Evidence map›Paper›PMID 31417895›Full record

ArticleFrontiers in chemistry2019

A Metabolomics-Inspired Strategy for the Identification of Protein Covalent Modifications.

João Nunes, Catarina Charneira, Carolina Nunes, Sofia Gouveia-Fernandes, Jacinta Serpa, Judit Morello, Alexandra M M Antunes

Abstract read
In one paragraph

Article in Frontiers in chemistry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. A Novel Adductomics Workflow IncorporatingEnvironmental science & technology · 2024
    Article
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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

7 authors.

João NunesCentro de Química Estrutural, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
Catarina CharneiraCentro de Química Estrutural, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
Carolina NunesCEDOC, Chronic Diseases Research Centre, Faculdade de Ciências Médicas, NOVA Medical School, Universidade NOVA de Lisboa, Lisbon, Portugal.
Sofia Gouveia-FernandesCEDOC, Chronic Diseases Research Centre, Faculdade de Ciências Médicas, NOVA Medical School, Universidade NOVA de Lisboa, Lisbon, Portugal.
Jacinta SerpaCEDOC, Chronic Diseases Research Centre, Faculdade de Ciências Médicas, NOVA Medical School, Universidade NOVA de Lisboa, Lisbon, Portugal.
Judit MorelloCentro de Química Estrutural, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
Alexandra M M AntunesCentro de Química Estrutural, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identification of protein covalent modifications (adducts) is a challenging task mainly due to the lack of data processing approaches for adductomics studies. Despite the huge technological advances in mass spectrometry (MS) instrumentation and bioinformatics tools for proteomics studies, these methodologies have very limited success on the identification of low abundant protein adducts. Herein we report a novel strategy inspired on the metabolomics workflows for the identification of covalently-modified peptides that consists on LC-MS data preprocessing followed by statistical analysis. The usefulness of this strategy was evaluated using experimental LC-MS data of histones isolated from HepG2 and THLE2 cells exposed to the chemical carcinogen glycidamide. LC-MS data was preprocessed using the open-source software MZmine and potential adducts were selected based on the

Indexed as

acrylamideadductomicschemometricsglycidamidehistonesmass spectrometrymetabolomicstoxicology

Identifiers

PMID31417895
PMCPMC6684772

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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.