Evidence map›Paper›PMID 21437206›Full record

ArticlePloS one2011

Screening of random peptide library of hemagglutinin from pandemic 2009 A(H1N1) influenza virus reveals unexpected antigenically important regions.

Wanghui Xu, Lu Han, Zhanglin Lin

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.2field-weighted citation impact, top 22% 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

11 citing papers in PubMed, 15 citations in OpenAlex.

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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

3 authors at 1 institution in 1 country.

Wanghui XuDepartment of Chemical Engineering, Tsinghua University, Beijing, China.
Lu Han
Zhanglin Lin
Tsinghua University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The antigenic structure of the membrane protein hemagglutinin (HA) from the 2009 A(H1N1) influenza virus was dissected with a high-throughput screening method using complex antisera. The approach involves generating yeast cell libraries displaying a pool of random peptides of controllable lengths on the cell surface, followed by one round of fluorescence-activated cell sorting (FACS) against antisera from mouse, goat and human, respectively. The amino acid residue frequency appearing in the antigenic peptides at both the primary sequence and structural level was determined and used to identify "hot spots" or antigenically important regions. Unexpectedly, different antigenic structures were seen for different antisera. Moreover, five antigenic regions were identified, of which all but one are located in the conserved HA stem region that is responsible for membrane fusion. Our findings are corroborated by several recent studies on cross-neutralizing H1 subtype antibodies that recognize the HA stem region. The antigenic peptides identified may provide clues for creating peptide vaccines with better accessibility to memory B cells and better induction of cross-neutralizing antibodies than the whole HA protein. The scheme used in this study enables a direct mapping of the antigenic regions of viral proteins recognized by antisera, and may be useful for dissecting the antigenic structures of other viral proteins.

Indexed as

PandemicsPeptide LibraryAnimalsAntigens, ViralCell MembraneFlow CytometryGenetic VectorsGoatsHemagglutinin Glycoproteins, Influenza VirusHumansImmune SeraImmunizationInfluenza A Virus, H1N1 SubtypeInfluenza, HumanInfluenza VaccinesMiceAntigens, ViralHemagglutinin Glycoproteins, Influenza VirusImmune SeraInfluenza VaccinesPeptide LibraryPeptides

Identifiers

PMID21437206
PMCPMC3060926
OpenAlexW1992243226

What OpenQuestion holds

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LicenceCC BY
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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.