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<p>Oral microbiota is related to the severity and recovery of SARS-CoV-2 infection. This study aims to predict clinical classification after SARS-CoV-2 infection using oral microbiota before infection. Herein, we collected tongue-coating samples before infection and then monitored clinical information after infection. Oral microbiota was detected by MiSeq sequencing. We randomly assigned participants from Zhengzhou into discovery and validation cohorts to develop a predictive model and conducted cross-region verification using Xinyang and Hangzhou cohorts. Sixteen asymptomatic patients (AP), 257 mild patients (MP), 106 common patients (CP), and 7 severe patients (SP) were enrolled. Oral microbiota diversity was decreased in CP versus MP. At <i>genus</i> level, 11 microorganisms, including <i>Rothia</i> and <i>Gemella</i>, were increased, while 5 microorganisms, including <i>Selenomonas</i> and <i>Lachnoanaerobaculum</i>, were decreased in CP versus MP. Moreover, the classifier based on 15 optimal markers showed high prediction efficiency in discovery cohort (area under the curve [AUC]: 98.35%), validation cohort (AUC: 81.91%), Xinyang cohort (AUC: 74.34%), and Hangzhou cohort (AUC: 94.44%). Interestingly, a higher abundance of <i>Selenomonas</i> was associated with milder clinical symptoms. In conclusion, our study established a good model to predict clinical classification after SARS-CoV-2 infection using oral microbiota before infection, providing a novel strategy for precise prevention and treatment.</p>

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Prediction of baseline oral microbiota for clinical classification post Omicron variant of SARS-CoV-2 infection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月15日

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  • Junyi Sun,
  • Shanshuo Liu,
  • Daming Wang,
  • Jia Yu,
  • Bowen Liu,
  • Hong Luo,
  • Feng Gao,
  • Yawen Zou,
  • Ying Sun,
  • Haiyu Wang,
  • Xueping Wang,
  • Yongjian Zhou,
  • Lei Li,
  • Zhongwen Wu,
  • Zujiang Yu,
  • Zhigang Ren

摘要

Oral microbiota is related to the severity and recovery of SARS-CoV-2 infection. This study aims to predict clinical classification after SARS-CoV-2 infection using oral microbiota before infection. Herein, we collected tongue-coating samples before infection and then monitored clinical information after infection. Oral microbiota was detected by MiSeq sequencing. We randomly assigned participants from Zhengzhou into discovery and validation cohorts to develop a predictive model and conducted cross-region verification using Xinyang and Hangzhou cohorts. Sixteen asymptomatic patients (AP), 257 mild patients (MP), 106 common patients (CP), and 7 severe patients (SP) were enrolled. Oral microbiota diversity was decreased in CP versus MP. At genus level, 11 microorganisms, including Rothia and Gemella, were increased, while 5 microorganisms, including Selenomonas and Lachnoanaerobaculum, were decreased in CP versus MP. Moreover, the classifier based on 15 optimal markers showed high prediction efficiency in discovery cohort (area under the curve [AUC]: 98.35%), validation cohort (AUC: 81.91%), Xinyang cohort (AUC: 74.34%), and Hangzhou cohort (AUC: 94.44%). Interestingly, a higher abundance of Selenomonas was associated with milder clinical symptoms. In conclusion, our study established a good model to predict clinical classification after SARS-CoV-2 infection using oral microbiota before infection, providing a novel strategy for precise prevention and treatment.