Human Gut Microbiota from Autism Spectrum Disorder Promote Behavioral Symptoms in Mice

doi:10.1016/j.cell.2019.05.004

. 2019 May 30;177(6):1600-1618.e17.

doi: 10.1016/j.cell.2019.05.004.

Human Gut Microbiota from Autism Spectrum Disorder Promote Behavioral Symptoms in Mice

Gil Sharon¹, Nikki Jamie Cruz², Dae-Wook Kang³, Michael J Gandal⁴, Bo Wang², Young-Mo Kim⁵, Erika M Zink⁵, Cameron P Casey⁵, Bryn C Taylor⁶, Christianne J Lane⁷, Lisa M Bramer⁸, Nancy G Isern⁵, David W Hoyt⁵, Cecilia Noecker⁹, Michael J Sweredoski², Annie Moradian², Elhanan Borenstein¹⁰, Janet K Jansson⁵, Rob Knight¹¹, Thomas O Metz⁵, Carlos Lois², Daniel H Geschwind¹², Rosa Krajmalnik-Brown¹³, Sarkis K Mazmanian¹⁴

Affiliations

¹ Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA. Electronic address: gsharon@caltech.edu.
² Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
³ Biodesign Swette Center for Environmental Biotechnology, Arizona State University, Tempe, AZ 85287, USA; Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, AZ 85287, USA.
⁴ Center for Autism Research and Treatment, Program in Neurobehavioral Genetics, Semel Institute, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Neurology, Semel Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
⁵ Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA.
⁶ Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA.
⁷ Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA.
⁸ National Security Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA.
⁹ Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.
¹⁰ Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA; Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA; Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 6997801, Israel; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel; Santa Fe Institute, Santa Fe, NM 87501, USA.
¹¹ Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA; Department of Biongineering, University of California, San Diego, La Jolla, CA 92093, USA.
¹² Center for Autism Research and Treatment, Program in Neurobehavioral Genetics, Semel Institute, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Neurology, Semel Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA.
¹³ Biodesign Swette Center for Environmental Biotechnology, Arizona State University, Tempe, AZ 85287, USA; Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, AZ 85287, USA; School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287, USA.
¹⁴ Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA. Electronic address: sarkis@caltech.edu.

PMID: 31150625
PMCID: PMC6993574
DOI: 10.1016/j.cell.2019.05.004

Human Gut Microbiota from Autism Spectrum Disorder Promote Behavioral Symptoms in Mice

Gil Sharon et al. Cell. 2019.

. 2019 May 30;177(6):1600-1618.e17.

doi: 10.1016/j.cell.2019.05.004.

Authors

Affiliations

¹ Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA. Electronic address: gsharon@caltech.edu.
² Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
³ Biodesign Swette Center for Environmental Biotechnology, Arizona State University, Tempe, AZ 85287, USA; Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, AZ 85287, USA.
⁴ Center for Autism Research and Treatment, Program in Neurobehavioral Genetics, Semel Institute, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Neurology, Semel Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
⁵ Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA.
⁶ Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA.
⁷ Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA.
⁸ National Security Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA.
⁹ Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.
¹⁰ Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA; Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA; Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 6997801, Israel; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel; Santa Fe Institute, Santa Fe, NM 87501, USA.
¹¹ Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA; Department of Biongineering, University of California, San Diego, La Jolla, CA 92093, USA.
¹² Center for Autism Research and Treatment, Program in Neurobehavioral Genetics, Semel Institute, University of California Los Angeles, Los Angeles, CA 90095, USA; Department of Neurology, Semel Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA.
¹³ Biodesign Swette Center for Environmental Biotechnology, Arizona State University, Tempe, AZ 85287, USA; Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, AZ 85287, USA; School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287, USA.
¹⁴ Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA. Electronic address: sarkis@caltech.edu.

PMID: 31150625
PMCID: PMC6993574
DOI: 10.1016/j.cell.2019.05.004

Abstract

Autism spectrum disorder (ASD) manifests as alterations in complex human behaviors including social communication and stereotypies. In addition to genetic risks, the gut microbiome differs between typically developing (TD) and ASD individuals, though it remains unclear whether the microbiome contributes to symptoms. We transplanted gut microbiota from human donors with ASD or TD controls into germ-free mice and reveal that colonization with ASD microbiota is sufficient to induce hallmark autistic behaviors. The brains of mice colonized with ASD microbiota display alternative splicing of ASD-relevant genes. Microbiome and metabolome profiles of mice harboring human microbiota predict that specific bacterial taxa and their metabolites modulate ASD behaviors. Indeed, treatment of an ASD mouse model with candidate microbial metabolites improves behavioral abnormalities and modulates neuronal excitability in the brain. We propose that the gut microbiota regulates behaviors in mice via production of neuroactive metabolites, suggesting that gut-brain connections contribute to the pathophysiology of ASD.

