Hollister EB¹, Oezguen N², Chumpitazi BP³, Luna RA², Weidler EM⁴, Rubio-Gonzales M², Dahdouli M², Cope JL², Mistretta TA², Raza S², Metcalf GA⁵, Muzny DM⁵, Gibbs RA⁵, Petrosino JF⁶, Heitkemper M⁷, Savidge TC², Shulman RJ⁴, Versalovic J⁸. J Mol Diagn. 2019 May;21(3):449-461. doi: 10.1016/j.jmoldx.2019.01.006. Epub 2019 Apr 17.

Author information

1 Department of Pathology and Immunology, Baylor College of Medicine, Houston, Texas; Texas Children's Microbiome Center, Texas Children's Hospital, Houston, Texas; Diversigen, Inc., Houston, Texas; Department of Pathology, Texas Children's Hospital, Houston, Texas.

2 Department of Pathology and Immunology, Baylor College of Medicine, Houston, Texas; Texas Children's Microbiome Center, Texas Children's Hospital, Houston, Texas; Department of Pathology, Texas Children's Hospital, Houston, Texas.

3 Department of Pediatrics, Baylor College of Medicine, Houston, Texas; Section of Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, Texas Children's Hospital, Houston, Texas.

4 Department of Pediatrics, Baylor College of Medicine, Houston, Texas; Section of Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, Texas Children's Hospital, Houston, Texas; Children's Nutrition Research Center, Houston, Texas.

5 Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas.

6 Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas; Alkek Center for Metagenomics and Microbiome Research, Baylor College of Medicine, Houston, Texas.

7 Department of Biobehavioral Nursing and Health Informatics, University of Washington, Seattle, Washington.

8 Department of Pathology and Immunology, Baylor College of Medicine, Houston, Texas; Texas Children's Microbiome Center, Texas Children's Hospital, Houston, Texas; Department of Pathology, Texas Children's Hospital, Houston, Texas; Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, Texas. Electronic address: jamesv@bcm.edu.

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.