Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2019 Sep 6;11(9):2128.
doi: 10.3390/nu11092128.

New and Preliminary Evidence on Altered Oral and Gut Microbiota in Individuals with Autism Spectrum Disorder (ASD): Implications for ASD Diagnosis and Subtyping Based on Microbial Biomarkers

Xuejun Kong  1   2 , Jun Liu  3   4 , Murat Cetinbas  5 , Ruslan Sadreyev  5 , Madelyn Koh  3 , Hui Huang  6 , Adetaye Adeseye  6 , Puhan He  6 , Junli Zhu  7 , Hugh Russell  8 , Clara Hobbie  3 , Kevi Liu  3 , Andrew B Onderdonk  9
Affiliations

New and Preliminary Evidence on Altered Oral and Gut Microbiota in Individuals with Autism Spectrum Disorder (ASD): Implications for ASD Diagnosis and Subtyping Based on Microbial Biomarkers

Xuejun Kong et al. Nutrients. .

Abstract

Autism Spectrum Disorder (ASD) is a complex neurological and developmental disorder characterized by behavioral and social impairments as well as multiple co-occurring conditions, such as gastrointestinal abnormalities, dental/periodontal diseases, and allergies. The etiology of ASD likely involves interaction between genetic and environmental factors. Recent studies suggest that oral and gut microbiome play important roles in the pathogenesis of inflammation, immune dysfunction, and disruption of the gut-brain axis, which may contribute to ASD pathophysiology. The majority of previous studies used unrelated neurotypical individuals as controls, and they focused on the gut microbiome, with little attention paid to the oral flora. In this pilot study, we used a first degree-relative matched design combined with high fidelity 16S rRNA (ribosomal RNA) gene amplicon sequencing in order to characterize the oral and gut microbiotas of patients with ASD compared to neurotypical individuals, and explored the utility of microbiome markers for ASD diagnosis and subtyping of clinical comorbid conditions. Additionally, we aimed to develop microbiome biomarkers to monitor responses to a subsequent clinical trial using probiotics supplementation. We identified distinct features of gut and salivary microbiota that differed between ASD patients and neurotypical controls. We next explored the utility of some differentially enriched markers for ASD diagnosis and examined the association between the oral and gut microbiomes using network analysis. Due to the tremendous clinical heterogeneity of the ASD population, we explored the relationship between microbiome and clinical indices as an attempt to extract microbiome signatures assocociated with clinical subtypes, including allergies, abdominal pain, and abnormal dietary habits. The diagnosis of ASD currently relies on psychological testing with potentially high subjectivity. Given the emerging role that the oral and gut microbiome plays in systemic diseases, our study will provide preliminary evidence for developing microbial markers that can be used to diagnose or guide treatment of ASD and comorbid conditions. These preliminary results also serve as a starting point to test whether altering the oral and gut microbiome could improve co-morbid conditions in patients with ASD and further modify the core symptoms of ASD.

Keywords: abdominal pain; allergy; autism spectrum disorders; biomarker discovery; co-occurring conditions; dysbiosis; gut microbiota; oral microbiota.

PubMed Disclaimer

Conflict of interest statement

Author Hugh Russell was employed by company Precidiag INC. Xuejun Kong served as a short term consultant for Precidiag more than two years ago. All other authors declare no competing interests. The funding agencies and Precidiag INC have no role in the study design, implementation, the interpretation of the results.

