Gastrointestinal microbiome signatures of pediatric patients with irritable bowel syndrome

Abstract

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: Using 16S 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.

Figure 1. Global phylogenetic tree comparing the intestinal microbiomes of healthy children and children with IBS

Phylogenetic tree was generated using QIIME and drawn with iTOL , including data from 22 healthy children (69 samples) and 22 children with IBS (71 samples). Map colored by phyla (exterior text), patient status (IBS - light red; Healthy - light green) and family (inset). Data were generated by 454 pyrosequencing (V1–V3 region).

Figure 2. The pediatric gut microbiomes of children with IBS are characterized by greater abundance of Gammaproteobacteria

A) Percentage of all bacterial classes represented. B) Percentage of bacterial taxa found in lower abundance (< 5% of total bacteria). Healthy children: 29 samples from 22 subjects, IBS: 42 samples from 22 patients. #: Significantly different between IBS and healthy children (P <.05). Data were generated by 454 pyrosequencing (V1–V3 region).

Figure 3. Relative abundance of bacterial genera differentiates the distal intestinal microbiomes of healthy children and children with IBS

Healthy (H) = 29 samples from 22 subjects, IBS (IBS): 42 samples from 22 patients (V1–V3 region or V3–V5). The data were generated by 454 pyrosequencing, and relative amounts were significantly different between IBS and healthy children (P <.05) except when labeled with *.

Figure 4. The pediatric gut microbiomes in children with IBS are enriched in Proteobacteria (Gammaproteobacteria)

Healthy: 27 samples from 21 subjects IBS: 28 samples from 17 patients. A) Percentage of bacterial phyla represented in healthy children and children with IBS. B) Bacterial phyla representing less than 5% of total bacteria in healthy children and children with IBS. Data were generated by PhyloChip hybridization.

Figure 5. Differential distribution of bacterial taxa in patients with recurrent abdominal pain was correlated with the relative frequency of abdominal pain

Bacterial taxa (specified in leftmost column) were defined by randomForest and confirmed by feature selection using Boruta. The list is sorted first by Mann-Whitney U score followed by the largest disparity in medians for each group. Taxa represent the lowest taxonomic depth (Genus) that are labeled by RDP Classifier. Red rectangles display the HM recurrent abdominal pain phenotype. Light blue rectangles display the L0 recurrent abdominal pain phenotype. Boxes represent the first quartile, median, and third quartile of the OTU distributions for each pain group. Empty circles represent outliers that are 1.5× greater than the respective interquartile ranges. A) OTUs with greater abundance in patients with HM versus L0 recurrent abdominal pain phenotypes. B) OTUs with reduced abundance in patients with HM versus L0 recurrent abdominal pain phenotypes.

Figure 6. Distal gut microbiomes of children segregate the IBS-C and IBS-U subtypes

IBS-C: IBS with constipation (n=41 samples), IBS-U: unsubtyped IBS (n=22 samples). Bray-Curtis analysis was used to generate a matrix of pairwise sample dissimilarities between communities. The scatterplot was generated from the matrix of distances using principal components analysis. Data were generated by 454 pyrosequencing (V1–V3 region only, 2 replicates per sample).