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Article
Understanding the ADHD-Gut Axis by Metabolic
Network Analysis
Ezgi Taş and Kutlu O. Ülgen *

Department of Chemical Engineering, Bogazici University, Istanbul 34342, Turkey; ezgi.tas@boun.edu.tr

* Correspondence: ulgenk@boun.edu.tr

Abstract: Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder di-

agnosed with hyperactivity, impulsivity, and a lack of attention inconsistent with the patient’s
development level. The fact that people with ADHD frequently experience gastrointestinal (GI)
dysfunction highlights the possibility that the gut microbiome may play a role in this condition.
The proposed research aims to determine a biomarker for ADHD by reconstructing a model of
the gut-microbial community. Genome-scale metabolic models (GEM) considering the relationship
between gene-protein-reaction associations are used to simulate metabolic activities in organisms
of gut. The production rates of dopamine and serotonin precursors and the key short chain fatty
acids which affect the health status are determined under three diets (Western, Atkins’, Vegan) and
compared with those of healthy people. Elasticities are calculated to understand the sensitivity of
exchange fluxes to changes in diet and bacterial abundance at the species level. The presence of
Bacillota (genus Coprococcus and Subdoligranulum), Actinobacteria (genus Collinsella), Bacteroidetes
(genus Bacteroides), and Bacteroidota (genus Alistipes) may be possible gut microbiota indicators of
ADHD. This type of modeling approach taking microbial genome-environment interactions into
account helps us understand the gastrointestinal mechanisms behind ADHD, and establish a path to
improve the quality of life of ADHD patients.

Keywords: gut microbiota; ADHD; short chain fatty acids; neurotransmitter precursors; probiotic

Citation: Taş, E.; Ülgen, K.O.

1. Introduction
Understanding the ADHD-Gut Axis
by Metabolic Network Analysis. Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder
Metabolites 2023, 13, 592. https:// diagnosed with hyperactivity, impulsivity, and lack of attention inconsistent with the
doi.org/10.3390/metabo13050592 patient’s development level [1]. Today, ADHD is one of the most common children’s
psychiatric disorders [2] and it is usually continuous throughout life [3]. Up to 85% of
Academic Editor: Andreas
diagnosed patients report that the symptoms continue during adolescence and 60% of them
Stadlbauer
point out that ADHD affected their adult life too [4,5]. The relationship between biological
Received: 18 March 2023 gender and the incidence of the disorder is around 1:5 to 1:9 in girls and boys respectively [6].
Revised: 19 April 2023 ADHD diagnosis depends on psychological evaluation, there is neither a blood test nor
Accepted: 19 April 2023 other physical tests [7]. There are two types of diagnostic criteria for ADHD—inattention
Published: 26 April 2023 and hyperactivity/impulsivity—but a biomarker has not been identified yet. There are
possible risk factors, e.g., birth weight below 1.5 kg increases the risk of ADHD two to three
times but not all children with low birth weight have ADHD [1]. Environmental factors
during the prenatal period such as maternal smoking, alcohol consumption, heavy metal
Copyright: © 2023 by the authors.
exposure, and nutrition habits, and toxin exposure after birth, may cause ADHD [8]. There
Licensee MDPI, Basel, Switzerland.
This article is an open access article
is also a genetic predisposition to this disorder, that is children who have parents and/or
distributed under the terms and
siblings with ADHD are at high risk.
conditions of the Creative Commons
There is a bidirectional relationship between the brain and the gut, called the gut-brain
Attribution (CC BY) license (https:// axis [9]. This axis is responsible for cognitive functions like mood and motivation [10,11].
creativecommons.org/licenses/by/ The mechanism of brain-gut axis communication contains neuro-immuno-endocrine me-
4.0/). diators [12]. In terms of communication networks, the central nervous system (CNS),

Metabolites 2023, 13, 592. https://doi.org/10.3390/metabo13050592 https://www.mdpi.com/journal/metabolites

Metabolites 2023, 13, 592 2 of 31

autonomic nervous system (ANS), enteric nervous system (ENS), and hypothalamic pi-
tuitary adrenal (HPA) axis participate in the gut-brain axis [13]. Recent studies showed
that some neuroactive molecules like gamma-aminobutyric acid (GABA), serotonin, cat-
echolamines like dopamine and norepinephrine (noradrenaline), acetylcholine, and his-
tamine are produced by gut microbiota and influence the gut-brain axis network (Table 1).
This interaction might be a result of an intestinal barrier modulation [13]. Gut microbiota
affects gut permeability, and different metabolic cycles, such as carbohydrate metabolism
and immunity.
Carbohydrate metabolism in the human gut results in short-chain fatty acid and gas
production [14]. Acetate, butyrate, and propionate have the highest abundance in the
human intestine. The ratio between them is 3:1:1, and butyrate is considered the most
important short chain fatty acid for a healthy metabolism [14,15]. There are several bacteria
responsible for acetate production, but butyrate and propionate-producing bacteria are
more specific [16,17]. Firmicutes bacteria, such as some Lachnospiraceae family and species
Faecalibacterium prausnitzii, are the major butyrate producers in the human intestine [14,18].
Propionate production is mostly handled by Bacteroides with the help of Negativicutes
and Clostridium bacteria [14]. SCFAs have a variety of advantageous effects on several
facets of human energy metabolism; however, our knowledge of the underlying molecular
pathways is still limited. The lack of information regarding the actual fluxes of SCFAs and
the metabolic processes, which SCFAs regulate, has so far hampered the field.
The composition of the gut microbiome depends mostly on dietary habits. Drug use,
nicotine and alcohol consumption, and stress also alter the gut microbiome [19]. These
changes (dysbiosis) are associated with several health problems such as irritable bowel
syndrome (IBS) [20], autism spectrum disorder (ASD) [21], depression, and anxiety [22].
The gut microbiota is also known to affect behavioral symptoms in neurodevelopmental
diseases. A Dutch sample of 42 adolescents and young adults with ADHD revealed a
significant increase in a genus from the family Ruminococcaceae, which was associated with
inattention symptoms [23]. Prevotella and Alistipes species as well as Akkermansia muciniphila,
a novel mucin-degrading bacterium, have been reported as the most prevalent in ASD
children [24–26]. Decreased levels of some health-promoting bacteria (e.g., Bifidobacterium)
and metabolites (e.g., FAA, SCFA) were also reported in autistic children [24]. Due to
their anti-inflammatory effects on the central nervous system, SCFA-producing bacteria
have been demonstrated to potentially contribute to ADHD and autism through a number
of gut-brain pathways [27]. Short-chain fatty acid (SCFA) producers include Prevotella
species and some Coprococcus species. Thus, children with ADHD had lower levels of
the genus Prevotella than did controls [27]. Some bacterial species are more active in the
production of SCFAs, such as Bacteroides spp. and Clostridiae spp. [23]. Faecalibacterium and
Ruminococcus and Bifidobacterium genera were positively correlated with total SCFA [24].
High levels of SCFA worsened the symptoms of autistic individuals [28,29]. Propionic
acid showed neurobiological effects in test animals such as rats [24,30]. At present, there
are contradictory conclusions in the literature regarding the role of the microbiome in
neuropsychiatric disorders.
The lack of deep information on the brain-gut axis for ADHD individuals leads us to
investigate gastrointestinal reasons (gut metabolic products) by reconstructing a model
of the gut-microbial community with the ultimate aim of determining a biomarker for
ADHD. Community growth and elasticities are predicted to understand the sensitivity of
exchange fluxes to changes in diet (Western, Atkins’, and Vegan) and bacterial abundance
at the species level. This type of modeling approach taking microbial genome–environment
interactions into account may help us understand the gastrointestinal mechanisms behind
ADHD and offer prebiotic/probiotic treatment options, establishing a path to improve the
quality of life of ADHD patients.
Metabolites 2023, 13, 592 3 of 31

