Oropharyngeal microbiome profiled at admission is predictive of the need for respiratory support among COVID-19 patients.
Oropharyngeal microbiome profiled at admission is predictive of the need for respiratory support among COVID-19 patients.
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DOI：
10.3389/fmicb.2022.1009440
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发表时间：
2022
影响因子：
5.2
通讯作者：
Haran, John P.
中科院分区：
文献类型：
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作者：
Bradley, Evan S.;Zeamer, Abigail L.;Bucci, Vanni;Cincotta, Lindsey;Salive, Marie-Claire;Dutta, Protiva;Mutaawe, Shafik;Anya, Otuwe;Tocci, Christopher;Moormann, Ann;Ward, Doyle V.;McCormick, Beth A.;Haran, John P.
关键词：
口咽微生物组是上呼吸道微生物群落的集体基因组，被认为会影响呼吸道病毒感染的临床过程，包括严重急性呼吸道综合征冠状病毒2（SARS-CoV-2），2019年冠状病毒传染病（COVID-19）的病原体。在这项研究中，我们检查了到急诊室就诊的疑似COVID-19患者的口咽部微生物组和具有急性COVID-19症状的住院COVID-19单位。在最初入组的115名患者中，50名患者的COVID-19+分子检测呈阳性，症状持续时间不超过14天。这些患者被进一步分析，因为疾病进展最有可能归因于急性COVID-19，而不太可能是继发过程。其中，38例（76%）继续需要某种形式的补充氧气支持。为了识别与需要呼吸支持的呼吸系统疾病相关的功能模式，我们将可解释的随机森林分类机器学习管道应用于鸟枪宏基因组测序数据并选择临床协变量。当与临床因素相结合时，发现基于物种和代谢途径丰度的模型对呼吸支持的需求具有高度预测性（微生物的F1评分为0.857，功能途径为0.821）。为了确定微生物组中具有生物学意义和高度预测性的信号，我们将稳定和可解释的规则集应用于模型的输出。该分析显示，两种肠道微生物，唾液普雷沃氏菌或韦荣氏菌的低丰度（分别<4.2%和1.7%），以及与LPS生物合成相关的途径的低丰度（< 0.1%）高度预测发展为急性呼吸支持的需要（分别为82%和91.4%）。这些发现表明，COVID-19患者口咽部微生物组的组成可能在决定谁将遭受严重疾病表现方面发挥作用。
The oropharyngeal microbiome, the collective genomes of the community of microorganisms that colonizes the upper respiratory tract, is thought to influence the clinical course of infection by respiratory viruses, including Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the causative agent of Coronavirus Infectious Disease 2019 (COVID-19). In this study, we examined the oropharyngeal microbiome of suspected COVID-19 patients presenting to the Emergency Department and an inpatient COVID-19 unit with symptoms of acute COVID-19. Of 115 initially enrolled patients, 50 had positive molecular testing for COVID-19+ and had symptom duration of 14 days or less. These patients were analyzed further as progression of disease could most likely be attributed to acute COVID-19 and less likely a secondary process. Of these, 38 (76%) went on to require some form of supplemental oxygen support. To identify functional patterns associated with respiratory illness requiring respiratory support, we applied an interpretable random forest classification machine learning pipeline to shotgun metagenomic sequencing data and select clinical covariates. When combined with clinical factors, both species and metabolic pathways abundance-based models were found to be highly predictive of the need for respiratory support (F1-score 0.857 for microbes and 0.821 for functional pathways). To determine biologically meaningful and highly predictive signals in the microbiome, we applied the Stable and Interpretable RUle Set to the output of the models. This analysis revealed that low abundance of two commensal organisms, Prevotella salivae or Veillonella infantium (< 4.2 and 1.7% respectively), and a low abundance of a pathway associated with LPS biosynthesis (< 0.1%) were highly predictive of developing the need for acute respiratory support (82 and 91.4% respectively). These findings suggest that the composition of the oropharyngeal microbiome in COVID-19 patients may play a role in determining who will suffer from severe disease manifestations.
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影响因子：
7.7
作者：
Beghini F;McIver LJ;Blanco-Míguez A;Dubois L;Asnicar F;Maharjan S;Mailyan A;Manghi P;Scholz M;Thomas AM;Valles-Colomer M;Weingart G;Zhang Y;Zolfo M;Huttenhower C;Franzosa EA;Segata N
通讯作者：
Segata N
影响因子：
5.4
作者：
Avadhanula, V;Rodriguez, CA;Adderson, EE
通讯作者：
Adderson, EE
影响因子：
15.9
作者：
Goldberg, JB;Pier, GB
通讯作者：
Pier, GB
Trimmomatic：用于 Illumina 序列数据的灵活修剪器。
DOI：
10.1093/bioinformatics/btu170
发表时间：
2014-08-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Bolger AM;Lohse M;Usadel B
通讯作者：
Usadel B
影响因子：
21.1
作者：
通讯作者：
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