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<p>Acute upper respiratory tract infections (AURI) can lead to an imbalance in the oropharyngeal microbiome. Specific bacteria may be responsible for a sore throat. We divided 278 AURI patients from outpatient clinics in China into sore throat (ST) and non-sore throat (NST) groups. Then, we performed 16S ribosomal ribonucleic acid (rRNA) gene sequencing on throat swab samples of all patients. And explore whether there are compositional and functional differences between the two groups of microorganisms, and conduct Sparse Correlations for Compositional Data (SparCC) and machine learning analysis. There were no statistically significant differences in age, gender, BMI, or peripheral blood cell counts between patients with sore throat and non-sore throat groups. The composition of OTUs in the two groups was significantly different. Compared to the ST group, the NST group showed an increasing trend in α-diversity. The abundance of <i>Campylobacter</i>, <i>Prevotella</i>, and <i>Actinomycetaceae</i> was higher in the NST group. In terms of functional prediction, the ST group showed a higher expression of non-homologous end-joining repair mechanisms and xenobiotic biodegradation. The Support Vector Machine (SVM) model exhibited the best predictive performance. Machine learning models at the genus level identified <i>Peptostreptococcus</i> as a potential microbial feature associated with sore throat. Notably, further species-level analysis revealed complexity within this genus. The mechanism of sore throat may be related to differences in the oral and pharyngeal microbiota and functional expression.</p>

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Oropharyngeal Microbiota Associated with Sore Throat in Acute Upper Respiratory Tract Infections

  • 期刊论文

  • 发表日期：

    2026年3月30日

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  • Changrui Huang,
  • Hao Zou,
  • Yashi Cai,
  • Hongchun Zhang,
  • Xiaohui Zou

摘要

Acute upper respiratory tract infections (AURI) can lead to an imbalance in the oropharyngeal microbiome. Specific bacteria may be responsible for a sore throat. We divided 278 AURI patients from outpatient clinics in China into sore throat (ST) and non-sore throat (NST) groups. Then, we performed 16S ribosomal ribonucleic acid (rRNA) gene sequencing on throat swab samples of all patients. And explore whether there are compositional and functional differences between the two groups of microorganisms, and conduct Sparse Correlations for Compositional Data (SparCC) and machine learning analysis. There were no statistically significant differences in age, gender, BMI, or peripheral blood cell counts between patients with sore throat and non-sore throat groups. The composition of OTUs in the two groups was significantly different. Compared to the ST group, the NST group showed an increasing trend in α-diversity. The abundance of Campylobacter, Prevotella, and Actinomycetaceae was higher in the NST group. In terms of functional prediction, the ST group showed a higher expression of non-homologous end-joining repair mechanisms and xenobiotic biodegradation. The Support Vector Machine (SVM) model exhibited the best predictive performance. Machine learning models at the genus level identified Peptostreptococcus as a potential microbial feature associated with sore throat. Notably, further species-level analysis revealed complexity within this genus. The mechanism of sore throat may be related to differences in the oral and pharyngeal microbiota and functional expression.