Evidence map›Paper›PMID 25874603›Full record

ReviewBiomolecules2015

Mass spectrometry-based methods for identifying oxidized proteins in disease: advances and challenges.

Ivan Verrastro, Sabah Pasha, Karina Tveen Jensen, Andrew R Pitt, Corinne M Spickett

Abstract readReview
In one paragraph

Review in Biomolecules, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

45 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ivan VerrastroSchool of Life and Health Sciences, Aston University, Aston Triangle, Birmingham, B4 7ET, UK. verrasti@aston.ac.uk.
Sabah PashaSchool of Life and Health Sciences, Aston University, Aston Triangle, Birmingham, B4 7ET, UK. pashas@aston.ac.uk.
Karina Tveen JensenSchool of Life and Health Sciences, Aston University, Aston Triangle, Birmingham, B4 7ET, UK. k.tveen-jensen@aston.ac.uk.
Andrew R PittSchool of Life and Health Sciences, Aston University, Aston Triangle, Birmingham, B4 7ET, UK. a.r.pitt@aston.ac.uk.
Corinne M SpickettSchool of Life and Health Sciences, Aston University, Aston Triangle, Birmingham, B4 7ET, UK. c.m.spickett@aston.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Protein Processing, Post-TranslationalAdvanced Oxidation Protein ProductsAnimalsCardiovascular DiseasesHumansMass SpectrometryOxidation-ReductionAdvanced Oxidation Protein Products

Identifiers

PMID25874603
PMCPMC4496678

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.