Table 1. Neurochemical compounds and genus relationship [31].

Neurotransmitter
Precursors Found in Neurotransmitters Genus References
the Human GI Tract
Lactobacillus,
Bifidobacterium,
L-Glutamate GABA Bacteroides, [32,33]
Parabacteroides,
Escherichia
Streptococcus, Escherichia, Enterococcus,
L-tryptophan Serotonin [34,35]
Lactococcus, Lactobacillus
L-tyrosine,
Norepinephrine Escherichia, Bacillus [35,36]
Phenylalanine
Escherichia, Bacillus,
L-tyrosine, Lactococcus,
Dopamine [34–36]
Phenylalanine Lactobacillus,
Streptococcus
Choline Acetylcholine Lactobacillus, Bacillus [37–40]
Lactobacillus,
Lactococcus,
L-histidine Histamine [41–43]
Streptococcus,
Enterococcus

2. Materials and Methods

2.1. Data
The work of Aarts et al. [44] provide bacterial abundance data in the gut of adoles-
cents with ADHD, containing 19 people diagnosed with ADHD and 77 healthy people,
96 samples in total. The average age of ADHD subjects is 19.5 with a standard deviation of
2.5. On the other hand, the mean age of control subjects is 27.1 but the standard deviation
in the group is 14.3 years which is significant. In addition, while 53.2% of the Control
group population is male, this ratio raises to 68.4% for ADHD subjects. 25% of the people
in the Control group were siblings of ADHD patients. The unaffected siblings of ADHD
cases (21/77), perhaps living in the same home and eating comparable foods, are analyzed
here, i.e., only the matching subjects (pairs) are considered as given in Table 2. In the
end, 20 samples out of 96 samples were determined to be usable. The dataset is handled
according to age and gender differences.

Table 2. Age and gender-matched sub-samples from Aarts et al. [44].

Sample ID Age Gender

CONTROL01 15 Female
ADHD01 15 Female
CONTROL02 17 Female
ADHD02 17 Female
CONTROL03 18 Female
ADHD03 18 Female
CONTROL04 20 Male
ADHD04 20 Male
CONTROL05 20 Male
ADHD05 20 Male
CONTROL06 20 Female
ADHD06 20 Female
CONTROL07 21 Male
ADHD07 21 Male
CONTROL08 21 Male
ADHD08 21 Male
CONTROL09 22 Male
ADHD09 22 Male
CONTROL10 23 Male
ADHD10 23 Male
Metabolites 2023, 13, 592 4 of 31

2.2. Reconstruction of Community Model

Using the bacterial abundance data retrieved from Aarts et al. [44], we collected the or-
ganisms from AGORA database [45], which comprises manually curated metabolic models
of more than 7000 strains, including those of the human gut microbiome. MICOM software
is employed for the reconstruction of the gut community model. MICOM is based on
the COBRApy Python package for constraint-based modeling of biological networks [46].
By MICOM, the species-specific genome scale metabolic models are combined to create
Metabolites 2023, 13, x FOR PEER REVIEW 5 of 30
a unified community model that simultaneously takes into account the exchange fluxes
between individuals and between individuals and their environment. To determine the
optimum growth of the gut microbiota, the cooperative trade-off technique in MICOM is
fat, low carb
employed anddiet
the given in VMH
objective function [49].isThe amount
selected of energy
as the provided
maximization by the diet,
of microbial as de-
commu-
fined
nity in VMH,
biomass consists
growth. Theofrelative
1.7% carbohydrates,
abundances of70% lipids, and
the different 24% proteins
species are used [50]. Vegan
to account
Diet
for theiscommunity
a plant-based diet, with during
composition any type theofsimulations,
food of animal origin
and the removed
cutoff from
value for it. The
bacterial
energy source
abundance is selected as 10−of
distribution 4 . the
TheVegan Diet is 15.66%
most feasible growthproteins, 20.99%
rate for the lipids, and 63.35%
gut community model
iscarbohydrates,
obtained by aas tradeoff
given invalue
VMH of [51].
0.7. During communitybuilding,
After community growth, the
the analyses
lowest possible
include
metabolite
community intake is aimed
growth, at due
tradeoff, andtoelasticities.
the possibleThe lack of resources.
impact IBMinterventions
of targeted CPLEX [47] ison used
the
as
netthe optimizationorsolver
consumption (quadratic
production solver)as
of SCFAs inwell
all the
as model reconstructions
neurotransmitter and analyses,
precursors by the
and the cutoff
microbiota value is selected as 0.0001 (Figure 1).
is predicted.

