Evidence map›Paper›PMID 25995265›Full record

ArticleJournal of virology2015

Repeated Low-Dose Influenza Virus Infection Causes Severe Disease in Mice: a Model for Vaccine Evaluation.

Yufeng Song, Xiang Wang, Hongbo Zhang, Xinying Tang, Min Li, Jufang Yao, Xia Jin, Hildegund C J Ertl, Dongming Zhou

Open access · bronzeAbstract read
In one paragraph

Article in Journal of virology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
1.7field-weighted citation impact, top 16% 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

24 citing papers in PubMed, 37 citations in OpenAlex.

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  15. LRCH1 deficiency enhances LAT signalosome formation and CD8Proceedings of the National Academy of Sciences of the United States of America · 2020
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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

9 authors at 4 institutions in 2 countries.

Yufeng SongVaccine Research Center, Key Laboratory of Molecular Virology and Immunology, Institut Pasteur of Shanghai, Chinese Academy of Science, Shanghai, China.
Xiang WangVaccine Research Center, Key Laboratory of Molecular Virology and Immunology, Institut Pasteur of Shanghai, Chinese Academy of Science, Shanghai, China Institute of Biology and Medical Sciences, Soochow University, Suzhou, China.
Hongbo ZhangVaccine Research Center, Key Laboratory of Molecular Virology and Immunology, Institut Pasteur of Shanghai, Chinese Academy of Science, Shanghai, China.
Xinying TangVaccine Research Center, Key Laboratory of Molecular Virology and Immunology, Institut Pasteur of Shanghai, Chinese Academy of Science, Shanghai, China.
Min LiDepartment of Laboratory Medicine, Ren Ji Hospital School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Jufang YaoDepartment of Animal Centre, Ren Ji Hospital School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Xia JinVaccine Research Center, Key Laboratory of Molecular Virology and Immunology, Institut Pasteur of Shanghai, Chinese Academy of Science, Shanghai, China.
Hildegund C J ErtlThe Wistar Institute, Philadelphia, Pennsylvania, USA.
Dongming ZhouVaccine Research Center, Key Laboratory of Molecular Virology and Immunology, Institut Pasteur of Shanghai, Chinese Academy of Science, Shanghai, China dmzhou@ips.ac.cn.
Institut Pasteur of Shanghai · CNRenji Hospital · CNSoochow University · CNThe Wistar Institute · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

unlabelledInfluenza infection causes severe disease and death in humans. In traditional vaccine research and development, a single high-dose virus challenge of animals is used to evaluate vaccine efficacy. This type of challenge model may have limitations. In the present study, we developed a novel challenge model by infecting mice repeatedly in short intervals with low doses of influenza A virus. Our results show that compared to a single high-dose infection, mice that received repeated low-dose challenges showed earlier morbidity and mortality and more severe disease. They developed higher vial loads, more severe lung pathology, and greater inflammatory responses and generated only limited influenza A virus-specific B and T cell responses. A commercial trivalent influenza vaccine protected mice against a single high and lethal dose of influenza A virus but was ineffective against repeated low-dose virus challenges. Overall, our data show that the repeated low-dose influenza A virus infection mouse model is more stringent and may thus be more suitable to select for highly efficacious influenza vaccines. IMPORTANCE: Influenza epidemics and pandemics pose serious threats to public health. Animal models are crucial for evaluating the efficacy of influenza vaccines. Traditional models based on a single high-dose virus challenge may have limitations. Here, we describe a new mouse model based on repeated low-dose influenza A virus challenges given within a short period. Repeated low-dose challenges caused more severe disease in mice, associated with higher viral loads and increased lung inflammation and reduced influenza A virus-specific B and T cell responses. A commercial influenza vaccine that was shown to protect mice from high-dose challenge was ineffective against repeated low-dose challenges. Overall, our results show that the low-dose repeated-challenge model is more stringent and may therefore be better suited for preclinical vaccine efficacy studies.

Indexed as

Disease Models, AnimalMiceAnimalsAntibodies, ViralChick EmbryoFemaleHumansInfluenza A Virus, H1N1 SubtypeInfluenza, HumanInfluenza VaccinesLungMice, Inbred C57BLAntibodies, ViralInfluenza Vaccines

Identifiers

PMID25995265
PMCPMC4505666
OpenAlexW1817025876

What OpenQuestion holds

Textmetadata
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.