Harvard School of Dental Medicine, Boston, Massachusetts, United States of America.
Tufts University School of Dental Medicine, Boston, Massachusetts, United States of America.
PLoS One. 2024 Jul 25;19(7):e0300408. doi: 10.1371/journal.pone.0300408. eCollection 2024.
SARS-CoV-2, a severe respiratory disease primarily targeting the lungs, was the leading cause of death worldwide during the pandemic. Understanding the interplay between the oral microbiome and inflammatory cytokines during acute infection is crucial for elucidating host immune responses. This study aimed to explore the relationship between the oral microbiome and cytokines in COVID-19 patients, particularly those with and without sputum production. Saliva and blood samples from 50 COVID-19 patients were subjected to 16S ribosomal RNA gene sequencing for oral microbiome analysis, and 65 saliva and serum cytokines were assessed using Luminex multiplex analysis. The Mann-Whitney test was used to compare cytokine levels between individuals with and without sputum production. Logistic regression machine learning models were employed to evaluate the predictive capability of oral microbiome, salivary, and blood biomarkers for sputum production. Significant differences were observed in the membership (Jaccard dissimilarity: p = 0.016) and abundance (PhILR dissimilarity: p = 0.048; metagenomeSeq) of salivary microbial communities between patients with and without sputum production. Seven bacterial genera, including Prevotella, Streptococcus, Actinomyces, Atopobium, Filifactor, Leptotrichia, and Selenomonas, were more prevalent in patients with sputum production (p<0.05, Fisher's exact test). Nine genera, including Prevotella, Megasphaera, Stomatobaculum, Selenomonas, Leptotrichia, Veillonella, Actinomyces, Atopobium, and Corynebacteria, were significantly more abundant in the sputum-producing group, while Lachnoanaerobaculum was more prevalent in the non-sputum-producing group (p<0.05, ANCOM-BC). Positive correlations were found between salivary IFN-gamma and Eotaxin2/CCL24 with sputum production, while negative correlations were noted with serum MCP3/CCL7, MIG/CXCL9, IL1 beta, and SCF (p<0.05, Mann-Whitney test). The machine learning model using only oral bacteria input outperformed the model that included all data: blood and saliva biomarkers, as well as clinical and demographic variables, in predicting sputum production in COVID-19 subjects. The performance metrics were as follows, comparing the model with only bacteria input versus the model with all input variables: precision (95% vs. 75%), recall (100% vs. 50%), F1-score (98% vs. 60%), and accuracy (82% vs. 66%).
SARS-CoV-2 是一种主要影响肺部的严重呼吸道疾病，是大流行期间全球范围内的主要死亡原因。了解急性感染期间口腔微生物组与炎症细胞因子之间的相互作用对于阐明宿主免疫反应至关重要。本研究旨在探讨 COVID-19 患者口腔微生物组与细胞因子之间的关系，特别是那些有痰和无痰患者之间的关系。对 50 名 COVID-19 患者的唾液和血液样本进行 16S 核糖体 RNA 基因测序，以分析口腔微生物组，并使用 Luminex 多重分析评估 65 种唾液和血清细胞因子。使用 Mann-Whitney 检验比较有痰和无痰患者之间细胞因子水平的差异。使用逻辑回归机器学习模型评估口腔微生物组、唾液和血液生物标志物对痰液产生的预测能力。有痰和无痰患者的唾液微生物群落的成员组成（Jaccard 不相似性：p=0.016）和丰度（PhILR 不相似性：p=0.048；metagenomeSeq）存在显著差异。有 7 个细菌属，包括普雷沃氏菌属、链球菌属、放线菌属、阿托波菌属、纤维杆菌属、勒特氏菌属和希瓦氏菌属，在有痰患者中更为常见（p<0.05，Fisher 精确检验）。有 9 个细菌属，包括普雷沃氏菌属、巨球形菌属、唾液杆菌属、希瓦氏菌属、勒特氏菌属、韦荣球菌属、放线菌属、阿托波菌属和棒状杆菌属，在有痰组中丰度显著更高，而lachnoanaerobaculum 在无痰组中更为常见（p<0.05，ANCOM-BC）。唾液 IFN-γ和 Eotaxin2/CCL24 与痰液产生呈正相关，而血清 MCP3/CCL7、MIG/CXCL9、IL1 beta 和 SCF 与痰液产生呈负相关（p<0.05，Mann-Whitney 检验）。仅使用口腔细菌输入的机器学习模型在预测 COVID-19 患者痰液产生方面优于包含所有数据（血液和唾液生物标志物以及临床和人口统计学变量）的模型。比较仅使用细菌输入的模型与包含所有输入变量的模型的性能指标如下：精确率（95% vs. 75%）、召回率（100% vs. 50%）、F1 分数（98% vs. 60%）和准确度（82% vs. 66%）。
