which carbonyl groups are first derivatized with BHZ to form a Schiff base. The Schiff base is then reduced with sodium cyanoborohydride to prevent reversal of derivatization. Excess BHZ is removed before avidin affinity chromatography by either dialysis or precipitation with trichloroacetic acid (TCA). Because the interaction of biotin with native tetrameric avidin affinity chromatography columns is very difficult to disrupt, monomeric avidin columns are frequently used in affinity chromatography. The binding of biotin to monomeric avidin is still highly specific, but much weaker. Monomeric avidin is also easily immobilized and has been used to select oxidized proteins. Elution of biotinylated proteins from monomeric avidin can be affected with 2 mM biotin or 0.1 M glycine. Biocytin hydrazide is similar to biotin hydrazide in structure and reactivity and has been used with streptavidin to isolated and identify carbonylated proteins from aged mice. The BHZ approach has know been used to study oxidized proteins in yeast, rats, and humans.
[0089] Additionally, 2-D gel electrophoresis (2DGE) and SDS-PAGE has been used to separate biotinylated proteins after which they were detected with labeled avidin. The limit of detection in gels using avidin FITC (fluorescein isothiocyanate) has been reported to be 10 ng. Detection of biotinylated proteins in gels using streptavidin-conjugated peroxidase for amplification is even more sensitive.
[0090] Avidin capture of a protein from a biotinylated does not prove the protein is oxidized. Affinity columns can bind protein complexes. This, when an avidin affinity column captures a biotinylated member of a complex, non-biotinylated, non-oxidized members of the complex can be captured as well. These non-oxidized members of the complex may show up during subsequent shotgun proteomic analyses. Non-oxidized proteins can also bind non-specifically to the chromatographic support matrix or avidin. Proof that a protein is oxidized comes from identification of the oxidation site.

Girard's P Reagent

[0091] Derivatization with Girard's P reagent (GPR, (1-(2-hydrazino-2-oxoethyl)pyridinium chloride)) provides another route for the selection of carbonylated peptides. GPR contains i) a hydrazide group that reacts readily with carbonyl groups to form hydrazones and ii) a quaternary amine that can be selected by strong cation exchange (SCX) resin at pH 6.0. Following trypsin digestion of proteins derivatized with GRP, quaternary amine-containing peptides are selected from mixtures with a SCX column and then further fractionated and identified by RPC-MS/MS. Two features of this approach are, first, that excess derivatizing reagent does not have to be removed before chromatographic analysis and, second, derivatization with GPR enhances peptide ionization through quaternization.

Oxidation-Dependent Element Coded Affinity Tags (O-ECAT)

[0092] Isolation, identification, and quantification of carbonylation sites also has been achieved with ((S)-2-(4-(2-aminooxy)-acetamido)-benzyl)-1,4,7,10-tetraazacyclododecane-N,N′,N″,N′″-tetraacetic acid (O-ECAT). After derivatization of carbonyl groups in a sample, the O-ECAT moiety is used to chelate a rare earth metal such as Tb (158.92 Da) or Ho (164.93 Da). Treating samples with different rare earth metals according to sample origin allows differential coding of samples. Native and oxidized human serum albumin samples may be allowed to react with this reagent and after coding and mixing were tryptic digested. Coded peptide fragments can be selected with an immunosorbent column targeting the derivatizing agent. Peptides selected in this manner can be analyzed by nanoRPC-FTICR mass spectrometry.

Identification Strategies

[0093] Affinity chromatography, coupled with modern proteomics methods, is now widely used to study many types of post-translational modifications including, for example, carbonylation. Proteomic analysis of carbonylated proteins has been achieved in three ways (FIG. 37).

Targeting PTM-Bearing Peptides

[0094] One route of identification involves immediately digesting biotinylated samples with, for example, trypsin or glu-C and selecting only the carbonylated and biotinylated peptides from samples by avidin affinity chromatography. Carbonylated peptide mixtures thus selected may be then analyzed by RPC-MS/MS. Because arginine and lysine residues can be oxidized, trypsin cleavage at these sites can be blocked, resulting in some fragment that may be larger than theoretically expected based on an expectation that all trypsin digestion sites will be available for digestion.

Identifying Carbonylated Proteins as a Group.

[0095] A second approach is to target native proteins. With this method carbonylated proteins are first biotinylated and then selected by avidin affinity chromatography. Following proteolysis, unoxidized peptide fragments from these proteins can be identified by RPC-MS/MS methods common to shotgun proteomics. There are several advantages with this approach. One is that both unmodified and PTM-bearing peptides are available for identification. When ionization of a carbonylated peptide is suppressed or a PTM-bearing peptide does not ionize at all, other peptides from the protein are available for identification. Another advantage is that many carbonylated proteins have also undergone additional types of protein oxidation such as, for example, methionine oxidation, sulfhydryl oxidation, and tyrosine nitrosylation. The fact that addition types of oxidation are co-selected with carbonylation may be fortuitous. One disadvantage of this strategy is that modification sites may not be identified if PTM-bearing peptides are not seen and sequenced. Non-specifically bound proteins could also be mistakenly identified as bearing the PTM if the PTM site is not identified.

Multidimensional Fractionation

[0096] A third strategy involves further fractioning affinity-selected proteins by liquid chromatography before proteolysis and identification of peptide fragments by RPC-MS/MS. Beyond affinity selection in the first dimension of chromatography, fractionation is generally achieved by reversed-phase chromatography (RPC) in the second dimension. Protein fractions collected from the RPC column may be trypsin digested and the peptide fragments identified by RPC-MS/MS. Carbonylation sites from 87 yeast proteins have identified in this manner. Again, oxidatively modified and unmodified tryptic peptides from a protein may appear together, facilitating protein identification based on peptide sequence analysis. The unmodified peptides may be used to identify the protein parent while labeled carbonylated peptides may be used to identify oxidation sites.
[0097] Proteins sometimes appear in multiple peaks during RPC fractionation. These peaks can arise from in viva cleavage, protein:protein cross-linking, and/or protein:RNA cross-linking. Isoforms of a protein will generally be missed by the other two procedures described above. Advantages of this approach include the ability to identify isoforms of a protein and the ability to identify the most oxidized proteins. A disadvantage is that it is more laborious than other approaches.

Analysis of Oxidation Mechanisms

[0098] Carbonylation of a protein can occur in at least three ways; by direct oxidation with reactive oxygen species (ROS), through Michael addition of lipid peroxidation products, and through formation of advanced glycation end products (FIG. 38).

ROS Oxidation.

[0099] Cleavage of amino acid side chains is generally associated with oxidation by reactive oxygen species. Some of these cleavage products are listed in Table 2. Each is linked to a unique change in mass that can be programmed into most peptide/protein identification software. But even so, the molecular weight of the modified peptide can be very similar to that of an unmodified peptide in the proteome. This may be addressed by acquiring data with a high mass accuracy analyzer capable of differentiating PTM-bearing peptides from unmodified peptides on the basis of mass alone. Through hydrogen peroxide-induced oxidative stress in yeast cultures and biotin hydrazide derivatization to select carbonylated proteins, 415 proteins have been identified along with specific sites of oxidation in 87 instances (Mirzaei et al., J. Proteome Res., 2006, 5(9):2159-2168). Thirty-two cases were seen in which proteins appear in multiple, non-adjacent peaks during reversed-phase chromatography. This generally indicates differences in post-translational modification, fragmented forms of the protein, or some type of cross-linking. Typically gel electrophoresis has been used to study protein fragmentation, but RPC works equally well.
[0100] Cross-linking is often seen in ROS-induced protein oxidation, occurring in at least six different ways include, for example, a) forming disulphide bonds between proteins through cysteine oxidation, b) Schiff base formation between a carbonyl group on an oxidized amino acid side chain of one protein and a lysine residue on another, c) Schiff base formation between the carbonyl group on an HNE adduct of one protein and a lysine residue on another, d) Schiff base formation between the carbonyl group on a malondialdehyde adduct of one protein and a lysine residue on another, e) Schiff base formation between a carbonylated AGE product and another protein, or f) by free radical cross-linking involving carbon-centered radicals.
[0101] Co-elution in multiple separation systems is a good way to recognize cross-linked proteins. This approach has been exploited in recognizing cross-linking of ribosomal proteins to rRNA (Mirzaei et al., J Chromatogr A, 2007, 1141(1):22-31). Proteins from H2O2-stressed yeast were biotinylated, avidin selected, and then further fractionated by RPC. Ribosomal proteins were noted to elute in three different peaks during RPC under strongly denaturing conditions. The ribosomal proteins were subjected to tryptic digestion and further chromatography of the peptides on a borate affinity column that selected species bearing a vicinal diol present in RNA bases. Mass spectral analysis of captured peptides indicated that the peptides had covalently appended nucleotide bases, and b) identified the specific bases involved (Mirzaei et al., J Proteome Res, 2006, 5(12):3249-3259). This procedure was used to identify 37 ribosomal proteins from yeast that were cross-linked to rRNA, along with sites in the proteins and on rRNA at which cross-linking occurred.
[0102] Although protein oxidation is non-enzymatic and may be expected to be random, it is not. Oxidation occurred at very specific sites. Many of these new, non-genetically-coded oxidation sites conveyed a new biological activity to the oxidized protein and have been named “allotypic active sites” (Mirzaei et al., J. Proteome Res., 2006, 5(9):2159-2168). The type of oxidizing agent may influence the site of protein oxidation. While metal-catalyzed oxidation of human serum albumin in vitro has resulted in carbonylation at Lys-97 and Lys-186, oxidation with hypochlorous acid has resulted in carbonylation at five sites: Lys-130, Lys-257, Lys-438, Lys-499, and Lys-598 (Temple et al., J. Am. Soc. Mass Spectrom., 2006, 17(8):1172-1180).
[0103] A substantial amount of work on oxidative stress has been done in model systems. Protein oxidation in vitro, however, may not always the same as in vivo. Metals have long been known to influence oxidation by reactive oxygen species, often leading to primary structure cleavage. When the oxidation of human serum albumin was catalyzed with FeEDTA, in vitro cleavage was more extensive than that seen in vivo (Lee et al., J. Proteome Res., 2006, 5(3):539-547). This may be because fewer proteins are in the in vitro mixture and reactive oxygen species are not depleted as quickly.

Lipid Peroxidation Adducts.

Examining the Protein-Aldehyde Adducts.

[0104] The first direct proof of lipid conjugation to proteins was in oxidized low density lipoproteins (LDL). LDL is composed of a single apolipoprotein B-100 (Apo B-100) with adsorbed fatty acids that together are water-soluble. Oxidizing LDL makes it susceptible to uptake by scavenger receptors inside the endothelium and can lead to the formation of “foam cells.” Accumulation of foam cells is the first stage of atherosclerotic plaque formation. One of the degradation products resulting from lipid oxidation is 4-hydroxynonenal-lysine (HNE). HNE can become attached to proteins through either Michael addition or Schiff base formation.
[0105] Oxidized LDL has been reduced with NaBH4 (to stabilize the Michael adduct formed between HNE and histidine), de-lipidated to remove non-covalently linked lipid, and digested with trypsin to generate peptides for RPC-MS/MS analysis. Mass spectra of all peptides containing the HNE moiety showed an m/z 268 product ion corresponding to the histidine immonium ion modified by HNE. Product ion scanning of all second dimension mass spectra for this m/z 268 ion was used to locate peptides in the RPC eluent carrying HNE. Peptide sequence and the location of HNE in the peptide were extracted from the spectra of these peptides. Modified residues were found to be located on the surface of LDL.
[0106] Michael addition also can occur on lysine and cysteine residues. Moreover, direct addition of carbonyl groups from malondialdehyde (MDA) and 4-hydroxynonenal (HNE) onto lysine is possible.
[0107] A recent in vitro study with hemoglobin and β-lactoglobulin under near physiological conditions has shown it is possible to differentiate between Michael addition and Schiff base formation through mass spectrometry. Michael addition of HNE adds 158 Da of mass to the peptide while Schiff base addition of HNE adds 138 Da. Based on mass spectral analysis, Michael adduct formation dominates Schiff base formation by a 99:1 ratio (Bruenner et al., Chem. Res. Toxicol., 1995, 8(4):552-559). When HNE was adducted to apomyoglobin, addition occurred predominantly at histidine residues (Bolgar et al., Anal. Chem., 1996, 68(14):2325-2330). Product ion scanning of immonium ions showed that 3-10 histidine residues were derivatized. In contrast, the adduct ratio of HNE to human serum albumin (HSA) was dependent on the molar ratio of HNE to HSA. Moreover, cysteine, histidine, and lysine were all modified (Aldini et al., J. Mass Spectrom., 2006, 41(9):1149-1161). Cytochrome c forms adducts with histidine, lysine, and arginine. The importance of this is that cytochrome C binds to complexes III and IV in the electron transport chain through lysine residues. Thus, HNE-Lys adduct formation could impact electron transport. In another study amyloid peptide was shown to form one or more HNE adducts in the residue 6-16 region of the primary structure (Magni et al., Rapid Commun Mass Spectrom, 2002, 16(15):1485-1493). Addition of reducing agents (e.g., NaCNBH3 or NaBH4) can affect the type of adduct formed as well at the site of modification. Interestingly, if NaCNBH3 is added early in the incubation process, Schiff base formation can be more prominent than Michael addition. In addition, the N-terminal amino acid rather than a histidine residue would be modified. On the other hand, addition of NaBH4 at the end of the reaction between the protein and HNE resulted in the reduction of the Michael adduct formed.
[0108] Polypeptide structure can also affect lipid peroxidation product modification. The degree of HNE and 4-oxo-2-nonenal modification in apomyoglobin, which possesses a more open structure, was greater than with myoglobin, which possesses a less open structure.
[0109] A variety of mass spectrometers have been used in the analysis of protein oxidation. One of the more important issues is the mass accuracy of the instrument. FTICR-MS was used to characterize HNE modifications in apomyoglobin where it was found that three to nine Michael adducts were formed. Schiff base adducts were observed as well, but with less intensity as expected from the discussion above. An advantage of high mass accuracy and resolving power is that it allows the resolution of fragment ions of very similar mass. Because peptide sequencing by collision-induced dissociation (CID) results in neutral loss and only partial sequence coverage, electron transfer dissociation (ETD) has been used as an alternative and shown to be superior in the characterization of modification sites due to the production of c and z ions. FTICR-MS has also be used to characterize HNE adducts of creatine kinase isoforms in brain were it was shown that cysteine and histidine residues were most likely to be derivatized. A new method for the characterization of HNE-protein adducts has also been developed using a hybrid linear ion trap-FTICR mass spectrometer (LTQ-FT). In this method, both the usual data-dependent mode of acquisition and a neutral loss driven MS3 (NL-MS3) data dependent acquisition mode were utilized. The later depended on the isolation and fragmentation of any ion showing a difference of m/z 78, 52 or 39 from the precursor ion. Twenty four HNE modification sites were observed on fifteen mitochondrial proteins of which six were seen using NL-MS3 data dependent acquisition.

Affinity Purification of the Adducts.

[0110] It has been shown above that monitoring immonium product ions contain HNE is a powerful tool for recognizing HNE bearing peptides. Unfortunately, cysteine and lysine do not produce intense HNE bearing immonium product ions. Several new methods have been developed with the aim of enriching these adducts to circumvent this problem. The first is based on the use of an anti-HNE immunosorbent in which the antibody was immobilized on CNBr-activated Sepharose. This immunosorbent has been employed to enrich adducts formed between HNE and peptides from either a tryptic digest of apomyoglobin or a model peptide (residues 87-99) of myelin basic protein. A unique feature of the antibody chosen for HNE selection was that it was specific for Michael adducts only. Anti-dinitrophenyl antibodies have been used as well to select HNE Michael and malondialdehyde Schiff base adducts derivatized with dinitrophenyl hydrazine (DNP). Enrichment and recovery of DNP derivatized peptides was virtually quantitative.
[0111] As noted above, enrichment of biotin adducts through avidin affinity purification is another route. Biotinylated hydroxylamine can react with Michael adducts and has been used for enrichment through avidin affinity chromatography. Forming an oxime rather than hydrazone eliminates the need for the reduction step while still allowing determination of the sites of modification (Table 3). Another way to isolate these adducts is by using biotin hydrazide. This allows enrichment of HNE-modified peptides in HNE-spiked yeast lysate. Mapping the HNE modification sites showed that sixty-seven proteins were modified, generally on histidine. The first step in identifying HNE-modified proteins from adipose tissues was incubating biotin hydrazide with adipose tissue from obese mice. Proteins thus biotinylated were captured by avidin affinity chromatography, digested with trypsin, and identified by RPC-MS/MS with online database searches to identify peptides. Among the proteins identified was HNE-modified adipocyte fatty acid-binding protein, which is involved in insulin resistance. Additionally, treating yeast lysates in vitro with HNE resulted in the identification of 67 different proteins carrying 125 HNE modification sites. HNE adducts seen in the in vitro study were not observed in the in vivo study of yeast.

Oxidation of Advanced Glycation End Products

[0112] Reducing sugars add to amines in proteins through the Maillard reaction. Addition products thus formed often undergo an Amadori rearrangement and, in the course of doing so, form an isomeric mixture of products with long-term stability known as advance glycation end (AGE) products. An increase in the concentration of AGEs and their oxidation products is associated with OS-associated damage in, for example, diabetes, renal failure, and aging. A series of methods have been developed to assess the nature of these adducts.
[0113] Advanced glycation end products of proteins formed by sugar addition are highly complex (Lapolla et al., Journal of Mass Spectrometry, 2001, 36(4):370-378). One concern with glycation is that it may modify the biological activity of a protein. With glutathione peroxidase, for example, methylglyoxal can irreversibly modify residues R184 and R185 and inactivate the enzyme. Loss of this oxidative repair enzyme can lead to rising levels of reactive oxygen species.
[0114] Glycation also can modify the susceptibility of proteins to proteolysis. This might have biological significance by increasing the half-life of a glycated protein. As seen with human serum albumin, glycation reduces the number of tryptic peptides formed. The same is true with digestion by endoproteinase Lys-C. Proteinase K is better able to digest glycated proteins and more nearly mimics the AGE-protein degrading enzymes occurring in vivo.
[0115] One promising advance for analyzing glycated proteins is electron transfer dissociation (ETD)-based analysis in addition to collision-induced dissociation (CID) in ESI-MS instruments. With ETD, a nearly full series of c and z type ions are produced with glycated peptides, allowing easier peptide sequencing. CID, in contrast, produces lower intensity b and y ions and the spectra are filled with ions corresponding to neutral loss of water and furylium ions. Actually, both forms of fragment ion generation have unique applications. Scanning for CID neutral loss of −162 amu is a powerful tool for recognizing glycated peptides. Using an electrospray inlet on a Q-TOF instrument operated at both low and high collision energies has permitted the identification of 31 out of 59 lysine residues in HSA that were glycated. The mode of ionization is also important in glycated- and glycosylated-peptide analysis. In addition to the ESI instrumentation described above, MALDI coupled to tandem TOF/TOF mass spectrometers has proven to be a powerful tool in structure analysis. MALDI-MS of glycosylated peptides is more successful when 2,5-dihydroxybenzoic acid is used as the matrix to initiate laser induced ionization.
[0116] Methods for the isolation of AGE products are important as well. Affinity chromatography with meta-amino phenyl boronic acid (mAPBA) (Table 3) columns has proven to be a valuable tool in the isolation of diol species. mAPBA forms a reversible covalent ester with 1,2- and 1,3-diols in aqueous media that captures glycated peptides and proteins, among a variety of other diol-containing species. Glycated peptides have been isolated in this manner and analyzed by tandem mass spectrometry using ETD and CID fragmentation. Five times as many peptides were identified by ETD as with CID. Using mAPBA to isolate glycated proteins and peptides from the human plasma and erythrocytes and ETD in sequencing has shown that individuals with impaired glucose tolerance or type 2 diabetes were likely to have slightly more glycated peptides than normal subjects (Zhang et al. Journal Proteome Res, 2008, 7(5):2025-2032). AGE studies have also been carried out using mAPBA on MALDI chips with minimal interference from nonspecific binding.

Quantification

Stable Isotope Coding in Comparative Proteomics.

[0117] Most quantification studies to date have involved relative comparisons of concentration between samples involving both staining and stable isotope coding methods. The advantage of stable isotope coding is the relative error in quantification is 6-8%, irrespective of the number of steps involved in the analytical process. Multiple isotopomers of dinitrophenyl hydrazine, GRP, and O-ECAT have been prepared and used in relative quantification studies of protein carbonylation. Advantages of in vitro coding strategy include, for example, that it can be used with small quantities of sample, quantification can be achieved with any biological system after the in vivo component of an experiment is completed, and multiple samples can be examined simultaneously. An oxidized sample was split into equal parts and after differential derivatization according to sample origin with d0-GRP and d5-GRP, the samples were mixed in a 1:1 ratio and examined by RPC-MS/MS. Carbonylated peptides appeared as doublet clusters of ions separated by 5 Da, or a multiple thereof according to the number of carbonyls in the peptide. The possibility of false positive identification was minimized by performing both RPC-MS/MS and MALDI-MS/MS along with parameter filtering including tag number, retention time, resolution, and the correct concentration ratio (Mirzaei et al., Journal of Chromatography, A, 2006, 1134(1-2):122-133). A limitation of this strategy is that derivatization may not be quantitative with low abundance proteins.
[0118] (13C6)-DNPH was used to differentially code OS samples and the unlabeled form of DNPH to code control samples to evaluate stable isotope coding for relative quantification. After differential derivatization of samples with the DNPH isotopomers according to sample origin, samples were mixed and examined by shotgun proteomics using reversed-phase chromatography to separate peptide fragments and electrospray ionization tandem mass spectrometry (ESI-MS/MS) for peptide identification.
[0119] Another modification of the biotin hydrazide tag called hydrazide functionalized isotope-coded affinity tag (HICAT) was used to achieve relative quantification of the oxylipid-protein conjugates in the heart mitochondrial proteins. In this method an ENE-peptide adduct is synthesized and derivatized in vitro with a 13C-label (13C4-HICAT). HNE-peptide adducts from the tryptic digest of a sample are then coded with an isotopically light version of HICAT. The light and heavy isotopomers of HICAT vary by 4 amu due to the presence of four 13C atoms in the heavy form. After mixing the differentially labeled isoforms, HNE-peptide adducts are enriched and further fractionated by RPC before analysis by MALDI-MS/MS analysis. Because proteins can be oxidatively modified at multiple sites, quantification of a single-site oxidative modification can involve multiple isoforms of a protein. It is likely that more than a hundred oxidatively modified isoforms of some proteins may occur in vivo.

MRM Methods

[0120] Absolute quantification can evaluate the absolute load of oxidized proteins being generated in a cell and/or the fraction of any particular protein being oxidized in a particular pathway. For many years, absolute quantification has been achieved through the addition of heavy isotope labeled internal standards. With proteins the internal standard can either be a heavy isotope labeled isotopomer of a protein generated biosynthetically or a synthetic 13C-labeled peptide that matches a proteolytic fragment derived from the protein. Use of 13C-labeled peptides precludes the possibility that peptide isotopomers will be separated by chromatographic or electrophoretic methods before quantification in the mass spectrometer. The internal standard method is often referred to as “multiple reaction monitoring” (MRM) when multiple analytes are being quantified in a single analysis. Triple quadrupole mass spectrometers with special MRM compatible software are capable of quantifying more than 100 pairs of peptide isotopomers in a single analysis. MRM methods can be used to study OS proteomics in several ways. One way is to determine the concentration of several non-oxidized peptides from each protein being targeted for absolute quantification. After affinity selection the oxidized protein fraction should be tryptic digested, 13C-labeled internal standard peptides added in known amounts, and the mixture analyzed by RPC-MS/MS to determine the isotope ratio of the targeted peptides. Isotope ratio measurements can then be used to compute the absolute concentration of specific proteins.
[0121] Internal standards also can be used to determine the concentration of protein isoforms that are oxidized at a particular site. For example, one can determine the absolute quantity of HNE adducts on cysteine- and histidine-containing peptides. Such a method was validated using H-Tyr-His-OH as an internal standard for absolute quantification of HNE adducts on glutathione (GSH), carnosine (CAR), and anserine (ANS) using the MRM approach. The method can be implemented to quantify HNE-Michael adducts in rat skeletal muscle. CAR-HNE was shown to be elevated in the case of lipid peroxidation of excitable tissues.