Figure1.1.Illustration
Figure Illustrationof
ofthe
theresearch
researchmethod.
method.

The flux balance analysis is performed by MICOM. By assuming that all fluxes in the
2.3. Diversity Analyses
biological system are in a steady state and maximizing a biomass reaction that is specific
to theTwo diversity
organism, fluxmeasures, Alpha and
balance analysis can Beta diversities, are of
get approximations here
theused
fluxestofor
characterize
a certain
communities quantitatively by using the number of species
organism. This can be expressed as a constrained linear programming problem and the number of individuals
for the
on anyvlevel,
fluxes, i (in sample
millimoles or community.
per gram dryThe first
weight compartment
per hour) of
using alpha
the diversity
stoichiometric is the indices
matrix S.,
of species
which has richness,
metabolites alsoinknown
its rowsas variety indices.in
and reactions Theitsspecies
columns. richness indexthe
Typically, is defined
biomass as
the total number of species, S, in the sample. Among the several indices
reaction vbm is normalized so that it will yield 1 g of biomass, which corresponds to a unit used to determine
diversity
1/h in the sample,
representing Chao’s growth
the organism’s index, Shannon’s index,and
rate. The upper andlower
Simpson’s index provide
boundaries are em-
ployed here. The other compartment is called the equability
thermodynamic restrictions to the fluxes or limit interactions with the environment,index and shows the relative
i.e.,
abundance
exchange of different
fluxes. species ofisatypically
Each organism community in termsin
embedded ofan
their evenness
external of distribution.
compartment (the
Thelumen)
gut evenness thatofrepresents
a community the depends
community on how close the in
environment, number
order ofto individuals of differ-
depict a community
ent species
model is in theseveral
that contains community.
speciesInwith
the different
case of dominating
abundancesspecies,
(in grams the dry
evenness
weight) index
for
decreases.
each organism. In order
With tothe
compare
use of alpha
MICOM, diversity
a fluxresults
balance between
analysis groups, twoout
is carried statistical
by addingtests
are used: F-test,
exchanges for thefor variance comparison,
environment compartment and the
and Mann-Whitney
exchanges between U atest, to understand
specific organism
howthe
and similar the groupsThe
environment. are. input for MICOM is abundances, which it uses to forecast
growth BetaratesDiversity is estimated
and fluxes. by the Bray-Curtis
The gut metabolic community modelDissimilarity
includesIndex (Equation
58 microbial (1)),
species
and
which968studies
exchange themetabolites with theirofexchange
mutual microbiota two samplesreactions showing
and the the overall
population in each metabolic
sample
capacity
[52]. Theofdissimilarity
the microbialindex community.
returns with a value between 0 and 1; if both samples are
identical the dissimilarity is returned as zero. In order to calculate the dissimilarity index
between two samples, first, mutual species are selected, and then the less numerous spe-
cies are summed to get Cij value. Si and Sj given in Equation (1) represent the population
of each sample.
Metabolites 2023, 13, 592 5 of 31

The reconstructed gut community model is introduced to different diets to observe

how the bacterial community in the gut will react to different external factors. Western,
Atkins’, and Vegan diets are selected as the community growth media. Western Diet
represents a typical American diet and is retrieved from the VMH (virtual metabolic
human) database [48] as well as from MICOM documents. Atkins’ Diet is derived from the
high fat, low carb diet given in VMH [49]. The amount of energy provided by the diet, as
defined in VMH, consists of 1.7% carbohydrates, 70% lipids, and 24% proteins [50]. Vegan
Diet is a plant-based diet, with any type of food of animal origin removed from it. The
energy source distribution of the Vegan Diet is 15.66% proteins, 20.99% lipids, and 63.35%
carbohydrates, as given in VMH [51]. After community building, the analyses include
community growth, tradeoff, and elasticities. The impact of targeted interventions on the
net consumption or production of SCFAs as well as neurotransmitter precursors by the
microbiota is predicted.

2.3. Diversity Analyses

Two diversity measures, Alpha and Beta diversities, are here used to characterize
communities quantitatively by using the number of species and the number of individuals
on any level, sample or community. The first compartment of alpha diversity is the indices
of species richness, also known as variety indices. The species richness index is defined
as the total number of species, S, in the sample. Among the several indices used to
determine diversity in the sample, Chao’s index, Shannon’s index, and Simpson’s index are
employed here. The other compartment is called the equability index and shows the relative
abundance of different species of a community in terms of their evenness of distribution.
The evenness of a community depends on how close the number of individuals of different
species is in the community. In the case of dominating species, the evenness index decreases.
In order to compare alpha diversity results between groups, two statistical tests are used:
F-test, for variance comparison, and the Mann-Whitney U test, to understand how similar
the groups are.
Beta Diversity is estimated by the Bray-Curtis Dissimilarity Index (Equation (1)), which
studies the mutual microbiota of two samples and the population in each sample [52]. The
dissimilarity index returns with a value between 0 and 1; if both samples are identical the
dissimilarity is returned as zero. In order to calculate the dissimilarity index between two
samples, first, mutual species are selected, and then the less numerous species are summed
to get Cij value. Si and Sj given in Equation (1) represent the population of each sample.

2Cij
BCij = 1 − (1)
Si + S j

Unweighted UniFrac Metric measures diversity in a qualitative manner and is based

on the phylogenetic tree [53]. Multiple samples can be compared with this metric in
a synchronized manner. Unweighted UniFrac metric uses branch length percentage to
evaluate how far away communities are from each other.
Diversity calculations are done by using the microbiome package of R with raw
abundance data retrieved from Aarts et al. [44] at the species level. In total, 96 samples and
98 species are used as input. The given CSV file is turned into the phyloseq format with the
help of the phyloseq package.

