Association between the oral microbiome and brain resting state connectivity in smokers

Abstract

Recent studies have shown a critical role of the gastrointestinal microbiome in brain and behavior via the complex gut-microbiome-brain axis. However, the influence of the oral microbiome in neurological processes is much less studied, especially in response to the stimuli, such as smoking, within the oral microenvironment. Additionally, given the complex structural and functional networks in brain, our knowledge about the relationship between microbiome and brain function through specific brain circuits is still very limited. In this pilot study, we leveraged next generation sequencing for microbiome and functional neuroimaging technique to enable the delineation of microbiome-brain network links as well as their relationship to cigarette smoking. Thirty smokers and 30 age- and sex-matched nonsmokers were recruited for 16S sequencing of their oral microbial community. Among them, 56 subjects were scanned by resting-state functional magnetic resonance imaging to derive brain functional networks. Statistical analyses were performed to demonstrate the influence of smoking on the oral microbial composition, functional network connectivity, and the associations between microbial shifts and functional network connectivity alternations. Compared to nonsmokers, we found a significant decrease of beta diversity (P = 6 × 10^-3) in smokers and identified several classes (Betaproteobacteria, Spirochaetia, Synergistia, and Mollicutes) with significant alterations in microbial abundance. Pathway analysis on the predicted KEGG pathways shows that the microbiota with altered abundance are mainly involved in pathways related to cell processes, DNA repair, immune system, and neurotransmitters signaling. One brain functional network connectivity component was identified to have a significant difference between smokers and nonsmokers (P = 0.032), mainly including connectivity between brain default network and other task-positive networks. This brain functional component was also significantly associated with smoking related microbiota, suggesting a correlated cross-individual pattern between smoking-induced oral microbiome dysbiosis and brain functional connectivity alternation, possibly involving immunological and neurotransmitter signaling pathways. This work is the first attempt to link oral microbiome and brain functional networks, and provides support for future work in characterizing the role of oral microbiome in mediating smoking effects on brain activity.

Keywords: Functional connectivity; Microbiome; Neuroimaging; Saliva; Smoking.

Fig. 1.

The analysis overflow for resting state fMRI and oral microbiome 16S sequencing data in smokers and nonsmokers.

Fig. 2.

PCoA analysis of microbial composition between smokers and nonsmokers. The microbial composition was evaluated based on (A) Unweighted UniFrac distance and (B) Bray-Curtis distance, respectively. Dark blue circle and triangle point indicates the center of eclipse for nonsmokers and smokers, respectively. Axis_1 and Axis_2 are the top two principle coordinate vectors (i.e., eigenvectors) from each distance matrix to visualize the distances among subjects in 2-D space.

Fig. 3.

The loading and spatial mapping of identified FNC component. (A) The loading of identified FNC component in smokers compared to nonsmokers; (B) Top FNCs with z-scored weights absolute (z-score)> 2.5 in the component; (C) The top contributing connectivity among the functional networks from the selected component; (D) Brain regions of those functional networks involved the top FNCs.

Fig. 4.

Pathway enrichment analysis based on the predicted metagenomics. The 23 out of 262 KEGG functional pathways show significant changes in abundance between smokers and nonsmokers (FDR < 0.15). Those pathways were predicted from 16S rRNA microbiome sequencing using the PICRUSt algorithm. Mean proportion (colored bar) indicates the relative abundance of the pathway in each group. The difference of mean proportions between groups as well as the 95% confidence interval indicates the effect size of relative abundance change for each pathway.