Robust Microbiota-Based Diagnostics for Inflammatory Bowel Disease

Abstract

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.

Nucleotide-pair encoding of 16S rRNA sequences for host phenotype and biomarker detection

“…The description of the standard pipeline is provided in §2.3 generating a list of significant differently expressed OTUs for both phenotypes. We then compare the significantly enriched OTU sequences (FDR corrected P values < 0.05, LEfSe) and significant (FDR < 0.05) subsequences determined by DiTaxa as biomarkers with the ground-truth 16S V4 region using global nucleotide alignment with blastn v. 2 Each point represents an average of 100 resamples belonging to 10 randomly selected 16S rRNA samples. Higher vocabulary size require higher sampling rates to produce self-consistent and representative samples.…”

“…Microbial communities vary widely in their taxonomic structures and compositions [1,2,3]. The human microbiota fulfills important functions in supporting, regulating, and causing adverse conditions in their environment, motivating methods for inferring relationships between microbial taxa or functions associated with certain host phenotypes.…”

“…Although k-mer features and some other non-OTU features have been also used [18,21], the most common representation of 16S rRNA gene sequences is based on OTUs. Random Forest was reported as the most effective classification approach for several diseases [2,3,21,22,23]. Recently, we have shown that using k-mer representations of shallow-subsamples is computationally inexpensive (being reference-and alignment-free) and marginally outperforms OTUs in host phenotype and environment classification tasks [21].…”

MicroPheno: Predicting environments and host phenotypes from 16S rRNA gene sequencing using a k-mer based representation of shallow sub-samples

“…Detecting disease status based on 16S gene sequencing is becoming more and more popular, with applications in the prediction of Type 2 Diabetes [36] (patients: 53 samples, healthy:43 -Best accuracy: 0.67), Psoriasis (151 samples for 3 classes -Best accuracy: 0.225), IBD (patients: 49 samples, healthy:59 -Best AUC:0.95) [33], (patients: 91 samples, healthy: 58 samples -Best AUC:0.92) [34]. Similar to body-site classification datasets, the datasets used for disease prediction were also relatively small.…”

Dynamic relationships among tumor, immune response, and microbiota

“…Recently, there have been many studies and much research on the relationships between the gut microbiota and human disease [8][9][10][11] . Gut microbiota seem to be strongly correlated with various kinds of disease, such as inflammatory bowel disease (IBD) [12][13][14][15] , diabetes mellitus (DM) [16][17][18] , central nervous disorder (CND) [19][20][21][22][23][24] , allergic diseases [25][26][27][28][29] , and infectious diseases [30] . On the other hand, the relationship between tumor and microbiota is not well clarified, especially from the viewpoint of immunology.…”