2.4. Elasticities
The elasticity coefficient is used to analyze how sensitive exchange fluxes are to
changes in exchange flux bounds or changes in bacterial abundance in the selected level.
The elasticity coefficient formula (Equation (2)), implemented in the MICOM library, is
used where v represents the exchange flux and p represents the parameter.

∂ln|v|
εvp = (2)
∂ln| p|
 Metabolites 2023, 13, 592 6 of 31

Flux direction is ignored with the absolute value but is defined separately to avoid
information loss. The differentiation step size was selected as 0.1 on the log scale, resulting
in a 10.5% increase in bacterial abundance in the native scale.

2.5. Probiotic Addition

Lactobacillus rhamnosus is selected as the probiotic provided to see how much it affects
the selected metabolite fluxes and previously mentioned possible biomarkers. The amount
of L. rhamnosus is determined with the least squares method, where the relative abundances
of each ADHD sample’s gut microbiome are compared to a randomly selected Control
sample’s abundance values. The abundance value of Lactobacillus rhamnosus to be added to
the community model is calculated as the abundance, which gives the lowest difference
between the Control sample and ADHD samples so that the gut microbiome of an ADHD
subject becomes closer to the healthy one. In this study, the additional L. rhamnosus
abundance is 0.2. After the probiotic addition, the same pipeline is followed for community
model building and community growth in different media.

3. Results
3.1. Abundances
Bacterial abundance data retrieved from Aarts et al. [44] are processed, and only those
of matching pairs in terms of age and gender (Table 3) are here considered for analysis
and gut community model building. The pairs (1 ADHD and 1 neurotypical Control at
similar age) are used to compare microbial abundances and diversities in the gut. At the
genus level, Bifidobacterium and Bacteroides have higher abundances in ADHD gut. Since
the data of Aarts et al. [44] are used for Control and ADHD comparison, the abundance
of the genus Bifidobacterium in ADHD gut is expected to be higher. At the species level,
Bifidobacterium adolescentis, Bifidobacterium dentium and Bifidobacterium pseudocatenulatum
have higher abundances in ADHD gut (Figure 2). In literature [54,55], Bacteroides genus
olites 2023, 13, x FOR PEER REVIEW shows lower abundance in ADHD gut, but B. uniformis has a higher abundance at species 7
level in our study. B. coprocola, B. plebeius, and B. vulgatus, on the other hand, are found to
have lower abundances in the guts of ADHD patients.

Actinomyces Akkermansia Alistipes Anaerococcus Anoxybacillus

1.00
0.75
0.50
0.25
Atopobium Bacillus Bacteroides Bifidobacterium Blautia
1.00
0.75
Taxa prevalence

0.50 Status

0.25
ADHD
Control

Butyricimonas Clostridium Coprobacillus Coprococcus Corynebacterium Diet

1.00 Atkins

0.75 Vegan
Western

0.50
0.25
−20

−10

Desulfovibrio Dialister Dorea Catenibacterium

1.00
0

0.75
0.50
0.25
−20

−10

−20

−10

−20

−10

−20

−10
0

Average count abundance (log scale)

FigureFigure
2. Taxa prevalence
2. Taxa prevalenceatatGenus level.
Genus level.

Table 3. Higher abundances in Control and ADHD Pairs.

Organism Pair 1 Pair 2 Pair 3 Pair 4 Pair 5 Pair 6 Pair 7 Pair 8 Pair 9 Pai
obacterium ad-
Control ADHD Control ADHD ADHD Same ADHD ADHD Control AD
olescentis
insella aerofa-
Metabolites 2023, 13, 592 7 of 31

Table 3. Higher abundances in Control and ADHD Pairs.

Organism Pair 1 Pair 2 Pair 3 Pair 4 Pair 5 Pair 6 Pair 7 Pair 8 Pair 9 Pair 10
Bifidobacterium adolescentis Control ADHD Control ADHD ADHD Same ADHD ADHD Control ADHD
Collinsella aerofaciens Control ADHD Control Control Control Same ADHD Control Control ADHD
Bifidobacterium angulatum Control ADHD Control Same Control Same Control ADHD ADHD Control
Prevotella buccae Same Same Same Control Same Same Same Same Same Same
Prevotella copri Control Control ADHD Same Same Same Same Control Same Control
Bacteroides coprocola ADHD Same Same Same Same Same Control Control Same Control
Bifidobacterium dentium Same Same ADHD Same Same Same Same ADHD ADHD ADHD
Parabacteroides distasonis Same Same Control Same Same Same Same Same ADHD Same
Enterococcus faecium ADHD Same Same Same Same Same Same Same Same Same
Parabacteroides goldsteinii Same Same Same Same Same Same Same Same ADHD Same
Parabacteroides merdae Control Control ADHD Control Control Control ADHD Control ADHD Same
Bacteroides plebeius ADHD Same Same Same Same Same Control Same Same ADHD
Bifidobacterium pseudocatenulatum Control ADHD Control ADHD Control ADHD Control ADHD ADHD Control
Prevotella ruminicola ADHD Same Same Same Same Same Same Control Control Same
Bacteroides thetaiotaomicron Same Same Same Same Same Same Same Same Same Same
Bacteroides uniformis Control Control ADHD Control ADHD Control ADHD ADHD Control ADHD
Bacteroides vulgatus Control Control ADHD Control Same Control ADHD Control Control ADHD
Paraprevotella xylaniphila Same Same Same Same Same Same Same Same Same Same
Metabolites 2023, 13, 592 8 of 31