Biological Consequences of Protein Oxidation

[0122] Despite the availability of methods such as those described above, the consequences of oxidative stress have not been widely studied. When identification of carbonylation sites is an objective, studies have frequently been done in vitro, often on model proteins with hydrogen peroxide, and metal catalysis, or through HNE addition. HNE addition in vitro to glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was shown to occur in a sequential manner, first at His-164 and Cys-281, then on Cys-244, and finally at His-327 and Lys-331. All of these residues are located on the surface of the enzyme and easily accessible to HNE and reactive oxygen species. The sequential nature of site modifications in GAPDH suggests a cascade of conformational changes may be necessary for later stage additions. The chaperon activity of Rat Hsp90 is lost after HNE modification of a single cysteine residue at Cys-572, again suggesting HNE addition can cause a conformational change. Carbonylation in adipose tissue of obese insulin-resistant mice produced a 10-fold reduction in the affinity of fatty acid-binding protein for fatty acids. Fatty acid-binding protein was modified by HNE at Cys-117 in vitro. Few carbonylation sites were detected in vivo. For example, carbonylation of Hsp 70-1 in the cornu Anunonis of the macaque monkey occurred at a single site (Arg469) after transient whole-brain ischemia and reperfusion. In another example, ADP/ATP translocase 1 is found in cardiac mitochondrial to be carbonylated by HNE and acrolein at Cys-256.
[0123] Detecting smaller numbers of carbonylation sites in vivo could occur for several reasons. One could be that there are so many isoforms that none occurs in a detectable amount. A further complication could be that isoforms are separated in preliminary fractionation and are difficult to locate. Insufficient recovery from gels for mass spectral identification could be another reason. Many of the studies on protein carbonylation have been done with 2-D gel electrophoresis. Difficulty identifying carbonylated peptides that are biotinylated could be another problem. Fragmentation of biotin can cause the introduction of noise peaks, which lowers identification scores. Another problem is that fragmentation of some peptides during CID sequencing is hard to interpret. Use of electron capture dissociation (ECD) might reduce this problem. The CID and ETD modes of fragmentation are complimentary, facilitating the location of modification sites when combined with the hydrazide purification techniques described above.
[0124] How protein oxidation impacts biological systems is a major issue. Much of the discussion encompassing this subject has focused on bulk phenomena such as the propensity of oxidized proteins to cross-link and precipitate, difficulties in their degradation, and their cellular toxicity. Alterations in the activity of specific enzymes following oxidation are important as well. It is clear from the discussion above that i) some proteins are more likely to be oxidized than others and ii) oxidative modifications can alter biological activity. Enzymes whose activity may be attenuated by OS-associated oxidation include, for example, GAPDH (discussed above), creatine kinase, and carbonic anhydrase. Carbonylation of creatine kinase and carbonic anhydrase under oxidative stress in the vestus lateralis muscle of patients with COPD leads to a reduction in their activity. Sepsis induced by injecting E. coli lipopolysaccharides into the diaphragm of rats produced other examples: enolase 3b, triosphosphate isomerase 1, aldolase, creatine kinase, aconitase 2, dihydrolipoamide dehydrogenase, carbonic anhydrase III and electron transfer flavoprotein all underwent elevation of HNE addition during treatment. In vitro incubation of the enolase with HNE following the in vivo experiment showed a significant reduction of its activity. Oxidized proteins also can be immunogenic, as has been seen in systemic lupus erythematosus.
[0125] Detecting, defining, and/or quantifying oxidative stress biomarkers can permit diagnosis and monitoring of oxidative stress-related diseases. Defining these biomarkers also can permit evaluation of the efficacy of dietary antioxidants, enable validation of new therapeutic approaches for controlling oxidative stress, and establish relationships between the oxidative patterns of biomarker proteins and pathological hallmarks of certain diseases. Despite the connection between oxidative stress and certain diseases, the detection of oxidative stress biomarkers is not used to diagnose these diseases.
[0126] Some proteins may be more prone to carbonylation than others. Moreover, the level of protein oxidation can vary between species. The total amount of oxidized proteins in rat plasma, for example, may be substantially higher than in swine (Madian, A. G., unpublished data, 2008). As another example, albumin and α1-macroglobulin have been reported to have the highest level of AGE-related carbonylation in mouse plasma; albumin and transferrin have been reported as the major oxidized proteins in rat plasma; and only albumin has reported as carbonylated by this route in Rhesus monkey plasma (Jana et al., Arch. Biochem. Biophys., 2002, 397(2):433-439; Dalle-Donne et al., J Cell Mol Med, 2006, 10(2):389-406).
[0127] We have developed methods involving biotin labeling of carbonyl groups in oxidized peptides—which, in many cases may be derived from carbonylated parent peptides (e.g., full-length proteins), then affinity selecting the biotin-labeled oxidized peptides from complex peptide mixtures. The affinity selection permits significant enrichment of the oxidized peptides prior to analysis. The methods described herein permit spectral analysis of oxidized peptides, reduce the presence—in some cases, even eliminates the presence—of non-oxidized peptides except those that exist in complexes with oxidized peptides, identify the sites of oxidation in peptides, and can permit identifying the mechanism by which a peptide is oxidized.
[0128] The methods described herein can therefore provide more sensitive and/or specific tests for certain diseases than current tests based on the presence of a single peptide in the plasma or the activity of enzymes in the plasma that are related to a disease of interest.
[0129] Generally, the methods described herein can be used to determine, depending upon the particular biomarker or biomarkers being detected, the presence or absence of a disease in an individual. The methods may be employed to ascertain one or more of the following: (i) the disease status of the individual from whom a sample is obtained, (ii) the stage of the disease at the time the sample was obtained, (iii) response of the individual to the disease, (iv) response of the disease to treatment, (v) the general health of the individual, (vi) the biological age of an organ, and (vii) the extent to which the disease is recurring in an individual.
[0130] Through analysis of appropriate biomarkers, the presence or absence of an oxidative stress-associated condition such as, for example, aging or a disease such as, for example, diabetes mellitus, breast cancer, Parkinson's disease, or other oxidative stress-associated condition can be monitored. In certain circumstances, the monitoring for a disease may involve an initial diagnosis by, for example, comparing a sample obtained from an individual with a reference that reflects the presence of the disease or a reference that reflects the absence of disease. In other circumstances, the monitoring may involve comparing an analysis performed on samples taken from the same individual at different times so that a time course of the progression, regression, and/or recurrence of the disease may become evident.
[0131] In another aspect, methods described herein may be used to detect and/or measure a specified set of oxidized peptides in a sample obtained from an individual. The oxidation state of a particular peptide may relate to the presence of at least one marker of oxidative stress such as, for example, 2-amino-3-oxo-butanoic acid; 2-amino-3-oxo-butanoic acid; hydroxylation; glutamate semialdehyde; cysteic acid (sulfonic acid); sulfnic acid; sulfenic acid; formylkynurenin; kynurenin; hydroxykynurenin; 2,4,5,6,7 hydroxylation of tryptophan; oxolactone; 4-hydroxy glutamate; conversion of histidine to asparagine; conversion of histidine to aspartate; 2-oxo-histidine; aminoadipic semialdehyde; an Amadori adduct; a 3-deoxyglucosone adduct; a glyoxal adduct; a methylglyoxal adduct; conversion of proline to pyroglutamic acid; conversion of proline to pyrrolidinone; a 4-HNE (4-hydroxynonenal) adduct; and malondialdehyde adducts and any other oxidative modification.
[0132] In some embodiments, the oxidized peptides may be labeled. The oxidized peptides may be labeled using any suitable method known to those of ordinary skill in the art. The labeling may be performed in vitro or in vivo, as appropriate for the given method. Suitable methods for labeling oxidized peptides include, for example, isotope coded affinity tagging, stable isotope labeling of amino acids in cell culture (SILAC), isobaric tagging for relative and absolute quantification (iTRAQ™), ICAT labeling, labeling using fluorinated affinity tags, amino-terminal sulphonation, dimethyl labeling, global internal standard technology (GIST), 160/180 labeling, or labeling using any combination of such methods.
[0133] Some of these labeling methods and other methods of sample preparation and analysis that are suitable for use in one or more aspects or embodiments of the methods described herein are further described in U.S. Pat. No. 6,864,099, U.S. Pat. No. 6,872,575, U.S. Patent Publication No. 2003/0129769, U.S. Patent Publication No. 2008/0145863, International Patent Application Publication No. WO 2001/86306, U.S. Pat. No. 7,449,170, U.S. Patent Publication No. 2009/0148952, International Patent Application Publication No. WO 2003/027682, and International Patent Application Publication No. WO 2009/134439.
[0134] In some embodiments, the oxidized peptides may be isolated or purified to some extent from a sample having additional components. Oxidized peptides may be isolated or purified from a sample by, for example, affinity chromatography. In some cases, oxidized peptides, previously biotinylated, can be isolated using avidin.
[0135] In another aspect, methods described herein can involve detecting changes in an oxidized peptidic and/or an oxidized proteomic profile that is associated with the presence of a particular disease, absence of a particular disease, or a certain change in status of a particular disease. As used herein, an “oxidized peptidic profile” refers to at least two oxidized peptides and/or oxidized proteins that are obtained from an individual and that can be used to identify the presence, absence, or status of a particular disease in the individual. For example, a particular combination of a plurality of specific oxidized peptides and/or oxidized proteins—i.e., an “oxidized peptidic profile” or, simply, a “profile”—in an individual's sample may identify the absence of a particular disease, while another profile of the particular oxidized peptides and/or oxidized proteins may indicate a disease state, while a further profile of the particular oxidized peptides and/or oxidized proteins may identify a patient's response to therapy and/or the severity, recurrence, or progression of the disease. An “oxidized peptidic profile” can include the identity of oxidized peptides, particular oxidation sites that are oxidized, the particular oxidation at each oxidation site, and/or the frequency or ratio of any of these characteristics in a population of molecules of a particular peptide. Thus, one may compare the oxidation status of a particular oxidation of a peptide with the corresponding oxidation site on other molecules of the same peptide—i.e., other molecules having the same amino acid sequence to determine, for example, whether a particular oxidation site of a particular peptide may have been subject to complete or incomplete oxidation and/or which form or forms of oxidation occur at a particular oxidation site in a sample. Any one or any combination of these characteristics can be sufficient, in certain analyses, to provide information regarding a patient's response to therapy and/or the severity, recurrence, or progression of the disease, or of the patient's general health. Thus, various oxidized peptide profiles may correlate with various states of the disease.
[0136] In another aspect, the methods described herein can provide for tailored treatment of a disease in an individual. By analyzing oxidized peptides and/or oxidized proteins from samples obtained from the individual in the non-diseased state and the diseased state, the individual can serve as an appropriate control for monitoring oxidized peptide profiles taken at different times.
[0137] Example 1 and FIGS. 1-6 report proteomic-based identification and characterization of oxidized proteins in human plasma. The study was conducted by isolating carbonylated proteins from the plasma of male subjects (age 32-36) with avidin affinity chromatography subsequent to biotinylation of carbonyl groups with biotin hydrazide and sodium cyanoborohydride reduction of the resulting Schiff's bases. Avidin-selected proteins were digested with trypsin and the peptide fragments separated by C18 reversed-phase chromatography, identified and characterized by both electrospray ionization and matrix assisted laser desorption ionization mass spectrometry. Approximately 0.2% of the total protein in plasma was selected with this method. Sixty-five high, medium, and low abundance proteins were identified, the majority appearing in all subjects. One feature of the oxidized proteins isolated was that in addition to carbonylation they often bore other types of oxidative modification referred to as an OSi˜PTM. Twenty-four oxidative modifications were mapped in fourteen proteins. Fifteen carbonylation sites carried on seven proteins were detected. Methionine oxidation was the most frequent single type of oxidative modification, i.e., OSi˜PTM, followed by tryptophan oxidation. Apolipoprotein B-100 had 20 oxidative modifications, the largest number for any protein observed in this study. Among the organs contributing oxidized proteins to plasma, kidney, liver, and soft tissues were the most frequent donors. One outcome of this work was that mass spectral analysis allowed differentiation between different biological mechanisms of oxidation in individual proteins. For the first time, oxidation products arising from direct reactive oxygen species (ROS) oxidation of amino acid side chains in proteins, formation of advanced glycation end products (AGEs) adducts, and formation of adducts with lipid peroxidation products were simultaneously recognized and assigned to specific sites in proteins.
[0138] Blood proteins have been widely used in the assessment of human health, primarily in single protein assays (Jacobs et al. J. Proteome Res., 2005, 4(4):1073-85; Issaq et al., Chem Rev, 2007, 107(8):3601-20). We show that new proteomics methods can be used to identify and assess groups of proteins that change in either structure or concentration in association with disease progression. These disease-specific molecular signatures can be more diagnostic than single proteins. Moreover, in many cases, the disease-specific molecular signatures include one or more peptides analyzed from a blood sample that are non-blood peptides. As used herein, a “non-blood peptide” is a peptide that is typically localized in a non-blood tissue such as, for example, the liver, kidney, neural tissue, or another soft tissue. A non-blood peptide may be present in the blood, plasma, or a blood product because the peptide is shed or released into the circulatory system as a result of OS-induced cell death—whether apoptosis, necrosis, or some other mechanism—as opposed to having a typical blood-related function such as, for example, nutrient transport, oxygen transport, endocrine function, immunological function, maintaining osmolarity, and the like.
[0139] One class of diseases amenable to this type of analysis is oxidative stress-associated diseases. Oxidative stress is a phenomenon in which reactive oxygen species can accumulate in cells, organs, or extracellular sites in the circulatory system to a level that proteins, DNA, RNA, and lipids are irreversibly damaged by oxidation. Oxidative stress has been implicated in multiple ailments ranging from neurological diseases such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis to a variety of diseases ranging from atherosclerosis, diabetes mellitus, chronic renal failure, and chronic lung disease, to cancer. Antioxidants and cellular catalysts such as catalase, selenium-dependent glutathione, superoxide dismutase, and thioredoxin hydroperoxidase are sufficiently abundant in most subjects that reactive oxygen species (e.g., hydrogen peroxide, singlet oxygen, peroxynitrite, or superoxide) are destroyed before causing irreversible protein oxidation. However, when the capacity of cells to destroy reactive oxygen species is exceeded, the system may become “oxidatively stressed.” This can lead to protein carbonylation and oxidative injury to cells. However, as a prerequisite to the identification of disease markers that correlate with oxidative stress-related diseases, it is necessary to understand the “normal” distribution of oxidized proteins in plasma. Prior to this disclosure, there is almost no data on oxidized proteins in human blood.
[0140] One possible reason for the paucity of data on oxidized proteins in plasma may be the difficulty of finding oxidized proteins in complex matrices, the multiplicity of possible oxidative modifications, and low abundance (Barelli et al., Proteomics: Clin. Appl., 2007, 2(2):142-157). Methods described herein circumvent some of these problems.
[0141] Methods described herein involve assessing human oxidative stress through detection and analysis of carbonylation in the plasma proteome. Plasma samples taken from normal, undedicated, non-smoking male subjects from 32-36 years of age were obtained, prepared, and analyzed as described in Example 1. MS/MS analyses were carried out in both the matrix assisted laser desorption and electrospray ionization modes. Oxidative stress is thought to be influenced by factors ranging from aging, gender, and diet to smoking, diseases, and medications. Thus, donors were of the same gender (males), non-smoking, had no diagnosed diseases, were not receiving medications, and were age matched (32-36 years of age).
[0142] The analytical protocol used in these studies (FIG. 1) is a modified version of a method first described for the analysis of the yeast proteome (Mirzaei et al., Anal. Chem., 2005, 77(8):2386-2392). A fresh plasma sample (6 ml) was drawn from a donor and carbonyl groups biotinylated with biotin hydrazide and the resulting Schiff bases reduced. Samples thus derivatized were dialyzed to remove excess biotin hydrazide along with low molecular weight biotinylated species including small polypeptides. Following dialysis only high molecular weight species remained, most of which were proteins. Avidin affinity chromatography was then used to select and enrich carbonylated species for identification of oxidized proteins, sites of oxidation, and the particular types of oxidation involved.
[0143] Oxidized proteins were identified in two ways. One was by RPC fractionation of the avidin-selected fraction followed by proteolysis of RPC fractions and MS-based identification with MALDI-MS/MS. A second approach was to tryptic digest the affinity-selected fraction and proceed directly to RPC- and MS-based identification. Because the second method is simpler and provided similar numbers of identifications to the first, the second method was used in this work. Avidin-selected proteins were digested with trypsin, the peptide cleavage fragments fractionated with a C18 RPC column, and the peptides from RPC identified by MALDI-MS/MS (4800 plus, Applied Biosystems, Inc., Foster City, Calif.) and then further characterized with an LTQ Orbitrap XL. Protein Pilot and Mascot were used for the analysis of the mass spectra respectively as described in Example 1.
[0144] In order to isolate and analyze carbonylated proteins from the plasma proteome, one must consider that carbonyl groups react readily with free amine groups on proteins as they sit on the bench or are stored, thereby forming Schiff's bases and making the carbonyl groups inaccessible to biotin hydrazide derivatization. Thus, we added a large excess of biotin hydrazide to plasma samples within a few minutes of the time they are drawn. Once biotin hydrazide has been added, samples can be frozen and stored indefinitely. Further, we added a protease inhibitor cocktail to the samples to inhibit the activity of intrinsic cysteine and serine proteases and preserve the samples.
[0145] An avidin affinity column was used in our procedure. Plasma samples were applied directly onto the affinity column without abundant protein removal. The column was used in processing approximately 50 samples over six months without significant loss of capacity, based on the binding of standard biotinylated bovine serum albumin at periodic intervals.
[0146] Although abundant protein removal is widely used in discovery proteomics studies, that approach was not used in this work for several reasons. One reason is that oxidized proteins could be associated non-covalently with abundant proteins and be removed. Recent studies in which 129 low abundance proteins were found to be associated with abundant proteins gives credibility to this abundant protein “sponge effect” hypothesis (Issaq et al., Chem Rev, 2007, 107(8):3601-20; Gong et al, J. Proteome Res. 2006, 5(6):1379-1387). A second reason is that peptides from abundant proteins did not interfere with oxidized protein identification. In fact, there was no relationship between the presence of peptides from abundant proteins and their concentration in affinity selected fractions. A third reason is that washing affinity columns with 15 or more column volumes of loading buffers can elute most proteins bound to columns with low affinity. In our procedure, sixty column volumes of loading buffer were pumped through the affinity column after loading. During this washing step, eluent absorbance returned to zero (FIG. 2), as expected.
[0147] The avidin affinity column captured approximately 0.2% of the protein in plasma, based on absorbance at 280 nm (Table 4). It should be noted that naturally biotinylated proteins, proteins naturally complexed with or cross-linked to the biotinylated proteins, and non-specifically bound proteins are included in this affinity-selected fraction as well. The relative standard deviation of peak areas among the four human plasma samples was 0.057%.
[0148] 
[00004] [TABLE-US-00004]
  TABLE 4
 
  The relative amounts of the affinity-purified protein
  from normal human plasma samples selected by
  avidin affinity chromatography
      % affinity-purified proteins
    Donor number   (based on the 280 nm absorbance)
   
    Donor number 1   0.20%
    Donor number 2   0.23%
    Donor number 3   0.14%
    Donor number 4   0.28%
    Average   0.21%
    Standard deviation   0.057
   
[0149] Because MALDI-MS/MS and ESI-MS/MS used together often can identify more proteins than either method alone, both types of ionization were used in these studies. Analysis of affinity-selected proteins from the four plasma samples generated an average of 7724 spectra in the ESI-MS mode as opposed to 601 spectra in the MALDI-MS mode. An average of sixty-five proteins were identified based on 525 unique peptides identified by ESI-MS/MS and 225 by MALDI-MS/MS. Twenty-four proteins were identified by both MALDI-MS and the ESI-MS. Thirty-two proteins were identified by ESI-MS alone and five proteins were identified by MALDI-MS only (FIG. 3). The data showed good reproducibility. Fifty-eight proteins were identified in all the four donors, two proteins were identified in three donors only and five proteins were identified in two donors only
[0150] The list of proteins identified (Table 5 and Table 6) includes highly abundant proteins (e.g., alpha-1-antitrypsin precursor, alpha-2-macroglobulin precursor, apolipoprotein A-I precursor, apolipoprotein B-100 precursor), some of moderate abundance (e.g., hempexin and kininogen-1 precursor) and several low abundance proteins (e.g., tetranectin precursor and transthyretin precursor). Tissue origin of the proteins found in this study was obtained from the Human Protein Atlas (version 4.0). This is a database containing approximately five million immunohistochemical-based images produced using 6120 antibodies against 5067 protein coding human genes. Kidney (cells in the tubules), soft tissues and liver (hepatocytes) contributed the largest numbers of proteins found in this study (FIG. 5).
[0151] 
[00005] [TABLE-US-00005]
  TABLE 5
 
  Proteins identified using the LTQ Orbitrap XL.
    Donor 1   Donor 2   Donor 3   Donor 4
        Number of       Number of       Number of       Number of  
        unique peptides       unique peptides       unique peptides       unique peptides
      Sequence   assigned to the     Sequence   assigned to the     Sequence   assigned to the     Sequence   assigned to the   Mascot
  Accessions   Name   coverage   protein   Mascot score   coverage   protein   Mascot score   coverage   protein   Mascot score   coverage   protein   score
 