Keywords: autism; autism spectrum disorder; bacterial metabolites; gut microbiome; gut-brain axis; metabolome; microbiota; mouse model.

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Conflict of interest statement

CONFLICT OF INTERESTS:

D.W.K. and R.K-B. have pending/approved patent applications related to the use of FMT and/or probiotics for various conditions including ASD. G.S. and S.K.M. have filed a pending patent application for the use of specific microbes and metabolites for various neurodevelopmental conditions. S.K.M is a co-founder of Axial Biotherapeutics and member of its scientific advisory board.

Figures

**Figure 1.. Colonization of mice with ASD microbiomes reproduces human behaviors.**
(A) Experimental design: germ-free mice were colonized with fecal samples from TD or ASD donors at weaning and bred at 7–8 weeks of age. Offspring were behaviorally tested starting at 6 weeks of age, and various tissues and samples were collected at P45. (B) Donor metadata. Metadata for sixteen donors used for mouse colonization, of which eight were followed up on with downstream analysis (in bold). See Table S1. (C) α-diversity in human TD (circles) and ASD (mild ASD-blue squares, ASD-red squares) donors as measured by observed amplicon sequence variants (ASVs) from 16S rRNA gene sequencing of human TD and ASD donors. Eight samples used downstream are in dark-grey. Differences in medians tested by Kruskal-Wallis. (D) First two axes of a principal coordinate analysis (PCoA) of unweighted UniFrac distances from 16S rRNA gene sequencing of human TD and ASD donors. N_TD=5, Nmild ASD=3, N_ASD=8. Darker symbols denote samples in bold in Figure 1B. Group differences were tested by pairwise PERMANOVA. (E-G) Behavioral phenotypes in humanized mice: (E) Number of marbles buried in Marble burying (MB) test (Cohen’s d_oTD-oASD= 0.64), (F) time socializing in direct social interaction (DSI)(Cohen’s d_oTD-oASD= −0.45), and (G) distance traveled in open field testing (OFT)(Cohen’s doTD-oASD= −0.58); in colonized offspring colored by donor. Hypothesis testing for differences of the means were done by a mixed effects analysis using donor diagnosis and mouse sex as fixed effects and donor ID as a random effect. p-values were derived from a chi-square test. N_oASD= 121, N_oTD= 85 (8–23 mice per donor, per gender). Data presented is the aggregate of all experiments. (H) Spearman’s rank correlation between mouse behavior and donor metadata (see Table S1). Benjamini-Hochberg adjusted p-values for significant (α≤0.05) correlations are noted. Color scale denotes Spearman’s ρ from purple (positive correlation) to green (negative correlation). GSI: Gastrointestinal Severity Index; StdADOS: standardized Autism Diagnostic Observation Schedule score; PDDBI: Pervasive Developmental Disorder Behavior Inventory. (I) α-diversity in humanized oTD and oASD mice as measured by observed amplicon sequence variants (ASVs) from 16S rRNA gene sequencing of human TD and ASD donors. Differences in medians tested by Kruskal-Wallis. Data are colored by donor. N=4–7 male offspring per donor. (J) First three axes of a PCoA of unweighted UniFrac distances from oTD (circles) and oASD (squares) male offspring mice (colored by donor) from 16S rRNA gene sequencing of human TD and ASD donors. Group differences were tested by pairwise PERMANOVA. N=4–7 male offspring per donor. (K) GraPhLan plot of LefSe linear discriminant analysis of microbiome profiles up to the genus level from 16S rRNA gene sequencing of human TD and ASD donors. Highlights denote significant taxonomic differences between oTD and oASD mice. N=4–7 male offspring per donor. See also Figures S1–S3, Table S1.

