Evidence map›Paper›PMID 29596458›Full record

ArticlePloS one2018

Quantitative N-glycoproteomics reveals altered glycosylation levels of various plasma proteins in bloodstream infected patients.

Sakari Joenvaara, Mayank Saraswat, Pentti Kuusela, Shruti Saraswat, Rahul Agarwal, Johanna Kaartinen, Asko Järvinen, Risto Renkonen

Abstract read
In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

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  15. SerumJournal of clinical medicine · 2021
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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

8 authors.

Sakari JoenvaaraTransplantation laboratory, Haartmaninkatu 3, University of Helsinki, Helsinki, Finland.
Mayank SaraswatTransplantation laboratory, Haartmaninkatu 3, University of Helsinki, Helsinki, Finland.ORCID 0000-0002-5021-8150
Pentti KuuselaHUSLAB, Helsinki University Hospital, Helsinki, Finland.
Shruti SaraswatTransplantation laboratory, Haartmaninkatu 3, University of Helsinki, Helsinki, Finland.
Rahul AgarwalDepartment of Reproductive Biology, All India Institute of Medical Sciences, New Delhi, India.
Johanna KaartinenEmergency Medicine and Services, Helsinki University Hospital, Helsinki, Finland.
Asko JärvinenHUSLAB, Helsinki University Hospital, Helsinki, Finland.
Risto RenkonenTransplantation laboratory, Haartmaninkatu 3, University of Helsinki, Helsinki, Finland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bloodstream infections are associated with high morbidity and mortality with rates varying from 10-25% and higher. Appropriate and timely onset of antibiotic therapy influences the prognosis of these patients. It requires the diagnostic accuracy which is not afforded by current gold standards such as blood culture. Moreover, the time from blood sampling to blood culture results is a key determinant of reducing mortality. No established biomarkers exist which can differentiate bloodstream infections from other systemic inflammatory conditions. This calls for studies on biomarkers potential of molecular profiling of plasma as it is affected most by the molecular changes accompanying bloodstream infections. N-glycosylation is a post-translational modification which is very sensitive to changes in physiology. Here we have performed targeted quantitative N-glycoproteomics from plasma samples of patients with confirmed positive blood culture together with age and sex matched febrile controls with negative blood culture reports. Three hundred and sixty eight potential N-glycopeptides were quantified by mass spectrometry and 149 were further selected for identification. Twenty four N-glycopeptides were identified with high confidence together with elucidation of the peptide sequence, N-glycosylation site, glycan composition and proposed glycan structures. Principal component analysis, orthogonal projections to latent structures-discriminant analysis (S-Plot) and self-organizing maps clustering among other statistical methods were employed to analyze the data. These methods gave us clear separation of the two patient classes. We propose high-confidence N-glycopeptides which have the power to separate the bloodstream infections from blood culture negative febrile patients and shed light on host response during bacteremia. Data are available via ProteomeXchange with identifier PXD009048.

Indexed as

ProteomicsAgedAged, 80 and overBacteremiaBlood ProteinsFemaleGlycopeptidesGlycosylationHumansMaleMiddle AgedProtein Processing, Post-TranslationalBlood ProteinsGlycopeptides

Identifiers

PMID29596458
PMCPMC5875812

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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.