Most of the experimental studies on gut microbiome have reported their findings
at the genus level. The genus Parabacteroides is expected to show a lower abundance in
ADHD gut [56]. In our gut community model, the species P. distasonis and P. merdae show
parallel behavior with literature findings, whereas P. goldsteinii has similar abundance
values for both Control and ADHD cases. Collinsella genus is expected to be higher in
ADHD gut [57]. At the species level, however, C. aerofaciens has lower abundance in most
of the pairs and consequently in our gut community model. Like Collinsella, Prevotella is a
possible biomarker at the genus level, and is expected to have a lower abundance in ADHD
gut but a higher abundance in Control gut [56]. At species level, P. buccae, P. copri, and
P. ruminicola satisfy this condition in the gut model. Enterococcus is a possible biomarker at
genus level [54] and E. faecium is the only species passing the lower limit of abundance in
our studied samples. Literature data show a lower abundance of Enterococcus in ADHD
gut compared to Control samples. However, in the given data of Aarts et al. [44] abundance
values are mostly similar for ADHD and Control cases. The same scenario applies to
Parabacteroides goldsteinii and P. xylaniphila [58]; the latter has zero abundances for all pairs,
and is therefore ignored in any future discussion.
Possible biomarkers of ADHD have been reported in the literature, but as these studies
are conducted by different groups, these biomarkers are defined at different taxonomic
levels (Table 4). For compatibility purposes, only samples at the species level are taken for
data analysis and comparison.

Table 4. Gut microbiota compositions in ADHD patients compared to Control groups.

Phylum Family Genus Species Higher in ADHD Lower in ADHD

Actinobacteria [57]
Bifidobacteriaceae Bifidobacterium [44] [54]
B. longum [57]
B. adolescentis [57]
Coriobacteriaceae Collinsella [57]
Bacteroidetes Bacteroidaceae [56]
Bacteroides [54]
B. uniformis [55]
B. ovatus [55]
B. caccae [58]
B. coprocola [55]
Prevotellaceae [56]
Prevotella [56]
Pararevotella P. xylaniphila [58]
Porphyromonadaceae [56]
Parabacteroides [56]
Odoribacteraceae [58]
Odoribacter O. splanchnicus [58]
Firmicutes [59] [44]
Catabacteriaceae [56]
Clostridiaceae Clostridium C. histolyticum [54]
Enterococcaceae [58]
Enterococcus [54]
Lachnospiraceae [58]
Lactobacillus [54,55]
Ruminococcaceae [58]
[58,60]
Faecalibacterium F. prausnitzii [58]
Ruminococcus R. gnavus [58]
Veillonellaceae Veillonella [58]
V. parvula [58]
Proteobacteria Neisseriaceae [56]
Neisseria [56]
Desulfovibrionaceae Desulfovibrio [59]
Sutterellaceae Sutterella S. stercoricanis [55]
Fusobacteria [55]
Fusobacteriaceae Fusobacterium [55]
 Proteobacteria [56]
fovibrionaceae rio
[59]
Sutterellaceae Sutterella S. stercoricanis [55]
[55]
Fusobacteria Fusobacteriaceae Fusobacterium
Metabolites 2023, 13, 592 [55] 9 of 31

3.2. Diversity
Diversities are used to characterize communities quantitatively by using the number
3.2. Diversity
of species and the number of individuals on any level, sample, habitat or community.
Diversities are used to characterize communities quantitatively by using the number
There are three diversity measures: Alpha, Beta, and Gamma diversity. Alpha diversity
of species and the number of individuals on any level, sample, habitat or community. There
shows the diversity in a habitat or community, and measures the richness and evenness
are three diversity measures: Alpha, Beta, and Gamma diversity. Alpha diversity shows
of species in the habitat. Beta diversity shows diversity between different habitats or com-
the diversity in a habitat or community, and measures the richness and evenness of species
munities. Gamma diversity handles landscape level diversity [61]. Since the samples ex-
in the habitat. Beta diversity shows diversity between different habitats or communities.
amined in this research are in the same landscape, gamma diversity will not be discussed
Gamma diversity handles landscape level diversity [61]. Since the samples examined in
here.
this research are in the same landscape, gamma diversity will not be discussed here.
To characterize communities, alpha diversity is evaluated based on three indices:
To characterize communities, alpha diversity is evaluated based on three indices:
Chao, Shannon, and Simpson (Figure 3a–i). Chao index and Shannon index indicate that
Chao, Shannon, and Simpson (Figure 3a–i). Chao index and Shannon index indicate that
the Control gut has a higher median value than that of the ADHD gut. Even though both
the Control gut has a higher median value than that of the ADHD gut. Even though
Control and ADHD samples have similar distributions in the number of species, the Con-
both Control and ADHD samples have similar distributions in the number of species, the
trol gut has a higher number of species than the ADHD gut (Figure 3a,d). Simpson index
Control gut has a higher number of species than the ADHD gut (Figure 3a,d). Simpson
indicated that the deviation from the median for both groups (control and ADHD) is al-
index indicated that the deviation from the median for both groups (Control and ADHD)
most equal, but they have significantly different mode values (the value that appears the
is almost equal, but they have significantly different mode values (the value that appears
most often in the data set) where ADHD subjects have a higher mode (Figure 3g). This
the most often in the data set) where ADHD subjects have a higher mode (Figure 3g). This
means the number of dominant species is higher for the ADHD group compared to the
means the number of dominant species is higher for the ADHD group compared to the
Control group. There are some samples in the Control group with more dominant species,
Control group. There are some samples in the Control group with more dominant species,
but there is no homogeneity. For all these three indexes, the Mann-Whitney U test does
but there is no homogeneity. For all these three indexes, the Mann-Whitney U test does not
not find a significant difference between the median values of the number of species in the
find a significant difference between the median values of the number of species in the guts
guts of control and ADHD individuals.
of Control and ADHD individuals.

Metabolites 2023, 13, x FOR PEER REVIEW 10 of 30

(a) (b) (c)

(d) (e) (f)

Figure 3. Cont.
Metabolites 2023, 13, 592 10 of 31

(d) (e) (f)

(g) (h) (i)

(j) (k) (l)

Figure3.3. Distributions
Figure Distributions of
of alpha
alpha diversity
diversity metrics
metrics based
based on
on health
healthstatus
statusand
andgender:
gender:(a–c)
(a–c)Chao’s
Chao’s
Index; (d–f) Shannon Index; (g–i) Simpson’s Dominance Index; (j–l) Evenness Index.
Index; (d–f) Shannon Index; (g–i) Simpson’s Dominance Index; (j–l) Evenness Index.

