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. 2017 Sep 1;12(9):e0183509.
doi: 10.1371/journal.pone.0183509. eCollection 2017.

Gut Microbiome in ADHD and Its Relation to Neural Reward Anticipation

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Free PMC article

Gut Microbiome in ADHD and Its Relation to Neural Reward Anticipation

Esther Aarts et al. PLoS One. .
Free PMC article

Abstract

Background: Microorganisms in the human intestine (i.e. the gut microbiome) have an increasingly recognized impact on human health, including brain functioning. Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder associated with abnormalities in dopamine neurotransmission and deficits in reward processing and its underlying neuro-circuitry including the ventral striatum. The microbiome might contribute to ADHD etiology via the gut-brain axis. In this pilot study, we investigated potential differences in the microbiome between ADHD cases and undiagnosed controls, as well as its relation to neural reward processing.

Methods: We used 16S rRNA marker gene sequencing (16S) to identify bacterial taxa and their predicted gene functions in 19 ADHD and 77 control participants. Using functional magnetic resonance imaging (fMRI), we interrogated the effect of observed microbiome differences in neural reward responses in a subset of 28 participants, independent of diagnosis.

Results: For the first time, we describe gut microbial makeup of adolescents and adults diagnosed with ADHD. We found that the relative abundance of several bacterial taxa differed between cases and controls, albeit marginally significant. A nominal increase in the Bifidobacterium genus was observed in ADHD cases. In a hypothesis-driven approach, we found that the observed increase was linked to significantly enhanced 16S-based predicted bacterial gene functionality encoding cyclohexadienyl dehydratase in cases relative to controls. This enzyme is involved in the synthesis of phenylalanine, a precursor of dopamine. Increased relative abundance of this functionality was significantly associated with decreased ventral striatal fMRI responses during reward anticipation, independent of ADHD diagnosis and age.

Conclusions: Our results show increases in gut microbiome predicted function of dopamine precursor synthesis between ADHD cases and controls. This increase in microbiome function relates to decreased neural responses to reward anticipation. Decreased neural reward anticipation constitutes one of the hallmarks of ADHD.

Conflict of interest statement

Competing Interests: JKB has been in the past 3 years a consultant / member of advisory board and/or speaker for Janssen Cilag BV, Eli Lilly, Shire, Medice, Lundbeck and Servier. He is not an employee of any of these companies, and not a stock shareholder of any of these companies. He has no other financial or material support, including expert testimony, patents, royalties. BF received educational speaking fees from Merz and Shire. Commercial company NIZO provided support in the form of salaries for authors JB, HMT and SAFTH. This does not alter our adherence to PLOS ONE policies on sharing data and materials. EA, THAE, JN, MPZ, SPS, MGN, and AAV report no competing interests.

Figures

Fig 1
Fig 1. Potential routes in which precursors of monoamines could influence brain functioning.
The large neutral amino acids tryptophan, phenylalanine, and tyrosine, which are absorbed in the intestine [20], are precursors of monoamines. Tryptophan and phenylalanine are essential amino acids, meaning that they cannot be synthesized by the human body itself [21]. 5-HTP = 5-Hydroxytryptophan.
Fig 2
Fig 2. Microbiome sample, fMRI sample, and their overlap.
Fig 3
Fig 3. The strongest differentially abundant microbial taxa for ADHD cases (n = 19) versus healthy controls (n = 77), shown in the graphical Cytoscape visualization [32].
Nodes represent taxa (node size represents average relative abundance, for both experimental groups combined), edges (dashed lines) link the different taxonomic levels. The weighed fold-change (node color) is calculated as the 2log of the ratio of the relative abundance between control and ADHD (0 = no difference between genotypes, 1 = twice as abundant in control, etcetera). In other words: yellow to red indicates an overrepresentation in control, hence an underrepresentation in ADHD, and vice versa for light- to dark blue. The significance (node border width) is expressed as the p-value of a Mann–Whitney U test, uncorrected for multiple comparisons.
Fig 4
Fig 4. The ADHD microbiome contains significantly increased levels of predicted cyclohexadienyl dehydratase (CDT; KEGG Ortholog K01713; EC:4.2.1.51), responsible for phenylalanine synthesis (Fig B in S1 Appendix).
This analysis is based on functional predictions deriving from 16S profiles of the microbiome, as performed by PICRUSt [33]. Box plots represent the relative abundance of predicted CDT, with 5–95% percentile whiskers (dots represent outliers). The significance was tested with a non-parametric MWU (* p = 0.038), Bonferroni-corrected for 15 K numbers identified.
Fig 5
Fig 5. fMRI results.
A. Main effect of reward anticipation, cluster-level corrected at the whole-brain level (pFWE < 0.05). Color bars reflect T-values. B. Diagnosis effects in the anatomical region of interest (ROI) of the ventral striatum. C. Negative correlation of the microbiome function CDT (see Fig 4) with reward anticipation responses across the whole-brain (n = 28), intensity threshold at p < 0.001 uncorrected (T = 3.45). The clusters in bilateral ventral striatum (x = -11, y = 11, z = -9, cluster size = 8, p(FWE, cluster) = 0.024; x = 11, y = 6, z = -11, cluster size = 2, p(FWE, cluster) = 0.036) are significant after correcting for multiple comparisons across the search volume (cluster-level pFWE < 0.05, SVC), i.e. the anatomically defined ventral striatum shown in panel B. SVC = small volume correction. * indicates p < 0.05.

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Grant support

The NeuroIMAGE study was supported by NIH Grant R01MH62873 (www.nih.gov), NWO Large Investment Grant 1750102007010 (www.nwo.nl), and matching grants from Radboud University Nijmegen Medical Center, University Medical Center Groningen and Accare, and Vrije Universiteit Amsterdam. This work was funded by an Aspasia grant to BF (grant number 015.008.009) (www.nwo.nl), and the European Community’s Horizon 2020 Program (H2020/2014 – 2020, https://ec.europa.eu/programmes/horizon2020/) under grant agreement n° 643051 (MiND). AAV was supported by the Netherlands Organization for Scientific Research (www.nwo.nl) Program Food, Cognition and Behavior (grant number FCB 057-14-005). EA was supported by a Veni grant (grant number 016.135.023) and BF by a Vici grant (grant 016-130-669) from the Netherlands Organization for Scientific Research (www.nwo.nl), MGN by an ERC Grant (#310372) (https://erc.europa.eu/). Additional funding for this work came from the European Community’s Seventh Framework Programme (FP7/2007-2013, https://ec.europa.eu/research/fp7/) under grant agreement no. 278948 (TACTICS) and n° 602805 (Aggressotype) and from the ZONMW grant 80-83700-98-52029 (www.zonmw.nl). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Commercial company NIZO provided support in the form of salaries for authors JB, HMT and SAFTH, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.
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