Heritability of Oral Microbiota and Immune Responses to Oral Bacteria

Abstract

Maintaining a symbiotic oral microbiota is essential for oral and dental health, and host genetic factors may affect the composition or function of the oral microbiota through a range of possible mechanisms, including immune pathways. The study included 836 Swedish twins divided into separate groups of adolescents (n = 418) and unrelated adults (n = 418). Oral microbiota composition and functions of non-enzymatically lysed oral bacteria samples were evaluated using 16S rRNA gene sequencing and functional bioinformatics tools in the adolescents. Adaptive immune responses were assessed by testing for serum IgG antibodies against a panel of common oral bacteria in adults. In the adolescents, host genetic factors were associated with both the detection and abundance of microbial species, but with considerable variation between species. Host genetic factors were associated with predicted microbiota functions, including several functions related to bacterial sucrose, fructose, and carbohydrate metabolism. In adults, genetic factors were associated with serum antibodies against oral bacteria. In conclusion, host genetic factors affect the composition of the oral microbiota at a species level, and host-governed adaptive immune responses, and also affect the concerted functions of the oral microbiota as a whole. This may help explain why some people are genetically predisposed to the major dental diseases of caries and periodontitis.

Keywords: 16S rDNA; antibody; heritability; immunoblotting; microbiota; saliva.

Figure 1

Pie and bar charts illustrating proportions of (a) identified phyla, (b) top identified genera, and (c) top eHOMD matched species.

Figure 2

Overall microbiota comparisons between MZ and DZ groups. (a) alpha diversity of MZ, DZ-SS, and DZ-OS twin groups based on rarefaction curves of identified ASVs per sample, and (b) number of eHOMD-identified species in the same groups. (c) Box plot of Jaccard diversity index based on (c) ASV profiles and (d) eHOMD species detection. Violin plots showing (e) pair-wise proportions of shared and non-shared species in the three zygosity groups and (f) MZ and merged DZ twin groups. Intraclass qualitative estimation agreement based on Fleiss’ kappa values for (g) MZ, DZ-SS, and DZ-OS twin groups and (h) MZ and merged DZ groups. The Mann–Whitney U tests were used for group comparisons.

Figure 3

Scatter plot of intraclass correlation coefficient in MZ versus DZ twins. (a) bacterial species intraclass correlation coefficient (ICC) plotted for MZ versus DZ-SS twins, (b) MZ versus DZ-OS twins, (c) DZ-SS versus DZ-OS twins, and (d) MZ versus all DZ twins. ICC values are presented for species with a prevalence ≥5%. Each dot represents a single species with the dot-size indicating the species prevalence. The line represents the diagonal where X = Y. The Mann–Whitney U tests were used for group comparisons. Density was predicted using the Kernel estimation.

Figure 4

Oral microbiota heritability. (a) additive genetic (A) versus unique environment (E) scores for bacterial species with sex, birth-year, and Illumina run adjusted statistically significant A score for species abundance; (b) comparison of A scores for abundance and scores by the Falconer’s formula (2 × (ICCmz-ICCdz); (c) A and E score for species detection of species. Variance explained by component C in (a) and (c) is shown in white. (d) Venn diagram showing bacterial species with a statistically significant A score for both detection and abundance (n = 27); (e) heatmap showing ICC scores for bacterial species with a statistically significant A score by MZ and DZ and caries status (caries-free versus caries-affected).

Figure 5

Effect of the host genotype on the microbiota predicted functions. (a) ICC between MZ and DZ-SS twins of the predicted functions (n = 3252, 100% prevalence). Density was predicted using the Kernel estimation. (b) 2D-scattered plots of three selected functions suggested to be prominently influenced by host additive genetic effects (A). Trend line with 95% CI is shown. (c) Area plot of ACE model estimation for the top 100 selected functions. Variance explained by component C is shown in white. Models were adjusted for birth year, sex, and sequencing batch, and p-values were adjusted using a Bonferroni correction.

Figure 6

Serum IgG antibodies to oral bacteria. (a) show a representative checker-board analysis of two twin pairs and the negative controls (TBS) (left), heatmap for detection (middle), and bars representing additive genetic variance (A) estimates (right). Dark grey bars indicated significant attribution to additive genetic variance with the dotted line representing 50%; (b) scatter plot of intraclass correlation coefficient (ICC) of IgG antibody signals in MZ versus DZ twins. Each dot represents a single tested species with the dot-size indicating the IgG signal strength. The line represents the diagonal where X = Y. Density was predictable using the Kernel estimation. Mann–Whitney U tests were used for group comparisons. Error bars in (a) represents 95% CI. * indicates statistical significance p < 0.05 after adjustment for multiple comparisons. P. gingivalis (i) refers to strain W381, and (ii) refers to strain W83.