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Background: Host–microbe interactions at the intestinal mucosal–luminal interface (MLI) are critical factors in the biology of inflammatory bowel disease (IBD). Methods: 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. Results: 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. Conclusions: 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;)

Microorganisms appear to play important yet ill-defined roles in the etiologies of Crohn's disease (CD) and ulcerative colitis (UC).1–3 CD and UC occur in regions of the intestine where enteric bacteria are found in the highest concentrations.4 Contact with intestinal contents triggers mucosal inflammation in CD,5 while diversion of the fecal stream promotes intestinal healing.6 The most direct evidence for the importance of microorganisms in disease etiology comes from investigations with animals. In numerous rodent models, colitis is absent when the animals are kept in a “germ-free” state, but it rapidly develops when standard intestinal microorganisms are introduced.7 Evidence also indicates that disease etiology involves specific bacteria, and or an aberrant immunological response to specific intestinal microorganisms. In animal studies, monoassociation experiments have shown that the type of colitis is dependent on the bacterial species.8,9 In addition, various antibiotics, possessing different taxonomic targets, have been shown to exhibit varying abilities to prevent and treat colitis in HLA-B27 rats10 and IL-10–/– mice.11 In human studies, evidence for the involvement of specific bacteria includes the differing abilities of various antibiotic therapies to induce disease remission.12–14 Evidence that the immunological responses to these bacteria are involved in disease etiology include the ability of CBir1-selective T cells to cause colitis when transferred to immunodeficient mice15 and the ability to utilize seroreactivity to specific microbial antigens for disease stratification.16 Prior investigations in our laboratories have demonstrated the utility of population-based approaches for identifying microorganisms involved in specific in situ processes such as plant-pathogen suppression in soil.17,18 The first step in this approach is to create or identify a series of samples/subjects with various levels of a specific functional parameter/phenotype. Extensive microbial community analyses are then performed on these samples or subjects. Finally, analyses are performed to identify associations between the abundance of specific taxa and the levels of the functional parameter/phenotype. In this study we used this experimental approach to identify bacteria and bacterial rRNA genes associated with the development of colitis in IL-10–/– mice. Various levels of colitis were examined by monitoring bacterial populations in 2 different environments, where the disease progression rates were different, and over a 6-month period, throughout the development of colitis. Bacteria and bacterial rRNA genes with both positive and negative associations with colitis were identified. Although such trends can be interpreted in several ways, we suggest that negative associations may represent particularly important trends in inflammatory bowel disease (IBD), because they may facilitate the identification of resident microbiota that are being targeted by aberrant immunological responses.

How environmental cues regulate adult stem cell and cancer cell activity through surface receptors is poorly understood. Angiopoietin-like proteins (ANGPTLs), a family of seven secreted glycoproteins, are known to support the activity of haematopoietic stem cells (HSCs) in vitro and in vivo. ANGPTLs also have important roles in lipid metabolism, angiogenesis and inflammation, but were considered 'orphan ligands' because no receptors were identified. Here we show that the immune-inhibitory receptor human leukocyte immunoglobulin-like receptor B2 (LILRB2) and its mouse orthologue paired immunoglobulin-like receptor (PIRB) are receptors for several ANGPTLs. LILRB2 and PIRB are expressed on human and mouse HSCs, respectively, and the binding of ANGPTLs to these receptors supported ex vivo expansion of HSCs. In mouse transplantation acute myeloid leukaemia models, a deficiency in intracellular signalling of PIRB resulted in increased differentiation of leukaemia cells, revealing that PIRB supports leukaemia development. Our study indicates an unexpected functional significance of classical immune-inhibitory receptors in maintenance of stemness of normal adult stem cells and in support of cancer development.

Oligonucleotide fingerprinting of rRNA genes (OFRG) is a method for identifying arrayed ribosomal RNA genes (rDNA) through a series of hybridization experiments with short oligonucleotide probes. Due to its low cost and high speed, it is an effective tool for analyzing microbial communities. OFRG relies on probe sets that can discriminate large collections of clones. Although the currently used probe design algorithm produces probe sets whose theoretical accuracy is close to optimum, those probes often do not hybridize in a consistent and predictable manner in actual biological experiments. We assume that these failures occur following an unknown probability distribution. In this paper, a nonparametric kernel density estimation method is proposed to estimate this distribution and to predict probe reliability. These predictions are used to reduce the number of unreliable probes chosen by the probe design algorithms. Our preliminary results show that the application of this method leads to a significant decrease in the number of unreliable probes.

Nature 485, 656–660 (2012); doi:10.1038/nature11095 The surname of author HoangDinh Huynh was inadvertently misspelled as Hyunh. This has been corrected online in the PDF and HTML of the original paper.