  A1AT_HUMAN   Alpha-1-antitrypsin   20.1   7   342   25   15   353   53.1   39   1801   49   15   1203
  precursor (Homo
  sapiens (Human)
  A2AP_HUMAN   Alpha-2-antiplasmin   3.5   1   23   13   2   128   11.4   12   106   7.9   2   125
  precursor Homo
  sapiens (Human)
  FETUA_HUMAN   Alpha-2-HS-   ND   ND   ND   14   4   218   22.9   7   506   32   6   488
    glycoprotein
    precursor (Fetuin-A)-
  Homo sapiens
    (Human)
  ANT3_HUMAN   Antithrombin-III   ND   ND   ND   13   3   214   7.8   14   75   16   4   224
    Precursor (ATIII)-
  Homo sapiens
    (Human)
  APOA1_HUMAN   Apolipoprotein A-I   51.3   17   1953   60   20   3570   60.7   6   2835   52   18   2639
    precursor (Apo-AI)
  Homo sapiens
    (Human)
  APOA2_HUMAN   Apolipoprotein A-II   43   4   691   59   5   1246   28   3   382   25   2   373
    precursor (Apo AII)
  (Homo sapiens
    (Human)
  APOA4_HUMAN   Apolipoprotein A-IV   30.8   10   527   27   8   678   10.6   22   136   25   6   360
    precursor (Apo-AIV)
  (Homo sapiens
    (Human)
  APOB_HUMAN   Apolipoprotein B-100   18.7   63   3123   21   69   4342   ND   ND   ND   0.6   6   33
    precursor (Apo B-100)
  Homo sapiens
    (Human)
  APOC1_HUMAN   Apolipoprotein C-I   26.5   4   685   13   3   240   45.8   5   891   27   3   642
    precursor (Apo-CI)
  Homo sapiens
    (Human)
  APOC3_HUMAN   Apolipoprotein C-III   27.3   2   477   27   2   189   62.6   4   1419   30   4   669
    precursor (Apo-CIII)
  Homo sapiens
    (Human)
  APOD_HUMAN   Apolipoprotein D   26.5   6   497   19   3   235   20.1   5   273   20   3   351
    precursor (Apo D)
  Homo sapiens
    (Human)
  APOE_HUMAN   Apolipoprotein E   12.9   4   148   27   7   523   10.1   10   89   9.5   2   117
    precursor (Apo-E)-
  Homo sapiens
    (Human)
  APOM_HUMAN   Apolipoprotein M   39.9   6   253   26   3   174   29.3   2   272   33   2   191
  (Apo-M) Homo
  sapiens (Human)
  APOH_HUMAN   Beta-2-glycoprotein 1   19.1   3   65   22   4   229   40.6   32   875   33   10   594
    precursor (Beta-2-
    glycoprotein I)
    (Apolipoprotein H)
  (Homo sapiens
    (Human)
  C4BP_HUMAN   C4b-binding protein   48.1   24   8900   58   25   9153   17.6   8   573   12   4   419
    alpha chain precursor
  (C4bp) Homo sapiens
    (Human)
  C4BB_HUMAN   C4b-binding protein   34.9   6   1014   26   4   563   12.3   6   55   12   2   114
    beta chain precursor-
  Homo sapiens
    (Human)
  CADH5_HUMAN   Cadherin-5 precursor   ND   ND   ND   ND   ND   ND   5.2   7   86   5.2   6   63
    (Vascular endothelial-
    cadherin) (VE-
  cadherin) Homo
  sapiens (Human)
  CD5L_HUMAN   CD5 antigen-like   20.7   5   297   40   11   612   ND   ND   ND   ND   ND   ND
    precursor (SP-alpha)
  Homo sapiens
    (Human)
  CLUS_HUMAN   Clusterin precursor   20.3   8   462   23   6   611   22.9   3   729   27   8   918
  Homo sapiens
    (Human)
  FA5_HUMAN   Coagulation factor V   0.7   3   29   0.6   2   39   4.1   4   181   3.9   8   436
  precursor Homo
  sapiens (Human)
  C1QB_HUMAN   Complement C1q   20.7   3   279   21   4   232   ND   ND   ND   ND   ND   ND
    subcomponent
    subunit B precursor-
  Homo sapiens
    (Human)
  C1QC_HUMAN   Complement C1q   16.3   2   307   17   3   175   ND   ND   ND   ND   ND   ND
    subcomponent
    subunit C precursor-
  Homo sapiens
    (Human)
  CO3_HUMAN   Complement C3   5.5   6   1474   15   10   1083   5   6   608   2.6   0   610
  precursor-Homo
  sapiens (Human)
  CO4B_HUMAN   Complement C4-B   4.6   4   571   4.6   4   651   ND   ND   ND   ND   ND   ND
  precursor Homo
  sapiens (Human)
  CFAH_HUMAN   Complement factor H   4.5   3   146   7.1   8   397   1.5   5   61   1.9   1   141
    precursor (H factor 1)-
  Homo sapiens
    (Human)
  FIBA_HUMAN   Fibrinogen alpha chain   31.1   16   5885   27   22   6213   32.3   32   9346   26   16   7145
  precursor Homo
  sapiens (Human)
  FIBB_HUMAN   Fibrinogen beta chain   30.3   11   428   42   13   782   27.3   3   387   20   10   617
  precursor Homo
  sapiens (Human)
  FIBG_HUMAN   Fibrinogen gamma   24.1   5   263   37   10   782   28   5   342   26   5   601
    chain precursor-
  Homo sapiens
    (Human)
  FINC_HUMAN   Fibronectin precursor   1.5   3   61   3.5   7   262   ND   ND   ND   ND   ND   ND
  (FN) Homo sapiens
    (Human)
  HPTR_HUMAN   Haptoglobin-related   12.1   3   164   16   3   100   14.1   1   226   14   1   314
    protein precursor-
  Homo sapiens
    (Human)
  HBA_HUMAN   Hemoglobin subunit   25.4   3   261   15   1   59   48.6   2   158   42   2   143
  alpha Homo sapiens
    (Human)
  HBB_HUMAN   Hemoglobin subunit   21.8   3   353   22   3   254   6.8   8   33   16   2   95
  beta Homo sapiens
    (Human)
  HEMO_HUMAN   Hemopexin precursor   1.7   3   40   6.3   3   105   18.4   6   219   17   4   251
  Homo sapiens
    (Human)
  HRG_HUMAN   Histidine-rich   10.5   2   131   5.7   2   91   20.8   26   444   21   6   465
    glycoprotein
    precursor (HPRG)-
  Homo sapiens
    (Human)
  IGHA1_HUMAN   Ig alpha-1 chain C   45.3   8   2006   48   10   1732   47.3   2   1318   48   9   2094
  region-Homo sapiens
    (Human)
  IGHG1_HUMAN   Ig gamma-1 chain C   59.4   15   3676   58   13   4293   50.9   4   2693   52   13   2288
  region-Homo sapiens
    (Human)
  IGHG2_HUMAN   Ig gamma-2 chain C   40.8   7   1629   41   7   1985   41.1   5   1241   42   8   1191
  region-Homo sapiens
    (Human)
  IGHG3_HUMAN   Ig gamma-3 chain C   29.7   5   655   29   2   1056   22.8   4   681   20   2   520
  region Homo sapiens
    (Human)
  IGHG4_HUMAN   Ig gamma-4 chain C   44.6   3   1459   44   2   1373   31.8   2   937   37   2   912
  region-Homo sapiens
    (Human)
  KAC_HUMAN   Ig kappa chain C   86.8   7   7925   82   7   6495   86.8   4   3469   91   9   2345
  region-Homo sapiens
    (Human)
  LAC_HUMAN   Ig lambda chain C   70.5   4   2437   71   5   2016   46.7   3   574   47   3   842
  regions-Homo
  sapiens (Human)
  MUC_HUMAN   Ig mu chain C region-   58.6   17   6770   63   25   7084   36.3   4   1348   31   8   507
  Homo sapiens
    (Human)
  K1C10_HUMAN   Keratin, type I   20.7   6   463   28   8   577   35.6   4   2523   40   19   2575
    cytoskeletal 10
    (Cytokeratin-10)-
  Homo sapiens
    (Human)
  K1C9_HUMAN   Keratin, type I   14   4   349   17   4   412   34.8   6   1794   41   15   1875
    cytoskeletal 9
    (Cytokeratin-9)-
  Homo sapiens
    (Human)
  K2C1_HUMAN   Keratin, type II   18.3   8   1062   11   7   689   28.7   1   2608   27   14   2994
    cytoskeletal 1
  (Cytokeratin-1) Homo
  sapiens (Human)
  K22E_HUMAN   Keratin, type II   7.1   3   238   5.6   6   404   25.4   2   1207   25   10   1171
    cytoskeletal 2
    epidermal
    (Cytokeratin-2e)
  (Homo sapiens
    (Human)
  K2C5_HUMAN   Keratin, type II   ND   ND   ND   ND   ND   ND   5.9   3   298   9   2   411
    cytoskeletal 5
  (Cytokeratin-5) Homo
  sapiens (Human)
  KNG1_HUMAN   Kininogen-1 precursor   ND   ND   ND   7.5   4   115   23.3   5   861   21   10   452
    (Alpha-2-thiol
    proteinase inhibitor)
  Homo sapiens
    (Human)
  LBP_HUMAN   Lipopolysaccharide-   6   2   87   10   3   228   14.6   10   724   15   5   322
    binding protein
    precursor (LBP)-
  Homo sapiens
    (Human)
  LYSC_HUMAN   Lysozyme C precursor   26.4   3   209   39   4   614   20.3   2   288   20   3   385
  (EC 3.2.1.17) Homo
  sapiens (Human)
  PHLD1_HUMAN   Phosphatidylinositol-   8.8   5   245   10   4   218   2.6   1   82   8.7   7   243
    glycan-specific
    phospholipase D 1
  precursor Homo
  sapiens (Human)
  PCOC1_HUMAN   Procollagen C-   12.7   5   450   23   5   299   36.5   2   729   28   7   324
    endopeptidase
    enhancer 1 precursor
  Homo sapiens
    (Human)
  PRG4_HUMAN   Proteoglycan-4   9.2   17   889   9.7   20   725   9.2   8   608   11   11   790
    precursor (Lubricin)
    (Human)
  TRFE_HUMAN   Serotransferrin   ND   ND   ND   8.5   5   123   8   2   229   6.3   3   120
    precursor
  (Transferrin) (Homo
  sapiens (Human)
  ALBU_HUMAN   Serum albumin   32.2   12   784   38   14   1204   24.8   3   1446   25   12   1070
  precursor-Homo
  sapiens (Human)
  PON1_HUMAN   Serum   37.5   8   1493   46   10   1730   47.3   1   882   53   10   1246
    paraoxonase/arylesterase
  1-Homo sapiens
    (Human)
  PON3_HUMAN   Serum   2.5   1   59   2.5   9   122   2.5   4   56   2.5   1   128
    paraoxonase/lactonase
  3 Homo sapiens
    (Human)
  TETN_HUMAN   Tetranectin precursor   ND   ND   ND   ND   ND   ND   5.9   3   63   23   4   197
    (TN) 4-binding
  protein)-Homo
  sapiens (Human)
  PROS_HUMAN   Vitamin K-dependent   24.7   13   1189   23   12   1348   22.9   2   569   23   12   866
    protein S precursor-
  Homo sapiens
    (Human)
  VTNC_HUMAN   Vitronectin precursor   14.4   5   419   22   5   787   23   2   988   20   6   1053
    (Serum-spreading
  factor) Homo sapiens
    (Human)
  TTHY_HUMAN   Transthyretin   15   2   76   ND   ND   ND   23.8   11   180   15   1   130
  precursor Homo
  sapiens (Human)
 
[0152] 
[00006] [TABLE-US-00006]
  TABLE 6
 
  Proteins identified using MALDI-MS/MS (ABI 4800 plus)
    Donor 1   Donor 2   Donor 3   Donor 4
        Number of       Number of       Number of       Number of  
        unique       unique       unique       unique  
        peptides       peptides       peptides       peptides  
        assigned       assigned       assigned       assigned  
      Sequence   to the   ProteinPilot   Sequence   to the   ProteinPilot   Sequence   to the   ProteinPilot   Sequence   to the   ProteinPilot
  Accessions   Name   coverage   protein   score   coverage   protein   score   coverage   protein   score   coverage   protein   score
 
  P01009|A1AT_HUMAN   Alpha-1-antitrypsin   11.72   1   2   3.349   1   2   10.5   2   4   3.35   2   2
  precursor Homo                        
  sapiens (Human)                        
  P01023|A2MG_HUMAN   Alpha-2-macroglobulin   8.005   4   8   17.3   9   18   19.3   14   29.02   10   4   7.5
    precursor (Alpha-2-M)-                        
  Homo sapiens                        
    (Human)                        
  P01008|ANT3_HUMAN   Antithrombin-III   23.71   3   6.24   13.15   3   6   20.5   3   6   12.5   2   4
    precursor (ATIII)-                        
  Homo sapiens                        
    (Human)                        
  P02647|APOA1_HUMAN   Apolipoprotein A-I   44.57   5   10   52.06   6   11.7   62.5   7   14.33   50.9   6   12
    precursor (Apo-AI)                        
  Homo sapiens                        
    (Human)                        
  P04114|APOB_HUMAN   Apolipoprotein B-100   10.96   3   7.73   7.364   1   2   12.5   3   6.48   13   4   8
    precursor (Apo B-100)                        
  Homo sapiens                        
    (Human)                        
  P02654|APOC1_HUMAN   Apolipoprotein C-I   24.1   3   4   13.25   2   4   13.3   2   4   32.5   3   4
    precursor (Apo-CI)                        
    (ApoC-I)-Homo                        
    sapiens (Human)                        
  P02649|APOE_HUMAN   Apolipoprotein E   27.44   2   4.37   44.48   2   5.36   45.1   6   12   28.7   2   4
    precursor (Apo-E)-                        
  Homo sapiens                        
    (Human)                        
  P04003|C4BP_HUMAN   C4b-binding protein   21.27   4   9.61   26.97   6   12.9   23.3   5   10.35   33.8   7   15
    alpha chain precursor                        
  (C4bp) Homo sapiens                        
    (Human)                        
  P00450|CERU_HUMAN   Ceruloplasmin   5.446   2   4.44   10.99   2   4.08   14.3   3   5.7   13.9   2   4
    precursorHomo                        
    sapiens (Human)                        
  P10909|CLUS_HUMAN   Clusterin precursor   21.38   6   14.9   21.38   4   11   30.5   4   11.35   24.3   4   11
  Homo sapiens                        
    (Human)                        
  P00736|C1R_HUMAN   Complement C1r   17.73   3   6   23.55   6   10.8   19.4   4   8.2   27.2   5   12
    subcomponent                        
  precursor Homo                        
  sapiens (Human)                        
  P01024|CO3_HUMAN   Complement C3   17.14   7   14.7   21.89   8   19.7   25.2   11   25.16   19.7   12   21
  precursor-Homo                        
  sapiens (Human)                        
  P0C0L5|CO4B_HUMAN;   Complement C4-B   12.67   3   6.98   18.86   3   6.88   17.2   4   8.13   20.1   3   6.3
  P0C0L4|CO4A_HUMANprecursor Homo                        
  sapiens (Human)                        
  P02671|FIBA_HUMAN   Fibrinogen alpha chain   40.88   23   44.4   41.8   23   50.4   49.1   37   85.8   39.1   20   45
  precursor Homo                        
  sapiens (Human)                        
  P02675|FIBB_HUMAN   Fibrinogen beta chain   58.04   20   55.7   63.75   27   66   70.9   28   74.05   62.7   29   69
  precursor Homo                        
  sapiens (Human)                        
  P02679|FIBG_HUMAN   Fibrinogen gamma   55.63   12   26.3   64.9   15   30   77.9   20   43.09   63.8   14   30
    chain precursor-                        
  Homo sapiens                        
    (Human)                        
  P02751|FINC_HUMAN   Fibronectin precursor   33.91   22   52.6   47.49   47   103   28   49   105.6   40.6   47   100
  (FN) Homo sapiens                        
    (Human)                        
  P68871|HBB_HUMAN   Hemoglobin subunit   51.7   4   7.82   27.21   2   5.22   27.2   2   4   ND   ND   ND
  beta Homo sapiens                        
    (Human)                        
  P02790|HEMO_HUMAN   Hemopexin precursor-   22.94   5   10.9   37.66   9   17.5   29.9   4   9.22   32.5   7   13
  Homo sapiens                        
    (Human)                        
  P01876|IGHA1_HUMAN   Ig alpha-1 chain C   ND   ND   ND   ND   ND   ND   20.4   2   4.16   33.4   5   9.7
  region-Homo sapiens                        
    (Human)                        
  P01877|IGHA2_HUMAN;   Ig alpha-2 chain C   10   3   6   7.941   2   3.52   ND   ND   ND   35.3   6   9.7
  P01876|IGHA1_HUMANregion-Homo sapiens                        
  (Human); Homo                        
  sapiens (Human)                        
  P01857|IGHG1_HUMAN   Ig gamma-1 chain C   39.7   6   12   44.24   5   10   44.2   7   15.38   50   8   15
  region-Homo sapiens                        
    (Human)                        
  P01859|IGHG2-HUMAN   Ig gamma-2 chain C   36.2   5   10   32.82   4   8.02   ND   ND   ND   38.7   9   17
  region-Homo sapiens                        
    (Human)                        
  P01861|IGHG4_HUMAN   Ig gamma-4 chain C   ND   ND   ND   ND   ND   ND   37   5   12   47.7   6   15
  region-Homo sapiens                        
    (Human)                        
  P01842|LAC_HUMAN   Ig lambda chain C   14.29   1   2   14.29   1   2   33.3   2   4   14.3   6   2
  regions-Homo sapiens                        
    (Human)                        
  P01871|MUC_HUMAN   Ig mu chain C region-   22.69   6   12   19.16   6   12   39.9   7   15.53   27.3   5   11
  Homo sapiens                        
    (Human)                        
  P02787|TRFE_HUMAN   Serotransferrin   ND   ND   ND   10.74   3   5.53   14.6   2   4   21.5   2   4
    precursor (Transferrin)                        
  Homo sapiens                        
    (Human)                        
  P02768|ALBU_HUMAN   Serum albumin   30.54   6   12.4   27.42   8   14.2   32.8   13   27.75   48.4   10   20
  precursor-Homo                        
  sapiens (Human)                        
  P27169|PON1_HUMAN   Serum   28.45   4   8   25.63   4   8   21.7   4   6   35.2   5   10
    paraoxonase/arylesterase                        
  1 Homo sapiens                        
    (Human)                        
  P07225|PROS_HUMAN   Vitamin K-dependent   6.657   2   5.15   20.71   5   10   7.84   3   6.33   16.1   3   6.4
    protein S precursor-                        
  Homo sapiens                        
    (Human)                        
  P04004|VTNC_HUMAN   Vitronectin precursor   23.85   5   11.7   26.57   6   12.3   26.8   10   21.44   27.6   7   16
    (Serum-spreading                        
  factor) Homo sapiens                        
    (Human)
 
[0153] Seventy-four oxidative stress-induced post-translational modifications (OSi˜PTM) were mapped to these proteins (FIG. 6). Among the sites identified, oxidation of methionine-to-methionine sulphoxide appeared more frequently than any other OSi˜PTM. Twenty-two proteins with a methionine sulphoxide OSi˜PTM were mapped. The oxidation of tryptophan to dihydroxytryptophan, kynurenin, hydroxykynurenin, or hydroxytryptophan came second in frequency of oxidation. The high frequency of methionine oxidation agrees with literature reports that sulfur amino acids (methionine and cysteine) are more sensitive to oxidation by reactive oxygen species than other amino acids (Stadtman et al., Biochim. Biophys. Acta, Proteins Proteomics, 2005, 1703(2):135-140). That only one cysteine oxidation product was detected is not surprising for several reasons. One reason is that disulfide and sulfenic acid reduction occurs naturally in biological systems. Another reason is that detecting the oxidation of sulfhydryls to disulfides can be precluded by the reduction of disulfide bonds with dithiothreitol and iodoacetamide alkylation during sample preparation.
[0154] Carbonyl groups generated directly (e.g., by oxidation of threonine, arginine, lysine, and proline) and those formed indirectly (e.g., by the formation of Amadori, glyoxal, methylglyoxal, 3-doxyglucosone, and 2-hydroxy-nonenal adducts) could be isolated with the same procedure. Thus, it is possible to simultaneously identify oxidation products involving direct ROS oxidation of amino acid side chains residues, oxidation of adducts from AGE product addition, and adducts from lipid peroxidation products.
[0155] Fourteen proteins were shown to have at least one oxidation site as seen in Table 7. Apolipoprotein B-100 in contrast was found to undergo 20 oxidative modifications; the largest number of OSi˜PTMs of any protein observed. Interestingly, this protein was carbonylated due to the formation of both glycation/AGEs (3-deoxyglucosone and methylglyoxal) and lipid peroxidation (2-hydroxy-nonenal) adducts. Fifteen carbonylation sites carried on seven proteins were detected. These proteins are: alpha-2-HS-glycoprotein precursor (fetuin-A), antithrombin-III precursor (ATIII), apolipoprotein B-100 precursor (Apo B-100), apolipoprotein E precursor (Apo-E), C4b-binding protein alpha chain precursor, clusterin precursor and coagulation factor V precursor.
[0156] 
[00007] [TABLE-US-00007]
  TABLE 7
 
  Proteins identified in the pooled sample with at least one oxidation site
  Protein description   Oxidative modifications
 
  Alpha-1-antitrypsin precursor - 16M398, 16M382 15F376
Homo sapiens (Human)  
  Alpha-2-HS-glycoprotein precursor 16M321, 15F320, 1T158, 3R159
(Fetuin-A) Homo sapiens (Human)  
  Antithrombin-III precursor (ATIII) - 23K182
Homo sapiens (Human)  
  Apolipoprotein A-I precursor (Apo-AI) -16M110, 6W74, 6W96, 9W96, 16M136,
Homo sapiens (Human)9W74, 12N73, 11D97, 15F95
  Apolipoprotein A-II precursor (Apo-AII) -22L48, 2Y44
Homo sapiens (Human)  
  Apolipoprotein B-100 precursor (Apo B-100)16M3280, 13P3281, 16M1266, 21K3229,
Homo sapiens (Human)18K3234, 16M1881, 11D1880, 16M1189,
  16M2526, 22L1060, 16M901, 16M2597,
  16M812, 16M495, 16M499, 18K305,
  19K314, 16M2042, 18K766, 23K2147
  Apolipoprotein C-I precursor (Apo-CI) 7W67, 6W67, 9W67, 16M64, 8W67, 15F68
(ApoC-I) - Homo sapiens (Human)  
  Apolipoprotein C-III precursor (Apo-CIII)7W85, 6W85, 7W62, 8W62, 9W62, 10W62,
(ApoC-III) - Homo sapiens (Human)6W62, 6W74, 9W62, 11D65, 2Y73, 9W74
  Apolipoprotein E precursor (Apo-E) - 14P30
Homo sapiens (Human)  
  C4b-binding protein alpha chain precursor16M249, 6W163, 11D243, 22L555, 4C468,
(C4bp) - Homo sapiens (Human)22L466, 5C468, 2Y470, 9W163, 1T592, 1T419
Cadherin-5 precursor - Homo sapiens16M396
  (Human)  
Clusterin precursor Homo sapiens (Human)1T93, 17K94
  Coagulation factor V precursor (Activated20K2132
protein C cofactor) Homo sapiens (Human)  
  Complement C1q subcomponent subunit B16M147
precursor - Homo sapiens (Human)
 
