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Multikingdom and functional gut microbiota markers for autism spectrum disorder

Abstract

Associations between the gut microbiome and autism spectrum disorder (ASD) have been investigated although most studies have focused on the bacterial component of the microbiome. Whether gut archaea, fungi and viruses, or function of the gut microbiome, is altered in ASD is unclear. Here we performed metagenomic sequencing on faecal samples from 1,627 children (aged 1–13 years, 24.4% female) with or without ASD, with extensive phenotype data. Integrated analyses revealed that 14 archaea, 51 bacteria, 7 fungi, 18 viruses, 27 microbial genes and 12 metabolic pathways were altered in children with ASD. Machine learning using single-kingdom panels showed area under the curve (AUC) of 0.68 to 0.87 in differentiating children with ASD from those that are neurotypical. A panel of 31 multikingdom and functional markers showed a superior diagnostic accuracy with an AUC of 0.91, with comparable performance for males and females. Accuracy of the model was predominantly driven by the biosynthesis pathways of ubiquinol-7 or thiamine diphosphate, which were less abundant in children with ASD. Collectively, our findings highlight the potential application of multikingdom and functional gut microbiota markers as non-invasive diagnostic tools in ASD.

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Fig. 1: Associations between ASD and faecal microbiome composition.
Fig. 2: Associations between ASD and faecal microbiome functions.
Fig. 3: Random forest models for the diagnosis of ASD.
Fig. 4: Validation of the random forest models.

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Data availability

The metagenomic sequencing data generated in this study have been deposited in the NCBI Sequence Read Archive database under accession code PRJNA943687. The publicly available raw sequencing data were downloaded through the retrieved accession numbers from cited papers, including GSE113540, PRJNA516054, PRJNA782533, CRA004105, PRJEB23052 and CRA001746. Mammalian genomes including hg38, felCat8, canFam3, mm10, rn6, susScr3, galGal4 and bosTau8 are available in UCSC Genome Browser at https://genome.ucsc.edu. Bacterial plasmids, complete plastomes, UniVec sequences and reference genomes database consisting of bacterial, archaeal and viral reference genomes are available in NCBI RefSeq database at https://www.ncbi.nlm.nih.gov/refseq. Fungal reference genomes are available in NCBI RefSeq database (https://www.ncbi.nlm.nih.gov/refseq), FungiDB (http://fungidb.org) and Ensemble (http://fungi.ensembl.org). Source data are provided with this paper.

Code availability

All software used are from publicly available sources. Codes used for the microbiome analyses or figures are available via GitHub at https://github.com/qsu123/ASD_multi-kingdom_diagnosis ref. 81.

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Acknowledgements

We thank F. Mo, C. Shea and C. Ho for facilitating the clinical participants’ enrolment. This study was supported by InnoHK (F.K.L.C, S.C.N.), the Government of Hong Kong, Special Administrative Region of the People’s Republic of China, The D. H. Chen Foundation (F.K.L.C., S.C.N.), and the New Cornerstone Science Foundation through the New Cornerstone Investigator Program (S.C.N.).

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Authors and Affiliations

Authors

Contributions

Q.S. and O.W.H.W. conceived the study, ran analyses and drafted the paper. W.L., Y.W., L.Z., W.X., M.K.T.L., C.L. and T.F.L. contributed to part of metagenomic sequencing. C.P.C., J.Y.L.C. and P.K.C. contributed to participant recruitment, sample collection and biobank management. S.C. and P.L. contributed to participant recruitment and clinical assessment. F.K.L.C. contributed to the study design and data interpretation. S.C.N. oversaw the entire study and contributed to the study design, data analysis and interpretation, and paper writing. All authors gave final approval for the version to be published.

Corresponding author

Correspondence to Siew C. Ng.