Forgender-based
For gender-basedcomparison,
comparison,therethereare
are17
17male
maleandand1313female
femalesamples;
samples;the
thegender
genderof
of the rest of the samples is not known. The male group has eight Control and
the rest of the samples is not known. The male group has eight Control and nine ADHD nine ADHD
subjectswhile
subjects while the
the female
female group
group has
has eight
eight Control
Control and
and six
sixADHD
ADHDsubjects.
subjects.Corresponding
Corresponding
to the variance difference, the Mann-Whitney U test does not find a significantdifference
to the variance difference, the Mann-Whitney U test does not find a significant difference
betweenControl
between Control and
and ADHD
ADHD subjects’
subjects’ median
median values
values in
ineither
eithermale
maleororfemale
femalepopulations
populations
(Figure 3b,e,h). Gender-specified data is not enough to make compelling guesses. However,
Shannon’s index is more reliable than Chao’s index (in terms of gender-based comparison
of Control and ADHD groups), since it is not dependent on sample size. Mann-Whitney
U test finds a difference between median values of Control and ADHD groups for female
subjects but not for males (Figure 3e).
In status-based comparison (different genders in the same status), there is no significant
difference between the diversity values (Chao, Shannon, Simpson) of different genders
in the same status. Mann-Whitney U test does not find any difference between male and
female groups’ species richness (Figure 3c,f,i). Simpson’s index indicated that male Control
subjects have a higher median value, but a lower mode value. This can be evaluated thus:
the male Control group has lower dominance, but it is more homogeneous compared to
female Control subjects under the same status (Figure 3i). The female ADHD group has a
higher mode value, indicating a higher dominance in female ADHD subjects compared
to males.
Evenness is a measure of equality. The value of evenness of a sample refers to the
closeness of the number of individuals belonging to a species. Simpson’s Evenness Index
Metabolites 2023, 13, 592 11 of 31

is only used for this evaluation (Figure 3j–l). The Mann-Whitney U test gives the same
results for status-based comparison, i.e., there is a location shift in both Control and ADHD
populations. In the Control population, the female group has a higher median value and
more symmetrical distribution. However, the male group has a higher mode compared to
the female group. This can be interpreted as meaning that evenness in the male Control
group is higher, but distribution is not homogeneous compared to the female Control group.
In the case of the ADHD population, the female group has both higher mode and median
values compared to the male group. The differences are not as significant as in the Control
population, but they show a higher evenness for the female group.
To understand the differences between the two samples, Beta Diversity is employed
with three metrics: Bray-Curtis Dissimilarity Index, Unweighted UniFrac Metric, and
Weighted UniFrac Metric. Bray-Curtis’s dissimilarity index studies the mutual microbiota
of two samples and the population in each sample [53]. The downside of the Bray-Curtis
index is that the results depend on the sample population size. If the samples taken into
account have small populations, small differences in species populations can have a sig-
nificant effect on the dissimilarity index value. Although Control subjects have a higher
variance (Figure 4a) in the BrayCurtis Dissimilarity test, there is no significant difference
between the two environments (ADHD and Control) according to the Mann-Whitney U
test (pmwu = 0.49). The Unweighted UniFrac Metric shows how different families can adapt
to specific living conditions and how their adaptation differs. In Figure 4b, each point
represents a sample of the population. Blue points belong to ADHD gut environment
and orange points show subjects with a healthy gut. There is no significant difference or
similarity between the two environments. The results do not provide conclusive discrimi-
nation between the two environments. Weighted UniFrac [62] is derived from Unweighted
UniFrac as a quantitative metric, and it allows us to understand the effects of the relative
Metabolites 2023, 13, x FOR PEER REVIEW 12 of 30
abundance of species in a sample on the population. Weighted UniFrac gives better results
compared to Unweighted UniFrac but, the results are still inconclusive (Figure 4c).

(a) (b) (c)

Figure
Figure4.4.Beta
Betadiversity
diversityindices:
indices:(a)
(a)Bray-Curtis
Bray-Curtisdissimilarity
dissimilarityindex
indexresults
resultsfor
forControl
Controland
andADHD
ADHD
samples; (b) Weighted UniFrac distance results for Control and ADHD samples; (c) Unweighted
samples; (b) Weighted UniFrac distance results for Control and ADHD samples; (c) Unweighted
UniFrac distance results for Control and ADHD samples.
UniFrac distance results for Control and ADHD samples.

Although
Althoughthethediversity
diversitymetrics
metricsuseusedifferent
differentassumptions
assumptionsandandformulas
formulasfor
forcalcula-
calcula-
tions and are affected by changes in the sample (such as changes in species composition,
tions and are affected by changes in the sample (such as changes in species composition,
adding
addingororsubtracting
subtractingspecies
speciestotothe
thecommunity,
community,and anddominant
dominantororhidden
hiddenspecies),
species),there
there
are
areno
nosignificant
significantdifferences in in
differences thethe
results of the
results indices
of the usedused
indices to calculate alphaalpha
to calculate and beta
and
diversities.
beta diversities.

