Variability of core microbiota in newly diagnosed treatment-naïve paediatric inflammatory bowel disease patients.

BACKGROUND & AIMS: Intestinal microbiota is considered to play a crucial role in the aetiology of inflammatory bowel disease (IBD). We aimed to describe faecal microbiota composition and dynamics in a large cohort of children with de novo (naïve) IBD, in comparison to healthy paediatric controls (HC). METHODS: In this prospective study, performed at two tertiary centres, faecal samples from newly diagnosed, treatment-naïve paediatric IBD patients were collected prior to bowel cleansing for colonoscopy (t0) and 1, 3 and 6 weeks and 3 months after initiation of therapy. The microbial profiles of Crohn's disease (CD) and Ulcerative colitis (UC) patients were compared with HC and linked to therapeutic response. Microbiota composition was analysed by IS-pro technology. RESULTS: Microbial profiles of 104 new IBD-patients (63 CD, 41 UC, median age 14.0 years) were compared to 61 HC (median 7.8 years). IBD was mainly characterised by decreased abundance of Alistipes finegoldii and Alistipes putredinis, which characterize a healthy state microbial core. The classifier including these core species as predictors achieved an AUC of the ROC curve of .87. Core bacteria tended to regain abundance during treatment, but did not reach healthy levels. CONCLUSION: Faecal microbiota profiles of children with de novo CD and UC can be discriminated from HC with high accuracy, mainly driven by a decreased abundance of species shaping the microbial core in the healthy state. Paediatric IBD can therefore be characterized by decreased abundance of certain bacterial species reflecting the healthy state rather than by the introduction of pathogens.

Interpretation of microbiota-based diagnostics by explaining individual classifier decisions.

BACKGROUND: The human microbiota is associated with various disease states and holds a great promise for non-invasive diagnostics. However, microbiota data is challenging for traditional diagnostic approaches: It is high-dimensional, sparse and comprises of high inter-personal variation. State of the art machine learning tools are therefore needed to achieve this goal. While these tools have the ability to learn from complex data and interpret patterns therein that cannot be identified by humans, they often operate as black boxes, offering no insight into their decision-making process. In most cases, it is difficult to represent the learning of a classifier in a comprehensible way, which makes them prone to be mistrusted, or even misused, in a clinical environment. In this study, we aim to elucidate microbiota-based classifier decisions in a biologically meaningful context to allow their interpretation. RESULTS: We applied a method for explanation of classifier decisions on two microbiota datasets of increasing complexity: gut versus skin microbiota samples, and inflammatory bowel disease versus healthy gut microbiota samples. The algorithm simulates bacterial species as being unknown to a pre-trained classifier, and measures its effect on the outcome. Consequently, each patient is assigned a unique quantitative estimation of which species in their microbiota defined the classification of their sample. The algorithm was able to explain the classifier decisions well, demonstrated by our validation method, and the explanations were biologically consistent with recent microbiota findings. CONCLUSIONS: Application of a method for explaining individual classifier decisions for complex microbiota analysis proved feasible and opens perspectives on personalized therapy. Providing an explanation to support a microbiota-based diagnosis could guide decisions of clinical microbiologists, and has the potential to increase their confidence in the outcome of such decision support systems. This may facilitate the development of new diagnostic applications.

Robust Microbiota-Based Diagnostics for Inflammatory Bowel Disease.

Strong evidence suggests that the gut microbiota is altered in inflammatory bowel disease (IBD), indicating its potential role in noninvasive diagnostics. However, no clinical applications are currently used for routine patient care. The main obstacle to implementing a gut microbiota test for IBD is the lack of standardization, which leads to high interlaboratory variation. We studied the between-hospital and between-platform batch effects and their effects on predictive accuracy for IBD. Fecal samples from 91 pediatric IBD patients and 58 healthy children were collected. IS-pro, a standardized technique designed for routine microbiota profiling in clinical settings, was used for microbiota composition characterization. Additionally, a large synthetic data set was used to simulate various perturbations and study their effects on the accuracy of different classifiers. Perturbations were validated in two replicate data sets, one processed in another laboratory and the other with a different analysis platform. The type of perturbation determined its effect on predictive accuracy. Real-life perturbations induced by between-platform variation were significantly greater than those caused by between-laboratory variation. Random forest was found to be robust to both simulated and observed perturbations, even when these perturbations had a dramatic effect on other classifiers. It achieved high accuracy both when cross-validated within the same data set and when using data sets analyzed in different laboratories. Robust clinical predictions based on the gut microbiota can be performed even when samples are processed in different hospitals. This study contributes to the effort to develop a universal IBD test that would enable simple diagnostics and disease activity monitoring.