Evidence map›Paper›PMID 29621258›Full record

ArticlePloS one2018

DIVA metabolomics: Differentiating vaccination status following viral challenge using metabolomic profiles.

Darren W Gray, Michael D Welsh, Fawad Mansoor, Simon Doherty, Olivier P Chevallier, Christopher T Elliott, Mark H Mooney

Open access · goldAbstract 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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 37% of its field
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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Review
  2. Metabolomics Analysis of Viral Therapeutics.Methods in molecular biology (Clifton, N.J.) · 2021
    Article
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 at 2 institutions in 1 country.

Darren W GrayInstitute for Global Food Security (IGFS), School of Biological Sciences, Queen's University Belfast (QUB), Belfast, Northern Ireland, United Kingdom.ORCID 0000-0002-5804-9785
Michael D WelshVeterinary Sciences Division (VSD), Agri-Food and Biosciences Institute (AFBI), Belfast, Northern Ireland, United Kingdom.
Fawad MansoorVeterinary Sciences Division (VSD), Agri-Food and Biosciences Institute (AFBI), Belfast, Northern Ireland, United Kingdom.
Simon DohertyVeterinary Sciences Division (VSD), Agri-Food and Biosciences Institute (AFBI), Belfast, Northern Ireland, United Kingdom.ORCID 0000-0001-5908-1050
Olivier P ChevallierInstitute for Global Food Security (IGFS), School of Biological Sciences, Queen's University Belfast (QUB), Belfast, Northern Ireland, United Kingdom.ORCID 0000-0002-3786-4318
Christopher T ElliottInstitute for Global Food Security (IGFS), School of Biological Sciences, Queen's University Belfast (QUB), Belfast, Northern Ireland, United Kingdom.
Mark H MooneyInstitute for Global Food Security (IGFS), School of Biological Sciences, Queen's University Belfast (QUB), Belfast, Northern Ireland, United Kingdom.
Queen's University Belfast · GBAgri Food and Biosciences Institute · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bovine Respiratory Disease (BRD) is a major source of economic loss within the agricultural industry. Vaccination against BRD-associated viruses does not offer complete immune protection and vaccine failure animals present potential routes for disease spread. Serological differentiation of infected from vaccinated animals (DIVA) is possible using antigen-deleted vaccines, but during virus outbreaks DIVA responses are masked by wild-type virus preventing accurate serodiagnosis. Previous work by the authors has established the potential for metabolomic profiling to reveal metabolites associated with systemic immune responses to vaccination. The current study builds on this work by demonstrating for the first time the potential to use plasma metabolite profiling to differentiate between vaccinated and non-vaccinated animals following infection-challenge. Male Holstein Friesian calves were intranasally vaccinated (Pfizer RISPOVAL®PI3+RSV) and subsequently challenged with Bovine Parainfluenza Virus type-3 (BPI3V) via nasal inoculation. Metabolomic plasma profiling revealed that viral challenge led to a shift in acquired plasma metabolite profiles from day 2 to 20 p.i., with 26 metabolites identified whose peak intensities were significantly different following viral challenge depending on vaccination status. Elevated levels of biliverdin and bilirubin and decreased 3-indolepropionic acid in non-vaccinated animals at day 6 p.i. may be associated with increased oxidative stress and reactive oxygen scavenging at periods of peak virus titre. During latter stages of infection, increased levels of N-[(3α,5β,12α)-3,12-dihydroxy-7,24-dioxocholan-24-yl]glycine and lysophosphatidycholine and decreased enterolactone in non-vaccinated animals may reflect suppression of innate immune response mechanisms and progression to adaptive immune responses. Levels of hexahydrohippurate were also shown to be significantly elevated in non-vaccinated animals from days 6 to 20 p.i. These findings demonstrate the potential of metabolomic profiling to identify plasma markers that can be employed in disease diagnostic applications to both differentially identify infected non-vaccinated animals during disease outbreaks and provide greater information on the health status of infected animals.

Indexed as

MetabolomeMetabolomicsAnimal DiseasesAnimalsBiomarkersCattleCattle DiseasesChromatography, High Pressure LiquidDatabases, FactualMaleRespiratory Syncytial Virus, BovineVaccinationViral VaccinesBiomarkersViral Vaccines

Identifiers

PMID29621258
PMCPMC5886402
OpenAlexW2795475381

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.