通过机器学习整合呼吸道微生物组和宿主免疫反应,用于呼吸道感染诊断

IF 7.8 1区 生物学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY npj Biofilms and Microbiomes Pub Date : 2024-09-12 DOI:10.1038/s41522-024-00548-y
Hongbin Chen, Tianqi Qi, Siyu Guo, Xiaoyang Zhang, Minghua Zhan, Si Liu, Yuyao Yin, Yifan Guo, Yawei Zhang, Chunjiang Zhao, Xiaojuan Wang, Hui Wang
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引用次数: 0

摘要

目前,下呼吸道感染(LRTIs)的诊断十分困难,迫切需要更好的诊断方法。本研究在 2020 年至 2021 年期间招募了 136 名患者,并收集了支气管肺泡灌洗液(BALF)标本。我们利用元转录组分析了下呼吸道微生物组(LRTM)和宿主免疫反应。LRTIs患者下呼吸道微生物组的多样性明显降低,表现为正常微生物群数量减少,机会性病原体数量增加。LRTIs组中上调的差异表达基因(DEGs)主要富集在感染免疫反应相关通路中。肺炎克雷伯菌在 LRTIs 中的丰度增加最为显著,与宿主感染或炎症基因 TNFRSF1B、CSF3R 和 IL6R 密切相关。我们结合 LRTM 和宿主转录组数据构建了一个机器学习模型,并筛选出 12 个特征来区分 LRTI 和非 LRTI。结果表明,在验证集中由随机森林训练的模型性能最佳(ROC AUC:0.937,95% CI:0.832-1)。独立外部数据集显示,该模型的准确率为 76.5%。这项研究表明,整合 LRTM 和宿主转录组数据的模型可以成为诊断 LRTIs 的有效工具。
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Integrating respiratory microbiome and host immune response through machine learning for respiratory tract infection diagnosis

At present, the diagnosis of lower respiratory tract infections (LRTIs) is difficult, and there is an urgent need for better diagnostic methods. This study enrolled 136 patients from 2020 to 2021 and collected bronchoalveolar lavage fluid (BALF) specimens. We used metatranscriptome to analyze the lower respiratory tract microbiome (LRTM) and host immune response. The diversity of the LRTM in LRTIs significantly decreased, manifested by a decrease in the abundance of normal microbiota and an increase in the abundance of opportunistic pathogens. The upregulated differentially expressed genes (DEGs) in the LRTIs group were mainly enriched in infection immune response-related pathways. Klebsiella pneumoniae had the most significant increase in abundance in LRTIs, which was strongly correlated with host infection or inflammation genes TNFRSF1B, CSF3R, and IL6R. We combined LRTM and host transcriptome data to construct a machine-learning model with 12 screened features to discriminate LRTIs and non-LRTIs. The results showed that the model trained by Random Forest in the validate set had the best performance (ROC AUC: 0.937, 95% CI: 0.832–1). The independent external dataset showed an accuracy of 76.5% for this model. This study suggests that the model integrating LRTM and host transcriptome data can be an effective tool for LRTIs diagnosis.

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来源期刊
npj Biofilms and Microbiomes
npj Biofilms and Microbiomes Immunology and Microbiology-Microbiology
CiteScore
12.10
自引率
3.30%
发文量
91
审稿时长
9 weeks
期刊介绍: npj Biofilms and Microbiomes is a comprehensive platform that promotes research on biofilms and microbiomes across various scientific disciplines. The journal facilitates cross-disciplinary discussions to enhance our understanding of the biology, ecology, and communal functions of biofilms, populations, and communities. It also focuses on applications in the medical, environmental, and engineering domains. The scope of the journal encompasses all aspects of the field, ranging from cell-cell communication and single cell interactions to the microbiomes of humans, animals, plants, and natural and built environments. The journal also welcomes research on the virome, phageome, mycome, and fungome. It publishes both applied science and theoretical work. As an open access and interdisciplinary journal, its primary goal is to publish significant scientific advancements in microbial biofilms and microbiomes. The journal enables discussions that span multiple disciplines and contributes to our understanding of the social behavior of microbial biofilm populations and communities, and their impact on life, human health, and the environment.
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