iMetaOmics · 2026
Comprehensive spatial profiling reveals transitional microbiome dynamics and microbial heterogeneity in pediatric adenoid hypertrophy.
Письмо в редакциюНазвание и аннотация: Перевод пока не подготовлен
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Chen Kaining, Wan Xin, Guo Wenli, Yu Hongyan, Tong Yuantao, Ke Huiwen, Fang Junjie, Luo Xilian, Che Di, Hou Chao, Mei Xu, Hu Bingxue, Liu Yuehong, Yu Xiaoxiao, Xiang Daoman, Chen Wensi, Zeng Yingjia, Xu Yufen, Shen Jun, Chen Qiuxia, Pi Lei, Fu Lanyan, Zhou Huazhong, Liang Bingshao, Zhang Meng, Dong Yifan, Chen Yanqiu, Zhou Wenhao, Gu Xiaoqiong
Аннотация
This study characterizes the upper respiratory microbiome in 276 children (101 Adenoid hypertrophy (AH) 119 Adenotonsillar hypertrophy (ATH), 11 Tonsil hypertrophy (TH), and 45 healthy controls by analyzing 1149 samples across five distinct niches: nasopharyngeal swabs (NS), adenoid swab (AS), and tonsil swabs (TS), plus adenoid tissues (AT) and tonsil tissues (TT). Utilizing a workflow of 16S rRNA sequencing, qPCR, and metagenomics, we identified significant spatial heterogeneity and niche-specific dominance, such as Staphylococcus in the nasopharynx and Streptococcus on mucosal surfaces. AT exhibited a transitional microbiome with intermediate composition and unique Ralstonia enrichment. Machine learning models demonstrate that AT microbial profiles (area under curve, AUC = 0.752) offer superior predictive power for AH severity compared to surface swabs. SHAP analysis identified key contributing genera, such as Roseburia and Flavobacterium, as primary drivers of these predictive outcomes. These findings highlight how tissue-infiltrating microbial dynamics drive chronic inflammation and pediatric hypertrophy.