Children with Autism spectrum disorder are associated with gut microbiota disorder

Sun, Hairong; You, Zhong; Jia, Libo; Wang, Fang

doi:10.21203/rs.2.16845/v2

Cited by 6 publications

(7 citation statements)

References 7 publications

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“…In autism, groups consisting primarily Ruminococcaceae and Porphyromonadaceae families were enriched in the autism cohort, and Veillonellaceae underenriched in the autism cohort. This is in agreement with a previous studies [ 47 ], which found Ruminococcaceae enriched in children with autism and gastrointestinal disturbances, and Veillonellaceae underenriched in children with autism [ 48 ]. Porphyromonadaceae has been linked to major depressive disorder, suggesting a role for it in the gut-brain axis [ 49 ].…”

Section: Resultssupporting

confidence: 94%

Improved detection of disease-associated gut microbes using 16S sequence-based biomarkers

et al. 2021

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Background Sequencing partial 16S rRNA genes is a cost effective method for quantifying the microbial composition of an environment, such as the human gut. However, downstream analysis relies on binning reads into microbial groups by either considering each unique sequence as a different microbe, querying a database to get taxonomic labels from sequences, or clustering similar sequences together. However, these approaches do not fully capture evolutionary relationships between microbes, limiting the ability to identify differentially abundant groups of microbes between a diseased and control cohort. We present sequence-based biomarkers (SBBs), an aggregation method that groups and aggregates microbes using single variants and combinations of variants within their 16S sequences. We compare SBBs against other existing aggregation methods (OTU clustering and Microphenoor DiTaxa features) in several benchmarking tasks: biomarker discovery via permutation test, biomarker discovery via linear discriminant analysis, and phenotype prediction power. We demonstrate the SBBs perform on-par or better than the state-of-the-art methods in biomarker discovery and phenotype prediction. Results On two independent datasets, SBBs identify differentially abundant groups of microbes with similar or higher statistical significance than existing methods in both a permutation-test-based analysis and using linear discriminant analysis effect size. . By grouping microbes by SBB, we can identify several differentially abundant microbial groups (FDR <.1) between children with autism and neurotypical controls in a set of 115 discordant siblings. Porphyromonadaceae, Ruminococcaceae, and an unnamed species of Blastocystis were significantly enriched in autism, while Veillonellaceae was significantly depleted. Likewise, aggregating microbes by SBB on a dataset of obese and lean twins, we find several significantly differentially abundant microbial groups (FDR<.1). We observed Megasphaera andSutterellaceae highly enriched in obesity, and Phocaeicola significantly depleted. SBBs also perform on bar with or better than existing aggregation methods as features in a phenotype prediction model, predicting the autism phenotype with an ROC-AUC score of .64 and the obesity phenotype with an ROC-AUC score of .84. Conclusions SBBs provide a powerful method for aggregating microbes to perform differential abundance analysis as well as phenotype prediction. Our source code can be freely downloaded from http://github.com/briannachrisman/16s_biomarkers.

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Section: Resultssupporting

confidence: 94%

Improved detection of disease-associated gut microbes using 16S sequence-based biomarkers

et al. 2021

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“…It is necessary to collect information about children's residential environment in follow‐up studies to explore the relationship between residential environment and gut microbiota. Cao et al and Sun et al reported reduced levels of Lachnospiraceae and Prevotellaceae (Cao et al, 2021; Sun et al, 2019). There is growing evidence demonstrating that inflammatory activity, caused by disordered gut microbiota, might underlie the microbiota‐gut‐brain axis (Liu, Li, Wu, et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Alteration of the fecal microbiota in Chinese children with autism spectrum disorder

