Department of Plant Pathology and Microbiology, University of California, Riverside, CA 90095, USA.
Inflamm Bowel Dis. 2012 Mar;18(3):409-17. doi: 10.1002/ibd.21793. Epub 2011 Jun 22.
Host-microbe interactions at the intestinal mucosal-luminal interface (MLI) are critical factors in the biology of inflammatory bowel disease (IBD).
To address this issue, we performed a series of investigations integrating analysis of the bacteria and metaproteome at the MLI of Crohn's disease, ulcerative colitis, and healthy human subjects. After quantifying these variables in mucosal specimens from a first sample set, we searched for bacteria exhibiting strong correlations with host proteins. This assessment identified a small subset of bacterial phylotypes possessing this host interaction property. Using a second and independent sample set, we tested the association of disease state with levels of these 14 "host interaction" bacterial phylotypes.
A high frequency of these bacteria (35%) significantly differentiated human subjects by disease type. Analysis of the MLI metaproteomes also yielded disease classification with exceptional confidence levels. Examination of the relationships between the bacteria and proteins, using regularized canonical correlation analysis (RCCA), sorted most subjects by disease type, supporting the concept that host-microbe interactions are involved in the biology underlying IBD. Moreover, this correlation analysis identified bacteria and proteins that were undetected by standard means-based methods such as analysis of variance, and identified associations of specific bacterial phylotypes with particular protein features of the innate immune response, some of which have been documented in model systems.
These findings suggest that computational mining of mucosa-associated bacteria for host interaction provides an unsupervised strategy to uncover networks of bacterial taxa and host processes relevant to normal and disease states. (Inflamm Bowel Dis 2012;).
肠道黏膜-腔界面(MLI)的宿主-微生物相互作用是炎症性肠病(IBD)生物学的关键因素。
为了解决这个问题,我们进行了一系列的研究,整合了对克罗恩病、溃疡性结肠炎和健康人类受试者的 MLI 细菌和元蛋白质组的分析。在对第一组样本的黏膜标本进行这些变量的定量分析后,我们寻找与宿主蛋白呈强相关性的细菌。这种评估确定了具有这种宿主相互作用特性的一小部分细菌分类群。使用第二组和独立的样本集,我们测试了这些 14 种“宿主相互作用”细菌分类群的疾病状态与水平之间的关联。
这些细菌的高频率(35%)显著地根据疾病类型区分了人类受试者。MLI 元蛋白质组的分析也以极高的置信水平进行了疾病分类。使用正则化典型相关分析(RCCA)分析细菌和蛋白质之间的关系,将大多数受试者按疾病类型排序,支持宿主-微生物相互作用参与 IBD 生物学的概念。此外,这种相关分析确定了通过方差分析等标准基于方法无法检测到的细菌和蛋白质,并确定了特定细菌分类群与先天免疫反应特定蛋白质特征的关联,其中一些在模型系统中已有记录。
这些发现表明,对黏膜相关细菌进行宿主相互作用的计算挖掘提供了一种非监督策略,可以揭示与正常和疾病状态相关的细菌分类群和宿主过程的网络。(炎症性肠病 2012 年;)。