Evidence map›Paper›PMID 32560624›Full record

ArticleBMC bioinformatics2020

Implementing sequence-based antigenic distance calculation into immunological shape space model.

Christopher S Anderson, Mark Y Sangster, Hongmei Yang, Thomas J Mariani, Sidhartha Chaudhury, David J Topham

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2020. 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
0.4field-weighted citation impact, top 35% 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

4 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Review
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

6 authors at 3 institutions in 1 country.

Christopher S AndersonDepartment of Pediatrics, University of Rochester Medical Center, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA. christopher_anderson@urmc.rochester.edu.ORCID http://orcid.org/0000-0002-8560-3438
Mark Y SangsterNew York Influenza Center of Excellence at David Smith Center for Vaccine Biology and Immunology, Department of Microbiology and Immunology, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA.
Hongmei YangDepartment of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA.
Thomas J MarianiDepartment of Pediatrics, University of Rochester Medical Center, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA.
Sidhartha ChaudhuryCenter for Enabling Capabilities, Walter Reed Army Institute of Research, Silver Spring, MD, USA.
David J TophamNew York Influenza Center of Excellence at David Smith Center for Vaccine Biology and Immunology, Department of Microbiology and Immunology, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA. david_topham@urmc.rochester.edu.
University of Rochester Medical Center · USUniversity of Rochester · USWalter Reed Army Institute of Research · US

Funding

VISUAL INDICES OF NEUROTOXICITYP30ES001247 · NIEHS · UNIVERSITY OF ROCHESTER · PI Martha Susiarjo · 1985 to 2026
$42.8M
MULTIDISCIPLINARY TRAINING IN PULMONARY RESEARCHT32HL066988 · NHLBI · UNIVERSITY OF ROCHESTER · PI GEORAS, STEVE N, O'REILLY, MICHAEL A · 2001 to 2021
$8.1M
NHLBI NIH HHS T32 HL066988NHLBI NIH HHS T32-HL066988NIAID NIH HHS HHSN272201400005CNIEHS NIH HHS P30 ES001247University of Rochester (US) OP211341
6 · The paper itself

Abstract

backgroundIn 2009, a novel influenza vaccine was distributed worldwide to combat the H1N1 influenza "swine flu" pandemic. However, antibodies induced by the vaccine display differences in their specificity and cross-reactivity dependent on pre-existing immunity. Here, we present a computational model that can capture the effect of pre-existing immunity on influenza vaccine responses. The model predicts the region of the virus hemagglutinin (HA) protein targeted by antibodies after vaccination as well as the level of cross-reactivity induced by the vaccine. We tested our model by simulating a scenario similar to the 2009 pandemic vaccine and compared the results to antibody binding data obtained from human subjects vaccinated with the monovalent 2009 H1N1 influenza vaccine.

resultsWe found that both specificity and cross-reactivity of the antibodies induced by the 2009 H1N1 influenza HA protein were affected by the viral strain the individual was originally exposed. Specifically, the level of antigenic relatedness between the original exposure HA antigen and the 2009 HA protein affected antigenic-site immunodominance. Moreover, antibody cross-reactivity was increased when the individual's pre-existing immunity was specific to an HA protein antigenically distinct from the 2009 pandemic strain. Comparison of simulation data with antibody binding data from human serum samples demonstrated qualitative and quantitative similarities between the model and real-life immune responses to the 2009 vaccine.

conclusionWe provide a novel method to evaluate expected outcomes in antibody specificity and cross-reactivity after influenza vaccination in individuals with different influenza HA antigen exposure histories. The model produced similar outcomes as what has been previously reported in humans after receiving the 2009 influenza pandemic vaccine. Our results suggest that differences in cross-reactivity after influenza vaccination should be expected in individuals with different exposure histories.

Indexed as

Models, ImmunologicalAmino Acid SequenceAntibodies, ViralAntigens, ViralComputer SimulationCross ReactionsHemagglutinin Glycoproteins, Influenza VirusHumansInfluenza A Virus, H1N1 SubtypeInfluenza VaccinesAntibodies, ViralAntigens, ViralH1N1 virus hemagglutininHemagglutinin Glycoproteins, Influenza VirusInfluenza Vaccines2009 pandemicAntigenic distanceAntigenic sitesArtificial immune systemsComputational immunologyEpitopesGillespie algorithmH1N1HAHemagglutininHumoral immune systemInfluenzapH1N1Shape spaceSimulationsStalkStemVaccines

Identifiers

PMID32560624
PMCPMC7303933
OpenAlexW3036603810

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.