Figures

Figure 1
Figure 1
Bar plots of bacterial phylum-level relative abundances of the salivary (A) and gut (B) microbiomes. Each bar represents one subject. (C) Salivary microbiome class-level heatmap expression profile. (D) Gut microbiome class-level heatmap expression profile.
Figure 2
Figure 2
PCA of bacterial beta diversity of saliva (A) and gut (B) microbiomes based on the Bray–Curtis dissimilarity for ASD and neurotypical subjects. ASD and neurotypical subjects are colored in blue and red, respectively. (C) The major contributing phyla of gut and oral microbiome, in ASD and control subjects. The values used to compose the figures represent group mean relative abundances. (D,E) Box plots depicting relative abundances of the most differentially abundant salivary or gut bacterial phyla between patients with ASD and control subjects. Single asterisk indicates p < 0.1 with adjusted FDR > 0.2; double asterisk indicates p < 0.05 with adjusted FDR > 0.2, triple asterisk indicates p < 0.05 and adjusted FDR < 0.2, Kruskal–Wallis test.
Figure 3
Figure 3
(A,B) Box plot representations of the relative abundances of differentially abundant salivary or gut bacterial genera in patients with Autism Spectrum Disorder (ASD) and control subjects. (C) Box plots representation of gut phylum-level dysbiosis marker Firmicutes/Bacteroidetes ratio, in patients with ASD and control subjects. ASD and neurotypical subjects are colored in blue and red, respectively. Single asterisk indicates p < 0.1 with adjusted FDR > 0.2; double asterisk indicates p < 0.05 with adjusted FDR > 0.2, triple asterisk indicates p < 0.05 and adjusted FDR < 0.2, Kruskal–Wallis test. (D) receiver operator characteristics (ROC) curve of the 3 differentially abundant gut or oral genera and dysbiosis markers that have the highest area under the curve (AUC), and p < 0.05 based on two-sided Z-test for ROC.
Figure 4
Figure 4
(A,B) Overlap of differentially abundant gut or salivary genera based on Kruskal–Wallis test and paired Wilcoxon test. Results are for taxa with unadjusted p < 0.05. (C,D) Paired-test representation of the relative abundances of top most differentially abundant salivary bacterial genera between ASD patient–family member control pairs. (E,F) Paired-test representation of the relative abundances of top most differentially abundant gut bacterial genera between ASD patient–family member control pairs. Single asterisk indicates p < 0.1 with adjusted FDR > 0.2; double asterisk indicates p < 0.05 with adjusted FDR > 0.2, triple asterisk indicates p < 0.05 and adjusted FDR < 0.2, Wilcoxon’s paired test.
Figure 5
Figure 5
(A) Phylum-level heat map expression profiles of gut and oral microbiomes in ASD patients. (B) PCA of bacterial beta diversity based on Bray–Curtis dissimilarity for saliva and gut (all subjects are represented). Saliva and gut microbiome are colored in yellow and green, respectively. (C–E) Gut and oral microbiome phylum level co-occurrence network using the Sparse Correlations for Compositional data (SparCC) method with a correlation cut-off >0.3 ((C) all subjects, (D) control only, (E) ASD only). Each node represents a saliva (Sl) or stool (St) phylum, and saliva and stool microbiomes are colored in yellow and green, respectively. The dotted red circle highlights a co-occurrence cluster with the greatest inter-nodal correlations.
Figure 6
Figure 6
Box plot representation of the relative abundances of oral (A–A”) and gut (B–B”) bacterial phyla correlating with the allergy status of the subjects enrolled in this study. (A) Oral SR1 relative abundance in all subjects with no allergy and those with allergy; (A’) Oral SR1 relative abundance in ASD subjects with no allergy and patients with allergy; (A”) Oral SR1 relative abundance in neurotypical subjects with no allergy and neurotypical subjects with allergy. (B) Gut Proteobacteria relative abundance in all subjects with no allergy and those with allergy; (B’) Gut Proteobacteria relative abundance in ASD subjects with no allergy and patients with allergy; (B”) Gut Proteobacteria relative abundance in neurotypical subjects with no allergy and neurotypical subjects with allergy. Box plot representation of the gut alpha diversity (Shannon index) that correlated with the allergy status of the subjects enrolled in this study. (C) Gut alpha diversity in all subjects with no constipation and those with constipation; (C’) Gut alpha diversity in ASD subjects with no constipation and patients with constipation; (C”) Gut alpha diversity in neurotypical subjects with no constipation and neurotypical subjects with constipation. Single asterisk indicates p < 0.1 with adjusted FDR > 0.2; double asterisk indicates p < 0.05 with adjusted FDR > 0.2, triple asterisk indicates p < 0.05 and adjusted FDR < 0.2, Kruskal–Wallis test.
Figure 7
Figure 7
Bar plot representation of the relative abundances of gut (A–A’) and oral (B–B’) bacterial genera correlating with the abdominal status of the subjects enrolled in this study. (A) The most differentially abundant gut genera in all subjects with no abdominal pain and those with abdominal pain; (A’) The most differentially abundant gut genera in ASD patients with no abdominal pain and patients with abdominal pain. (B) The most differentially abundant oral genera in all subjects with no abdominal pain and those with abdominal pain; (B’) The most differentially abundant oral genera in ASD patients with no abdominal pain and patients with abdominal pain. Single asterisk indicates p < 0.1; double asterisk indicates p < 0.05, Kruskal–Wallis test.
Figure 8
Figure 8
Box plot representation of abnormal dietary habit severity scores in ASD and control subjects. (A) Unwilling to try new foods. (B) Diet lacks variety. (C) Refuse to eat certain foods. Single asterisk indicates p < 0.1; double asterisk indicates p < 0.05, Mann–Whitney U test. (D) Spearman’s correlation matrix between habit scores, allergy/autoimmunity scores, gastrointestinal severity indices (GSI) total score, and selected ASD gut microbiome markers in patients with ASD (results with FDR < 0.05 were shown).
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Baio J., Wiggins L., Christensen D.L., Maenner M.J., Daniels J., Warren Z., Kurzius-Spencer M., Zahorodny W., Robinson Rosenberg C., White T., et al. Prevalence of Autism Spectrum Disorder among Children Aged 8 Years-Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2014. MMWR Surveill. Summ. 2018;67:1–23. doi: 10.15585/mmwr.ss6706a1. - DOI - PMC - PubMed
    1. Karimi P., Kamali E., Mousavi S., Karahmadi M. Environmental factors influencing the risk of autism. J. Res. Med. Sci. 2017;22:27. - PMC - PubMed
    1. Wu M., Wu Y., Yu L., Liu J., Zhang M., Kong X., Wu B. A Survey of Epidemiological Studies and Risk Factors of ASD, with a Focus on China. N. Am. J. Med. Sci. 2017;10:1–9.
    1. Siniscalco D., Schultz S., Brigida A., Antonucci N. Inflammation and Neuro-Immune Dysregulations in Autism Spectrum Disorders. Pharmaceuticals. 2018;11:56. doi: 10.3390/ph11020056. - DOI - PMC - PubMed
    1. Alam R., Abdolmaleky H.M., Zhou J.-R. Microbiome, inflammation, epigenetic alterations, and mental diseases. Am. J. Med. Genet. B Neuropsychiatr. Genet. 2017;174:651–660. doi: 10.1002/ajmg.b.32567. - DOI - PMC - PubMed