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Identification of an intestinal microbiota signature associated with severity of irritable bowel syndrome

机译:鉴定与肠易激综合征严重程度有关的肠道菌群特征

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摘要

BACKGROUND & AIMS: We have limited knowledge about the association between the composition of the intestinal microbiota and clinical features of irritable bowel syndrome (IBS). We collected information on the fecal and mucosa-associated microbiota of patients with IBS and evaluated whether these were associated with symptoms. METHODS: We collected fecal and mucosal samples from adult patients who met the Rome III criteria for IBS at a secondary/tertiary care outpatient clinics in Sweden, as well as from healthy subjects. The exploratory set comprised 149 subjects (110 with IBS and 39 healthy subjects); 232 fecal samples and 59 mucosal biopsy samples were collected and analyzed by 16S ribosomal RNA targeted pyrosequencing. The validation set comprised 46 subjects (29 with IBS and 17 healthy subjects); 46 fecal samples, but no mucosal samples, were collected and analyzed. For each subject, we measured exhaled H-2 and CH4, oro-anal transit time, and the severity of psychological and gastrointestinal symptoms. Fecal methanogens were measured by quantitative polymerase chain reaction. Numerical ecology analyses and a machine learning procedure were used to analyze the data. RESULTS: Fecal microbiota showed covariation with mucosal adherent microbiota. By using classic approaches, we found no differences in fecal microbiota abundance or composition between patients with IBS vs healthy patients. A machine learning procedure, a computational statistical technique, allowed us to reduce the 16S ribosomal RNA data complexity into a microbial signature for severe IBS, consisting of 90 bacterial operational taxonomic units. We confirmed the robustness of the intestinal microbial signature for severe IBS in the validation set. The signature was able to discriminate between patients with severe symptoms, patients with mild/moderate symptoms, and healthy subjects. By using this intestinal microbiota signature, we found IBS symptom severity to be associated negatively with microbial richness, exhaled CH4, presence of methanogens, and enterotypes enriched with Clostridiales or Prevotella species. This microbiota signature could not be explained by differences in diet or use of medications. CONCLUSIONS: In analyzing fecal and mucosal microbiota from patients with IBS and healthy individuals, we identified an intestinal microbiota profile that is associated with the severity of IBS symptoms.
机译:背景与目的:我们对肠道菌群的组成与肠易激综合征(IBS)的临床特征之间的关联知之甚少。我们收集了有关IBS患者粪便和黏膜相关菌群的信息,并评估了它们是否与症状有关。方法:我们从瑞典的二级/三级医疗门诊和健康受试者中收集了符合IBS罗马III标准的成年患者的粪便和粘膜样品。探索性研究包括149名受试者(110名患有IBS的受试者和39名健康受试者);收集232份粪便样品和59份粘膜活检样品,并通过16S核糖体RNA靶向焦磷酸测序进行分析。验证集包括46名受试者(29名患有IBS的受试者和17名健康受试者);收集并分析了46份粪便样本,但没有粘膜样本。对于每个受试者,我们测量了呼出气中的H-2和CH4,经肛门的时间以及心理和胃肠道症状的严重程度。通过定量聚合酶链反应测量粪便产甲烷菌。数值生态学分析和机器学习程序被用来分析数据。结果:粪便菌群与黏膜黏附菌群共变异。通过使用经典方法,我们发现IBS患者与健康患者之间的粪便微生物群丰度或组成没有差异。机器学习程序(一种计算统计技术)使我们能够将16S核糖体RNA数据的复杂性降低为严重IBS的微生物特征,其中包括90个细菌操作分类单位。我们在验证集中确认了肠道微生物签名对严重IBS的稳健性。该签名能够区分具有严重症状的患者,具有轻度/中度症状的患者和健康受试者。通过使用此肠道菌群特征,我们发现IBS症状严重程度与微生物丰富度,呼出的CH4,产甲烷菌和富含梭菌或小球藻种类的肠型呈负相关。这种微生物群的特征不能用饮食或药物使用的差异来解释。结论:在分析IBS患者和健康个体的粪便和粘膜微生物群时,我们确定了与IBS症状严重程度相关的肠道微生物群特征。

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