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首页> 外文期刊>MBio >Microbiome Data Distinguish Patients with Clostridium difficile Infection and Non-C.?difficile-Associated Diarrhea from Healthy Controls
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Microbiome Data Distinguish Patients with Clostridium difficile Infection and Non-C.?difficile-Associated Diarrhea from Healthy Controls

机译：微生物组数据从健康对照中区分难辨梭状芽孢杆菌感染和非艰难梭菌相关性腹泻的患者

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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. >IMPORTANCE 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.
机译：抗生素的使用是医院获得性艰难梭菌感染的最常见风险因素。风险增加是由于本地微生物组的破坏以及随后被细菌群落干扰而导致的定植抗性下降;然而，人们对导致风险增加的微生物组的具体变化知之甚少。我们开发了将微生物组数据与临床和人口统计数据相结合的统计模型，以更好地理解个人为什么会发展CDI。从338例患者的粪便中测序出16S rRNA基因，包括病例，腹泻对照和非腹泻对照。我们使用logit回归使用多个临床变量（包括年龄，抗生素使用，抗酸剂的使用以及其他已知风险因素）对CDI和腹泻状态进行建模。使用多样性指标，社区类型或特定细菌种群，将该基本模型与并入微生物组数据的模型进行比较，以确定与CDI敏感性或耐药性相关的微生物组的特征。当比较病例或腹泻对照与非腹泻对照时，微生物组数据的添加显着提高了我们区分CDI状态的能力。但是，只有将样本分配给社区类型后，才有可能将病例与腹泻对照区分开。案例中不存在 Ruminococcaceae ， Lachnospiraceae ， Bacteroides 和 Porphyromonadaceae 中的几种细菌，并且与非腹泻控制。我们基于微生物组的模型的改进的区分能力证实了影响微生物组的因素影响CDI的理论。 >重要性肠道微生物组由胃肠道中的数万亿细菌组成，负责宿主内的许多关键功能。这些包括消化，免疫系统刺激和抗定植性。微生物在定植抗性中的作用是防止和限制病原体定殖和生长的能力，是抵抗艰难梭菌感染的关键。然而，尚未阐明对于定植抗性重要的细菌。使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某些细菌种群的损失，包括 Bacteroides ， Lachnospiraceae 和 Ruminococcaceae < / em>与CDI相关联。我们的结果强调了考虑微生物组介导定植抗性的重要性，也可能指导未来多物种益生菌疗法的设计。

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