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Competing interests

F.K.L.C. is a Board Member of CUHK Medical Centre. He is a co-founder, non-executive Board Chairman, honorary Chief Medical Officer and shareholder of GenieBiome Ltd. He receives patent royalties through his affiliated institutions. He has received fees as an advisor and honoraria as a speaker for Eisai Co. Ltd., AstraZeneca, Pfizer Inc., Takeda Pharmaceutical Co., and Takeda (China) Holdings Co. Ltd. S.C.N. has served as an advisory board member for Pfizer, Ferring, Janssen, and Abbvie and received honoraria as a speaker for Ferring, Tillotts, Menarini, Janssen, Abbvie, and Takeda. She has received research grants through her affiliated institutions from Olympus, Ferring, and Abbvie, and is a scientific co-founder and shareholder of GenieBiome Ltd. She also receives patent royalties through her affiliated institutions. Q.S., L.Z., Y.W., F.K.L.C. and S.C.N. are named inventors of patent applications (no. 63/533,871, US, 2023) held by the CUHK and MagIC that cover the therapeutic and diagnostic use of microbiome. The remaining authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Overview of microbial composition in four kingdoms across cohorts.

a, Composition of archaea, bacteria and fungi was shown at phylum level and composition of virus was shown at family level. Top 5 abundant phyla or families are shown in the pie chart and others are summed into others. b, Principal components analysis of gut microbiome composition across different cohorts. c, Differential taxa across different cohorts identified by MaAsLin2.

Extended Data Fig. 2 The influence of individual phenotypic factor on the multi-kingdom microbiome (archaea, bacteria, fungi and viruses) composition assessed by multivariate PERMANOVA analysis (two-sided test) in the discovery cohort (709 ASD and 374 NT).

Phenotypic factors with a significant (p < 0.05) influence on each kingdom were marked with *. ASD, autism spectrum disorder; NT, neurotypical.

Source data

Extended Data Fig. 3 The influence of individual phenotypic factor on the microbiome function (KO genes and MetaCyc pathways) assessed by multivariate PERMANOVA analysis (two-sided test) in the discovery cohort (709 ASD and 374 NT).

Phenotypic factors with a significant (p < 0.05) influence on each kingdom were marked with *. KO, Kyoto Encyclopedia of Genes and Genomes orthology. ASD, autism spectrum disorder; NT, neurotypical.

Source data

Extended Data Fig. 4 Matched confounders between ASD (n = 301) and NT (n = 301) in the balanced sub-cohort constructed by one-by-one pairing algorithm, including age (a), BMI (b), sex (c), sequencing batch (d), GI parameters (e) and dietary factors (f).

Continuous data were compared using two-sided Mann-Whitney U test (two-sided test) and shown via the median (centre line), 25th and 75th percentiles (box limits) and the 5th and 95th percentiles (whiskers). Categorical variables were presented as proportions and were compared using two-sided Chi-squire test or Fisher’s exact test (expected count<5). Dietary patterns were assessed by principal components analysis. BMI, body mass index; GI, gastrointestinal. ASD, autism spectrum disorder; NT, neurotypical.

Source data

Extended Data Fig. 5 Validation of trained models in children under 6 years of age (14 ASD and 17 NT) in the independent hospital cohort.

AUC were calculated after adjustment for technical factors and available covariates including age, sex, body mass index, Bristol stool form scale, functional constipation and defecation disorders. ASD, autism spectrum disorder; NT, neurotypical.

Source data

Extended Data Fig. 6 Associations between ASD and 31 markers in the independent community cohort (stratified by age) assessed by MaAsLin 2 (two-sided test).

The Coef value of each association was only marked when p value less than 0.05. ASD, autism spectrum disorder; NT, neurotypical.

Source data

Extended Data Fig. 7 Validation of model using 31 markers on the public dataset.

a, Construction of an external validation cohort of ASD from six published studies. b, Area under the Curve of model using 31 markers tested in the public dataset. AUC were calculated after adjustment for available covariates from public datasets including age, sex, and batch effects (defined by different studies). P values were calculated by Wilcoxon rank-sum test (two-sided test). ASD, autism spectrum disorder; NT, neurotypical.

Source data

Extended Data Fig. 8 Decreased abundance of ubiquinol-7 and thiamine diphosphate biosynthesis genes in ASD children across different cohorts.

Associations between ubiquinol-7 and thiamine diphosphate biosynthesis genes and ASD were assessed by MaAsLin 2 (two-sided test). The Coef value of each association was only marked when p value less than 0.05. ASD, autism spectrum disorder; NT, neurotypical.

Source data

Extended Data Table 1 Demographics of subjects recruited in this study
Extended Data Table 2 Validation of model using 31 markers on the independent community cohort

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Su, Q., Wong, O.W.H., Lu, W. et al. Multikingdom and functional gut microbiota markers for autism spectrum disorder. Nat Microbiol (2024). https://doi.org/10.1038/s41564-024-01739-1

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