**Figure 2.. Lachnospiraceae, Bacteroides, and Parabacteroides are differentially abundant in the oTD and oASD microbiomes.**
(A) Volcano plot of differential bacterial abundance analysis as calculated by DESeq2 from 16S rRNA gene sequencing. Fold change as a factor Benjamini-Hochberg corrected p-values are plotted for each taxon. Significantly different taxa (α≤0.001) are colored according to their phylum. (B) Heat map of differentially abundant amplicon sequence variants (ASVs) by DESeq2 (α≤0.001) from 16S rRNA gene sequencing. Features are named by their taxonomy with a unique feature identifier. Samples are clustered by Bray-Curtis distances. (C) Microbiome features (ASVs) contributing >1% to classification between oTD and oASD samples by RandomForest. Taxon abundances from offspring mice were used to train a supervised Random Forest classifier based on donor diagnosis (oTD/oASD; accuracy ratio over baseline: 1.75). Taxa are ordered by their contribution to correct classification of microbiomes by diagnosis. (D) Relative abundance of select taxa in the microbiome of male offspring, colored by donor. Hypothesis testing for differences of the means were tested by a linear mixed effects analysis with diagnosis as a fixed effect and donor ID as a random effect. N_oTD= 15, N_oASD= 20 (4–7 mice per donor). (E) Abundance of select taxa in the offspring microbiome is correlated with behavior of male offspring. Spearman’s rank correlation between the microbiome and mouse behavior, by donor (as in Figure 1). Benjamini-Hochberg adjusted p-values (α≤0.05) for significant correlations are noted. Color scale denotes Spearman’s ρ from purple (positive correlation) to green (negative correlation). (F-G) Relative abundance of *P. merdae* and *E. tayi* in the original human cohorts. Hypothesis testing by one-tailed Mann-Whitney U test. N_TD=32, N_ASD=42.

**Figure 3.. The microbiome impacts gene expression and alternative splicing of high confidence ASD genes in the mouse brain.**
(A–B) KEGG pathways upregulated (A) and downregulated (B) in the brains of oASD mice by Gene Set Enrichment Analysis (GSEA). (C) Venn diagram of differentially spliced genes in the STA and/or PFC between oTD and oASD mice (ASD microbiome Spliced Genes; FDR≤0.05), and their overlap with known ASD genes as curated by SPARK (odds ratio: 4.12 (95% CI 2.16–7.88), p < 0.0001) and SFARI Gene dataset (4 syndromic genes, 21 genes categories 1–3, and 13 genes category 4 and above; odds ratio: 1.39 (95% CI 1.01–1.92), p = 0.0401). Differential splicing events were identified by rMATS. (D) Cell-type enrichment analysis of differentially-splicing events in brains of oASD mice. Odds ratio and 95% confidence intervals are presented. (E) Enrichment of differentially-spliced events amongst previously reported targets of specific RNA-binding proteins and activity-dependent events in the brain. (F) Examples of differential splicing events (FDR ≤ 0.05) in genes present in both SPARK and SFARI Gene. Data points colored by donor. PFC: N_oASD= 19, N_oTD= 14; STR: N_oASD= 20, N_oTD= 14 (3–6 mouse samples per donor/tissue). Benjamini-Hochberg corrected p-values were calculated by rMATS. See also Figure S4, and Table S2.

**Figure 4.. oTD and oASD microbiomes imprint the colonic and serum metabolome of mice.**
(A–C) Volcano plots of differentially abundant metabolites identified by an untargeted metabolomics of (A) colon contents by GC-MS, (B) colon contents by ¹H NMR, and (C) serum by GC-MS. Significantly different metabolites with more than 50% difference are marked in red, and those with modest effects (<50%) are marked in yellow. N_oASD= 20, N_oTD= 15 (4–7 mice per donor). p-values were calculated using the maximum likelihood test of a mixed effect linear model. (D-F) Heat maps of differentially abundant metabolites identified by an untargeted metabolomics of (D) colon contents by GC-MS, (E) colon contents by ¹H NMR, and (F) serum by GC-MS. N_oASD= 20, N_oTD= 15 (4–7 mice per donor). Metabolite abundances were median-normalized and plotted based on the Z-score from purple for highly abundant metabolites, to green, for metabolites detected in low levels. (G–I) Median-normalized concentrations of (G) 5-aminovaleric acid, (H) taurine, (I) 3-aminoisobutyric acid, and (J) the isoflavones daidzein and genistein in colon contents. Data point color denotes donor. N_oASD= 20, N_oTD= 15 (4–7 mice per donor). Bar graphs denote mean and S.E.M. p-values were calculated using the maximum likelihood test of a mixed effect linear model. oTD and oASD are offspring of recipient mice. See also Figure S5 and Table S3.