1Biotinylated oxidized threonine;
2Tyrosine hydroxylation;
3Biotinylated oxidized Arginine;
4Cysteic acid (sulfonic acid);
5Sulfenic acid;
6Dihydroxytryptophan;
7Kynurenin;
8Hydroxykynurenin;
92,4,5,6,7 hydroxylation of tryptophan;
10Oxolactone;
11Aspartic acid hydroxylation;
12Asparagines hydroxylation;
13Proline hydroxylation;
14Biotinylated oxidized proline;
15Phenylalanine hydroxylation;
16Methionine oxidation (sulphoxide);
17Biotinylated oxidized Lysine;
18Biotinylation of Amadori adduct;
19Biotinylation of 3 deoxyglucosone adduct;
20Biotinylation of glyoxal adduct;
21Biotinylation of methylglyoxal adduct;
22hydroxy Leucine;
23Biotinylation of 2-Hydroxynonenal adduct.
[0157] Avidin affinity-selected proteins with no detectable carbonyls also were seen. This could be because they were cross-linked or complexed with carbonylated proteins. It could also be the result of non-specific binding to the avidin-agarose affinity system. Still another possibility is that two other unoxidized peptides from the proteins were not found as specified in our identification criteria. This strict evaluation criterion eliminated a large number of ambiguous carbonylation sites not confirmed by manual interpretation of the mass spectra.
[0158] In vivo oxidation of human plasma proteins has been most widely studied with colorimetric methods as in the cases of risk assessment with breast cancer and smoking (Rossner et al., J Cell Mol Med, 2007, 11(5):1138-1148; Yeh et al., Environ. Res., 2008, 106(2):219-225). These studies, however, do not identify the proteins being oxidized. In contrast, the methods described herein exploit biotin labeling of carbonyl groups and avidin affinity chromatography selection of the biotinylated species to isolate heavily oxidized proteins from the plasma proteome, identify the oxidized proteins, and characterize the sites and types of oxidation. Approximately 0.2% of the total protein in plasma was isolated by avidin affinity chromatography in these 32- to 36-year-old male subjects. Eleven different amino acids were found to be involved in at least 23 different types of OSi˜PTM. Sixty-five proteins were identified from the male subjects. With this degree of complexity it is possible that a peptide with an OSi˜PTM can be of very similar mass to an unmodified peptide, making it difficult to differentiate between them. Differentiation was most specifically achieved by going to high mass accuracy instrumentation such as the LTQ-Oribitrap XL mass analyzer with a mass accuracy approaching 1 ppm.
[0159] Oxidized proteins were found in these normal male subjects, indicating that irreversible oxidative stress with accompanying permanent alterations of protein structure is a part of normal metabolic processes. Thus, protein oxidation can be analyzed in an individual who is not exhibiting any signs or symptoms of a disease. The methods described herein can, therefore, be performed on samples obtained from normal individuals to establish a personal baseline against which analyses of samples obtained from the individual can be compared.
[0160] The presence of a conjugated metal in a protein increases the probability it will be carbonylated, presumably by internal metal catalysis. As expected, serotransferrin, ceruplasmin, hemoglobin, hemopexin, and fibrinogen were identified in our study. Fibrinogen is i) more likely to be oxidized than serum albumin, ii) the most susceptible protein to oxidation in plasma under intravenous administration of iron gluconate, iii) one of the best indicators of oxidative stress in coronary heart disease, and iv) readily oxidized to the level that it causes atherosclerosis and thrombosis along with changes in blood rheology. At least in the case of atherosclerosis, some of the same metalloproteins associated with the disease state are also seen in normal male subjects, albeit not at the same concentration.
[0161] Unsequestered metals play a major role in protein oxidation through the Fenton reaction as well, especially in diseases that reduce the concentration of proteins that chelate metals. Friedreich's Ataxia is one example in which production of the mitochondrial protein frataxin declines with disease progression. The function of frataxin is to sequester Fe+2 in mitochondria. As its concentration declines with disease progression, Fe+2 concentration increases in mitochondria and ROS production rises sharply as a result of the Fenton reaction, causing serious neurological problems similar to muscular dystrophy.
[0162] A series of other variables contribute to the presence of oxidized proteins in the plasma proteome as well. One is the mechanism by which proteins from organs and cells enter the plasma proteome and the location at which oxidation occurs. There is no evidence that oxidized proteins enter plasma by excretion from cells. Instead, cells die and lyse. As cells die and lyse, their soluble proteins may be released into the circulatory system. Thus, protein oxidation can occur in a biological compartment (organ, tissues, cells, or sites along the vascular endothelium) other than plasma and the oxidized proteins can be released into and diffuse throughout the plasma proteome, where they may be easily recovered, isolated, identified, and analyzed. Analyzing oxidized proteins can be a powerful tool in the study, diagnosis, and/or monitoring of diseases that involve sufficiently high incidence of cell death that the oxidized proteins resulting from that cell death may be recovered from the plasma proteome.
[0163] The location of a protein relative to sources of oxidative stress can influence the frequency and/or extent to which a protein may be oxidized. For example, membrane proteins are necessarily in relatively close proximity to fatty acids in the plasma membrane. Lysine, cysteine, or histidine residues on proteins can be subjected to addition, through a Michael addition mechanism, of fatty acid fragments that can result from lipid peroxidation. Integral membrane proteins and proteins in close proximity to membranes are the most likely to undergo this type of adduct formation. Proximity to sources of oxidation also may influence the oxidation of proteins in the circulatory system. The largest number of oxidative modifications in a plasma protein was found on apolipoprotein B-100, which is involved in oxidative modification of LDL and atherosclerosis (Obama et al., Proteomics, 2007, 7(13):2132-2141). α-1-Antitrypsin was also readily oxidized and is a component of LDL. This protein is another part of the complex that accumulates on arterial walls and contributes to early stage atherosclerosis. (Mashiba et al., Arterioscler., Thromb., Vasc. Biol., 2001, 21(11):1801-1808.) The results of Example 1 show that proteins associated with heart disease are being produced in high abundance in normal individuals in the 32- to 36-year age group.
[0164] Advanced glycation end (AGE) products were found in this study of normal individuals. Since glycation of a protein seems to have relatively little impact on biological activity, it is interesting that glycated proteins are being oxidized. The resulting carbonylated proteins would seem to be more dangerous to cells than the glycated starting material. Protein glycation along with the subsequent AGE oxidation is an issue in diabetes. The analytical protocol described here for the analysis of AGE oxidation products may be of value in the study of OS in diabetes.
[0165] Tracing oxidized proteins back to their tissue of origin using the Human Protein Atlas showed that kidney, liver, and soft tissues contributed more proteins than other organs. This observation is supported by literature showing that high levels of oxidative stress occur in the liver and kidney (Dhahbi et al., Biology of Aging and Its Modulation, 2003, 3:271-291; Nistala et al., Antioxid. Redox Signaling, 2008, 10(12):2047-2090). This may allow the identification of organs experiencing high oxidative stress through the analysis of blood.
[0166] Example 1 and FIGS. 1-6 show that approximately 0.2% of the protein in the plasma of normal, 32- to 36-year-old human male subjects can be selected by avidin affinity chromatography after biotinylation of carbonyl groups with biotin hydrazide and that this protein fraction has a series of characteristic, OS-related features. One feature is that roughly 25 different types of OS-induced post-translational modifications (OSi-PTMs) are present in normal plasma. Multiple forms of OSi-PTM were observed with the same amino acid species in addition to the same OSi-PTM occurring on multiple amino acids. A second feature is that more than half of the amino acid species used in protein synthesis were modified by some type of post-translational modification and that these modifications did not appear to have involved enzymatic catalysis. A third feature was that a protein could be oxidized at multiple sites in an unrelated, non-stoichiometric pattern, thus creating multiple oxidized isoforms of a protein.
[0167] A further observation is that some of the proteins reported to be a factor in several oxidative stress related diseases are present in the plasma of normal male subjects. Whether these individuals will later develop these disease is unknown. If they do, oxidized proteins might be useful as early indicators of impending health problems.
[0168] Finally, the probability that a plasma protein will be oxidized can depend on a series of variables. Among these variables are i) structural features such as the presence of conjugated metals and the position of specific amino acids in the structure of the protein, ii) the degree of oxidative stress in the compartment where the protein originated before being released into the circulatory system, iii) the extent of AGE generation in cells and plasma, and iv) the concentration of ROS at locations in the circulatory system such as with atherosclerosis. The fact that plasma proteins can be used to assess localized oxidative stress might make them useful as clinical diagnostic agents.
[0169] The methods described in Example 1 establish the technical foundation on which the remaining methods described herein are based. Examples 3-6, discussed in more detail below, reflect methods that exploit the technical platform described in Example 1. Examples 3-6 describe methods that isolate, detect, quantify, characterize the degree of oxidation, and/or identify oxidized peptides associated with particular conditions: breast cancer (Example 3), Parkinson's disease (Example 4), and diabetes mellitus (Example 5 and Example 6). The Examples, below, establish the universal utility of the methods described herein and permit one to practice the methods in order to isolate, detect, quantify, characterize the degree of oxidation, and/or identify oxidized peptides associated with any other condition of interest such as, for example, other cancers, neurodegenerative diseases, inflammatory diseases, atherosclerosis, aging, etc.
[0170] Moreover, Examples 3-6 demonstrate how the technical platform described in Example 1 can be employed as a diagnostic tool, to establish an OS profile of proteins for a disease (e.g., breast cancer (Example 3) and diabetes mellitus (Example 5)), and how to use data from samples collected from an individual at different times to assess the time-dependent course of a disease such as, for example, before and after a set duration of treatment (Example 6).

Breast Cancer

[0171] Example 3 demonstrates the utility of methods described herein for analyzing the oxidation of peptides to monitor cancers such as, for example, breast cancer. There is growing evidence that redox regulation is disrupted in various cancers, resulting in overproduction of reactive oxygen species (ROS). Indeed, 8-isoprostane, a widely used ROS indicator, was of significantly higher concentration in the plasma of breast cancer patients relative to controls. One outcome of ROS overproduction is the oxidation of proteins through, for example, the production of carbonyl groups. We detected differences in levels of carbonylated plasma proteins between breast cancer patients and undiagnosed controls.
[0172] Carbonyl groups in proteins from freshly drawn blood were prepared and analyzed as described in Example 3. Four hundred sixty proteins were identified and quantified in breast cancer patients, 98 of which exhibited an increase in concentration of at least 1.5-fold relative to the controls. Carbonylation was found to occur at a small number of specific sites in proteins. Other forms of oxidation also were found in the selected proteins. Nearly one fourth of the affinity-selected proteins were of cytoplasmic, nuclear, or of membrane origin.
[0173] Analysis of the data by unbiased knowledge assembly indicated the most likely disease associated with the proteins was breast neoplasm. Pathway analysis showed that oxidized proteins are strongly associated with breast cancer (e.g., Brcal, the breast cancer type-1 susceptibility protein). Exemplary molecular functions of the oxidized proteins included defense and immunity, nucleic acid binding, DNA helicase activity, serine protease inhibition, microtubule binding, and RNA helicase activity.
[0174] Reactive oxygen species appear to be involved in breast cancer pathogenesis (Chua et al., Experimental Biology and Medicine, 2009, 234:1086-1094; Sener et al., Cell Biochemistry and Function, 2007, 25:377-382). Overproduction of ROS and the oxidative stress associated with overproduction of ROS can occur in several ways. One way is by enhanced expression of enzymes such as, for example, thymidine phosphorylase and lactoperoxidase that elevate ROS production from within the tumor (Brown et al, Breast Cancer Research [online computer file] 2001, 3:323-327). Another way is by extracellular production of ROS through macrophage recruitment.
[0175] Extracellular ROS entering tumor cells can oxidatively damage mitochondria, nuclear DNA, ribosomal RNA, intercellular proteins, and lipids. This damage can further stimulate uncontrolled growth, ischemia, and glucose deprivation, which can result in reduced neovascularization and a further increase in oxidative stress.
[0176] Metabolites associated with oxidative stress have been associated with breast cancer. For example, elevated urinary levels of 15-F2t-isoprostane (15-F2t-IsoP) have been linked to breast cancer risk (Rossner et al., Cancer Epidemiology, Biomarkers & Prevention, 2006, 15:639-644). Also, urinary 8-oxo-2-deoxyguanosine and plasma malondialdehyde are elevated in breast cancer patients compared to normal controls (Sener et al., Cell Biochemistry and Function, 2007, 25:377-382; Gonenc et al., Journal of Clinical Pharmacy and Therapeutics, 2001, 26:141-144; Kuo et al., Mutation Research, Genetic Toxicology and Environmental Mutagenesis, 2007, 631:62-68). Two recent studies have shown that the total concentration of carbonylated plasma proteins is strongly connected to breast cancer risk (Rossner et al., J Cell Mol Med, 2007, 11:1138-1148; Zipprich et al., Cancer Res 2009, 69:2966-2972).
[0177] The methods described herein permit one to isolate carbonylated proteins from the plasma of breast cancer patients, identify the oxidized proteins, locate oxidation sites, and establish the mechanism by which oxidation occurred at various sites within a protein. Moreover, one can analyze proteins carrying OSi˜PTMs from oxidatively stressed tissue that are shed or released into blood and can provide an oxidative stress signature of the organ or tumor from which the oxidized proteins originated. Thus, the methods allow one to construct a profile of oxidative stress proteins associated with breast cancer. Such a profile can be used as a diagnotic tool for other individuals.
[0178] Breast cancer patients and cancer-free subjects were compared to determine whether the elevated levels of oxidative stress that occurs in the tumor influenced proteins that can be detected in the plasma. We detected qualitative and quantitative differences between oxidized proteins found in the plasma of six breast cancer patients and matched controls.
[0179] We first validated that breast cancer patients experienced elevated oxidative stress by measuring 8-isoprostane in their plasma. 8-Isoprostane is formed specifically as a consequence of free radical induced lipid peroxidation and is considered to be one of the most reliable indicators of in vivo oxidative stress. FIG. 9 shows that the level of 8-isoprostane was significantly higher in cancer patient plasma than in the controls. This result is consistent with a study in which isoprostanes were elevated in the urine of patients with invasive breast cancer.
[0180] The general protocols used in Example 3 are illustrated in FIG. 7 and FIG. 8. Initial analysis of the data (to be discussed later) indicated that a major portion of the biotinylated protein fraction was most likely immunoglobulins. This observation was further confirmed through a second approach in which biotinylated plasma samples were subjected to tandem affinity chromatography. The first of the tandem dimensions employed a protein A/G column to select immunoglobulins from the blood proteome while an avidin affinity column was used in the second dimension to select oxidized forms of immunoglobulins that had been biotinylated. Protein A/G was immobilized on a cross-linked agarose column. Following trypsin digestion of the tandem affinity-selected fraction, the total hydrolysate was subjected to RPC-MS/MS analysis in an effort to identify these immunoglobulins and characterize their oxidation sites (FIG. 8A). The large amount of oxidized immunoglobulins in the plasma of breast cancer patients suppressed the ionization of some of the non-immunoglobulins. Therefore, the immunoglobulin-stripped flow through fraction from the protein A/G column was used for avidin affinity selection in the second dimension. The eluted fraction from the avidin column was then digested with trypsin and the peptides were subjected to RPC-MS/MS analysis to characterize the oxidation sites of non-immunoglobulins (FIG. 8B).
[0181] By using this protocol, one finds that 1) multiple sites of oxidation are often found in a single protein, 2) oxidation can be non-stoichiometric across these sites, and 3) multiple forms of oxidation involving many amino acids can be found in a biotinylated protein. Thus, a single protein can exist in many different oxidized forms. The protocol does not necessarily capture nor recognize all oxidized forms of a given protein; only oxidation sites that are co-resident on a protein with a free carbonyl group are captured and analyzed. Although it is likely that all types of oxidation occurring in a biological system are seen with this method, not all forms of oxidation are quantitatively selected.
[0182] The initial phase of this study described immediately above generated an average of 3540 spectra. A total of 460 proteins were identified and quantified in the six breast cancer patients and their controls. Among these proteins, 98 proteins exhibited at least a 1.5-fold increase in concentration in cancer patients relative to controls (Table 8).
[0183] 
[00008] [TABLE-US-00008]
  TABLE 8
 
  Proteins that changed more than 50% in the plasma of in any of the breast cancer
  patients compared to their controls. The ratios were calculated as fold change
                  Breast
                Number of   cancer/
                peptides   control
  Protein       %         used in the   (fold
  number   Unused   Total   Cov   Accession #   Name   Species   identification   change)
 