3.3. Production of Short-Chain Fatty Acids and Minor Metabolites (Amino Acids) by Gut
Microbiome
Three different diets are applied to the reconstructed gut community model: Western,
Atkins, and Vegan. Western Diet is directly taken from the literature and represents the
average American diet. The other two diets are curated from virtual metabolic human
data [63]. Atkins and keto are alike in the sense of both being low-carb diets, but they have
Metabolites 2023, 13, 592 12 of 31

3.3. Production of Short-Chain Fatty Acids and Minor Metabolites (Amino Acids) by
Gut Microbiome
Three different diets are applied to the reconstructed gut community model: Western,
Atkins’, and Vegan. Western Diet is directly taken from the literature and represents the
average American diet. The other two diets are curated from virtual metabolic human
data [63]. Atkins’ and keto are alike in the sense of both being low-carb diets, but they
have some minor differences in terms of their protein content. The ketogenic diet is a
very lowcarb, moderate protein, high-fat diet plan. Atkins’ allows for a wider variety of
foods, such as more fruits and vegetables and some grains. Thus, the Atkins’ Diet is a less
restrictive approach.
SCFA production can reduce intestinal inflammation and may help to improve metabolic
health [46]. The phylum/genus/species contributions to each SCFA (acetate, butyrate,
formate, propionate) production/consumption, as well as their exchanges with the environ-
ment, are examined. The correlations with the diets, age, gender and health status (ADHD
vs. healthy) are investigated. The strain contributors to the key SCFAs are mainly by the
genera Bacteroides, Bifidobacterium, Coprococcus, Dialister, Collinsella, Dorea, Prevotella, and
Ruminococcus, and the species Alistipes putredinis and Subdoligranulum variabile.
We compared the flux rates of each metabolite produced and/or consumed in the gut
of ADHD subjects with those in healthy humans by flux balance analysis. The following
section summarizes our results with significant flux values obtained by the gut community
model. Alistipes putredinis is one of the species coming to the front as important SCFA
producers in this study. In addition to more significant acetate production in ADHD
subjects, small amounts of isobutyrate, isovalerate, and hydrogen are also produced by
Alistipes putredinis. For each sample where acetate is produced (ADHD and Control), the
Western Diet gives the lowest acetate export flux while the Vegan Diet gives the highest.
The propionate export fluxes are also significant in both ADHD and Control subjects.
Formate production of Bacteroides coprocola in the ADHD samples is approximately
10 times higher than that of the Control samples. Formate import fluxes are lower while
export fluxes are higher in B. uniformis for ADHD samples. In B. vulgatus, ADHD samples
export higher amounts of formate. The amount of acetate by B. coprocola exported by
the ADHD sample is almost 30 times higher than the neurotypical Control sample. In
B. uniformis, ADHD samples mostly import acetate while Control samples export it.
The genus Bifidobacterium arises some questions as there are contradictory findings in
the literature. Acetate has a considerable exchange rate in Bifidobacterium dentium, which
is found only in ADHD subjects in the present gut community model. There are lower
acetate export fluxes for the ADHD samples in Bifidobacterium pseudocatelunatum. Acetate
fluxes mostly show import behavior and all samples that produce lower amounts of acetate
are healthy. Acetate import fluxes for ADHD samples are high. The amount of formate
produced by Bifidobacterium pseudocatelunatum has one of the highest values in this study,
and this bacterium does not consume formate, while export is the only action (no formate
import). Our study shows high butyrate, acetate, and formate exchange fluxes (both import
and export) by Coprococcus catus, but the flux values of propionate are low and thus not
significant in both ADHD and Control subjects. Formate export is mostly done by ADHD
samples, and they have a lower relative abundance compared to Control samples. There
is no effect of diet (no correlation with diet) on flux values, but the Western Diet shows
significantly lower flux values for both ADHD and control cases. Acetate exchange rates,
both import and export fluxes, are higher, between pairs, for ADHD samples. C. catus
consumes acetate and butyryl–coa to produce acetyl–coa and this process limits butyrate
production in the gut since butyryl–coa is also used to synthesize butyrate. C. catus
is expected to have a negative effect on butyrate production, since it competes against
butyrate-producing organisms for the necessary intermediates. In C. catus, in general,
ADHD samples export more butyrate with lower abundance values. In the case of high
abundance, export fluxes are almost equal to controls. Butyrate exchange done by another
Coprococcus species, C. comes, also shows significant flux values. Acetate import and export
Metabolites 2023, 13, 592 13 of 31

fluxes by Coprococcus comes are also significant. The Western Diet results in the highest
import and lowest export fluxes for acetate exchange out of the three different diets.
In this study, Collinsella aerofacians was found in 18 samples out of 20. Acetate, formate,
and H2 exchange fluxes are significant compared to the fluxes of other metabolites; how-
ever, none of these fluxes outshine each other or are secerned. The SCFAs produced by
C. aerofacians show both import and export behavior. H2 is exported only (no import by
this bacterium) but export fluxes are mostly low with a few exceptions.
Under the genus Dialister, the asaccharolytic bacterium Dialister invisus is found in
more than half of the ADHD subjects. The only significant fluxes come from acetate
exchange, and the acetate export rate is higher for the ADHD sample. This bacterium is
usually found in oral flora and in endodontic and periodontal infections [64]; this might
be the reason why we encounter D. invisus in a high number of samples, though no
significant metabolite fluxes were observed in this bacterium. In short, ADHD subjects
might encounter this bacterium because of another underlying condition in a different part
of the body.
Formate and acetate exchange fluxes are also significant in Dorea formigeneras. Control
samples are prone to import formate. Control samples that export formate have lower
reaction rates. Acetate fluxes are higher than formate fluxes in Dorea formigeneras. Acetate
is only exported by D. formigeneras. Control samples produce acetate at a higher rate.
D. formigeneras is more abundant in Control samples. D. longicatena imports L-tryptophan
and exports H2 but fluxes are below 1 mmol/gDCW h. H2 export is an expected outcome
of D. longicatena based on the literature, but tryptophan consumption or indole production
is not mentioned previously for the gut of ADHD subjects in the literature. Formate and
acetate have high exchange fluxes in D. longicatena. Formate export is not observed, and
it is only imported by this species. Samples from subjects in their twenties mostly show
higher formate import fluxes for Control samples in pairs. Pairs of matching siblings below
the age of 20 do not show a correlation with flux. High amounts of acetate are exported by
the bacterium D. longicatena as expected, but the acetate flux does not show any relation
with age, health status, or diet.
In this study, the species Prevotella copri, Prevotella ruminicola, and Prevotella buccae
are expressed in gut samples. Prevotella copri is expressed in five samples, each belonging
to a different pair, with only one of them being an ADHD subject. Acetate, formate,
and propionate are exported by this species Prevotella copri, but propionate fluxes are not
significant. Acetate export by Prevotella copri is higher than formate export, as reported in
the literature. Acetate export flux is lowest under the Western Diet and highest under the
Vegan Diet. Additionally, for the genus Prevotella, in ADHD subjects, propionate, formate
and acetate are also produced by another species, P. ruminicola. Propionate export rates are
significant. Both subjects, ADHD and Control, show the highest propionate exchange rate
when under the Western Diet. ADHD subjects have lower export fluxes than the Control
sample, but subjects are not at the same age nor have the same gender. Acetate exchange is
bidirectional for P. ruminicola and exchange fluxes are mostly significant. ADHD samples
produce a higher amount of acetate than the Control samples, but these two types of
samples belong to different ages and genders.
Under the genus Ruminococcus, Ruminococcus bromii is expressed in 16 samples out
of 20, and six of these samples are amongst those diagnosed with ADHD. Formate and
acetate exchanges were observed. Formate exchange done by R. bromii is bidirectional and
exchange fluxes are low. Acetate is not produced, but only consumed by R. bromii, and
the fluxes are significantly high. In terms of gender, female ADHD samples show a higher
acetate import rate compared to their Control pairs with one exception. There are not
enough pairs to compare male samples. In this study, 11 samples (three pairs), six of them
diagnosed with ADHD, show the species R. albus. Formate and acetate are exported with
significant fluxes by R. albus. ADHD samples have a higher abundance of this bacterium,
as well as higher export fluxes compared to their Control pairs. The difference between
ADHD and Control samples is higher for male pairs. Additionally, export fluxes decrease
Metabolites 2023, 13, 592 14 of 31