**Figure 5.. Metagenomic analysis corroborates amino acid metabolism is deficient in oASD mice.**
(A) Putative bacterial contributors to variation in amino acids and other metabolites identified by a MIMOSA analysis, separated by their mechanism of action (synthesis/production, degradation/utilization, or both), and based on GC-MS (top) and NMR (bottom) metabolomic analysis and metagenomic analysis of colon contents in oASD mice. (B) Differentially abundant KEGG orthologs involved in amino acid metabolism from HUMAnN2 by limma-voom analysis. Fold change and p-values are presented for significantly different pathways. (C–E) Taurine production in oASD mice is deficient. (C) possible sources of taurine and KEGG orthologs involved. (D) abundance of K01442 and (E) K01580 copies in oTD and oASD mice quantified by HUMAnN2. Differences in Means were analyzed by a Kruskal-Wallis test. (F–I) Pathways providing substrates for Stickland fermentation to produce 5AV are deficient in 0ASD mice. (F) pathways upstream to 5AV production and KEGG orthologs involved. (G) abundance of *hypD* (by ShortBRED) and the abundance of (H) K13821 and (I) K00286 copies in oTD and oASD mice quantified by HUMAnN2. Differences in Means were analyzed by a Kruskal-Wallis test. See also Figure S5.

**Figure 6.. Microbial metabolites impact behaviors and brain physiology in the BTBR mouse model.**
(A) Spearman’s rank correlation between discrete metabolites and male mouse behavior (see Figure 1). Benjamini-Hochberg adjusted p-values for significant (α≤0.05) correlations are noted. Color scale denotes Spearman’s ρ from purple (positive correlation) to green (negative correlation). (B) Targeted metabolomics for 5AV and taurine in E18.5 dams orally administered metabolites at 10mM in drinking water from E0.5 and on. Normalized concentrations of 5AV and taurine in dam’s colon contents, serum, amniotic fluid, and fetal brains were measured. Group differences were tested by Kruskal-Wallis test, Dunn post-hoc, and Holm correction for multiple comparisons. N=3–4 dams per group. (C–E) 5AV and taurine ameliorate ASD-related behavioral deficits in the BTBR mouse model for ASD. Groups of mice were orally administered with either 10 mM taurine or 5AV in drinking water (*ad libitum*) before mating, and throughout their lifetime. Offspring were tested by (C) marble burying, (D) direct social interaction, and (E) open field tests, and compared to untreated vehicle controls. Results are aggregated from three independent experiments. N_Control= 42, N_5AV= 52, N_Taurine= 33. Hypothesis on differences in means were tested by one-way ANOVA on trimmed means (10%) and subsequent post-hoc tests. (F) Amplitude and frequency of mEPSCs in pyramidal neurons in the L5 of the mPFC in acute slices from 8–12 week old BTBR mice treated with 5AV, Taurine, or control from pregnancy to adulthood. tested by 1-way ANOVA on trimmed means (10%) and subsequent post-hoc tests. N_Control = 21 cells in 5 mice, N_5AV = 18 cells in 3 mice, N_Taurine = 20 cells in 4 mice. (G) Amplitude and frequency of mIPSCs in pyramidal neurons in the L5 of the mPFC in acute slices from 8–12 week old BTBR mice treated with 5AV, Taurine, or control from pregnancy to adulthood. tested by one-way ANOVA on trimmed means (10%) and subsequent post-hoc tests. N_Control = 21 cells in 4 mice, N_5AV = 17 cells in 3 mice, N_Taurine = 20 cells in 4 mice. (H) Excitability of pyramidal neurons in the L5 of the mPFC in acute slices from BTBR mice treated with 5AV, Taurine, or control, in response to step-wise injection of current, as measured by the number of action potential spikes. Two-way ANOVA and Dunnett’s post-hoc. * P < 0.05, ** P<0.01. See also Figure S6.

