Microbiome data distinguish patients with Clostridium difficile infection and non-C. difficile-associated diarrhea from healthy controls.
Microbiome data distinguish patients with Clostridium difficile infection and non-C. difficile-associated diarrhea from healthy controls.
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DOI：
10.1128/mbio.01021-14
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发表时间：
2014-05-06
期刊：
影响因子：
6.4
通讯作者：
Schloss PD
中科院分区：
文献类型：
--
作者：
Schubert AM;Rogers MA;Ring C;Mogle J;Petrosino JP;Young VB;Aronoff DM;Schloss PD
抗生素的使用是医院获得性艰难梭菌感染(CDI)最常见的危险因素。风险增加是由于受干扰的细菌群落破坏了本土微生物群，随后减少了对定植的抵抗力；然而，导致风险增加的微生物群中的具体变化尚不清楚。我们开发了统计模型，将微生物组数据与临床和人口数据结合起来，以更好地理解为什么个人会患上CDI。从338名患者的粪便中测序了16S rRNA基因，其中包括病例、腹泻对照组和非腹泻对照组。我们使用多种临床变量，包括年龄、抗生素使用、抗酸剂使用和其他已知危险因素，使用Logit回归对CDI和腹泻状况进行建模。该基本模型与结合了微生物组数据的模型进行了比较，使用多样性指标、群落类型或特定的细菌种群，以确定与CDI敏感性或耐药性相关的微生物组的特征。微生物组数据的添加显著提高了我们在比较病例或腹泻控制与非腹泻控制时区分CDI状态的能力。然而，只有当我们将样本分配给社区类型时，才有可能区分病例和腹泻控制。反刍球菌科、乳螺科、类杆菌和藻单胞菌科中的几种细菌在病例中基本不存在，与非腹泻控制高度相关。我们基于微生物组的模型的识别能力的提高证实了影响微生物组的因素影响CDI的理论。肠道微生物群由驻留在胃肠道中的数万亿细菌组成，负责宿主内部的一些关键功能。这些包括消化、免疫系统刺激和定植抵抗。微生物群在耐定植中的作用，即防止和限制病原体定植和生长的能力，是预防艰难梭菌感染的关键。然而，对定植抗性起重要作用的细菌还没有被阐明。利用统计建模技术和微生物组的不同表示，我们证明了几种群落类型和几种细菌种群的丧失，包括类杆菌属、乳螺科和反刍球菌科，与CDI有关。我们的结果强调了考虑微生物组在调节定植抗性中的重要性，并可能指导未来多物种益生菌治疗的设计。
Antibiotic usage is the most commonly cited risk factor for hospital-acquired Clostridium difficile infections (CDI). The increased risk is due to disruption of the indigenous microbiome and a subsequent decrease in colonization resistance by the perturbed bacterial community; however, the specific changes in the microbiome that lead to increased risk are poorly understood. We developed statistical models that incorporated microbiome data with clinical and demographic data to better understand why individuals develop CDI. The 16S rRNA genes were sequenced from the feces of 338 individuals, including cases, diarrheal controls, and nondiarrheal controls. We modeled CDI and diarrheal status using multiple clinical variables, including age, antibiotic use, antacid use, and other known risk factors using logit regression. This base model was compared to models that incorporated microbiome data, using diversity metrics, community types, or specific bacterial populations, to identify characteristics of the microbiome associated with CDI susceptibility or resistance. The addition of microbiome data significantly improved our ability to distinguish CDI status when comparing cases or diarrheal controls to nondiarrheal controls. However, only when we assigned samples to community types was it possible to differentiate cases from diarrheal controls. Several bacterial species within the Ruminococcaceae, Lachnospiraceae, Bacteroides, and Porphyromonadaceae were largely absent in cases and highly associated with nondiarrheal controls. The improved discriminatory ability of our microbiome-based models confirms the theory that factors affecting the microbiome influence CDI. The gut microbiome, composed of the trillions of bacteria residing in the gastrointestinal tract, is responsible for a number of critical functions within the host. These include digestion, immune system stimulation, and colonization resistance. The microbiome’s role in colonization resistance, which is the ability to prevent and limit pathogen colonization and growth, is key for protection against Clostridium difficile infections. However, the bacteria that are important for colonization resistance have not yet been elucidated. Using statistical modeling techniques and different representations of the microbiome, we demonstrated that several community types and the loss of several bacterial populations, including Bacteroides, Lachnospiraceae, and Ruminococcaceae, are associated with CDI. Our results emphasize the importance of considering the microbiome in mediating colonization resistance and may also direct the design of future multispecies probiotic therapies.