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Background <p>Respiratory syncytial virus (RSV) is a leading cause of lower respiratory tract infections in children, but effective treatment options remain limited. The gut-lung axis, which highlights the role of gut microbiota in regulating respiratory immunity, provides new opportunities for developing probiotic-based therapies. However, existing studies on RSV-associated gut microbiota are often small-scale and lack systematic integration. To address this gap, we conducted a comprehensive machine learning-based analysis of gut microbiota data from RSV-infected children and mice, integrating five public datasets comprising 319 samples (154 children, 165 mice).</p> Results <p>Pediatric samples were divided into control, infected, and recovery groups, while mouse samples included control and infected groups. Microbial diversity analysis revealed RSV infection disrupted gut microbiota structure in children, with reduced α-diversity in the infected group and significant β-diversity differences among groups (<i>P</i> &lt; 0.001). Mice exhibited higher α-diversity than children, with distinct dominant taxa: <i>Bifidobacteriaceae</i> and <i>Escherichia-Shigella</i> prevailed in children, whereas <i>Lachnospiraceae</i> and <i>Ligilactobacillus</i> dominated in mice. Using 13 machine learning algorithms, we developed disease-prediction models at the family and genus levels, achieving superior performance in pediatric data (maximum AUC = 0.952) compared to mouse data. Cross-species analysis identified 62 family-level and 54 genus-level high-importance taxa (e.g., <i>Bacteroidaceae</i>,<i> Bifidobacteriaceae</i>,<i> Romboutsia</i>) shared between the two host species, accounting for 36%-60% of feature microbiota. Functional profiles showed significant remodeling during recovery, characterized by the loss of native functions such as D-arabinitol 4-dehydrogenase but acquisition of novel metabolic capabilities, including fructan biosynthesis pathways. Notably, <i>Bacteroidaceae</i> contributed extensively to differential functions, particularly short-chain fatty acid metabolism via propionyl-CoA carboxylase, highlighting its role in gut-lung immune regulation.</p> Conclusion <p>These findings provide the first cross-species machine learning analysis of RSV-associated, though not necessarily RSV-specific, gut microbiota, offering insights into gut-lung axis mechanisms and identifying potential targets for probiotic-based interventions in RSV management.</p>

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Integrated analysis of human-mouse gut microbiota in RSV infection based on machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月28日

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  • You Duan,
  • Chen Guo,
  • Liang Xie,
  • Hanmin Liu,
  • Yang Liu

摘要

Background

Respiratory syncytial virus (RSV) is a leading cause of lower respiratory tract infections in children, but effective treatment options remain limited. The gut-lung axis, which highlights the role of gut microbiota in regulating respiratory immunity, provides new opportunities for developing probiotic-based therapies. However, existing studies on RSV-associated gut microbiota are often small-scale and lack systematic integration. To address this gap, we conducted a comprehensive machine learning-based analysis of gut microbiota data from RSV-infected children and mice, integrating five public datasets comprising 319 samples (154 children, 165 mice).

Results

Pediatric samples were divided into control, infected, and recovery groups, while mouse samples included control and infected groups. Microbial diversity analysis revealed RSV infection disrupted gut microbiota structure in children, with reduced α-diversity in the infected group and significant β-diversity differences among groups (P < 0.001). Mice exhibited higher α-diversity than children, with distinct dominant taxa: Bifidobacteriaceae and Escherichia-Shigella prevailed in children, whereas Lachnospiraceae and Ligilactobacillus dominated in mice. Using 13 machine learning algorithms, we developed disease-prediction models at the family and genus levels, achieving superior performance in pediatric data (maximum AUC = 0.952) compared to mouse data. Cross-species analysis identified 62 family-level and 54 genus-level high-importance taxa (e.g., Bacteroidaceae, Bifidobacteriaceae, Romboutsia) shared between the two host species, accounting for 36%-60% of feature microbiota. Functional profiles showed significant remodeling during recovery, characterized by the loss of native functions such as D-arabinitol 4-dehydrogenase but acquisition of novel metabolic capabilities, including fructan biosynthesis pathways. Notably, Bacteroidaceae contributed extensively to differential functions, particularly short-chain fatty acid metabolism via propionyl-CoA carboxylase, highlighting its role in gut-lung immune regulation.

Conclusion

These findings provide the first cross-species machine learning analysis of RSV-associated, though not necessarily RSV-specific, gut microbiota, offering insights into gut-lung axis mechanisms and identifying potential targets for probiotic-based interventions in RSV management.