Evidence map›Paper›PMID 42850654›Full record

ArticleMicrobiome2026

Lung microbiota dysbiosis exacerbates influenza virus infection.

Hai Chang Yin, Xin Yu Zhang, Song Liu, Xinjie Jiang, Tian Fei Yu, He Zhang, Di Liu, Chang You Xia

Abstract read
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Article in Microbiome, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Hai Chang Yin *College of Life Science and Agriculture Forestry, Qiqihar University, Qiqihar, Heilongjiang, 161006, China.
Xin Yu Zhang *College of Life Science and Agriculture Forestry, Qiqihar University, Qiqihar, Heilongjiang, 161006, China.
Song Liu *College of Life Science and Agriculture Forestry, Qiqihar University, Qiqihar, Heilongjiang, 161006, China.
Xinjie JiangCollege of Life Science and Agriculture Forestry, Qiqihar University, Qiqihar, Heilongjiang, 161006, China.
Tian Fei YuCollege of Life Science and Agriculture Forestry, Qiqihar University, Qiqihar, Heilongjiang, 161006, China.
He ZhangState Key Laboratory for Animal Disease Control and Prevention, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin, 150069, China. Zhanghe3789@163.com.
Di LiuPostdoctoral Research Workstation, Heilongjiang Academy of Agricultural Sciences, Harbin, China. liudi1963@163.com.
Chang You XiaState Key Laboratory for Animal Disease Control and Prevention, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin, 150069, China. xiachangyou@caas.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrevious studies have shown that microbiota in the respiratory tract play a significant role in shaping host immunity and reducing the risk of respiratory tract infections. Influenza, an acute respiratory infectious disease caused by the influenza virus (IAV), necessitates to have an in-depth understanding of the pathogenic mechanisms of viral infection and to utilize these mechanisms to prevent and control influenza infection.

resultsIn this study, we established models of different disease severity levels in mice infected with IAV, the relationships between changes in pulmonary microbiota and host interactions during IAV infection were analyzed. We found that these relationships were characterized by the enrichment of opportunistic pathogenic taxonomic groups and were related to disease severity. Multi-omics analysis combined with animal experiments clarified that colonization of Bacillus cereus in the lungs aggravates IAV infection through xanthosine, thereby downregulating the expression of nt5e and reducing the viral phagocytic ability of alveolar macrophages. Importantly, targeted removal of B. cereus by bacteriophages can alleviate IAV infection and the resulting pulmonary lesions.

conclusionsHighlighting provides a novel approach for preventing and controlling the progression of IAV infection by targeting the lung microbiota. IN BRIEF: Lung dysbiosis is associated with severe influenza virus (IAV) infection. Bacillus cereus aggravates IAV infection via the xanthosine-nt5e axis in SPF mice, which can be rescued by bacteriophage targeting. These findings highlight the potential of modulating the lung microbiota as a strategy for prevention and controlling IAV infection.

Indexed as

DysbiosisLungMicrobiotaOrthomyxoviridae InfectionsAnimalsBacillus cereusBacteriophagesDisease Models, AnimalFemaleInfluenza A virusInfluenza, HumanMacrophages, AlveolarMiceBacteriophagesB. cereusIAV infectionLung microbiotaNt5eXanthosine

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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 Socratic graph.