  1   2.02   3.78   5.3   O60318|MCM3A_HUMAN   80 kDa MCM3-   HUMAN   12    −2.1
            associated protein      
  2   4.79   4.79   13.4   P01009|A1AT_HUMAN   Alpha-1-antitrypsin   HUMAN   7   −1.8
          precursora      
  2   4.21   4.21   15.8   P01009|A1AT_HUMAN   Alpha-1-antitrypsin   HUMAN   7   1.8
          precursora      
  3   2.19   2.21   6.9   P01023|A2MG_HUMAN   Alpha-2-macroglobulin   HUMAN   13    2.5
            precursor      
  4   2.48   2.48   19   P02652|APOA2_HUMAN   Apolipoprotein A-II   HUMAN   3   1.9
            precursor      
  5   2   2   12.4   P06727|APOA4_HUMAN   Apolipoprotein A-IV   HUMAN   5   −2.0
          precursora      
  5   4   4.01   12.6   P06727|APOA4_HUMAN   Apolipoprotein A-IV   HUMAN   6   −2.0
          precursora      
  5   31.28   31.28   54.5   P06727|APOA4_HUMAN   Apolipoprotein A-IV   HUMAN   20    −1.7
          precursora      
  6   3.54   3.54   5   P04114|APOB_HUMAN   Apolipoprotein B-100   HUMAN   22    1.8
            precursor      
  7   4.29   4.29   30.1   P02654|APOC1_HUMAN   Apolipoprotein C-I   HUMAN   3   −2.0
            precursor      
  8   2   2   19.2   P02656|APOC3_HUMAN   Apolipoprotein C-III   HUMAN   2   −6.4
          precursora      
  8   2   2   21.2   P02656|APOC3_HUMAN   Apolipoprotein C-III   HUMAN   2   −8.1
          precursora      
  9   2   2   18.1   P55056|APOC4_HUMAN   Apolipoprotein C-IV   HUMAN   2   2.2
            precursor      
  10   10.3   10.3   18.9   P02649|APOE_HUMAN   Apolipoprotein E   HUMAN   6   1.9
          precursora      
  10   11.12   11.12   48.3   P02649|APOE_HUMAN   Apolipoprotein E   HUMAN   5   −1.6
          precursora      
  10   22.21   22.21   57.7   P02649|APOE_HUMAN   Apolipoprotein E   HUMAN   17    2.4
          precursora      
  11   2   2   3.8   O14791|APOL1_HUMAN   Apolipoprotein-L1   HUMAN1b   5.0
            precursor      
  12   2.07   3.29   7   O94833|BPAEA_HUMAN   Bulious pemphigoid   HUMAN   50    −2.5
            antigen 1, isoforms      
  13   8.64   8.68   15.2   P04003|C4BP_HUMAN   C4b-binding protein   HUMAN   11    1.7
            alpha chain precursor      
  14   6   6   13   O43866|CD5L_HUMAN   CD5 antigen-like   HUMAN   4   2.0
            precursor      
  15   2.84   3.11   4.2   P49454|CENPF_HUMAN   Centromere protein F   HUMAN   12    −5.5
  16   6.93   6.93   10.8   P00450|CERU_HUMAN   Ceruloplasmin   HUMAN   12    −2.1
          precursora      
  16   5.72   5.73   10   P00450|CERU_HUMAN   Ceruloplasmin   HUMAN   11    1.5
          precursora      
  16   11.71   11.74   15.6   P00450|CERU_HUMAN   Ceruloplasmin   HUMAN   17    1.6
          precursora      
  17   6   6   8.5   P05160|F13B_HUMAN   Coagulation factor XIII   HUMAN   3   −2.4
          B chain precursora      
  17   6   6   11.6   P05160|F13B_HUMAN   Coagulation factor XIII   HUMAN   8   −1.6
          B chain precursora      
  17   12.59   12.59   25   P05160|F13B_HUMAN   Coagulation factor XIII   HUMAN   7   1.5
          B chain precursora      
  18   2.14   2.14   12.2   P02745|C1QA_HUMAN   Complement C1q   HUMAN   3   2.0
            subcomponent      
            subunit A precursor      
  19   4   4   14.7   P02747|C1QC_HUMAN   Complement C1q   HUMAN   3   1.9
            subcomponent      
            subunit C precursor      
  20   63.46   63.46   43.4   P01024|CO3_HUMAN   Complement C3   HUMAN   37    −1.6
            precursor      
  21   17.23   17.26   18.2   P0C0L5|CO4B_HUMAN   Complement C4-B   HUMAN   8   1.5
            precursor      
  22   4.83   4.84   9.6   P08603|CFAH_HUMAN   Complement factor H   HUMAN   11    2.0
          precursora      
  22   2   4.04   17.6   P08603|CFAH_HUMAN   Complement factor H   HUMAN   8   2.0
          precursora      
  23   45.86   45.86   33.3   P02671|FIBA_HUMAN   Fibrinogen alpha chain   HUMAN   47    2.1
          precursora      
  23   51.54   51.54   41.7   P02671|FIBA_HUMAN   Fibrinogen alpha chain   HUMAN   50    −1.8
          precursora      
  23   93.83   93.85   60.7   P02671|FIBA_HUMAN   Fibrinogen alpha chain   HUMAN   123    −2.1
          precursora      
  23   96.2   96.2   63.6   P02671|FIBA_HUMAN   Fibrinogen alpha chain   HUMAN   114    2.5
          precursora      
  24   77.51   77.53   66.2   P02675|FIBB_HUMAN   Fibrinogen beta chain   HUMAN   115    2.0
          precursora      
  24   44.93   44.93   49.7   P02675|FIBB_HUMAN   Fibrinogen beta chain   HUMAN   35    1.9
          precursora      
  24   88.89   88.91   76.4   P02675|FIBB_HUMAN   Fibrinogen beta chain   HUMAN   125    −1.8
          precursora      
  25   14.99   14.99   30.9   P02679|FIBG_HUMAN   Fibrinogen gamma   HUMAN   7   1.7
          chain precursora      
  25   22.18   22.33   46.1   P02679|FIBG_HUMAN   Fibrinogen gamma   HUMAN   15    −1.7
          chain precursora      
  25   40.56   40.57   52.5   P02679|FIBG_HUMAN   Fibrinogen gamma   HUMAN   37    2.0
          chain precursora      
  25   43.53   43.53   68.9   P02679|FIBG_HUMAN   Fibrinogen gamma   HUMAN   38    −1.8
          chain precursora      
  26   79.06   79.06   33.9   P02751|FINC_HUMANFibronectin precursora   HUMAN   46    1.7
  26   143.08   143.1   45.2   P02751|FINC_HUMANFibronectin precursora   HUMAN   107    −2.1
  26   183.52   183.5   50.8   P02751|FINC_HUMANFibronectin precursora   HUMAN   172    −2.0
  27   4   4   9.2   Q08380|LG3BP_HUMAN   Galectin-3-binding   HUMAN   4   9.0
            protein precursor a      
  28   5.35   5.35   20.7   P00738|HPT_HUMAN   Haptoglobin precursor   HUMAN   7   2.2
  29   2.34   2.34   15.6   P68871|HBB_HUMAN   Hemoglobin subunit   HUMAN   2   −3.0
          betaa      
  29   4   4   23.8   P68871|HBB_HUMAN   Hemoglobin subunit   HUMAN   3   1.9
          betaa      
  29   10   10   45.6   P68871|HBB_HUMAN   Hemoglobin subunit   HUMAN   6   1.6
          betaa      
  30   6   6   27.2   P02042|HBD_HUMAN   Hemoglobin subunit   HUMAN   4   −2.2
            delta      
  31   6.96   6.96   18.4   P02790|HEMO_HUMANHemopexin precursora   HUMAN   9   1.5
  31   14.57   14.57   27.9   P02790|HEMO_HUMANHemopexin precursora   HUMAN   12    2.6
  32   8.01   8.01   17.3   P04196|HRG_HUMAN   Histidine-rich   HUMAN   11    1.7
            glycoprotein      
          precursora      
  32   1.7   1.7   12   P04196|HRG_HUMAN   Histidine-rich   HUMAN   7   −1.6
            glycoprotein      
          precursora      
  33   6.01   6.01   28.5   P13747|HLAE_HUMAN   HLA class I   HUMAN   9   −2.7
            histocompatibility      
            antigen, alpha chain E      
            precursor      
  34   2.12   2.12   3.9   Q96ED9|HOOK2_HUMAN   Hook homolog 2   HUMAN   4   2.6
  35   2   2   2.9   Q14520|HABP2_HUMAN   Hyaluronan-binding   HUMAN   2   1.6
            protein 2 precursor      
  36   12.48   12.48   18.4   P01876|IGHA1_HUMAN   Ig alpha-1 chain C   HUMAN   14    2.6
          regiona      
  36   8.21   8.21   18.1   P01876|IGHA1_HUMAN   Ig alpha-1 chain C   HUMAN   8   1.8
          regiona      
  37   5.1   5.1   17.4   P01877|IGHA2_HUMAN   Ig alpha-2 chain C   HUMAN   8   1.6
            region      
  38   20.36   20.36   54.8   P01857|IGHG1_HUMAN   Ig gamma-1 chain C   HUMAN   22    −1.6
          regiona      
  38   25.19   25.19   58.5   P01857|IGHG1_HUMAN   Ig gamma-1 chain C   HUMAN   21    1.6
          regiona      
  38   39.84   39.85   75.2   P01857|IGHG1_HUMAN   Ig gamma-1 chain C   HUMAN   27    −1.6
          regiona      
  38   4.48   19.81   62.4   P01857|IGHG1_HUMAN   Ig gamma-1 chain C   HUMAN   13    3.2
          regiona      
  39   6.65   13.78   51.2   P01859|IGHG2_HUMAN   Ig gamma-2 chain C   HUMAN   22    1.9
            region      
  40   2   9.92   64.8   P01860|IGHG3_HUMAN   Ig gamma-3 chain C   HUMAN   6   2.4
            region      
  41   3.2   10.06   31.8   P01861|IGHG4_HUMAN   Ig gamma-4 chain C   HUMAN   5   −2.8
            region      
  42   4.03   4.03   20.5   P23083|HV103_HUMAN   Ig heavy chain V-I   HUMAN   7   1.5
            region V35 precursor      
  43   2.16   3.52   37.1   P01781|HV320_HUMAN   Ig heavy chain V-III   HUMAN   5   1.9
            region GAL      
  44   2   3.27   20.6   P01772|HV311_HUMAN   Ig heavy chain V-III   HUMAN   3   1.7
            region KOL      
  45   3.51   3.6   30.3   P01777|HV316_HUMAN   Ig heavy chain V-III   HUMAN   4   6.5
            region TEI      
  46   2   2.42   26.2   P01762|HV301_HUMAN   Ig heavy chain V-III   HUMAN   4   2.1
            region TRO      
  47   10.04   10.04   70.8   P01834|KAC_HUMAN   Ig kappa chain C   HUMAN   11    −2.0
          regiona      
  47   23.45   23.46   84   P01834|KAC_HUMAN   Ig kappa chain C   HUMAN   25    2.3
          regiona      
  48   4.74   4.74   24.8   P04432|KV124_HUMAN   Ig kappa chain V-I   HUMAN   4   2.4
            region Daudi      
            precursor      
  49   2   2   31.5   P01610|KV118_HUMAN   Ig kappa chain V-I   HUMAN   2   1.6
            region WEA      
  50   2.59   5.52   28.7   P01610|KV118_HUMAN   Ig kappa chain V-I   HUMAN   3   1.7
            region WEA      
  51   2.36   4.37   42.7   P06309|KV205_HUMAN   Ig kappa chain V-II   HUMAN   5   −2.8
            region GM607      
            precursor      
  52   4.18   4.19   41.1   P04207|KV308_HUMAN   Ig kappa chain V-III   HUMAN   6   2.0
            region CLL precursor      
  53   3.2   5.21   55.8   P18135|KV312_HUMAN   Ig kappa chain V-III   HUMAN   9   2.2
            region HAH precursor      
  54   4.98   4.98   49.6   P18136|KV313_HUMAN   Ig kappa chain V-III   HUMAN   6   2.2
            region HIC precursor      
  55   6.06   6.07   49.3   P06314|KV404_HUMAN   Ig kappa chain V-IV   HUMAN   7   2.0
            region B17 precursor      
  56   2   2.02   33.1   P06313|KV403_HUMAN   Ig kappa chain V-IV   HUMAN   6   1.7
            region JI precursor      
  57   14.03   14.04   67.6   P01842|LAC_HUMAN   Ig lambda chain C   HUMAN   10    1.8
          regionsa      
  57   11.72   11.76   63.8   P01842|LAC_HUMAN   Ig lambda chain C   HUMAN   8   −1.8
          regionsa      
  58   3.71   3.71   34.2   P01701|LV103_HUMAN   Ig lambda chain V-I   HUMAN   3   1.7
            region NEW      
  59   2   2   28   P01717|LV403_HUMAN   Ig lambda chain V-IV   HUMAN   2   1.7
          region Hila      
  59   4   4   28   P01717|LV403_HUMAN   Ig lambda chain V-IV   HUMAN   3   1.6
          region Hila      
  59   2   2   17.8   P01717|LV403_HUMAN   Ig lambda chain V-IV   HUMAN   2   1.5
          region Hila      
  60   48.88   48.89   62.6   P01871|MUC_HUMANIg mu chain C regiona   HUMAN   41    2.7
  60   31.42   31.42   44.9   P01871|MUC_HUMANIg mu chain C regiona   HUMAN   19    −1.5
  61   1.4   22.78   45   P04220|MUCB_HUMAN   Ig mu heavy chain   HUMAN   13    −2.3
            disease protein      
  62   4   4   17.5   P01591|IGJ_HUMAN   Immunoglobulin J   HUMAN   3   1.6
            chain      
  63   4.01   4.01   3.7   P19827|ITIH1_HUMAN   Inter-alpha-trypsin   HUMAN   3   −6.3
            inhibitor heavy chain      
            H1 precursor      
  64   1.31   1.31   6.6   Q9BVA0|KTNB1_HUMAN   Katanin p80 WD40-   HUMAN   6   −3.4
            containing subunit B1      
  65   1.4   1.47   7.7   Q9C0H6|KLHL4_HUMAN   Kelch-like protein 4   HUMAN   10    −3.5
  66   1.7   1.7   3.9   Q8IXQ5|KLHL7_HUMAN   Kelch-like protein 7   HUMAN   2   −4.7
  67   1.7   1.71   7.4   Q96L93|SNX23_HUMAN   Kinesin-like motor   HUMAN   14    −2.3
            protein C20orf23      
  68   3.58   3.58   18.3   P01042|KNG1_HUMAN   Kininogen-1 precursor   HUMAN   8   1.9
  69   1.57   1.57   4.6   Q13753|LAMC2_HUMAN   Laminin subunit   HUMAN   6   −11.6
            gamma-2 precursor      
  70   2   2   3.2   Q8IUZ0|LRC49_HUMAN   Leucine-rich repeat-   HUMAN   2   −2.2
            containing protein 49      
  71   2   2   2.3   O00187|MASP2_HUMAN   Mannan-binding lectin   HUMAN   2   9.0
            serine protease 2      
            precursor      
  72   2.02   2.02   4.9   P27816|MAP4_HUMAN   Microtubule-   HUMAN   5   −3.0
            associated protein 4      
  73   2   2.01   2.5   Q8NEV4|MYO3A_HUMAN   Myosin IIIA   HUMAN   7   −1.6
  74   2.04   2.81   6.9   Q9ULV0|MYO5B_HUMAN   Myosin-5B   HUMAN   20    −1.5
  75   2.07   2.09   7.5   Q86WG5|MTMRD_HUMAN   Myotubularin-related   HUMAN   20    4.7
            protein 13      
  76   2.1   2.93   5   Q8NF91|SYNE1_HUMAN   Nesprin-1   HUMAN   56    −2.8
  77   1.52   2.23   7.6   O43929|ORC4_HUMAN   Origin recognition   HUMAN   4   −1.6
            complex subunit 4      
  78   1.53   1.66   5.3   P56715|RP1_HUMAN   Oxygen-regulated   HUMAN   18    −3.0
            protein 1      
  79   4   4   23.2   P32119|PRDX2_HUMAN   Peroxiredoxin-2   HUMAN   3   −9.2
  80   2   2.06   4.9   O60486|PLXC1_HUMAN   Plexin-C1 precursor   HUMAN   12    −5.1
  81   2.02   2.83   5.3   Q92954|PRG4_HUMAN   Proteoglycan-4   HUMAN   15    −1.5
            precursor      
  82   2.35   2.35   13.3   P00734|THRB_HUMAN   Prothrombin   HUMAN   11    −1.9
            precursor      
  83   2   2.06   5.2   Q9UHD2|TBK1_HUMAN   Serine/threonine-   HUMAN   5   −1.6
            protein kinase TBK1      
  84   4   4   12   P02787|TRFE_HUMAN   Serotransferrin   HUMAN   8   2.4
            precursor      
  85   49.75   49.75   50.7   P02768|ALBU_HUMAN   Serum albumin   HUMAN   41    2.7
            precursor      
  86   10.05   10.05   17.7   P27169|PON1_HUMAN   Serum   HUMAN   8   1.6
            paraoxonase/arylesterase 1      
  87   1.7   1.7   2.5   Q9Y5W8|SNX13_HUMAN   Sorting nexin-13   HUMAN   2   −3.4
  88   1.46   1.55   6.4   P02549|SPTA1_HUMAN   Spectrin alpha chain,   HUMAN   25    −4.1
            erythrocyte      
  89   1.45   1.47   13.7   P23246|SFPQ_HUMAN   Splicing factor,   HUMAN   10    −1.5
            proline- and      
            glutamine-rich      
  90   4.04   4.74   7.8   Q8WZ42|TITIN_HUMAN   Titin   HUMAN   201    −1.7
  91   4   4   24.5   P02766|TTHY_HUMAN   Transthyretin   HUMAN   2   −3.0
            precursor      
  92   1.4   1.4   14.7   Q9Y333|LSM2_HUMAN   U6 snRNA-associated   HUMAN   2   −3.6
            Sm-like protein LSm2      
  93   2.34   2.35   7.5   Q70EL4|UBP43_HUMAN   Ubiquitin carboxyl-   HUMAN   12    2.2
            terminal hydrolases 43      
  94   2.02   2.08   3.4   O75445|USH2A_HUMAN   Usherin precursor   HUMAN   27    −2.5
  95   1.4   1.51   5.3   Q5THJ4|VP13D_HUMAN   Vacuolar protein   HUMAN   29    −2.4
            sorting-associated      
          protein 13Da      
  95   2.04   2.08   3.4   Q5THJ4|VP13D_HUMAN   Vacuolar protein   HUMAN   22    −2.5
            sorting-associated      
          protein 13Da      
  96   33.26   33.26   49.4   P04004|VTNC_HUMAN   Vitronectin precursor   HUMAN   44    −1.6
  97   1.7   1.7   1.6   Q96FK6|WDR89_HUMAN   WD repeat protein 89   HUMAN1b   −4.5
  98   1.7   1.7   1.6   Q9Y493|ZAN_HUMAN   Zonadhesin precursor   HUMAN   4   −7.8
 
aThese proteins were identified in multiple donors
bThe mass spectra for any peptide associated with single hit proteins were further manually inspected.
[0184] Four biotinylated proteins: intestinal alkaline phosphatase, horseradish peroxidase, protein A, and protein G were mixed together and applied on the avidin purification system four times. The four purified fractions were, reduced, alicylated, digested and labeled with the 114, 115, 116, or 117-dalton iTRAQ™ labeling reagents. Then each two of these fractions were mixed with an expected ratio of 1:1. The greatest variability was seen with horseradish peroxidase in the sample labeled with an error of 0.16, which indicate that a change of 50% is almost 3-fold greater than error due to the iTRAQ™ labeling (Table 9).
[0185] 
[00009] [TABLE-US-00009]
  TABLE 9
 
  iTRAQ ™ ratios for proteins from each of the four analyzed aliquots
of a biotinylated proteins mixturea
  Accession     No.       Ratio
  no.   Protein ID   pept.   Unused   % Cov.   116:115   117:114
 
  sp|P19111|   Intestinal   87   78.6   68.9   1.04   0.95
    alkaline          
    phosphatase          
  sp|P02976|   Protein A   32   57.1   76.4   1.09   0.89
  sp|P00433|   Peroxidase   17   17.9   36   1.16   0.94
  sp|P19909|   Protein G   3   7.8   23.1   0.93   0.96
 
aTo evaluate the variability of iTRAQ ™ induced by the avidin affinity purification. Four aliquots of biotinylated proteins mixture (each of total protein concentration of 400ug) were analyzed in parallel. The theoretical ratios for all of the four proteins are 1:1.

Knowledge Assembly Analysis
[0186] Knowledge assembly is a term used here to describe the process of assembling new knowledge based on integration of findings reported in the literature and new experimental findings. An objective of this work was to examine whether organs or tissue undergoing elevated oxidative stress are shedding proteins into the circulatory system with oxidative stress induced oxidative modifications and providing a protein signature. Although a series of proteins were found to have undergone concentration changes exceeding 1.5-fold in cancer patient plasma relative to controls, they may not necessarily have come from a tumor. This issue was addressed by using pathway analysis software such as GeneGo™ and DAVID to determine whether an un-biased analysis of the scientific literature connects these proteins to cancer. GeneGo™ (St. Joseph, Mich.) is a comprehensive set of databases for pathway analysis that uses more than 50,000 signaling interactions based on 4.5 million reports collected from the literature over the last five years. The Database for Annotation, Visualization, and Integrated Discovery (DAVID), which is a tool available at no cost from NIAID, provides 40 annotation categories such as protein-protein interactions, disease associations, bio-pathways, gene function, and tissue expression using the most relevant gene ontology (GO) associated with genes. The degree to which these two differing data analysis suites gave the same interpretation of the data was a strong factor in evaluating the validity of the analyses.
[0187] DAVID GO by Molecular Function
[0188] Gene ontology (GO) analysis of the molecular function (FIG. 10) of the proteins identified showed that 23% function in defense and immunity (e.g., immunoglobulin heavy constant gamma). Nineteen percent are involved in nucleic acid binding (e.g., mitosin and splicing factor proline/glutamine-rich protein) while another 11% were related to DNA helicase (e.g., transthyretin and laminin gamma). Other molecular functions include 10% being serine protease inhibitors (e.g., set binding factor 2), 8% are functioning as microtubule binding proteins (e.g., chromosome 20 open reading frame 23), 8% are related to RNA helicase activity (e.g., proteoglycan 4), 8% serve as transfer (carrier) proteins (e.g., apolipoprotein e), 7% act as actin binding proteins (e.g., titin), 3% are extracellular matrix proteins (e.g., coagulation factor) and 5% are cytoskeletal proteins (e.g., titin).
[0189] DAVID GO by Biological Processes
[0190] GO analysis of the biological processes involving these proteins shows (FIG. 11) that 34% function in response to stimulus (e.g., complement factor h), 20% are involved in immune system processes (e.g., immunoglobulin kappa constant), 17% are part of protein transport (e.g., transferrin), 10% are used in positive regulation of biological process (e.g., katanin p80), 7% are part of acute inflammatory responses (e.g., complement component), 6% function in cell adhesion (e.g., zonadhesin), and 6% are involved in cytoskeleton organization and biogenesis (e.g., titin).
[0191] Network Analysis
[0192] The Build Network tool from GeneGo™ was used to identify protein-protein interactions and biological pathways shared by the proteins with an OSi˜PTMs found in this work as shown in FIG. 12. We built the network by allowing GeneGo™ to find the shortest path between our data set and transcription factors using the Analyze Network by Transcription Factors. This is because many of the proteins identified were involved in DNA binding (see GO by Molecular function), immune system regulation, and biological process regulation (see GO by Biological process). From the list of networks obtained, the most statistically significant was a network involved in “positive regulation of biological processes” with a P-val of 3.47e42 that correlated with results obtained by DAVID. This network includes proteins such as Brcal (the breast cancer type-1 susceptibility protein), TGFR-beta types I and II (transforming growth factor receptor beta), and proteins from the MAPKK pathway, all of which are involved in cancer.
[0193] Cellular Location
[0194] FIG. 13 shows the cellular locations of proteins that changed in concentration more than 50%. Three were located in the nucleus, 19 in the cytoplasm, two in membranes and the rest were in the extracellular compartment. The detection of nuclear, cytoplasmic, and the membrane proteins in the blood is an important piece of information in that it indicates these oxidized proteins were released by apoptosis and necrosis. They were not excreted. Excessive protein oxidation has been strongly associated with cell death in the literature.
[0195] Disease Distribution
[0196] Searching disease connections with GeneGo™ (FIG. 14), we found that breast neoplasm was the most likely disease associated with the proteins identified as having changed more than 50% in concentration. Atherosclerosis was the second most likely disease. Based on the fact that the “breast cancer patient” samples were known to have come from subjects diagnosed to have breast cancer and all of the blood donors were approximately 55 years of age, it is reasonable that GeneGo™ would associate these samples with breast neoplasia and atherosclerosis. The bars in FIG. 14 represent breast cancer donors compared to controls.
[0197] GeneGo™ GO Processes
[0198] The bars in FIG. 15 represent each of the six patients and indicate that the most commonly shared function of the oxidized proteins identified in the six patients involves immune system processes.
[0199] Amino acid side chain modifications identified in Example 3 are shown in FIG. 16. Oxidized proteins were recognized in data analysis by the identification of a peptide bearing an OSi˜PTM, the site of the modification, and/or the structure of the modification. Before an oxidation site or type of oxidation was considered to have been identified, the presence of the OSi˜PTM-bearing peptide and at least two unoxidized peptides from the same protein were required. No attempt was made to identify cross-linking sites. Mascot was used for the analysis of the mass spectra as described under Example 3. False positive identifications of OSi˜PTM-bearing peptides was eliminated by using instrumentation such as the LTQ-Orbitrap XL™ with a mass accuracy approaching 1 ppm.
[0200] Based on GeneGo™ and DAVID analysis, the largest fraction of proteins associated with cancer was connected to immunity, both in terms of molecular function and biological processes involved. As a consequence, it was decided that identification of the participating oxidized immunoglobulins might shed light on this process. Biotin hydrazide-derivatized samples were analyzed using a two-dimensional fractionation scheme. The total immunoglobulin fraction of plasma samples was selected in a first separation dimension by affinity chromatography using an immobilized protein A/G column. The affinity selected immunoglobulin fraction was then subjected to avidin affinity chromatography in which biotinylated immunoglobulins were selected in a second separation dimension. Oxidized immunoglobulins thus selected were tryptic digested and identified as before.
[0201] Oxidation sites were characterized by pooling the six breast cancer patient samples and control samples, respectively, and biotinylated immunoglobulins from the two pooled samples were isolated and analyzed using the two-dimensional affinity chromatography approach described immediately above. Peptide identification was achieved using the LTQ Orbitrap XL™ mass spectrometer (Table 10). Twelve immunoglobulins were identified in the pooled breast cancer plasma sample while only eight were identified in the pooled normal plasma sample. Carbonylation sites were detected in five of these immunoglobulins (Table 11). A total of seven carbonylation sites were detected representing the three routes of carbonylation. Direct carbonylation was seen at T55 from arginine oxidation in the immunoglobulin heavy chain region while the glyoxal adduct at K97 in IGHG1 was from reaction with an advance lipid peroxidation end product and the deoxyglucosone adduct at K75 in immunoglobulin lambda light chain VU region was from advanced glycation end product formation.
[0202] 
[00010] [TABLE-US-00010]
  TABLE 10
 
  Oxidized immunoglobulins detected in the normal and breast cancer plasma,
  respectively (analyzed LTQOrbitrap XL ™)
  Normal plasma   Breast cancer plasma
        Number of           Number of  
  Protein       peptides     Protein       peptides  
  accession     Protein   used in the   percent   accession     Protein   used in the   percent
  number   Protein name   score   identification   coverage   number   Protein name   score   identification   coverage
 
  gi|229601   Ig G1 H Nle   503   7   18.8   gi|229601   Ig G1 H Nle   551   6   20.5
  gi|11275302   anti TNF-alpha   372   4   28.5   gi|11275302   anti TNF-alpha antibody   320   4   28.5
    antibody light-chain           light-chain Fab fragment      
    Fab fragment                
  gi|442920   Chain B, Fab   243   2   13.9   gi|442920   Chain B, Fab Fragment Of   268   2   13.9
    Fragment Of           Humanized Antibody 4d5,      
    Humanized           Version 4      
    Antibody 4d5,                
    Version 4                
  gi|54778900   immunoglobulin mu   153   2   13.7   gi|54778900   immunoglobulin mu heavy   182   4   22.5
    heavy chain           chain      
  gi|46254112   immunoglobulin   156   2   14.3   gi|37777926   immunoglobulin heavy   176   2   20.1
    heavy chain           chain variable region      
  gi|12054078   immunoglobulin   246   2   11.9   gi|21669609   immunoglobulin lambda   137   3   8
    heavy chain           light chain VLI region      
    constant region                
    gamma 4                
  gi|185927   immunoglobulin   404   4   34.1   gi|10334541   immunoglobulin heavy   130   3   11.9
    kappa-chain VK-1           chain      
  gi|60688640   IGHG1 protein   288   4   13.3   gi|54780728   immunoglobulin mu heavy   90   4   13.3
              chain      
            gi|2765425   immunoglobulin lambda   362   5   12.4
              heavy chain      
            gi|9857759   recombinant IgG4 heavy   40   1   4.1
              chain      
            gi|127514   ig mu chain C region   181   6   15.6
            gi|15779222IGHG1 protein [Homo   379   12   34
            sapiens]
 
[0203] 
[00011] [TABLE-US-00011]
  TABLE 11
 
  Oxidation sites detected in the immunoglobulins in the breast
  cancer plasma (analyzed LTQOrbitrap XL)
  Accession     Oxidation  
  number   Protein name   site   Modification
 
  gi|15779222   IGHG1 protein   K97   Biotinylated glyoxal
        adduct
  gi|21669609   immunoglobulin   K75   Biotinylated
    lambda light     deoxyglucosone
    chain VLJ region     adduct
  gi|21669399   immunoglobulin   T220   Biotinylated 2-amino
    kappa light     3-ketobutyric acid
    chain VLJ region    
  gi|11119125   immunoglobulin   T55   Biotinylated 2-amino
    heavy chain     3-ketobutyric acid
  gi|392717   immunoglobulin   T90, R97   T: Biotinylated 2-amino
    heavy chain   and P99   3-ketobutyric acid,
    variable region     P and R: Biotinylated
        glutamic semialdehyde
 
[0204] In order to characterize the oxidation sites of non-immunoglobulin proteins, the two-dimensional fractionation process described above was modified. The total immunoglobulin fraction of biotinylated samples was removed in the first dimension with a protein A/G affinity column as before. The immunoglobulin-stripped flow through fraction from the protein A/G column was used for avidin affinity selection in the second dimension. The eluted fraction from the avidin purification was then digested and analyzed by RPC-MS using the LTQ-Orbitrap XL™ as describe above. This resulted in the detection of 67 non-immunoglobulins while only 32 non-immunoglobulins were detected when immunoglobulins were not removed.
[0205] Twenty-six proteins were shown to have at least one oxidation site (Table 12). A total of 21 carbonyls carried on eleven proteins were detected. Fifteen of them were direct carbonylation products (oxidized proline, arginine, threonine, or lysine). Six were formed by indirect carbonylation (glyoxal, methyl glyoxal, malondialdehyde, or Amadori adducts). Proteoglycan-4 precursor carried four carbonylation sites, the largest number of carbonylation sites observed for any single protein in this study. Each of the four sites present in proteoglycan-4 precursor was a site of direct ROS oxidation of an arginine, proline, or threonine residue. In the other proteins, direct oxidation sites included twenty-nine methionine sulfoxides, one methionine sulfone, two hydroxyl leucines, one hydroxylproline, one hydroxyphenylalanine, one oxo-histidine, and one cysteine sulfenic acid.
[0206] 
[00012] [TABLE-US-00012]
  TABLE 12
 