Xie

et al. 2022

Autism Research

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Autism spectrum disorder (ASD) is associated with altered gut microbiota. However, there has been little consensus on the altered bacterial species and studies have had small sample sizes. We aimed to identify the taxonomic composition and evaluate the changes in the fecal microbiota in Chinese children with ASD by using a relatively large sample size. We conducted a case–control study of 101 children with ASD and 103 healthy controls in China. Demographic information and fecal samples were collected, and the V3‐V4 hypervariable regions of the bacterial 16S ribosomal RNA (rRNA) gene were sequenced. The alpha and beta diversities between the two groups were significantly different. After correcting for multiple comparisons, at the phylum level the relative abundances of Actinobacteria and Proteobacteria in the case group were significantly higher than those in the control group. The relative abundance of the Escherichia‐Shigella genus in the case group was significantly higher than that of the control group, and the relative abundance of Blautia and unclassified_f__Lachnospiraceae in the control group were higher than that of the case group. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States analysis showed that children with ASD may have disturbed functional pathways, such as amino acid metabolism, cofactor and vitamin metabolism, and the AMP‐activated protein kinase signaling pathway. This study revealed the characteristics of the intestinal flora of Chinese children with ASD and provided further evidence of gut microbial dysbiosis in ASD. Lay Summary This study characterized the gut microbiota composition of 101 children with ASD and 103 healthy controls in China. The altered gut microbiota may contribute significantly to the risk of ASD, including significant increases in the relative abundances of Actinobacteria, Proteobacteria and Escherichia‐Shigella and significant decrease of Blautia and unclassified_f__Lachnospiraceae. This study provided further evidence of gut microbial dysbiosis in ASD.

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“…Recently, the GM has been suggested to be involved in the pathogenesis of ASD due to the frequent occurrence of gastrointestinal (GI) dysfunctions in ASD patients [14][15][16][17][18][19][20]. A large number of papers have revealed that the GM is significantly altered in patients with ASD or animal models of ASD when compared with that in their controls [21][22][23][24][25][26][27][28][29][30][31][32][33][34]. Colonization with GM from ASD individuals is sufficient to induce hallmark autistic behaviors in mice [35].…”

Section: Introductionmentioning

confidence: 99%

Sex-specific differences in the gut microbiota and fecal metabolites in an adolescent valproic acid-induced rat autism model

Gu¹,

Han²,

Liang³

et al. 2021

Front. Biosci. (Landmark Ed)

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Introduction 3. Materials and methods 3.1 Animals 3.2 Construction of the VPA-induced rat model of autism 3.3 Experimental groups 3.4 Collection of feces 3.5 16S rRNA gene sequencing 3.6 UHPLC-MS based untargeted metabolomics 3.7 Statistical analysis 4. Results 4.1 Taxonomical characterization of the gut microbiota in male and female VPA rats 4.2 Characteristics of fecal metabolites in male and female VPA rats 4.3 Differential metabolites and KEGG pathways in male and female VPA rats 4.4 Correlation between characteristic genera and characteristic fecal metabolites in male and female VPA rats 5. Discussion 6. Conclusions 7. Author contributions 8. Ethics approval and consent to participate 9. Acknowledgment 10. Funding 11. Conflict of interest 12. References FDR, false discovery rate; GI, gastrointestinal; GM, gut microbiota; KEGG, Kyoto Encyclopedia of Genes and Genomes; UHPLC-MS, ultra-high-performance liquid chromatography-mass spectrometry; LEfSe, linear discriminant analysis effect size; NS, normal saline; OPLS-DA, orthogonal partial least squares discrimination analy-sis; OTU, operational taxonomic unit; PCA, principal component analysis; PCoA, principal coordinate analysis; PCR, polymerase chain reaction; PND, postnatal day; QC, quality control; ROC, receiver operator characteristic; rRNA, ribosomal ribonucleic acid; VIP, variable important in pro-jection; VPA, valproic acid.

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Children with Autism spectrum disorder are associated with gut microbiota disorder

Cited by 6 publications

References 7 publications

Improved detection of disease-associated gut microbes using 16S sequence-based biomarkers

Improved detection of disease-associated gut microbes using 16S sequence-based biomarkers

Alteration of the fecal microbiota in Chinese children with autism spectrum disorder

Sex-specific differences in the gut microbiota and fecal metabolites in an adolescent valproic acid-induced rat autism model

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