Multi-omic analysis along the gut-brain axis points to a functional architecture of autism
- Creators
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Morton, James T.
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Jin, Dong-Min
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Mills, Robert H.
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Shao, Yan
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Rahman, Gibraan
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McDonald, Daniel
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Berding, Kirsten
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Needham, Brittany D.
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Zurita, María Fernanda
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David, Maude
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Averina, Olga V.
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Kovtun, Alexey S.
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Noto, Antonio
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Mussap, Michele
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Wang, Mingbang
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Frank, Daniel N.
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Li, Ellen
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Zhou, Wenhao
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Fanos, Vassilios
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Danilenko, Valery N.
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Wall, Dennis P.
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Cárdenas, Paúl
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Baldeón, Manuel E.
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Xavier, Ramnik J.
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Mazmanian, Sarkis K.
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Knight, Rob
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Gilbert, Jack A.
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Donovan, Sharon M.
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Lawley, Trevor D.
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Carpenter, Bob
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Bonneau, Richard
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Taroncher-Oldenburg, Gaspar
Abstract
Autism is a highly heritable neurodevelopmental disorder characterized by heterogeneous cognitive, behavioral and communication impairments. Disruption of the gut-brain axis (GBA) has been implicated in autism, with dozens of cross-sectional microbiome and other omic studies revealing autism-specific profiles along the GBA albeit with little agreement in composition or magnitude. To explore the functional architecture of autism, we developed an age and sex-matched Bayesian differential ranking algorithm that identified autism-specific profiles across 10 cross-sectional microbiome datasets and 15 other omic datasets, including dietary patterns, metabolomics, cytokine profiles, and human brain expression profiles. The analysis uncovered a highly significant, functional architecture along the GBA that encapsulated the overall heterogeneity of autism phenotypes. This architecture was determined by autism-specific amino acid, carbohydrate and lipid metabolism profiles predominantly encoded by microbial species in the genera Prevotella, Enterococcus, Bifidobacterium, and Desulfovibrio, and was mirrored in brain-associated gene expression profiles and restrictive dietary patterns in individuals with autism. Pro-inflammatory cytokine profiling and virome association analysis further supported the existence of an autism-specific architecture associated with particular microbial genera. Re-analysis of a longitudinal intervention study in autism recapitulated the cross-sectional profiles, and showed a strong association between temporal changes in microbiome composition and autism symptoms. Further elucidation of the functional architecture of autism, including of the role the microbiome plays in it, will require deep, multi-omic longitudinal intervention studies on well-defined stratified cohorts to support causal and mechanistic inference.
Additional Information
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license. Version 1: February 26, 2022; Version 2: March 9, 2022. We would like to thank Allan Packer, Paul Wang, Natalia Volfovsky, Kelsey Martin and John Spiro for their critical review of the manuscript. We'd also like to add Kevin Liu, Hannah Sherman and Xue-Jun Kong for insightful discussions. Y.S. and T.D.L. are supported by the Wellcome Trust (WT098051). Contributions: J.T.M. and G.T.-O. conceived and designed the study, developed the software, analyzed the data, interpreted the results and wrote the manuscript; R.B. contributed to study design, data analysis, result interpretation and manuscript editing; R.H.M. contributed to study design, data analysis and manuscript editing; R.J.X. and S.K.M. contributed to study design and manuscript editing; G.R. and B.C. contributed to software development and manuscript editing; D.-M.J. and Y.S. contributed to data analysis and manuscript editing; K.B., B.D.N., M.F.Z., M.D., O.V.A., A.S.K., A.N., M.M., M.W., D.N.F., E.L., W.Z., V.F., V.N.D., D.P.W., M.E.B., R.K., J.G., S.M.D. and T.D.L. provided access to data and contributed to manuscript editing. Conflict of Interest: R.H.M. is Scientific Director at Precidiag Inc.; T.D.L. is co-founder and Chief Scientific Officer of Microbiotica; S.K.M. is a co-founder and has equity in Axial Therapeutics; R.B is currently Executive Director of Prescient Design, a Genentech Accelerator; G.T.-O. is a Consultant-in-Residence at the Simons Foundation. Software Availability: Software implementation of our Bayesian age-sex matched differential ranking algorithm can be found at https://github.com/flatironinstitute/q2-matchmaker. We want to acknowledge Matplotlib [113], Seaborn [114], Scipy [115], Numpy [116], Xarray [117], Arviz [118], Scikit-learn [119], biom-format [120] and Scikit-bio [121] for providing the software foundation that this work was built upon.Attached Files
Submitted - 2022.02.25.482050v2.full.pdf
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Additional details
- Eprint ID
- 113621
- Resolver ID
- CaltechAUTHORS:20220228-619146000
- Wellcome Trust
- WT098051
- Created
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2022-02-28Created from EPrint's datestamp field
- Updated
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2022-03-15Created from EPrint's last_modified field
- Caltech groups
- Division of Biology and Biological Engineering