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References

1. Abrahams BS, Arking DE, Campbell DB, Mefford HC, Morrow EM, Weiss LA, Menashe I, Wadkins T, Banerjee-Basu S, and Packer A (2013). SFARI Gene 2.0: a community-driven knowledgebase for the autism spectrum disorders (ASDs). Mol. Autism 4, 36. - PMC - PubMed
1. Amir A, McDonald D, Navas-Molina JA, Kopylova E, Morton JT, Zech Xu Z, Kightley EP, Thompson LR, Hyde ER, Gonzalez A, et al. (2017). Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns. mSystems 2. - PMC - PubMed
1. Adams JB, Audhya T, McDonough-Means S, Rubin RA, Quig D, Geis E, Gehn E, Loresto M, Mitchell J, Atwood S, et al. (2011). Nutritional and metabolic status of children with autism vs. neurotypical children, and the association with autism severity. Nutr. Metab 8, 34. - PMC - PubMed
1. Aldred S, Moore KM, Fitzgerald M, and Waring RH (2003). Plasma amino acid levels in children with autism and their families. J. Autism Dev. Disord 33, 93–97. - PubMed
1. An J-Y, Lin K, Zhu L, Werling DM, Dong S, Brand H, Wang HZ, Zhao X, Schwartz GB, Collins RL, et al. (2018). Genome-wide de novo risk score implicates promoter variation in autism spectrum disorder. Science 362. - PMC - PubMed

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[1] Abrahams BS, Arking DE, Campbell DB, Mefford HC, Morrow EM, Weiss LA, Menashe I, Wadkins T, Banerjee-Basu S, and Packer A (2013). SFARI Gene 2.0: a community-driven knowledgebase for the autism spectrum disorders (ASDs). Mol. Autism 4, 36. - PMC - PubMed

[2] Abrahams BS, Arking DE, Campbell DB, Mefford HC, Morrow EM, Weiss LA, Menashe I, Wadkins T, Banerjee-Basu S, and Packer A (2013). SFARI Gene 2.0: a community-driven knowledgebase for the autism spectrum disorders (ASDs). Mol. Autism 4, 36. - PMC - PubMed

[3] Amir A, McDonald D, Navas-Molina JA, Kopylova E, Morton JT, Zech Xu Z, Kightley EP, Thompson LR, Hyde ER, Gonzalez A, et al. (2017). Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns. mSystems 2. - PMC - PubMed

[4] Amir A, McDonald D, Navas-Molina JA, Kopylova E, Morton JT, Zech Xu Z, Kightley EP, Thompson LR, Hyde ER, Gonzalez A, et al. (2017). Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns. mSystems 2. - PMC - PubMed

[5] Adams JB, Audhya T, McDonough-Means S, Rubin RA, Quig D, Geis E, Gehn E, Loresto M, Mitchell J, Atwood S, et al. (2011). Nutritional and metabolic status of children with autism vs. neurotypical children, and the association with autism severity. Nutr. Metab 8, 34. - PMC - PubMed

[6] Adams JB, Audhya T, McDonough-Means S, Rubin RA, Quig D, Geis E, Gehn E, Loresto M, Mitchell J, Atwood S, et al. (2011). Nutritional and metabolic status of children with autism vs. neurotypical children, and the association with autism severity. Nutr. Metab 8, 34. - PMC - PubMed

[7] Aldred S, Moore KM, Fitzgerald M, and Waring RH (2003). Plasma amino acid levels in children with autism and their families. J. Autism Dev. Disord 33, 93–97. - PubMed

[8] Aldred S, Moore KM, Fitzgerald M, and Waring RH (2003). Plasma amino acid levels in children with autism and their families. J. Autism Dev. Disord 33, 93–97. - PubMed

[9] An J-Y, Lin K, Zhu L, Werling DM, Dong S, Brand H, Wang HZ, Zhao X, Schwartz GB, Collins RL, et al. (2018). Genome-wide de novo risk score implicates promoter variation in autism spectrum disorder. Science 362. - PMC - PubMed

[10] An J-Y, Lin K, Zhu L, Werling DM, Dong S, Brand H, Wang HZ, Zhao X, Schwartz GB, Collins RL, et al. (2018). Genome-wide de novo risk score implicates promoter variation in autism spectrum disorder. Science 362. - PMC - PubMed

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Human Gut Microbiota from Autism Spectrum Disorder Promote Behavioral Symptoms in Mice

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Human Gut Microbiota from Autism Spectrum Disorder Promote Behavioral Symptoms in Mice

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