| Literature DB >> 25874603 |
Ivan Verrastro1, Sabah Pasha2, Karina Tveen Jensen3, Andrew R Pitt4, Corinne M Spickett5.
Abstract
Many inflammatory diseases have an oxidative aetiology, which leads to oxidative damage to biomolecules, including proteins. It is now increasingly recognized that oxidative post-translational modifications (oxPTMs) of proteins affect cell signalling and behaviour, and can contribute to pathology. Moreover, oxidized proteins have potential as biomarkers for inflammatory diseases. Although many assays for generic protein oxidation and breakdown products of protein oxidation are available, only advanced tandem mass spectrometry approaches have the power to localize specific oxPTMs in identified proteins. While much work has been carried out using untargeted or discovery mass spectrometry approaches, identification of oxPTMs in disease has benefitted from the development of sophisticated targeted or semi-targeted scanning routines, combined with chemical labeling and enrichment approaches. Nevertheless, many potential pitfalls exist which can result in incorrect identifications. This review explains the limitations, advantages and challenges of all of these approaches to detecting oxidatively modified proteins, and provides an update on recent literature in which they have been used to detect and quantify protein oxidation in disease.Entities:
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Year: 2015 PMID: 25874603 PMCID: PMC4496678 DOI: 10.3390/biom5020378
Source DB: PubMed Journal: Biomolecules ISSN: 2218-273X
Figure 1Structures of oxidized residues most commonly detected and studied by mass spectrometry. In mixed disulfides, R can be cysteine or glutathione (glutathionylation).
Figure 2Summary of advanced methods for identification of proteins and oxPTMs. Labeling and enrichment can also be carried out at the protein level, but this approach is less common.
Comparison of the advantages and disadvantages of the most commonly used search engines for peptide and protein identification.
| Search Engine | Method | Advantages | Disadvantages |
|---|---|---|---|
| Uses a probability modelling algorithm and protein database searching. Matches experimental peptide and fragment ion masses to ones generated | User-friendly interface. Provides an error-tolerant search facility. Sophisticated but complex data export possibilities. | Very reliant on user input for correct identification of oxPTMs, otherwise false positives and negatives occur. | |
| Uses an algorithm based on a cross correlation function, plus protein data base searching. Matches experimental peptide and fragment ion masses to ones generated | User-friendly interface. Provides an error-tolerant search facility. | Very reliant on user input for correct identification of oxPTMs, otherwise false positives and negatives occur. | |
| Uses a sequence tag method plus protein database searching. | User-friendly interface. Potentially better at identifying unsuspected modifications. | If the initial sequence tag is incorrectly identified, the experimental peptide will not be matched to the correct peptide. Long analysis run times. | |
| Spectral library searching against experimentally-derived data. | Has been reported to be better at identifying PTMs, and specifically at coping with the unusual fragmentation of peptides caused by PTMs. | Since this method uses a spectral library, the peptide will only be identified if the spectra are available in the spectral library. | |
| Based on a binomial distribution function. Protein data base searching. Matches experimental peptide and fragment ion masses to ones generated | Reported to be better at identifying peptides of higher | Very reliant on user input for correct identification of oxPTMs, otherwise false positives and negatives occur. |
Figure 3Incorrectly assigned oxidation to proline using a probability-based search engine. (a) Search engine identified 2 modifications on one peptide: methionine-7 mono-oxidation and proline-9 oxidation; (b) de novo sequencing showed that methionine is dioxidised.
Summary of recent studies where increased levels of oxPTMs in disease have been detected using MS techniques.
| Modification Type | Disease | Method | Sample Type | Protein Type | Oxidation Sites Identified? | Reference |
|---|---|---|---|---|---|---|
| Carbonylation | Alzheimer’s disease | DNPH, MALDI-TOF/MS | Blood (human) | Fibrinogen γ-chain precursor protein, α-1-Antitrypsin precursor | no | Choi |
| Carbonylation | Aging | Avidin affinity, LC-MS/MS | Brain tissue (mouse) | Brain proteins | yes | Soreghan |
| Carbonylation | Aging | FTCl-labeling; 2DE-MS | Liver tissue (mouse) | Cytosolic liver proteins | no | Chaudhuri |
| Carbonylation | Aging | ITRAQ/LC-MS/MS | Skeletal muscle (rat) | Mitochondrial muscle proteins | no | Feng |
| Carbonylation | Mild Cognitive impairment and Early Alzheimer’s disease | DNPH, MALDI-TOF/MS | inferior parietal lobule (human) | CA II, Syntaxin binding protein I, Hsp70, MAPK kinase I, FBA-C, PM-1, GFAP | no | Sultana |
| Carbonylation | Aging | ARP-labeling, MS/MS | Heart (rat) | Cardiac mitochondrial proteins | yes | Chavez |
| Carbonylation | Diabetes | ITRAQ/LS-MS/MS(SRM) | Plasma (rat) | Plasma proteins | yes | Madian |
| Carbonylation | Obesity-induced diabetes mellitus | ARP-labeling RPC-MS/MS | Plasma (human) | Plasma proteins | yes | Bollineni |
| Carbonylation | Breast cancer | iTRAQ | Plasma (human) | Plasma proteins | yes | Madian & Regnier, 2010 [ |
| Carbonylation | Ischemia/reperfusion | 2D-PAGE-MALDI-TOF/TOF/MS/MS, | Hippocampus (monkey) | Hsp70-1, DRP2 isoform 2, GFAP, β-actin | yes | Oikawa |
| Carbonylation, cysteic acid, MetO, MetO2 | Alzheimer’s disease, Parkinson’s disease | 2D-PAGE, MALDI-TOF/MS MALDI-TOF/TOF/MS/MS, HPLC-ESI/MS/MS MALDI-MS/MS | Brain (human) | DJ-1 | yes | Choi |
| 3-NO2Y | Cancer | NTAC-based MALDI–LTQ MS/MS | Non-functional pituitary adenoma tissue (human) | NTAC-enriched proteins | yes | Zhan & Desiderio, 2006 [ |
| 3-NO2Y, 3-Cl-Y | Influenza | LC-MS/MS | Serum (mouse) | Serum proteins | yes | Kumar |