Evidence map›Paper›PMID 25612906›Full record

Trial reportScientific reports2015

Glycomic signatures on serum IgGs for prediction of postvaccination response.

Jing-Rong Wang, Wen-Da Guan, Lee-Fong Yau, Wei-Na Gao, Yang-Qing Zhan, Liang Liu, Zi-Feng Yang, Zhi-Hong Jiang

Abstract readClinical Trial
In one paragraph

Trial report in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
  2. Article
  3. The Alterations of Serum N-glycome in Response to SARS-CoV-2 Vaccination.International journal of molecular sciences · 2023
    Article
  4. Review
  5. Robust and prototypical immune responses toward influenza vaccines in the high-risk group of Indigenous Australians.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  6. Review
  7. Review
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Microscale purification of antigen-specific antibodies.Journal of immunological methods · 2015
    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

8 authors.

Jing-Rong WangState Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau, China.
Wen-Da GuanState Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
Lee-Fong YauState Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau, China.
Wei-Na GaoState Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau, China.
Yang-Qing ZhanState Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
Liang LiuState Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau, China.
Zi-Feng YangState Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
Zhi-Hong Jiang1] State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau, China [2] State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Millions of individuals are vaccinated worldwide each year to stimulate their adaptive immune systems to produce protective antibodies and T-cell response against pathogens. Since glycosylation of the Fc region of immunoglobulin G (IgG) can be influenced by the host's immune status, it was inferred that glycosylation profile of IgG might be altered as a result of the immune response. Therefore, subclass-specific glycosylation profiles of serum IgGs from 26 healthy adults before and after vaccination with a trivalent subunit influenza virus vaccine were comprehensively analyzed to explore glycomic signatures for vaccination. The results showed that no significant changes in the glycosylation of total IgGs took place before and after vaccination, but distinct glycosylation profiles in responders (fourfold or more increase of HI titer after vaccination) and nonresponders (less than fourfold increase of HI titer) were observed. This difference between the responders and nonresponders occurred even in the resting state. On the basis of variable importance parameters, glycosylation markers that distinguish responders from nonresponders were identified. These markers can be used as molecular signatures to predict antibody titers after vaccination. This is the first study of serum IgG glycosylation profiles in healthy adults receiving a trivalent inactivated influenza vaccine.

Indexed as

Influenza A Virus, H1N1 SubtypeInfluenza A Virus, H3N2 SubtypeAdultAntibodies, ViralFemaleGlycosylationHumansImmunoglobulin Fc FragmentsImmunoglobulin GInfluenza VaccinesMaleVaccinationAntibodies, ViralImmunoglobulin Fc FragmentsImmunoglobulin GInfluenza Vaccines

Identifiers

PMID25612906
PMCPMC4303884

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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.