CLOUD: a non-parametric detection test for microbiome outliers.

BACKGROUND: Dysbiosis of the human gut microbiome is defined as a maladaptive or clinically relevant deviation of the community profile from the healthy or normal state. Dysbiosis has been implicated in an extensive set of metabolic, auto-immune, and infectious diseases, and yet there is substantial inter-individual variation in microbiome composition even within body sites of healthy humans. An individual's microbiome varies over time in a high-dimensional space to form their personal microbiome cloud. This cloud may or may not be similar to that of other people, both in terms of the average microbiome profile (conformity) and the diameter of the cloud (stability). However, there is currently no robust non-parametric test that determines whether a patient's microbiome cloud is an outlier with respect to a reference group of healthy individuals with widely varying microbiome profiles. METHODS: Here, we propose a test for outliers' detection in the human gut microbiome that accounts for the wide range of microbiome phenotypes observed in a typical set of healthy individuals and for intra-individual temporal variation. Our robust nonparametric outlier detection test, the CLOUD test, performs two assessments of a patient's microbiome health: conformity, the extent to which the patient's microbiome cloud is ecologically similar to a subset of healthy subjects; and stability, which compares the cloud diameter of a patient to those of healthy subjects. The CLOUD test is based on locally linear embedded ecological distances, allowing it to account for widely varying microbiome compositions among reference individuals. It also leverages temporal variability within patients and reference individuals to increase the robustness of the test. RESULTS: We describe the CLOUD test, and we apply it to one novel and two previously published cohorts of patients receiving fecal microbiota transplantation for recurrent Clostridium difficile colitis, as well as to two known healthy cohorts, demonstrating high concordance of the CLOUD conformity and stability indices with clinical outcomes. CONCLUSIONS: Although the CLOUD test is not, on its own, a test for clinical dysbiosis, it nonetheless provides a framework for outlier testing that could be incorporated into evaluation of suspected dysbiosis, which may play a role in diagnosis and prognosis of numerous pediatric and adult diseases.

Rethinking "enterotypes".

Classification of the human gut microbiome into distinct types, or "enterotypes," provides an attractive framework for understanding microbial variation in health and disease. However, as discussed here, several different methods of collapsing enterotype variation into a few discrete clusters suggest that enterotype distribution is continuous and can vary widely within an individual.

Multi-omics analysis of inflammatory bowel disease.

Crohn's disease and ulcerative colitis, known together as inflammatory bowel disease (IBD), are severe autoimmune disorders now causing gut inflammation and ulceration, among other symptoms, in up to 1 in 250 people worldwide. Incidence and prevalence of IBD have been increasing dramatically over the past several decades, although the causes for this increase are still unknown. IBD has both a complex genotype and a complex phenotype, and although it has received substantial attention from the medical research community over recent years, much of the etiology remains unexplained. Genome-wide association studies have identified a rich genetic signature of disease risk in patients with IBD, consisting of at least 163 genetic loci. Many of these loci contain genes directly involved in microbial handling, indicating that the genetic architecture of the disease has been driven by host-microbe interactions. In addition, systematic shifts in gut microbiome structure (enterotype) and function have been observed in patients with IBD. Furthermore, both the host genotype and enterotype are associated with aspects of the disease phenotype, including location of the disease. This provides strong evidence of interactions between host genotype and enterotype; however, there is a lack of published multi-omics data from IBD patients, and a lack of bioinformatics tools for modeling such systems. In this article we discuss, from a computational biologist's point of view, the potential benefits of and the challenges involved in designing and analyzing such multi-omics studies of IBD.