  Oxidation sites detected in the non-immunoglobulin proteins in the breast cancer
  plasma (analyzed LTQOrbitrap XL ™)
  Accession   Protein name   Oxidation site   Modification
 
  CO3_HUMAN   Complement   K678   Biotinylated oxidized lysine
    component C3     (aminoadipic semialdehyde)
      M42, M201, M990   Methionine sulfoxide
      P366   Hydroxyproline
  FINC_HUMAN   Fibronectin    P1759   Biotinylated oxidized proline
    precursor (FN)     (glutamic semialdehyde)
       K1586   Biotinylated amadori adduct
      M926, M1548   Methionine sulfoxide
  A1AT_HUMAN   Alpha-1-antitrypsin    K246,   Biotinylated oxidized lysine
    precursor (Alpha-1     (aminoadipic semialdehyde)
    protease inhibitor)   R247   Biotinylated oxidized arginine
        (glutamic semialdehyde)
      L377   Hydroxyleucine
  CLUS_HUMAN   Clusterin precursor   T63    Biotinylated oxidized threonine
        (2-amino-3-ketobutyric acid)
  C4BP_HUMAN   C4b-binding protein   K14, K16   Biotinylated oxidized lysine
    alpha chain     (aminoadipic semialdehyde)
    precursor (C4bp)    
  CFAH_HUMAN   Complement    R1149   Biotinylated oxidized arginine
    factor H precursor     (glutamic semialdehyde)
    (H factor 1)    T1151   Biotinylated oxidized threonine
        (2-amino-3-ketobutyric acid)
       P1160   Biotinylated oxidized proline
        (glutamic semialdehyde)
  PRG4_HUMAN   Proteoglycan-4    K1048   Biotinylated oxidized lysine
    precursor (Lubricin)     (aminoadipic semialdehyde)
    (Megakaryocyte    T1156   Biotinylated oxidized threonine
    stimulating factor)     (2-amino-3-ketobutyric acid)
       T1391   Biotinylated oxidized threonine
        (2-amino-3-ketobutyric acid)
       R1392   Biotinylated oxidized arginine
        (glutamic semialdehyde)
  HBA_HUMAN   Hemoglobin   P6     Biotinylated oxidized proline
    subunit alpha     (glutamic semialdehyde)
      T10    Biotinylated oxidized threonine
        (2-amino-3-ketobutyric acid)
  APOB_HUMAN   Apolipoprotein   K293   Biotinylated methyl glyoxal adduct
    B-100 precursor    K2425   Biotinylated glyoxal adduct
      M812, M901, M1234,   Methionine sulfoxide
      M2042, M2597, M3267  
      M564   Methionine sulfone
  ITIH2_HUMAN   Inter-alpha-trypsin   K902   Biotinylated methyl glyoxal adduct
    inhibitor heavy chain    
    H2 precursor    
  CFAB_HUMAN   Complement factor   K648   Biotinylated malondialdehyde adduct
    B precursor   K652   Biotinylated Amadori adduct
  ALBU_HUMAN   Serum albumin   C148   Sulfenic acid
    precursor   M353   Methionine sulfoxide
  A2MG_HUMAN   Alpha-2-macroglobulin   F155   Hydroxyphenylalanine
    precursor   M713, M1378   Methionine sulfoxide
  APOA1_HUMAN   Apolipoprotein A-I   M136   Methionine sulfoxide
    precursor    
  VTNC_HUMAN   Vitronectin precursor   M350, M359   Methionine sulfoxide
  CLUS_HUMAN   Clusterin precursor   H171   2-Oxo-histidine
      M431   Methionine sulfoxide
  FIBB_HUMAN   Fibrinogen beta chain   M468   Methionine sulfoxide
    precursor    
  APOE_HUMAN   Apolipoprotein E precursor   M153   Methionine sulfoxide
  FIBA_HUMAN   Fibrinogen alpha chain   M537   Methionine sulfoxide
    precursor    
  C4BP_HUMAN   C4b-binding protein alpha   M248   Methionine sulfoxide
    chain precursor    
  PON1_HUMAN   Serum   L14   Hydroxyleucine
    paraoxonase/arylesterase 1   M12    Methionine sulfoxide
  C1R_HUMAN   Complement C1r   M472   Methionine sulfoxide
    subcomponent precursor   M394   Methionine sulfoxide
  CFAH_HUMAN   Complement factor H   M162   Methionine sulfoxide
    precursor    
  TRFE_HUMAN   Serotransferrin precursor   M275   Methionine sulfoxide
  APOA4_HUMAN   Apolipoprotein A-IV   M322   Methionine sulfoxide
    precursor    
  FETUA_HUMAN   Alpha-2-HS-glycoprotein   M321   Methionine sulfoxide
    precursor
 
[0207] We show that the elevated concentration of oxidized proteins in plasma that correlates with breast cancer is the result of increased oxidation of a small group of proteins at specific sites. Based on affinity chromatographic selection of oxidized proteins and proteins with which they are conjugated, a total of 460 proteins were identified. Among these proteins, 98 changed in concentration by more than 50%. These proteins appear to be oxidized at a small number of specific sites.
[0208] Despite isolating the oxidized proteins from plasma samples, at least some of the oxidized proteins are of tumor origin and not native to the circulation. Network analysis with GeneGo™ showed a clear involvement of the tumor suppressor breast cancer type 1 (BRCA1) gene pathway. This pathway is responsible for maintaining the integrity of genes in response to DNA damage induced by several factors including, for example, oxidative stress. To perform this function, BRCA1 interacts with various repair proteins. Additionally, the loss or reduction of BRCA1 alters TGF-β growth inhibiting activity during cellular response to oxidative stress. Moreover, oxidative stress activates the migration of poorly invasive cancer cells through the activation of Erk signaling. Comparative network analysis of the age and oxidant-estrogen+ER52 gene signatures reveals two such pathways, the tumor necrosis factor (TNF) and transforming growth factor-β (TGF-β) signaling pathways, which are common to both oxidatively stressed and early-onset ER-positive breast cancers. Thus, some portion of the oxidized proteins in the plasma of breast cancer patients arose from tumor cells.
[0209] This conclusion is supported by gene ontology analyses in which the molecular function of the oxidized, elevated proteins was found to be broadly in the areas of immunity, nucleic acid binding, DNA helicase, serine protease inhibitors, microtubule binding proteins, RNA helicase, carrier proteins, actin binding proteins, and extracellular matrix proteins. An unbiased in silico analysis of biological function revealed that the oxidized proteins were associated with responses to immune system processes, protein transport, positive regulation of biological processes, acute inflammatory response, cell adhesion, cytoskeleton organization, and biogenesis. All of these processes are components of the Brcal (Breast cancer type-1 susceptibility protein) pathway.
[0210] Moreover, the proteins and their oxidation sites may represent a peptidic breast cancer signature or profile. The complete list of proteins that changed more than 1.5-fold strongly correlated with breast neoplasia based on their role in signaling pathways related to breast cancer. The plasma concentrations of these oxidized proteins changed by more than 50%, relative to controls, in the six breast cancer patients, despite the variability observed between patients (FIG. 11).

Parkinson's Disease

[0211] Example 4 describes using the methods described herein to analyze oxidized peptides associated with Parkinson's disease. Mutations in the gene encoding DJ-1 have been identified in patients with familial Parkinson's disease (PD) and are thought to inactivate a neuroprotective function. The oxidation of a cysteine residue, C106, to the sulfinic acid can enhance the protein's neuroprotective activity, whereas “over-oxidation” to the sulfonic acid may be detrimental. Some familial mutations may disrupt DJ-1 activity by interfering with the conversion of C106 to the sulfinic acid. Methods described in Example 4 may be used to measure the degree of oxidation at specific sites in mutant DJ-1 as compared to the wild-type protein.
[0212] Recombinant wild-type DJ-1 and M26I, a mutant involved in familial PD, were treated with different amounts of H2O2 and compared with untreated samples using a method involving trypsin digestion, 18O labeling, and tandem mass spectrometry. Seven sites of oxidative modification involving cysteine, methionine, and histidine residues were identified, and in each case the degree of oxidation was quantified. Treatment of wild-type DJ-1 with a 10-fold molar excess of H2O2 resulted in a robust oxidation of C106 to cysteine sulfuric acid, whereas this modification was approximately seven-fold less abundant in a sample of M26I exposed to identical conditions. These results were validated by 2D-PAGE data, which showed that the M26I mutant was oxidized less readily at C106 than the wild-type protein. These findings suggest that the M26I substitution (and potentially other DJ-1 mutations identified in familial PD patients) disrupts DJ-1 function by interfering with a site-specific oxidative modification required for optimal neuroprotective activity.
[0213] Parkinson's disease (PD) is a neurodegenerative disorder characterized by muscular rigidity, slowness of voluntary movement, poor balance, and tremor at rest. These symptoms are caused by the death of neurons in a region of the midbrain called the substantia nigra. The neurons that survive in this region exhibit a defect in complex I of the mitochondrial electron transport chain and show signs of oxidative damage. In addition, surviving neurons contain characteristic cytosolic inclusions named “Lewy bodies” that are enriched with aggregated forms of the presynaptic protein α-synuclein (aSyn). Reactive oxygen species may accumulate as a result of mitochondrial impairment and contribute to neurodegeneration by causing the oxidation of lipid, proteins, and DNA. A buildup of reactive oxygen species may promote the formation of harmful aSyn aggregates in the brains of PD patients. ROS accumulation is thought to occur preferentially in nigral dopaminergic neurons because of the catabolism and auto-oxidation of dopamine, reactions that result in the generation of H2O2.
[0214] Some patients with familial, early-onset, recessive PD have been found to harbor homozygous loss-of-function mutations (e.g., M26I, E64D, A104T, D149A, E163K, L166P) in the gene encoding DJ-1, a homodimeric protein (subunit molecular weight=20 kDa) that is abundant throughout the central nervous system. Dysfunction of wild-type DJ-1 as a result of destabilizing oxidative modifications is also thought to play a role in more common sporadic forms of PD. DJ-1 may suppress neurodegeneration in cellular and animal models by activating antioxidant responses, upregulating or carrying out a molecular chaperone function, and/or inducing pro-survival signaling responses. Cysteine 106 (C106) in the DJ-1 sequence may be readily oxidized to the sulfinic acid (or “2O”) form under oxidizing conditions. Structural data indicate that C106 is located in a pocket at the subunit interface lined with polar residues from both subunits, and these residues facilitate the oxidation of C106 to the sulfinic acid. The design of this pocket suggests that controlled oxidation of DJ-1 at position 106 is advantageous for optimal DJ-1 function. Consistent with this idea, the oxidation of C106 to the 2O form is apparently critical for the ability of DJ-1 to translocate to mitochondria or suppress fibril formation by recombinant α-synuclein. In contrast, overoxidation of DJ-1, leading to the conversion of C106 to the sulfonic acid (“3O”) form may result in thermodynamically instability and a loss of chaperone activity.
[0215] The L166P mutant has a pronounced protein-folding defect, resulting in impaired homodimer formation, rapid protein turnover, and a high propensity to form large protein complexes. In contrast, other substitutions destabilize the protein to a lesser extent, and it is unclear why they disrupt DJ-1 function. As one possibility, some of these mutants may have a lower ability to undergo oxidation at position 106 to yield the 2O form. Thus, we examined the impact of the M26I familial substitution on the propensity of DJ-1 to become oxidized at position 106 and other sites on the polypeptide chain. Using a novel mass spectrometry approach, we quantified site-specific oxidative modifications of M26I as compared to wild-type DJ-1 after treatment with different amounts of H2O2. Our results indicate that the M26I substitution has a profound disruptive effect on the ability of DJ-1 to undergo oxidation at C106.
[0216] Example 4 describes an analytical strategy that combines affinity purification, 18O labeling, and LC-MS analysis (FIG. 17). Recombinant wild-type DJ-1 and M26I isolated from E. coli were untreated or treated with a 10- or 500-fold molar excess of H2O2. After tryptic digestion, the proteins were labeled at the C-terminus with either 16O (untreated DJ-1) or 18O (H2O2-treated DJ-1) by incubation with trypsin in either H216O or H218O, respectively. The 16O- and 18O-labeled peptides were mixed and analyzed by nanoscale LC/MS/MS. Sequence-specific oxidative modifications were then quantitated by measuring the ratio of 18O- to 16O-labeled peptides. Next, the mass spectrometry data were validated by characterizing wild-type DJ-1 and M26I in terms of their propensities to undergo C106 oxidation via 2D-PAGE. Finally, to better understand why wild-type DJ-1 and M26I exhibit different degrees of oxidation, the two proteins were compared in terms of thermodynamic stability and quaternary structure.
[0217] Approximately 6400 spectra were obtained for each analysis. The following post-translational modifications were identified and quantified: (i) oxidation of two cysteine residues (C53 and C106) to the sulfuric or sulfonic acid; and (ii) oxidation of four methionine residues (M17, M26, M133, and M134), to the sulfoxide or sulfone.
[0218] Treatment of DJ-1 with H2O2 resulted in a dose-dependent oxidation of the protein's cysteine residues to the sulfinic or sulfonic acid form. FIG. 18 shows the MS/MS spectrum of peptide 99KGLIAAICAGPTALLAHEIGFGSK122 (SEQ ID NO:1) derived from wild-type DJ-1 following oxidation with a 10-fold molar excess of H2O2. The “y” and “b” ion peaks in the spectrum were assigned labels corresponding to their m/z values. The mass difference between the b(8) ion (m/z=802.464 Da) and the b(7) ion (m/z=667.47 Da) was equal to the mass of a cysteine sulfinic acid (134.99 Da), suggesting that residue C106 had undergone oxidation to the form under these conditions. FIG. 19 shows the MS/MS spectrum of peptide 100GLIAAICAGPTALLAHEIGFGSK122 (SEQ ID NO:2) derived from wild-type DJ-1 after oxidation with a 500-fold molar excess of H2O2. In this case the mass difference between b(7) (m/z=690.349 Da) and b(6) (m/z=539.355 Da) was equal to the mass of cysteine sulfonic acid (150.994 Da), suggesting that C106 was converted to the 30 form under the more stringent oxidizing conditions. Similarly, the MS/MS spectrum of peptide 49DVVICPDASLEDAKK63 (SEQ ID NO:3) derived from M26I following exposure with a 500-fold molar excess of H2O2 clearly revealed a difference between b(5) (m/z=578.249 Da) and b(4) (m/z=427.255 Da) equal to the mass of cysteine sulfonic acid (FIG. 20), suggesting that C53 was oxidized to the 30 form upon exposure to the high dose of peroxide.
[0219] The identification of oxidized methionine residues is more complex because methionine sulfoxide (MetSO) undergoes a characteristic neutral loss of methanesulfenic acid (CH3SOH, 63.998 Da) during collision-induced dissociation. The Mascot search engine accounts for this neutral loss when assigning peaks in the MS/MS spectrum. FIG. 21 shows the MS/MS spectrum of peptide 13GAEENEETVIPVDVMR27 (SEQ ID NO:4) derived from wild-type DJ-1 following oxidation with a 500-fold molar excess of H2O2. The mass difference between y(11) (m/z=1193.697 Da after the neutral loss of two CH3SOH groups) and y(10) (m/z=1110.631 Da after the neutral loss of one CH3SOH group) was equal to the mass of methionine sulfoxide minus the neutral loss mass (83 Da=147 Da−64 Da). Additionally, the mass difference between y(2) (m/z=258.105 Da after the neutral loss of CH3SOH) and y(1) (m/z=175.106 Da) was equal to the mass of methionine sulfoxide minus the neutral loss mass (83 Da). These results indicate that both methionine residues in the parent peptide (M17 and M26) were oxidized to methionine sulfoxide under these H2O2 treatment conditions. Additional MS/MS data revealed the presence of methionine sulfoxide at positions 133 (FIG. 22) and 134 (FIG. 23) of peptide 131DKMNINGGMYTYSENRVEK148 (SEQ ID NO:5) derived from wild-type DJ-1 oxidized with a 500-fold molar excess of H2O2.
[0220] Next we determined relative levels of post-translationally modified DJ-1 peptides generated in the presence of a low or high concentration of H2O2 via quantitative analysis of the MS peak intensities. The method involved dividing the peak intensity of each 18O-labeled peptide from H2O2-treated wild-type DJ-1 or M26I by the peak intensity of the identical 16O-labeled peptide derived from the untreated protein. Reproducible data from three independent experiments revealed remarkable differences in relative levels of cysteine-modified peptides when comparing wild-type DJ-1 and M26I exposed to a 10- or 500-fold molar excess of H2O2 (FIG. 24, Table 13). These differences include: (i) wild-type DJ-1 exhibited markedly higher relative levels of C106 sulfinic acid compared to M26I following incubation with a 10-fold molar excess of H2O2; (ii) the wild-type protein (but not M26I) exhibited higher relative levels of C106 sulfinic acid after exposure to a 10-fold molar excess of H2O2 compared to a 500-fold molar excess of the peroxide; (iii) wild-type DJ-1 exhibited dramatically higher relative levels of C106 sulfonic acid compared to M26I after incubation with a 500-fold molar excess of H2O2 whereas the “30” form of C106 was essentially undetectable in samples of wild-type DJ-1 or M26I exposed to a 10-fold molar excess of the peroxide); (iv) M26I (but not wild-type DJ-1) exhibited an increase in relative levels of C53 sulfonic acid following exposure to a 500-fold molar excess of H2O2 (whereas this modification didn't appear in any other forms of the protein). In contrast to the pronounced differences in relative levels of cysteine oxidation products outlined above, the relative abundance of peptides containing methionine sulfoxide varied little between wild-type DJ-1 and M26I following exposure of the proteins to a 10-fold or 500-fold molar excess of H2O2 (FIG. 24, Table 13).
[0221] 
[00013] [TABLE-US-00013]
  TABLE 13
 
Quantitation of oxidative modifications in wild-type DJ-1 and M26Ia
  Mass spectral ratioa
  oxidative 10-fold molar excess H2O2500-fold molar excess H2O2
  modification   wild-type DJ-1   M26I   wild-type DJ-1   M26I
 
  C53 sulfonic acidNDbNDbNDb   4.0 ± 0.4
  C106 sulfinic acid   12 ± 3    2.8 ± 0.8   5.1 ± 0.7   4.0 ± 0.6
  C106 sulfonic acidNDbNDb   30.3 ± 0.9    4.1 ± 0.4
  H115 asparagine   1.95 ± 0.03NDb   2.1 ± 0.7NDb
  M17 sulfoxide   0.3 ± 0.2 NDb   0.26 ± 0.01NDb
  M133 sulfoxide0.25 ± 0.22   0.2 ± 0.1   1 ± 2   2 ± 2
  M134 sulfoxide   0.2 ± 0.2   0.2 ± 0.1   0.9 ± 0.5   0.39 ± 0.02
 
aMass spectral ratios were determined by dividing the peak intensity of each 18O-labeled peptide derived from H2O2-treated DJ-1 by the peak intensity of the identical 16O-labeled peptide derived from untreated (control) protein. Mean ± SEM, N = 3.
bND, not detected.
[0222] Structural and functional data suggest that the oxidation of DJ-1 to the 2O form enhances the protein's protective function. M26I, a pathogenic mutation to DJ-1, causes significant changes in the degree of oxidation of various residues inside the protein. The oxidation sites quantitated were: dioxidation of C106, trioxidation of C106, trioxidation of C46, oxidation of methionines M17, M133, M134, and the oxidation of histidine 115 to asparagine. The most dramatic changes in the degree of oxidation were in the C106 oxidation that is involved in the function of DJ-1. The M26I mutation reduced oxidation C106 to sulfuric acid more than 6-fold. In the wild-type DJ-1, oxidation of C106 to sulfinic acid is involved in the inhibition of the α-synuclein fibrillation. The reduced of this oxidation by the M26I mutation may reduce the anti-aggregation activity against α-synuclein.
[0223] These changes can be attributed to the change in the geometry of the protein. M26I is located in the core of the protein dimer. Although the M26I mutation occurs in the helical region near the dimer interface, it is involved in the formation of the dimer. The replacement of the bulky methionine with the smaller isoleucine, in this mutation, causes a packing defect in the interior of the protein. CD and X-ray crystallographic data of the reduced M26I DJ-1 indicated that the protein maintained its secondary structure in solution and in crystal. On the other hand, under oxidative stress conditions, M26I DJ-1 has a reduced secondary structure, low stability, and tends to aggregate. These slight conformational changes may have caused the change in the degree of oxidation reported in our study. Additionally, NMR data suggest that residues near M26 had chemical shifts that may indicate alterations that could influence C106 oxidation.
[0224] Another modification in the M26I mutant is the trioxidation of C106 to cysteic acid. This modification is reduced more than 7-fold in the M26I mutant. The presence of both cysteine sulfinic acid and cysteic acid residues at C106 in both the wild-type DJ-1 and M26I DJ-1 proteins exposed to 500 molar-fold of hydrogen peroxide may indicate that the oxidation of C106 to cysteic acid passes first by the conversion to sulfinic acid then to cysteic acid.
[0225] Also, trioxidation of the C46 residue appeared in the M26I mutant, but only when oxidized with 500 mM hydrogen peroxide. To our knowledge, this is the first time that trioxidation of C46 in the M26I mutant has been reported. C46 is partially buried within the dimer interface. The C46 residue modulates the C106-dependent binding of DJ-1 to ASK1. This binding depends on the oxidation of C106 to disulphide bond. Therefore, the increased oxidation at C46 in the M26I mutant may have modulated the function of the C106 residue.
[0226] Changes in the degree of methionine oxidation at positions 17, 133, and 134 were also analyzed.
[0227] The data presented here, together with observations that oxidation of DJ-1 to the 2O form is necessary for inhibition of aSyn aggregation, suggest that M26I may have a reduced ability to suppress aSyn fibrillation than the wild-type protein. In addition, the oxidation of DJ-1 at position 106 is necessary for protection against toxicity related to complex I inhibition. Accordingly, the decreased propensity of M26I to undergo conversion to the 2O form as shown in Example 4 may result in various functional defects in familial PD patients, including a reduced ability of the protein to carry out a chaperone function in cytosolic and/or mitochondrial compartments.
[0228] Finally, this study exploited the unique power of mass spec-based proteomic techniques for the quantitation of the effect of the M26I mutation on the level of oxidation of different residues in the DJ1 protein. While mass spectrometry has been used before to identify DJ-1 post-translation modifications, mass spectrometry has not been used to quantitate post-translation modifications under the effect of the pathogenic mutations.

