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Microbiome. 2017 May 1;5(1):49. doi: 10.1186/s40168-017-0260-z.

Differences in gut microbial composition correlate with regional brain volumes in irritable bowel syndrome.

Author information

1
Division of Digestive Diseases, David Geffen School at UCLA, Los Angeles, CA, 90095, USA. jlabus@ucla.edu.
2
Oppenheimer Center for Neurobiology of Stress and Resilience, CHS 42-210 MC737818 10833 Le Conte Avenue, Los Angeles, CA, 90095-7378, USA. jlabus@ucla.edu.
3
Texas Children's Microbiome Center, Department of Pathology, Texas Children's Hospital, 1102 Bates Ave., Houston, TX, USA.
4
Department of Pathology & Immunology, Baylor College of Medicine, One Baylor Plaza, Houston, TX, USA.
5
Division of Digestive Diseases, David Geffen School at UCLA, Los Angeles, CA, 90095, USA.
6
Center for Human Nutrition, Washington University School of Medicine, St. Louis, MO, 63110, USA.
7
Oppenheimer Center for Neurobiology of Stress and Resilience, CHS 42-210 MC737818 10833 Le Conte Avenue, Los Angeles, CA, 90095-7378, USA.
8
Division of Digestive Diseases, David Geffen School at UCLA, Los Angeles, CA, 90095, USA. emayer@ucla.edu.
9
Oppenheimer Center for Neurobiology of Stress and Resilience, CHS 42-210 MC737818 10833 Le Conte Avenue, Los Angeles, CA, 90095-7378, USA. emayer@ucla.edu.

Abstract

BACKGROUND:

Preclinical and clinical evidence supports the concept of bidirectional brain-gut microbiome interactions. We aimed to determine if subgroups of irritable bowel syndrome (IBS) subjects can be identified based on differences in gut microbial composition, and if there are correlations between gut microbial measures and structural brain signatures in IBS.

METHODS:

Behavioral measures, stool samples, and structural brain images were collected from 29 adult IBS and 23 healthy control subjects (HCs). 16S ribosomal RNA (rRNA) gene sequencing was used to profile stool microbial communities, and various multivariate analysis approaches were used to quantitate microbial composition, abundance, and diversity. The metagenomic content of samples was inferred from 16S rRNA gene sequence data using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt). T1-weighted brain images were acquired on a Siemens Allegra 3T scanner, and morphological measures were computed for 165 brain regions.

RESULTS:

Using unweighted Unifrac distances with hierarchical clustering on microbial data, samples were clustered into two IBS subgroups within the IBS population (IBS1 (n = 13) and HC-like IBS (n = 16)) and HCs (n = 23) (AUROC = 0.96, sensitivity 0.95, specificity 0.67). A Random Forest classifier provided further support for the differentiation of IBS1 and HC groups. Microbes belonging to the genera Faecalibacterium, Blautia, and Bacteroides contributed to this subclassification. Clinical features distinguishing the groups included a history of early life trauma and duration of symptoms (greater in IBS1), but not self-reported bowel habits, anxiety, depression, or medication use. Gut microbial composition correlated with structural measures of brain regions including sensory- and salience-related regions, and with a history of early life trauma.

CONCLUSIONS:

The results confirm previous reports of gut microbiome-based IBS subgroups and identify for the first time brain structural alterations associated with these subgroups. They provide preliminary evidence for the involvement of specific microbes and their predicted metabolites in these correlations.

KEYWORDS:

Bacteroidetes; Brain-gut-microbiome axis; Firmicutes; Irritable bowel syndrome; Metagenome

PMID:
28457228
PMCID:
PMC5410709
DOI:
10.1186/s40168-017-0260-z
[Indexed for MEDLINE]
Free PMC Article

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