Leveraging Human Microbiome Features to Diagnose and Stratify Children with Irritable Bowel Syndrome

Emily B Hollister, Numan Oezguen, Bruno P Chumpitazi, Ruth Ann Luna, Erica M Weidler, Michelle Rubio-Gonzales, Mahmoud Dahdouli, Julia L Cope, Toni-Ann Mistretta, Sabeen Raza, Ginger A Metcalf, Donna M Muzny, Richard A Gibbs, Joseph F Petrosino, Margaret Heitkemper, Tor C Savidge, Robert J Shulman, James Versalovic, Emily B Hollister, Numan Oezguen, Bruno P Chumpitazi, Ruth Ann Luna, Erica M Weidler, Michelle Rubio-Gonzales, Mahmoud Dahdouli, Julia L Cope, Toni-Ann Mistretta, Sabeen Raza, Ginger A Metcalf, Donna M Muzny, Richard A Gibbs, Joseph F Petrosino, Margaret Heitkemper, Tor C Savidge, Robert J Shulman, James Versalovic

Abstract

Accurate diagnosis and stratification of children with irritable bowel syndrome (IBS) remain challenging. Given the central role of recurrent abdominal pain in IBS, we evaluated the relationships of pediatric IBS and abdominal pain with intestinal microbes and fecal metabolites using a comprehensive clinical characterization and multiomics strategy. Using rigorous clinical phenotyping, we identified preadolescent children (aged 7 to 12 years) with Rome III IBS (n = 23) and healthy controls (n = 22) and characterized their fecal microbial communities using whole-genome shotgun metagenomics and global unbiased fecal metabolomic profiling. Correlation-based approaches and machine learning algorithms identified associations between microbes, metabolites, and abdominal pain. IBS cases differed from controls with respect to key bacterial taxa (eg, Flavonifractor plautii and Lachnospiraceae bacterium 7_1_58FAA), metagenomic functions (eg, carbohydrate metabolism and amino acid metabolism), and higher-order metabolites (eg, secondary bile acids, sterols, and steroid-like compounds). Significant associations between abdominal pain frequency and severity and intestinal microbial features were identified. A random forest classifier built on metagenomic and metabolic markers successfully distinguished IBS cases from controls (area under the curve, 0.93). Leveraging multiple lines of evidence, intestinal microbes, genes/pathways, and metabolites were associated with IBS, and these features were capable of distinguishing children with IBS from healthy children. These multi-omics features, and their links to childhood IBS coupled with nutritional interventions, may lead to new microbiome-guided diagnostic and therapeutic strategies.

Copyright © 2019 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.

Figures

Figure 1
Figure 1
Study flowchart outlining subject recruitment, participation, and classification. GI, gastrointestinal.
Figure 2
Figure 2
Spearman correlations between abdominal pain–associated species and metabolites in 45 pediatric subjects. Relationships between whole-genome sequencing–based species abundances and metabolites (middle), metabolites and abdominal pain (bottom), and species and abdominal pain (right) are depicted. Each metabolite depicted herein correlates with abdominal pain frequency (PF) and/or mean abdominal pain (MP). All of the included species are significantly correlated with abdominal pain and/or abdominal pain–associated metabolites. False-discovery rate–corrected statistical significance is denoted as follows: *q < 0.05, **q < 0.01, and #q < 0.10. n = 23 pediatric subjects with IBS; n = 22 HCs. DiHome, (12Z)-9,10-dihydroxyoctadec-12-enoic acid.
Figure 3
Figure 3
A multi-omics network of bacterial species (green triangles), metagenomic pathways (yellow diamonds), and metabolite abundances (blue spheres) separates pediatric IBS cases (red squares) from HCs (cyan squares). Features (ie, species, pathways, and metabolites) were included if they had F values >7 in the comparison of IBS cases versus HCs. The edge-weighted, spring-embedded layout was used to visualize network structure. n = 23 pediatric IBS cases; n = 22 HCs. TCA, tricarboxylic acid.
Figure 4
Figure 4
Multivariate classification based on a lean set of multi-omics features correctly distinguished IBS cases from HCs with a high degree of accuracy. A: Receiver operating characteristic curve of the random forest (RF) classifier and its associated accuracy and precision rates. Classifier metrics were generated using fivefold cross validation. Random classification is represented by the dotted line. B: Principal component (PC) analysis of subjects based on the set of species, pathways, and metabolites used to train the RF classifier. Background shading indicates point density of IBS cases versus HCs.
Figure 5
Figure 5
Abundances and distributions of the metabolites (A), bacterial species (B), and functional pathways (C) on which the classifiers were trained. Boxplots depict median and first and third quartile values, whereas whiskers indicate 1.5 times the interquartile range (IQR). Species, pathway, and metabolite values were assessed in IBS cases and HCs. B: Only the bacterial species are significantly differentially abundant (false-discovery rate–corrected q = 0.02). n = 23 IBS cases (A–C); n = 22 HCs (A–C). MAD, median of the absolute deviations from the median; TCA, tricarboxylic acid.
Supplemental Figure S1
Supplemental Figure S1
Receiver operating characteristic (ROC) and calibration curves indicating the classification success and quality of the class-based (ie, IBS case versus HC) probability predictions generated by each of the classifier models considered [random forest (RF), support vector machine (SVM), and naïve Bayes (NB)]. A: ROC curves and their associated accuracy and precision metrics. Random classification is represented by the dotted line. B and C: Calibration curves for each of the classifiers are depicted relative to their ability to correctly classify HCs (B) and IBS cases (C). The calibration curve for an ideal model would plot along the 45-degree reference dashed line, whereas deviations from this line indicate tendencies to overpredict or underpredict class probabilities. For both HCs (B) and IBS cases (C), the RF and SVM models tend to be the best calibrated. AUROC, area under the ROC curve; CA, classification accuracy; LASSO, least absolute shrinkage selection operator.

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