SARS-CoV-2是一种主要针对肺部的严重呼吸道疾病,是大流行期间全球死亡的主要原因。了解急性感染期间口腔微生物组和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的关系。特别是那些有和没有痰生产。对50例COVID-19患者的唾液和血液样本进行16S核糖体RNA基因测序,用于口腔微生物组分析,使用Luminex多重分析评估了65种唾液和血清细胞因子。Mann-Whitney检验用于比较有痰液产生和没有痰液产生的个体之间的细胞因子水平。采用Logistic回归机器学习模型评估口腔微生物组的预测能力,唾液,和痰液产生的血液生物标志物。在有和没有痰产生的患者之间,唾液微生物群落的成员(Jaccard差异:p=0.016)和丰度(PhILR差异:p=0.048;宏基因组Seq)存在显着差异。七个细菌属,包括普雷沃氏菌,链球菌,放线菌,Atobobium,Filifactor,Leptotrichia,和硒单胞菌,在痰液产生的患者中更为普遍(p<0.05,Fisher精确检验)。九个属,包括普雷沃氏菌,Megasphaera,Stomatobaculum,硒单胞菌,Leptotrichia,Veillonella,放线菌,Atobobium,和棒状杆菌,在产痰组中明显更丰富,而Lachnoanaeracium在非产痰组中更为普遍(p<0.05,ANCOM-BC)。唾液IFN-γ和Eotaxin2/CCL24与痰液产量呈正相关,而血清MCP3/CCL7、MIG/CXCL9、IL1β、和SCF(p<0.05,Mann-Whitney检验)。仅使用口腔细菌输入的机器学习模型优于包含所有数据的模型:血液和唾液生物标志物。以及临床和人口统计学变量,预测COVID-19受试者的痰液产生。性能指标如下,将仅具有细菌输入的模型与具有所有输入变量的模型进行比较:精度(95%与75%),召回(100%与50%),F1得分(98%vs.60%),和准确性(82%与66%)。
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%).