TY - JOUR
T1 - Gastrointestinal microbiome signatures of pediatric patients with irritable bowel syndrome
AU - Saulnier, Delphine M.
AU - Riehle, Kevin
AU - Mistretta, Toni Ann
AU - Diaz, Maria Alejandra
AU - Mandal, Debasmita
AU - Raza, Sabeen
AU - Weidler, Erica M.
AU - Qin, Xiang
AU - Coarfa, Cristian
AU - Milosavljevic, Aleksandar
AU - Petrosino, Joseph F.
AU - Highlander, Sarah
AU - Gibbs, Richard
AU - Lynch, Susan V.
AU - Shulman, Robert J.
AU - Versalovic, James
N1 - Funding Information:
Funding Supported by the National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK) and the National Center for Complementary and Alternative Medicine (NCCAM) from the National Institute of Health (NIH) (grant UH2 DK083990-01 and UH3 DK083990-02 ). The work in J.V.'s laboratory is supported by NIDDK ( R01 DK065075 and P30 DK56338 ), NIH National Center for Complementary and Alternative Medicine ( R01 AT004326 and R21 AT003102 ), and NHGRI (HMP sampling Jumpstart 5U54 HG003273-08 , with R.G. as principal investigator). S.V.L. is supported by the Rainin Foundation.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2011/11
Y1 - 2011/11
N2 - Background & Aims: The intestinal microbiomes of healthy children and pediatric patients with irritable bowel syndrome (IBS) are not well defined. Studies in adults have indicated that the gastrointestinal microbiota could be involved in IBS. Methods: We analyzed 71 samples from 22 children with IBS (pediatric Rome III criteria) and 22 healthy children, ages 7-12 years, by 16S ribosomal RNA gene sequencing, with an average of 54,287 reads/stool sample (average 454 read length = 503 bases). Data were analyzed using phylogenetic-based clustering (Unifrac), or an operational taxonomic unit (OTU) approach using a supervised machine learning tool (randomForest). Most samples were also hybridized to a microarray that can detect 8741 bacterial taxa (16S rRNA PhyloChip). Results: Microbiomes associated with pediatric IBS were characterized by a significantly greater percentage of the class γ-proteobacteria (0.07% vs 0.89% of total bacteria, respectively; P < .05); 1 prominent component of this group was Haemophilus parainfluenzae. Differences highlighted by 454 sequencing were confirmed by high-resolution PhyloChip analysis. Using supervised learning techniques, we were able to classify different subtypes of IBS with a success rate of 98.5%, using limited sets of discriminant bacterial species. A novel Ruminococcus-like microbe was associated with IBS, indicating the potential utility of microbe discovery for gastrointestinal disorders. A greater frequency of pain correlated with an increased abundance of several bacterial taxa from the genus Alistipes. Conclusions: Using16S metagenomics by PhyloChip DNA hybridization and deep 454 pyrosequencing, we associated specific microbiome signatures with pediatric IBS. These findings indicate the important association between gastrointestinal microbes and IBS in children; these approaches might be used in diagnosis of functional bowel disorders in pediatric patients.
AB - Background & Aims: The intestinal microbiomes of healthy children and pediatric patients with irritable bowel syndrome (IBS) are not well defined. Studies in adults have indicated that the gastrointestinal microbiota could be involved in IBS. Methods: We analyzed 71 samples from 22 children with IBS (pediatric Rome III criteria) and 22 healthy children, ages 7-12 years, by 16S ribosomal RNA gene sequencing, with an average of 54,287 reads/stool sample (average 454 read length = 503 bases). Data were analyzed using phylogenetic-based clustering (Unifrac), or an operational taxonomic unit (OTU) approach using a supervised machine learning tool (randomForest). Most samples were also hybridized to a microarray that can detect 8741 bacterial taxa (16S rRNA PhyloChip). Results: Microbiomes associated with pediatric IBS were characterized by a significantly greater percentage of the class γ-proteobacteria (0.07% vs 0.89% of total bacteria, respectively; P < .05); 1 prominent component of this group was Haemophilus parainfluenzae. Differences highlighted by 454 sequencing were confirmed by high-resolution PhyloChip analysis. Using supervised learning techniques, we were able to classify different subtypes of IBS with a success rate of 98.5%, using limited sets of discriminant bacterial species. A novel Ruminococcus-like microbe was associated with IBS, indicating the potential utility of microbe discovery for gastrointestinal disorders. A greater frequency of pain correlated with an increased abundance of several bacterial taxa from the genus Alistipes. Conclusions: Using16S metagenomics by PhyloChip DNA hybridization and deep 454 pyrosequencing, we associated specific microbiome signatures with pediatric IBS. These findings indicate the important association between gastrointestinal microbes and IBS in children; these approaches might be used in diagnosis of functional bowel disorders in pediatric patients.
KW - 16S rRNA
KW - 454 Sequencing
KW - Functional Abdominal Pain
KW - Phylo-Chip
UR - https://www.scopus.com/pages/publications/80054851581
UR - https://www.scopus.com/inward/citedby.url?scp=80054851581&partnerID=8YFLogxK
U2 - 10.1053/j.gastro.2011.06.072
DO - 10.1053/j.gastro.2011.06.072
M3 - Article
C2 - 21741921
AN - SCOPUS:80054851581
SN - 0016-5085
VL - 141
SP - 1782
EP - 1791
JO - Gastroenterology
JF - Gastroenterology
IS - 5
ER -