Diabetes Mellitus

[0229] Example 5 describes using methods described herein to identify and analyze proteins associated with diabetes mellitus, by assessing qualitative and quantitative differences in carbonylated proteins shed, under oxidative stress, from organs and into the blood of diabetic and lean rats. Carbonylated proteins were obtained, prepared and analyzed as described in Example 5. The avidin affinity column bound approximately 1.7% of the proteins in the plasma of Zucker diabetic rats as compared to 0.98% in the case of plasma from lean rats.
[0230] Thirty proteins were identified and quantified. The concentration of four out of the thirty proteins changed significantly. The most significant changes occurred in the proteins Apo, AII, clusterin, and hemopexin precursor. Eighteen carbonylated peptides were detected and quantified, eleven of these eighteen exhibited significant concentration changes.
[0231] The oxidized proteins exhibited three types of carbonylation: direct oxidative cleavage by reactive oxygen species, oxidation of advanced glycation end products, and addition of lipid peroxidation end products. Carbonylation by direct oxidation of amino acid side chains was the most common form of protein oxidation observed. Hemoglobin was the most heavily oxidized protein. Carbonylation sites in proteins generally occurred in a non-stoichiometric ratio.
[0232] Finally, the analyses revealed molecular signatures associated with diabetes based on the identity of oxidized proteins in the plasma of a diabetic animal model and the sites of oxidation.
[0233] Diabetes is characterized by hyperglycemia, which can lead to oxidative stress that can drive flux increases in multiple pathways (Brownlee, M. Diabetes, 2005, 54:1615-1625). Hallmarks of hyperglycemia include flux elevation in the 1) polyol pathway, 2) formation of advanced glycation end products, 3) activation of protein kinase C, and/or 4) flow of metabolites in the hexosamine pathway, all of which can lead to tissue damage in diabetics (Brownlee, M. Nature, 2001, 414:813-820). Tissue damage from hyperglycemia may be the result of increased production of reactive oxygen species such as, for example, superoxide anions in mitochondria, which in turn disrupt cell signaling by protein oxidation (Evans et al., Diabetes, 2003, 52:1-8; Evans et al., Endocrine Reviews, 2002, 23:599-622; Brownlee, M. Diabetes, 2005, 54:1615-1625; Vincent et al., Endocrine Reviews, 2004, 25:612-628).
[0234] Oxidative stress in involved in diabetes-associated damage to the kidney, eyes, and the nervous system (Brownlee, M. Nature, 2001, 414:813-820). This diabetes-associated, oxidative stress-initiated organ damage is cumulative and is manifested in several organs. For example, short-term hyperglycemia in diabetic monkeys can induce the oxidation of arterial wall proteins by hydroxyl-like species (Pennathur et al., J Clin Invest, 2001, 107:853-860). Additionally, analysis of the diabetic Otsuka Long-Evans Tokushima Fatty (OLETF) rats showed elevated levels of multiple carbonylated proteins including desmin, actin, and myosin (Oh-Ishi et al., Free Radical Biology & Medicine, 2002, 34:11-22). Moreover, there was a 2- to 3-fold increase in total adipose protein carbonylation in obese, insulin-resistant mice compared to lean insulin sensitive mice (Grimsrud et al., Mol Cell Proteomics, 2007, 6:624-637).
[0235] Example 5 describes methods that exploit the observation that oxidized proteins released from damaged organs are shed into blood and can be used as indicators of organ damage and long-term consequences of chronic disease processes. These methods involve assessing qualitative and quantitative differences in the carbonylation of proteins that are shed into the blood of diabetic and lean rats.
[0236] Zucker diabetic rats develop metabolic disturbances characteristic of diabetes in which the animals become hyperglycemic by seven weeks of age on diet of PURINA 5008 CHOW. Hyperinsulinemia occurs during the development of diabetes in this animal and then decreases by nineteen weeks. As shown in Table 14, mean fasting glucose was significantly higher in the diabetic animal (449.3 mg/dl) than in a lean non-diabetic animal (93.4 mg/dl).
[0237] 
[00014] [TABLE-US-00014]
  TABLE 14
 
  The mean fasting glucose level for five diabetic rats
  and control lean rats at 16 weeks.
    Sample   Mean fasting glucose level at 16 weeks
   
    Lean plasma   93.4 mg/dl (SD = 11.3)
    Diabetic rat plasma   449.3 mg/dl (SD = 110.4)
   
[0238] Oxidative stress was demonstrated by urinary isoprostanes using competitive ELISA (Oxford Biomedical Research, Oxford, Mich.). Isoprostane is formed specifically as a consequence of free radical induced lipid peroxidation and is considered to be one of the most reliable indicators of in vivo oxidative stress. At weeks 3 and 6, 24-hour isoprostanes were significantly greater (p<0.05) in the diabetic than the controls rats (FIG. 26).
[0239] FIG. 27 shows the analytical strategy employed in these studies. Five diabetic Zucker rats were sacrificed and 6 mL of plasma withdrawn from each. The same procedure was used with non-diabetic control animals. After centrifugation, carbonylated proteins in plasma samples were biotinylated with biotin hydrazide (BH), Schiff base products of the reaction were reduced with sodium cyanoborohydride, and the samples were dialyzed to remove excess BH. Biotinylated proteins were subsequently selected from samples with avidin affinity chromatography and following proteolysis identified and quantified by LC-MS/MS.
[0240] Two analytical protocols were used. Samples from individual animals were examined in Protocol A; pooled samples were used in Protocol B. Following selection of biotinylated proteins from individual samples by avidin affinity chromatography in Protocol A, the selected proteins were digested with trypsin, labeled with iTRAQ™ coding agents, and further fractionated by C18 reversed-phase chromatography (RPC). Tryptic peptides in fractions collected from the RPC column were identified and quantified with an ABI 4800 plus MALDI/TOF/TOF mass spectrometer. Protein Pilot was used for the analysis of the mass spectra as described in Example 5.
[0241] Samples from diabetic and lean rats, respectively, were pooled in Protocol B for oxidation site identification. Five mg of pooled protein from either the diabetic or lean group of animals were applied to the avidin affinity column. Proteins thus selected were further fractionated on a C3 RPC column and collected for trypsin digestion. Following proteolysis, digested fractions were examined by the LC-MS/MS using a Waters nano-UPLC coupled to a QSTAR Pulsar mass spectrometer. Relative changes of the levels of the peptides carrying the carbonylation sites were quantitated with selective reaction monitoring (SRM) using an Agilent Triple Quad 6410 LC/MS.
[0242] After selecting the biotinylated proteins, avidin affinity columns were washed with approximately 60 column volumes of mobile phase. Elution of nonspecifically bound proteins was assessed by the degree to which absorbance had returned to zero after sample application (FIG. 28).
[0243] Using absorbance at 280 nm and assuming that affinity selected and unbound proteins have the same extinction coefficient, approximately 1.7% (SD=0.0014) of the protein in Zucker diabetic rat plasma was bound to the avidin affinity column compared to 0.98% (SD=0.46) from lean rat plasma (FIG. 28). This affinity-selected fraction includes naturally biotinylated proteins, proteins naturally complexed with or cross-linked to the biotinylated proteins, and non-specifically bound proteins.
[0244] Protocol A produced an average of 2604 mass spectra from the plasma samples. Thirty proteins were identified and quantified based on two peptides found in two or more pairs of diabetic or lean animals with a confidence level higher than 95% (FIG. 29 and Table 15). The procedure identified proteins oxidized in high abundance (e.g., fibrinogen alpha chain precursor), medium abundance (e.g., fibronectin) and low abundance (e.g., extracellular matrix protein 1 precursor) were found. Fourteen of these proteins were detected and quantified in five diabetic and lean pairs. Four proteins appeared in four diabetic and lean pairs, six proteins appeared in three diabetic and lean pairs, and another six proteins appeared in two diabetic and lean pairs. Apolipoprotein AII (Apo AII) precursor, clusterin precursor, and hemopexin precursor were elevated more than 1.5-fold in the diabetic animals. The protein from potassium voltage-gated channel subfamily H member 7 decreased more than 1.5-fold.
[0245] 
[00015] [TABLE-US-00015]
  TABLE 15
 
  Carbonylation sites quantitated using SRM
          Ratio   Ratio  
          (diabetic   (diabetic  
          plasma   plasma  
          pooled   pooled  
          sample/lean   sample/lean  
          plasma   plasma  
          pooled   pooled  
  Accession         sample)   sample)  
  number   Protein name   Oxidative modification   Site   replicate 1   replicate 2   Average
 
  gi|60678292   hemoglobin alpha 2   Biotinylated HNE   69   25.88   14.54   20.21
    chain   adduct        
  gi|204352   hemoglobin beta-chain   Biotinylated   49   0.79   1.25   1.02
      deoxyglucosone adduct        
  gi|60678292   hemoglobin alpha 2   Biotinylated   12   1.94   2.06   2.00
    chain   malodialdehyde        
  gi|60678292   hemoglobin alpha 2   Biotinylated glyoxal   17   0.52   0.87   0.69
    chain          
  gi|204570   major beta-hemoglobin   Biotinylated oxidized   77   7.19   2.21   4.70
      lysine        
  gi|56797757   fibrinogen, alpha   Biotinylated oxidized   770   6.05   6.00   6.03
    polypeptide isoform 1   arginine        
  gi|56797757   fibrinogen, alpha   Biotinylated oxidized   419   2.46   1.51   1.98
    polypeptide isoform 1   arginine        
  gi|404382   FABP-II = fatty acid-   K8: Biotinylated   8, 17   17.35   2.36   9.85
    binding protein {N-   malondialdehyde        
    terminal}   adduct,        
      K17: Biotinylated        
      methyl glyoxal adduct        
  gi|2493792   C4b-binding protein   K224: Biotinylated   224, 228   0.15   0.11   0.13
    alpha chain precursor   oxidized lysine, R: 228        
    (C4bp)   Biotinylated oxidized        
      arginine        
  gi|55391508Albumin [Rattus   Biotinylated oxidized   543   1.20   1.00   1.10
  norvegicus]   proline        
  gi|243866   immunoglobulin heavy   Biotinylated   345   1.69   3.63   2.66
    chain   malodialdehyde        
  gi|2292988   Inter-alpha-inhibitor H4   Biotinylated   161   1.25   0.66   0.95
    heavy chain   malodialdehyde        
  gi|8393024   complement component 3   Biotinylated oxidized   688   2.71   2.57   2.64
      arginine        
  gi|8393024   complement component 3   Biotinylated oxidized   656   1.62   1.09   1.35
      arginine        
  gi|12831225   Murinoglobulin 1   Biotinylated oxidized   973   0.02   0.02   0.02
    homolog   lysine        
  gi|12831225   Murinoglobulin 1   K347: biotinylated   347, 352   0.01   0.01   0.01
    homolog   deoxyglucosone        
      adduct, K352:        
      biotinylated        
      methylglyoxal        
  gi|12831225   Murinoglobulin 1   Biotinylated   682   0.86   0.98   0.92
    homolog   malodialdehyde        
  gi|55391508   Albumin   K548: Biotinylated   548, 549   1.18   1.34   1.26
      methylglyoxal adduct,        
      K549: biotinylated        
      Amadori adduct
 
[0246] The highest sequence coverage for the proteins identified and quantified came from fibrinogen beta chain precursor (92.3% coverage), fibrinogen gamma chain precursor (84% coverage), and fibronectin precursor (70.8% coverage).
[0247] We investigated the relationship between certain oxidized proteins and disease using GeneGo™ (FIG. 30). GeneGo™ analysis of disease ontology revealed that the group of proteins oxidized 50% or more than in the control correlated with nephrotic syndrome and other kidney pathologies in the diabetic rats. In addition, oxidized proteins correlated with lipid-related disorders. These findings are consistent with kidney diseases and coronary heart disease being complications of diabetes.
[0248] Carbonylation sites were identified through Protocol B in which samples from multiple diabetic or lean animals were pooled to enrich the sample for oxidized proteins. After avidin affinity chromatography, the selected proteins were further resolved by RPC on a column with a C3 alkyl silane stationary phase. Fractions collected from the RPC column were trypsin digested and the resulting peptides identified by LC-MS/MS using a QSTAR ESI/MS/MS. Quantification was achieved by selective reaction monitoring. Unmodified peptides were used to identify the protein parent while biotin-modified peptides were used to identify oxidation sites
[0249] An average of 10625 spectra was obtained per analysis. As shown in FIG. 29 and Table 15, eighteen carbonylation sites were detected using the QSTAR ESI/MS/MS. Carbonylation sites were characterized and quantified with the Agilent 6410 QqQ ESI/MS/MS using an SRM approach.
[0250] SRM analysis allowed precise, reliable, and highly sensitive quantitation of peptides below the detection limit of conventional LC/MS/MS methods. SRM analysis also allowed detection of carbonylated peptides fragment that were not seen in the initial product ion scan by conventional LC/MS/MS. In all cases, the peptide quantified with SRM should reside in the same fraction (i.e., after the separation of the purified proteins with C3-RPC and digestion) as the carbonylated peptide characterized initially by QSTAR/MS/MS. This appears to be the first use of SRM-MS to quantify carbonylated peptides (FIG. 29 and Table 15). An example of the transition used to quantify carbonylated peptides is shown in FIG. 31.
[0251] In all cases, QSTAR/MS/MS experiments were used to predict the major charge state of the precursor ion. As shown in FIG. 29, eighteen carbonylated peptides were quantitated using this strategy. The concentrations of eight of these peptides were significantly higher (at least 2-fold) in the diabetic pooled plasma compared to the lean pooled plasma. The concentrations of three proteins decreased significantly. Finally, a total of seven carbonylated peptides didn't show any significant change. The presence of a number of carbonylated peptides without any significant changes suggests that the changes at other carbonylation sites are not random.
[0252] All three mechanisms by which protein carbonylation occurs are represented in the proteins exhibiting increased oxidation levels in the diabetic rat plasma. Among the significantly changed modifications (i.e., either increased or decreased), seven originated from direct oxidation of an amino acid side chain (e.g., oxidation of arginine), four involved the formation of advanced lipid peroxidation product adducts (e.g., malondialdehyde adducts), one involved glycation and advanced glycation end product adducts (e.g., a deoxyglucosone adduct), two sites arose as a result of methylglyoxal adducts, arising from either advance glycation or advance lipid peroxidation end products (FIG. 32).
[0253] Among the identified oxidized proteins, hemoglobin and murinoglobulin 1 homolog had the largest number of carbonylation sites, possessing sites derived from all three types of carbonylation: direct carbonylation (e.g., oxidized lysine), glycoxidation of advanced glycation end products (AGE) (e.g., 3-deoxyglucosone adducts), and advanced lipid peroxidation end products (ALE) adducts (e.g., malondialdehyde adducts). The diabetic:lean ratio at carbonylation sites was surprisingly diverse. Whereas the deoxyglucosone adduct ratio was unchanged at 1.02, the HNE adduct, oxidized lysine, and malondialdehyde had ratios of 20, 4.7 and 2.00, respectively, favoring diabetic animals. Similar results were observed with murinoglobulin 1 homolog. The oxidized lysine, deoxyglucosone adduct, and methylglyoxal adduct had a ratio of nearly 0.01, favoring lean animals. The malondialdehyde adduct remained unchanged. Fibrinogen alpha polypeptide isoform 1 provided a similar example. The ratio of arginine oxidation between diabetic and lean animals at sites 419 and 770 was 1.98 and 6.03, respectively. A last example is the complement component 3 protein which had two sites of arginine oxidation. One was unchanged while the other increased more than two-fold in diabetic animals.
[0254] The oxidative stress environment inside cells is one factor that determines the degree of oxidation. Diabetes is a complex disease that involves oxidative stress, hyperlipidemia, and hyperglycemia. Thus, direct carbonylation is a common oxidation mechanism in diabetes. Hyperglycemia increases the superoxide anion in mitochondria. The superoxide anion may then leak out, causing the oxidation of other proteins. Additionally, the formation of ALE and AGE adducts is a result of increase reactive oxygen species levels. Diabetic liver mitochondria and microsomes, however, are unable to consume exogenous HNE to the same extent as the non-diabetic organelles. This may be due to the decreased activity of the enzymes that metabolize HNE in the liver, which may itself be a result of oxidative stress. On the other hand, high levels of reducing sugars, primarily glucose, in cells and plasma lead to Schiff base formation between the sugar and lysine residues on proteins. Following Amadori rearrangement a stable glycated protein product may be formed. Over time, oxidative degradation of the Amadori adducts leads to the formation of a reactive carbonyl or dicarbonyl functional group at the site. These compounds can be also produced by the autoxidation of glucose. A list of adducts that can be detected by the methodology described here is shown in FIG. 33.
[0255] The structure of proteins seems to play an important role in the propensity of particular sites to be oxidized. Half-life is important as well. Hemoglobin (Hb) is one of the most heavily oxidized proteins reported in this study. The half-life of red blood cells is 120 days. This makes them susceptible to the formation of adducts with AGEs and ALEs. The presence of iron in hemoglobin structure increases its potential for oxidation. The susceptibility of metal-containing proteins to be oxidation is reflected in the oxidation of serotransferrin, ceruplasmin, hemopexin, and fibrinogen as well.
[0256] The location of a protein relative to the source of reactive oxygen species may influence the probability that the protein will be oxidized, as seen with intestinal fatty acid binding protein (I-FABP). This protein is involved in the transport of fatty acids to the mitochondrion and their subsequent β-oxidation. The close proximity of I-FABP to mitochondria and the fatty acids might explain how malondialdehyde and methylglyoxal adducts were formed in diabetic rat plasma.
[0257] The effects of oxidative stress observed in Example 5 reflect diabetes-induced changes in multiple pathways, sometimes within a single protein as seen with hemoglobin, fibrinogen alpha polypeptide isoform 1, complement component 3, and murinoglobulin homolog 1. Multiple oxidation sites were observed in these proteins, each of which was oxidized to a different extent in diabetic rats.

Monitoring Antioxidant Treatment of Diabetes Mellitus

[0258] Example 6 describes the use of methods described herein to monitor the effect of therapy on the status of a disease. As noted above, chronic oxidative stress diseases can be associated with elevated levels of free radicals that can destroy cells and organs over time. Thus, the pathological effects associated with multiple oxidative stress diseases may be ameliorated with antioxidants. However, methods for elucidating the mechanisms by which antioxidants may reduce oxidative stress-related protein damage are lacking. Methods described herein report a novel method that can permit one to evaluate the efficacy of dietary antioxidant supplementation on the protection of protein oxidative damages in diabetic animals. We evaluated the efficacy of green tea, as an exemplary source of dietary antioxidants, for the ability to protect against oxidative stress-related oxidized peptide signatures in the plasma of diabetic Zucker rats. The mechanism of antioxidant protection was examined through the types and extent of OSi˜PTMs in oxidized proteins. Oxidative stress from advanced glycation end-product (AGE) oxidation was differentiated from lipid peroxidation- and reactive oxygen species (ROS)-initiated oxidation through the type of oxidation at particular sites in proteins.
[0259] Carbonylated proteins in freshly drawn blood samples were prepared and analyzed as described in Example 6. Eighteen carbonylated peptides were detected and quantified. Eight of the peptides changed significantly: eight decreased significantly while one increased significantly. Green tea changed the three routes for carbonylation: direct oxidative cleavage from reactive oxygen species, glycation and addition of advanced glycation end products (AGEs), and addition of lipid peroxidation products (ALEs). The major effect of green tea was on the AGEs followed by ALEs followed by direct carbonylation.
[0260] Existing in vitro assays for measuring antioxidant activity include, for example, the 1,1-diphenyl-2-picrylhydrazyl (DPPH) assay (Cos et al., Free Radical Res., 2002, 36(6):711-716) and horseradish peroxidase-luminol-hydrogen peroxide assay (Georgetti et al., AAPS PharmSci 2003, 5(2):E20). These assays, however, correlate poorly with actual in vivo efficacy of antioxidant compounds. The physiological environment, interactions with other food components in vivo, the in vivo degradation of the antioxidants, the bioavailability (absorption, distribution, metabolism and excretion) of the antioxidants, and the location of the antioxidants relative to the reactive oxygen species are some reasons for the poor correlation between the assays and the actual in vivo protective activity of antioxidants.
[0261] Existing in vivo assays for measuring antioxidant activity include, for example, assays that test the efficacy of antioxidant on oxidative lipid damage (e.g., thiobarbituric acid test, (Hermans et al., J. Chromatogr., B: Anal. Technol. Biomed Life Sci., 2005, 822(1-2):33-39)) and oxidative DNA damage (e.g., 8-hydroxy-2′-deoxyguanosine test, (Podmore et al., Nature, 1998, 392(6676):559)).
[0262] Assessing the utility of antioxidants in preventing the oxidative damage of proteins, however, has not been well explored, primarily due to the lack of evaluation tools. Measuring antioxidant efficacy protecting against oxidative protein damage has focused mainly on the determination of the total protein carbonyl content (PCC) by a colorimetric reaction (Levine et al., Methods Enzymol., 1990, 186(Oxygen Radicals Biol. Syst., Pt. B):464-478). This assay, however, doesn't provide the specific mechanism of oxidative stress (Hermans et al., Curr. Med. Chem., 2007, 14(4):417-430). Also, this assay doesn't provide information about the proteins involved in the protection process. Another test is the measurement of the total 3-nitrotyrosine released after the acid treatment or enzymatic digestion of the proteins (Salman-Tabcheh et al., Free Radical Biol. Med., 1995, 19(5):695-698). This assay only gives information about the efficacy of antioxidants in the prevention of the nitration of the proteins. Also, it doesn't give information about the proteins carrying the nitrated tyrosine residues. Finally, a recent method tested the efficacy of antioxidants on the protein expression levels (Weinreb, O. et al. Free Radical Biol. Med., 2007, 43(4):546-556; Santos-Gonzalez et al., Exp. Gerontol., 2007, 42(8):798-806; and Li et al, Chin. Med. J. (Engl. Ed), 2008, 121(24):2544-2552). This assay doesn't indicate whether these proteins are damaged, nor does it account for the fact that oxidative stress causes the degradation of these proteins making the results obtained about the expression of these proteins unreliable.
[0263] Generally, none the previous assays account for the enormous complexity by which oxidative damage occurs and the efficacy of antioxidants in mitigating the various forms of oxidative damage.
[0264] The methods described herein provide a new approach to directly measure the efficacy of antioxidants on the different kinds of oxidative damages of proteins. This measurement allows the elucidation of antioxidant protection mechanisms in vivo. The methods described herein can be used to assess the efficacy of antioxidants on the nature and/or extent of individual protein oxidation. The methods also may be used to identify the probable source of oxidation as having resulted from i) direct ROS-initiated oxidation of amino acids (e.g., Pro, Arg, Lys and Thr), ii) indirect oxidation through forming advanced glycation end products adducts (e.g., deoxyglucosone), or indirect oxidation through adding lipid peroxidation products (e.g., 4-hydroxy-2-noneal, 2-propenal, or malondialdehyde).
[0265] We analyzed the effects of Green tea (Camellia sinensis) on the Oxidative Stress-induced Post-translational Modifications (OSi˜PTMs) that are profiled for type II diabetes mellitus Example 5. Diabetes was chosen because pathological changes that are associated with the disease—e.g., renal failure, neuropathy, cardiovascular disease, and blindness—can be the result of four OS-induced pathways that commence at disease onset, when hyperglycemia and depletion of antioxidant defenses begin (Brownlee, M., Diabetes, 2005, 54(6):1615-1625). In Example 5, we characterize the oxidative stress signature for type II diabetes mellitus in the plasma of diabetic Zucker rats.
[0266] Green tea was selected because its content of epigallocatechin-3-gallate (EGCG) showed strong antioxidant efficacy. Its mechanism depends on the chelation of iron and copper and scavenging reactive oxygen and nitrogen species as well as the inhibition “pro-oxidant” enzymes and redox-sensitive transcription factors (Camargo et al., Nutr. Res., 2006, 26(12):626-631; Ounjaijean et al., Med. Chem., 2008, 4(4):365-370). This study is based on the assessment of the qualitative and quantitative differences in the proteins carbonylation sites shed in the blood of the green tea fed diabetic and control diabetic rats. This assessment involves labeling the proteins carbonyl groups with biotin hydrazide. The resultant Schiff's bases were reduced using sodium cyanoborohydride. The proteins thus biotinylated were dialyzed and enriched with avidin purification followed by separation, digestion, identification of the proteins, characterization and quantitation of their OSi˜PTMs.
[0267] Example 5 describes identifying and quantifying the effect of type II diabetes on the oxidation sites of proteins released into the plasma. Example 6 describes testing the effect of the administration of an antioxidant (green tea) on the oxidation sites characterized in Example 5. Thus, Example 6 describes a new method for determining the efficacy of antioxidants by testing their ability to affect the disease-related oxidative stress-induced PTMs. More broadly, example 6 describes a method for monitoring the effect of treatment. Example 6 uses a control population of diabetic rats as a baseline against which antioxidant-treated rats can be compared. In other embodiments, the control may be a sample obtained earlier from the individual receiving treatment. The previously obtained sample may be obtained prior to the initiation of treatment or at a time during the course of treatment.
[0268] The analytical protocol used in Example 6 is similar to the protocol used in Example 5 for the detection of OSi˜PTMs (FIG. 35). In Example 6, roughly 6 mLs of fresh plasma were withdrawn from each of five green tea-fed Zucker diabetic rats and each of their control Zucker diabetic rats. Biotin hydrazide was added to biotinylate the carbonylated proteins in the plasma, the samples were then dialyzed to remove excess reagent.
[0269] The OSi˜PTMs were first enriched by pooling the samples, either from green tea-fed diabetic rats or the control diabetic rats, respectively, and enriching the carbonylated proteins with avidin affinity purification. The desorbed proteins were further separated by a C3 reversed-phase column. The proteins separated were then digested and analyzed by the LC/MS/MS (nano-UPLC coupled to QSTAR Pulsar i mass spectrometer). The relative changes of the levels of the peptides carrying the carbonylation sites were then quantitated using selective reaction monitoring (SRM) using Agilent Triple Quad 6410 LC/MS.
[0270] Samples purified with avidin were further fractionated at the protein level using C3 reversed-phase chromatography. These fractions were then digested with trypsin and carbonylation sites were detected with QSTAR ESI/MS/MS and quantified with ESI/SRM. The separation was done at the protein level rather than at the peptide level as this allows the detection of the unmodified peptides, which are used for the identification of the protein; the modified peptides allows detection of the oxidation site. Generally, the use of SRM offered two advantages: a) increased sensitivity for the detection of carbonylated peptides present at levels below the detection limit for the conventional LC/ESI/MS/MS., and b) validation of the carbonylation sites detected by using product ions as transitions that weren't detected in the conventional LC/ESI/MS/MS analysis. As a practical matter, peptides quantitated using SRM must reside in the same fraction as those detected initially in the LC/ESI/MS/MS analyses. Two transitions were used for each peptide and they co-eluted perfectly at the same time.
[0271] An average of 10625 spectra were obtained for these analyses. As shown in Table 16, eighteen carbonylation sites were detected and quantified.
[0272] 
[00016] [TABLE-US-00016]
  TABLE 16
 
