Identification of an Intestinal Microbiota Signature Associated With Severity of Irritable Bowel Syndrome
Identification of an Intestinal Microbiota Signature Associated With Severity of Irritable Bowel Syndrome
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DOI：
10.1053/j.gastro.2016.09.049
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发表时间：
2017-01-01
期刊：
影响因子：
29.4
通讯作者：
Simren, Magnus
中科院分区：
文献类型：
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作者：
Tap, Julien;Derrien, Muriel;Simren, Magnus
背景与目的：我们对肠道微生物群的组成与肠易激综合征（IBS）临床特征之间的关联知之甚少。我们收集了 IBS 患者粪便和粘膜相关微生物群的信息，并评估这些是否与症状相关。方法：我们从瑞典二级/三级护理门诊符合 IBS 罗马 III 标准的成年患者以及健康受试者中收集了粪便和粘膜样本。探索组包括 149 名受试者（110 名患有 IBS 和 39 名健康受试者）；通过 16S 核糖体 RNA 靶向焦磷酸测序收集并分析了 232 份粪便样本和 59 份粘膜活检样本。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，我们测量了呼出的 H-2 和 CH4、口腔-肛门传输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。使用数值生态学分析和机器学习程序来分析数据。结果：粪便微生物群与粘膜粘附微生物群表现出共变。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。机器学习程序是一种计算统计技术，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，由 90 个细菌操作分类单元组成。我们在验证集中证实了严重 IBS 肠道微生物特征的稳健性。该签名能够区分严重症状的患者、轻度/中度症状的患者和健康受试者。通过使用这种肠道微生物群特征，我们发现 IBS 症状的严重程度与微生物丰富度、呼出的 CH4、产甲烷菌的存在以及富含梭菌目或普氏菌属的肠型呈负相关。这种微生物群特征无法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。
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.