Evidence map›Paper›PMID 19564921›Full record

ArticlePloS one2009

Generation of monoclonal antibodies against highly conserved antigens.

Hongzhe Zhou, Yunbo Wang, Wei Wang, Junying Jia, Yuan Li, Qiyu Wang, Yanfang Wu, Jie Tang

Abstract read
In one paragraph

Article in PloS one, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

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  8. Sialoglycan recognition is a common connection linking acidosis, zinc, and HMGB1 in sepsis.Proceedings of the National Academy of Sciences of the United States of America · 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.

Hongzhe ZhouCenter of Infection and Immunity, National Key Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, PR China.
Yunbo Wang
Wei Wang
Junying Jia
Yuan Li
Qiyu Wang
Yanfang Wu
Jie Tang

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTherapeutic antibody development is one of the fastest growing areas of the pharmaceutical industry. Generating high-quality monoclonal antibodies against a given therapeutic target is very crucial for the success of the drug development. However, due to immune tolerance, some proteins that are highly conserved between mice and humans are not very immunogenic in mice, making it difficult to generate antibodies using a conventional approach. METHODOLOGY/PRINCIPAL

findingsIn this report, the impaired immune tolerance of NZB/W mice was exploited to generate monoclonal antibodies against highly conserved or self-antigens. Using two highly conserved human antigens (MIF and HMGB1) and one mouse self-antigen (TNF-alpha) as examples, we demonstrate here that multiple clones of high affinity, highly specific antibodies with desired biological activities can be generated, using the NZB/W mouse as the immunization host and a T cell-specific tag fused to a recombinant antigen to stimulate the immune system. CONCLUSIONS/SIGNIFICANCE: We developed an efficient and universal method for generating surrogate or therapeutic antibodies against "difficult antigens" to facilitate the development of therapeutic antibodies.

Indexed as

AnimalsAntibodies, MonoclonalCell LineEnzyme-Linked Immunosorbent AssayFemaleHMGB1 ProteinHumansImmune SystemIntramolecular OxidoreductasesLipopolysaccharidesMacrophage Migration-Inhibitory FactorsMiceMice, Inbred BALB CModels, StatisticalRecombinant ProteinsTumor Necrosis Factor-alphaAntibodies, MonoclonalHMGB1 ProteinIntramolecular OxidoreductasesLipopolysaccharidesMacrophage Migration-Inhibitory FactorsMIF protein, humanRecombinant ProteinsTumor Necrosis Factor-alpha

Identifiers

PMID19564921
PMCPMC2699554

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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.