  Carbonylation sites quantitated using SRM
          Average   Average  
          (diabetic/   (green   SD
          lean rat   tea/diabetic   (green
          plasma   rat plasma   tea/diabetic
  Accession         pooled   pooled   rat
  number   Protein name   Oxidative modification   Site   sample)   sample)   plasma)
 
  gi|60678292   hemoglobin alpha 2   Biotinylated HNE adduct   69   20.21   0.03   0.01
    chain          
  Gi|204352   hemoglobin beta-   Biotinylated   49   1.02   0.13   0.03
    chain   deoxyglucosone adduct        
  gi|60678292   hemoglobin alpha 2   Biotinylated   12   2.00   1.28   0.13
    chain   malodialdehyde        
  gi|60678292   hemoglobin alpha 2   Biotinylated glyoxal   17   0.52   0.87   0.03
    chain          
  Gi|204570   major beta-   Biotinylated oxidized   77   4.70   0.73   0.06
    hemoglobin   lysine        
  gi|56797757   fibrinogen, alpha   Biotinylated oxidized   770   6.03   0.24   0.02
    polypeptide isoform 1   arginine        
  gi|56797757   fibrinogen, alpha   Biotinylated oxidized   419   1.98   1.03   0.24
    polypeptide isoform 1   arginine        
  Gi|404382   FABP-II = fatty acid-   K8: Biotinylated    8, 17   9.85   3.17   0.08
    binding protein {N-   malondialdehyde        
    terminal}   adduct, K17: Biotinylated        
      methyl glyoxal adduct        
  gi|2493792   C4b-binding protein   K224: Biotinylated   224, 228   0.13   0.66   0.05
    alpha chain   oxidized lysine, R: 228        
    precursor (C4bp)   Biotinylated oxidized        
      arginine        
  gi|55391508   Albumin   Biotinylated oxidized   543   1.10   0.66   0.04
      proline        
  Gi|243866   immunoglobulin   Biotinylated   345   2.66   1.20   0.39
    heavy chain   malodialdehyde        
  gi|2292988   Inter-alpha-inhibitor   Biotinylated   161   0.95   0.32   0.26
    H4 heavy chain   malodialdehyde        
  gi|8393024   complement   Biotinylated oxidized   688   2.64   1.10   0.09
    component 3   arginine        
  gi|8393024   complement   Biotinylated oxidized   656   1.35   0.18   0.18
    component 3   arginine        
  gi|12831225   Murinoglobulin 1   Biotinylated oxidized   973   0.02   1.01   0.02
    homolog   lysine        
  gi|12831225   Murinoglobulin 1   K347: biotinylated   347, 352   0.01   1.03   0.03
    homolog   deoxyglucosone adduct,        
      K352: biotinylated        
      methylglyoxal        
  gi|12831225   Murinoglobulin 1   Biotinylated   682   0.92   0.08   0.04
    homolog   malodialdehyde        
  gi|55391508   Albumin   K548: Biotinylated   548, 549   1.26   0.50   0.02
      methylglyoxal adduct,        
      K549: biotinylated        
      Amadori adduct
 
[0273] Plasma samples of green tea-fed animals had significantly reduced levels of seven of these peptides. On the other hand, it has significantly increased levels of one peptide.
[0274] Six carbonylated peptides didn't significantly change between the diabetic and lean samples. Of these six, green tea significantly reduced the level of five of them. The only carbonylated peptide that wasn't affected by green tea, in this category, was the peptide carrying oxidized proline residue at position 543 in albumin. Eight carbonylated peptides exhibited significantly increased concentrations in the diabetic plasma samples compared to plasma samples from lean rats. Of these eight, two proteins returned to the normal values using green tea: the peptides that form 1) an HNE adduct with the lysine residue at position 69 in the hemoglobin alpha 2 chain, and 2) a deoxyglucosone adduct with the lysine residue at position 49 in the hemoglobin beta chain.
[0275] On the other hand, the concentrations of the oxidized peptide forming adducts with malondialdehyde and methyl glyoxal at the lysine residues at positions 8 and 17, respectively, in the intestinal fatty acid binding protein were increased by more than three-fold in the green tea-fed diabetic rats plasma samples compared their control diabetic rats samples. Among the three carbonylated peptides that decreased significantly in the diabetic rat plasma samples compared to the lean rat plasma samples, green tea didn't have any significant change on these peptides.
[0276] Among a total of eight direct carbonylation sites, green tea was able to significantly reduce two of them. These are the oxidized arginines at positions 770 and 656 in fibrinogen alpha polypeptide isoform 1 and complement component 3, respectively. Although arginine was oxidized at another two positions on the same proteins, at positions 419 in fibrinogen alpha polypeptide isoform 1 and 688 in the complement component 3, green tea didn't have a significant effect on either of these other sites. Moreover, among the five advanced lipid peroxidation ALE adducts, green tea significantly reduced three of them and increased the levels of a peptide carrying an ALE modification (malondialdehyde adduct with lysine at position 8 in the intestinal fatty acid binding protein). Additionally, among the three AGE adducts, green tea significantly reduced two of them. Finally, methyl glyoxal adducts can be of either ALE or AGE origin. Among the three peptides carrying methyl glyoxals, green tea reduced one of them (at lysine 548 in albumin) and increased the level of another one (at lysine 17 in intestinal fatty acid binding protein). This indicates that green tea reduced the effect of three routes involved in carbonylation. AGE adducts were the most affected by the green tea, followed by the ALE adducts, followed by the direct carbonylation.
[0277] Generally, the most dramatic decreases in the levels of carbonylated peptides induced by green tea were the reduction of the HNE adduct at the lysine residue 69 in the hemoglobin alpha 2 chain. The second most dramatic decrease of the carbonylated peptides by the green tea was the reduction of the malondialdehyde adduct with the lysine residue at position 682 in the murinoglobulin 1 homolog.
[0278] Thus, Example 6 provides an analytical methodology for determining how dietary antioxidant supplementation impacts oxidative stress (OS) signatures in proteins. The assessment of the impact of antioxidant supplementation on OS in vivo was based on the site, extent, and type of OS-initiated post-translational modification (OSi˜PTM) in plasma proteins. By examining changes in OS signatures in association with supplementation it is possible to predict whether a given supplement may be beneficial in interrupting a specific disease process.
[0279] More broadly, Example 6 describes a general analytical methodology for monitoring the status of a disease over time, e.g., prior to and then after some event such as, for example, a course of treatment or a dietary change. The methodology can be used to identify changes in the population and/or extent of oxidized peptides to determine whether a disease has progressed, regressed, recurred, is responding to treatment, etc. The method may be employed simply to monitor the efficacy of a course of treatment. In other cases, the method may be used to screen potential treatments for efficacy. In either case, the method may be used to assist a health professional in determining the course of treatment most likely to be effective for a particular individual under a particular set of circumstances.
[0280] The method exploits several observations. Among these observations are i) carbonylation is a universal indicator of oxidative stress, ii) carbonylated proteins are easily selected from a proteome, iii) carbonylated proteins carry multiple forms of OSi˜PTMs, iv) specific sites and types of oxidative modification can be identified for an individual protein that is a component of a complex mixture of oxidized proteins, v) there are apparent differences in protein oxidation based on the level of OS and antioxidant treatment, vi) carbonylated signature proteins appear reproducibly in plasma, vi) oxidized proteins shed from organs can be readily identified in plasma, and vii) there seem to be no inherent differences in the way low and high abundance proteins are oxidized, suggesting that high abundance oxidation products are equally valid for testing the efficacy of antioxidants.
[0281] As part of the differential analysis of OSi˜PTMs, the sites of oxidation, the types of oxidative modifications, and the extent of oxidative modifications within individual proteins were examined. Carbonylation sites were identified using MS/MS and the sensitivity and selectivity of SRM was used to quantitate these sites. Using this methodology, we found that green tea significantly reduced seven of these sites while it increased one of them. This can be attributed to the fact that green tea is rich in polyphenolic compounds. One third of its dry weight is composed mainly of flavonols known as catechins. These include: epicatechin, epigallocatechin, epicatechin gallate, and epigallocatechin gallate (FIG. 34). Generally, these constituents showed large antioxidant activity. For example, the administration of green tea increased the antioxidant potential in the serum of normal and dyslipidemic subjects. Also, there is increasing evidence that green tea ameliorates oxidative stress in diabetes.
[0282] 
[00017] [TABLE-US-00017]
  TABLE 17
 
  SRM transitions used for the quantitation of the carbonylated peptides
  Accession     Oxidative               Frag-   Dwell  
  number   Protein name   modification   Site   Q1   Q3   z (+)   Ion   CE   mentor   time   R.T.
 
  gi|60678292   hemoglobin alpha   Biotinylated HNE   69   607.3   915.5   1   y5   22   140   50   7.25
    2 chain   adduct                  
  gi|60678292   hemoglobin alpha   Biotinylated HNE   69   607.3   599.4   1   y2-   22   140   50   7.25
    2 chain   adduct         NH3        
  gi|60678292   hemoglobin alpha   Biotinylated glyoxal   17   430.2   674.3   1   y3   11   130   100   8.28
    2 chain                    
  gi|60678292   hemoglobin alpha   Biotinylated glyoxal   17   430.2   745.4   1   y3   11   130   100   8.28
    2 chain                    
  gi|204352   hemoglobin beta-   Biotinylated   49   762.9   327.2   1   b4   24   130   50   6.6
    chain   deoxyglucosone                  
      adduct                  
  gi|204352   hemoglobin beta-   Biotinylated   49   762.9   598.4   1   b7   16   130   50   6.6
    chain   deoxyglucosone                  
      adduct                  
  gi|60678292   hemoglobin alpha   Biotinylated   12   794.5   500.3   2   y6   16   130   100   10.1
    2 chain   malodialdehyde                  
  gi|60678292   hemoglobin alpha   Biotinylated   12   794.5   428.3   1   y1   32   130   100   10.1
    2 chain   malodialdehyde                  
  gi|204570   major beta-   Biotinylated oxidized   77   730.4   251.2   2   y2   25   140   100   8.3
    hemoglobin   lysine                  
  gi|204570   major beta-   Biotinylated oxidized   77   730.4   129   1   b1   25   140   100   8.3
    hemoglobin   lysine                  
  gi|56797757   fibrinogen, alpha   Biotinylated oxidized   770   490.73   187.3   2   y1   27   130   100   6.4
    polypeptide   arginine                  
    isoform 1                    
  gi|56797757   fibrinogen, alpha   Biotinylated oxidized   770   490.74   244.1   2   y2   11   130   100   6.4
    polypeptide   arginine                  
    isoform 1                    
  gi|56797757   fibrinogen, alpha   Biotinylated oxidized   419   805.9   399.2   2   b8-   20   130   100   9.1
    polypeptide   arginine         NH3        
    isoform 1                    
  gi|56797757   fibrinogen, alpha   Biotinylated oxidized   419   805.9   584.3   2   y9   20   130   100   9.1
    polypeptide   arginine                  
    isoform 1                    
  gi|404382   FABP-II = fatty acid-   K8: Biotinylated    8, 17   767.1   283.7   2   b5   20   140   100   8.7
    binding protein   malondialdehyde                  
    {N-terminal}   adduct,                  
      K17: Biotinylated                  
      methyl glyoxal                  
      adduct                  
  gi|404382   FABP-II = fatty acid-   Biotinylated    8, 17   767.1   851.0   2   b14   20   140   100   8.7
    binding protein   malondialdehyde                  
    {N-terminal}   adduct, Biotinylated                  
      methyl glyoxal                  
      adduct                  
  gi|2493792   C4b-binding   K224: Biotinylated   224, 228   579.60   237.1   2   y2   12   130   100   8.2
    protein alpha   oxidized lysine, R: 228                  
    chain precursor   Biotinylated oxidized                  
    (C4bp)   arginine                  
  gi|2493792   C4b-binding   K224: Biotinylated   224, 228   579.60   388.2   2   b5-   8   130   100   8.2
    protein alpha   oxidized lysine, R: 228         NH3        
    chain precursor   Biotinylated oxidized                  
    (C4bp)   arginine                  
  gi|243866   immunoglobulin   Biotinylated   345   810.89   265.12   2   b4   28   130   100   11.52
    heavy chain   malodialdehyde                  
  gi|243866   immunoglobulin   Biotinylated   345   810.89   529.24   1   b3   20   130   100   11.52
    heavy chain   malodialdehyde                  
  gi|55391508   Albumin [Rattus   Biotinylated oxidized   543   714.0   416.2   2   y5   17   130   100   11.0
    norvegicus]   proline                  
  gi|55391508   Albumin [Rattus   Biotinylated oxidized   543   714.0   201.1   1   b2   25   130   100   11.0
    norvegicus]   proline                  
  gi|2292988   Inter-alpha-   Biotinylated   161   766.9   654.4   1   y3   15   130   100   9.5
    inhibitor H4 heavy   malodialdehyde                  
    chain                    
  gi|2292988   Inter-alpha-   Biotinylated   161   766.9   541.3   1   y2   31   130   100   9.5
    inhibitor H4 heavy   malodialdehyde                  
    chain                    
  gi|8393024   complement   Biotinylated oxidized   688   762.38   675.3   2   b10   23   130   100   9.5
    component 3   arginine                  
  gi|8393024   complement   Biotinylated oxidized   688   762.38   459.2   2   b6   27   130   100   9.5
    component 3   arginine                  
  gi|8393024   complement   Biotinylated oxidized   656   680.3   508.8   2   y7   16   130   100   9.6
    component 3   arginine                  
  gi|8393024   complement   Biotinylated oxidized   656   680.3   257.6   2   b5   28   130   100   9.6
    component 3   arginine                  
  gi|12831225   Murinoglobulin 1   K347: biotinylated   347, 352   618.3   ####   1   b7   30   130   100   8.9
    homolog   deoxyglucosone                  
      adduct, K352:                  
      biotinylated                  
      methylglyoxal                  
  gi|12831225   Murinoglobulin 1   K347: biotinylated   347, 352   618.3   483.3   2   y5   30   130   100   8.9
    homolog   deoxyglucosone                  
      adduct, K352:                  
      biotinylated                  
      methylglyoxal                  
  gi|12831225   Murinoglobulin 1   Biotinylated   682   754.4   655   1   y3   18   130   100   8.44
    homolog   malodialdehyde                  
  gi|12831225   Murinoglobulin 1   Biotinylated   682   754.4   452   2   y2   14   130   100   8.44
    homolog   malodialdehyde                  
  gi|55391508   Albumin   K548: Biotinylated   548, 549   738.4   386   1   b3   30   130   100   7.57
      methylglyoxal                  
      adduct,                  
      K549: biotinylated                  
      Amadori adduct                  
  gi|55391509   Albumin   K548: Biotinylated   548, 549   738.4   489   2   y2   30   130   100   7.57
      methylglyoxal                  
      adduct,                  
      K549: biotinylated                  
      Amadori adduct                  
  gi|12831225   Murinoglobulin 1   Biotinylated oxidized   973   458.96   587.97   3   y13   3   130   100   8.77
    homolog   lysine                  
  gi|12831225   Murinoglobulin 1   Biotinylated oxidized   973   458.96   382.2   3   y9   11   130   100   8.77
    homolog   lysine
 
[0283] For any method disclosed herein that includes discrete steps, the steps may be conducted in any feasible order. And, as appropriate, any combination of two or more steps may be conducted simultaneously.
[0284] Exemplary embodiments of the invention include:
[0285] 1. A method for monitoring the health of a subject comprising:
[0286] comparing a plurality of test peptides in a sample obtained from the subject, each test peptide having a detectable oxidation state, with a plurality of reference peptides, each reference peptide having a detectable oxidation state; and
[0287] detecting a difference in oxidation state between at least one test peptide and the oxidation state of a corresponding reference peptide, wherein the difference in oxidation state is indicative of the health status of the subject.
[0288] 2. The method of embodiment 1 wherein the plurality of test peptides, the plurality of reference peptides, or both are detectably labeled.
[0289] 3. The method of embodiment 1 or 2 wherein comparing the plurality of test peptides with the plurality of reference peptides comprises using global internal standard technology.
[0290] 4. The method of any previous embodiment wherein comparing the plurality of test peptides with the plurality of reference peptides comprises using isotopically labeled internal standard peptides.
[0291] 5. The method of any previous embodiment wherein comparing the plurality of test peptides with the plurality of reference peptides comprises using global isotopic coding.
[0292] 6. The method of any previous embodiment wherein the at least a portion of the plurality of test peptides, at least a portion of the reference peptides, or both are labeled using a procedure selected from stable mass isotope coding, radioisotope coding, isotope coded affinity tagging (ICAT), stable isotope labeling of amino acids in cell culture (SILAC), isobaric tagging for relative and absolute quantification (iTRAQ™), labeling using fluorinated affinity tags, amino terminal sulphonation, dimethyl labeling, global internal standard technology (GIST), 160/180 labeling, or any combination thereof.
[0293] 7. The method of embodiment 6 wherein the labeling is performed in vivo.
[0294] 8. The method of embodiment 6 wherein the labeling is performed in vitro.
[0295] 9. The method of embodiment 1 wherein neither the plurality of test peptides nor the plurality of reference peptides are detectably labeled, and wherein comparing the plurality of test peptides with the reference peptides comprises using a label-free method.
[0296] 10. The method of any previous embodiment wherein comparing the plurality of test peptides with the plurality of reference peptides comprises using mass spectrometry.
[0297] 11. The method of any previous embodiment wherein comparing the plurality of test peptides with the plurality of reference peptides comprises using mass spectrometry comprising electron transfer dissociation (ETD), collision-induced dissociation (CED), or both.
[0298] 12. The method of any previous embodiment wherein comparing the plurality of test peptides with the plurality of reference peptides comprises using liquid chromatography-mass spectrometry (LC/MS).
[0299] 13. The method of any previous embodiment wherein comparing the plurality of test peptides with the plurality of reference peptides comprises using a mass spectrometry method comprising selected ion monitoring (SIM), selected reaction monitoring (SRM), or multiple reaction monitoring (MRM), or any combination thereof.
[0300] 14. The method of any previous embodiment wherein comparing the plurality of test peptides with the plurality of reference peptides comprises using interferometry.
[0301] 15. The method of any previous embodiment wherein comparing the plurality of test peptides with the plurality of reference peptides comprises using fluorescence.
[0302] 16. The method of any previous embodiment wherein comparing the plurality of test peptides with the plurality of reference peptides comprises:
[0303] using liquid chromatography; and
[0304] absorbance or fluorescence monitoring.
[0305] 17. The method of any previous embodiment wherein comparing the plurality of test peptides with the plurality of reference peptides comprises performing an immunological assay on the sample obtained from the subject and a sample from at least one disease-free subject to detect biomarkers in the samples.
[0306] 18. The method of any previous embodiment further comprising using affinity selection to enrich for oxidized peptides.
[0307] 19. The method of any previous embodiment, wherein at least one of the plurality of test peptides or at least one of the plurality of reference peptides is oxidized, the method further comprising biotinylating at least one oxidized peptide.
[0308] 20. The method of embodiment 19 comprising affinity selecting the biotinylated peptides using avidin.
[0309] 21. The method of any previous embodiment wherein the difference in oxidation state between at least one test peptide and the corresponding reference peptide comprises:
[0310] a difference in the concentration of an oxidized test peptide relative to the reference peptide;
[0311] a difference in the level of oxidation of the test peptide relative to the reference peptide;
[0312] a difference in the molecular location of oxidation in the test peptide relative to the reference peptide;
[0313] or any combination thereof.
[0314] 22. The method of any previous embodiment wherein at least one test peptide or at least one reference peptide is oxidized at more than one site.
[0315] 23. The method of any previous embodiment wherein different forms of oxidation are present at a corresponding oxidation site in different molecules of at least one test peptide or different forms of oxidation are present at a corresponding oxidation site in different molecules of at least one reference peptide.
[0316] 24. The method of any previous embodiment wherein the plurality of test peptides, plurality of reference peptides, or both comprise multiple isoforms of a peptide, wherein the isoforms differ in their oxidation pattern.
[0317] 25. The method of any previous embodiment wherein the sample obtained from the subject is obtained from the blood or a blood product of the subject.
[0318] 26. The method any previous embodiment wherein the sample obtained from the subject was obtained from the plasma of the subject.
[0319] 27. The method of any previous embodiment wherein the plurality of test peptides and the plurality of reference peptides comprise non-blood peptides.
[0320] 28. The method of any previous embodiment wherein detecting a difference in oxidation state between at least one test peptide and the oxidation state of a corresponding reference peptide comprises detecting differences in oxidation state of a test non-blood peptide and a reference non-blood peptide.
[0321] 29. The method of any previous embodiment wherein comparing the plurality of test peptides with the plurality of reference peptides comprises using oxidative site mapping to identify at least one specific oxidative modification.
[0322] 30. The method of any previous embodiment wherein at least one test peptide, at least one reference peptide, or both comprises an oxidized peptide comprising a carbonyl group, wherein the carbonyl group is a product of, or is associated with:
[0323] oxidative cleavage of an amino acid side chain;
[0324] cleavage of the primary peptide structure;
[0325] glycation;