De La Cochetière M F, Durand T, Lalande V, Petit J C, Potel G, Beaugerie L
Thérapeutiques Cliniques et Expérimentales des Infections, EA 3826, UFR Médecine, Université de Nantes, Nantes Atlantique Universités, Rue G. Veil, Nantes, 44000, France.
Microb Ecol. 2008 Oct;56(3):395-402. doi: 10.1007/s00248-007-9356-5. Epub 2008 Jan 22.
The gastrointestinal tract is a complex ecosystem. Recent studies have shown that the human fecal microbiota is composed of a consortium of microorganism. It is known that antibiotic treatment alters the microbiota, facilitating the proliferation of opportunists that may occupy ecological niches previously unavailable to them. It is therefore important to characterize resident microbiota to evaluate its latent ability to permit the development of pathogens such as Clostridium difficile. Using samples from 260 subjects enrolled in a previously published clinical study on antibiotic-associated diarrhea, we investigated the possible relationship between the fecal dominant resident microbiota and the subsequent development of C. difficile. We used molecular profiling of bacterial 16S rDNA coupled with partial least square (PLS) regression analysis. Fecal samples were collected on day 0 (D0) before antibiotic treatment and on day 14 (D14) after the beginning of the treatment. Fecal DNA was isolated, and V6-to-V8 regions of the 16S rDNA were amplified by polymerase chain reaction with general primers and analyzed by temporal temperature gradient gel electrophoresis (TTGE). Main bacteria profiles were compared on the basis of similarity (Pearson correlation coefficient). The characteristics of the microbiota were determined using PLS discriminant analysis model. Eighty-seven TTGE profiles on D0 have been analyzed. The banding pattern was complex in all cases. The subsequent onset of C. difficile was not revealed by any clustering of TTGE profiles, but was explained up to 46% by the corresponding PLS model. Furthermore, 6 zones out of the 438 dispatched from the TTGE profiles by the software happened to be specific for the group of patients who acquired C. difficile. The first approach in the molecular phylogenetic analysis showed related sequences to uncultured clones. As for the 87 TTGE profiles on D14, no clustering could be found either, but the subsequent onset of C. difficile was explained up to 74.5% by the corresponding PLS model, thus corroborating the results found on D0. The non exhaustive data of the microbiota we found should be taken as the first step to assess the hypothesis of permissive microbiota. The PLS model was used successfully to predict C. difficile development. We found that important criteria in terms of main bacteria could be markedly considered as predisposing factors for C. difficile development. Yet, the resident microbiota in case of antibiotic-associated diarrhea has still to be analyzed. Furthermore, these findings suggest that strategies reinforcing the ability of the fecal microbiota to resist to modifications would be of clinical relevance.
胃肠道是一个复杂的生态系统。最近的研究表明，人类粪便微生物群是由一群微生物组成的。众所周知，抗生素治疗会改变微生物群，促进机会致病菌的增殖，这些机会致病菌可能占据它们以前无法利用的生态位。因此，表征常驻微生物群以评估其允许艰难梭菌等病原体发展的潜在能力非常重要。我们使用来自参与先前发表的关于抗生素相关性腹泻的临床研究的260名受试者的样本，研究了粪便优势常驻微生物群与随后艰难梭菌发展之间的可能关系。我们使用细菌16S rDNA的分子谱分析结合偏最小二乘（PLS）回归分析。在抗生素治疗前的第0天（D0）和治疗开始后的第14天（D14）采集粪便样本。分离粪便DNA，用通用引物通过聚合酶链反应扩增16S rDNA的V6至V8区域，并通过时间温度梯度凝胶电泳（TTGE）进行分析。根据相似性（皮尔逊相关系数）比较主要细菌谱。使用PLS判别分析模型确定微生物群的特征。已分析了D0时的87个TTGE谱。在所有情况下，条带模式都很复杂。TTGE谱的任何聚类都未揭示随后艰难梭菌的发病情况，但相应的PLS模型可解释高达46%的发病情况。此外，软件从TTGE谱中划分出的438个区域中有6个区域恰好是获得艰难梭菌的患者组所特有的。分子系统发育分析中的第一种方法显示出与未培养克隆的相关序列。至于D14时的87个TTGE谱，则也未发现聚类，但相应的PLS模型可解释高达74.5%的随后艰难梭菌的发病情况，从而证实了在D0时发现的结果。我们发现的微生物群的非详尽数据应被视为评估允许性微生物群假说的第一步。PLS模型成功用于预测艰难梭菌的发展。我们发现，就主要细菌而言重要的标准可明显被视为艰难梭菌发展的易感因素。然而，抗生素相关性腹泻情况下的常驻微生物群仍有待分析。此外，这些发现表明，增强粪便微生物群抵抗改变能力的策略将具有临床相关性。