under the influence of the Western Diet. The Atkins’ Diet provides the highest flux values,
but these rates are very close to those obtained under the Vegan Diet. There is no visible
correlation between flux rates and age. Acetate fluxes are also higher for ADHD samples
in pairs. However, there is no correlation between acetate fluxes and dietary habits, age,
or gender.
Our results showed a significant butyrate import and acetate export in ADHD subjects
with Subdoligranulum variabile. ADHD samples import more butyrate than the Control
sample, which is the opposite for some pairs. In terms of acetate export, ADHD subjects
show the lowest flux under the Western Diet effect. S. variabile also imports acetate for
ADHD cases and has the highest import flux under Western diet influence.
There are thousands of different species that live in the human gastrointestinal tract.
Some species, e.g., those belonging to Bacteroides species, are common in the gut population,
but some others show sample-specific abundance. The species that appeared in very few
samples will be discussed in Supplementary File S1.

3.4. Production of Precursors of Key Neurotransmitters in the Gut

Recent research revealed that gut bacteria can produce several neuroactive sub-
stances, such as gamma-aminobutyric acid (GABA), serotonin, and catecholamines such
as dopamine and norepinephrine, or the precursors of these key molecules, which have
an impact on ADHD. Theories about the neuroscience of ADHD place much emphasis on
abnormalities in the noradrenergic, dopaminergic, serotoninergic, and cholinergic path-
ways [65]. In the present study, the precursors of the neurotransmitters are produced by
the gut microbiota of age-matched siblings. Thus, glutamate (precursor of GABA), trypto-
phan (precursor of serotonin) and phenylalanine and tyrosin (precursors of dopamine) are
examined by our community model.
Bacteria and neural tissue follow the same biosynthetic pathway to produce GABA.
Glutamate is hydroxylated to GABA in the neuron cytoplasm [66]. Glutamate, the GABA
precursor, is exchanged by gut flora in the present gut community model. Among the
other neurotransmitter precursors, glutamate results in significant flux values in several
bacteria like Alistipes putredinis, Bacteroides vulgatus, Bifidobacterium angulatum, Coprococcus
catus, Subdoligranulum variabile. Glutamate fluxes also show a correlation with gender
or dietary habits. Alistipes putredinis does not produce glutamate; however, it consumes
this metabolite at a significant rate. The highest consumption rates are observed under
the Western Diet influence with one exception. The flux differences in male pairs are
more drastic compared to those in female pairs. Moreover, Bacillus licheniformis, Bacteroides
vulgatus, Blautia hydrogenotrophica, Coprococcus catus, and Prevotella ruminicola show higher
glutamate import fluxes under the Western Diet influence, whereas Finegoldia magna,
Mobiluncus curtisii, Parabacteroides ditasonis, Parabacteroides goldsteinii, and Prevotella copri
give the lowest glutamate exchange rates (Western Diet). Except for P. copri, all the species
import glutamate, and none of them have insignificant flux rates. Only F. magna has a
low glutamate import rate compared to other species. Vegan and Atkins’ diets give very
close exchange fluxes for F. magna species. Glutamate exchange held by P. ruminicola is
bidirectional, and, while the Western Diet provides the highest import rate, Atkins’ diet
gives the highest export rate. In the case of Holdemania filiformis, ADHD samples import
more glutamate than the Control sample. C. eutactus only imports glutamate, and female
pairs show high differences between ADHD and Control samples compared to male pairs.
For Prevotella copri, ADHD and neurotypical Control samples show almost equal glutamate
export fluxes. However, as the number of samples including these gut bacteria is very
limited, the results should be considered with care, and not taken as conclusive for this
kind of rarely-present bacteria.
ADHD samples show higher L-tryptophan (serotonin precursor) import fluxes by
A. putredinis compared to the Control, and no L-tryptophan export exists. This might result
from a tryptophan hydrolysis reaction where tryptophan is hydrolyzed to indole, pyruvate,
and ammonium with tryptophanase catalysis. There is no correlation between L-tryptophan
Metabolites 2023, 13, 592 15 of 31

import and the three diet types. In terms of gender, the difference between Control and
ADHD sample fluxes of tryptophan is more drastic for male pairs than for female pairs.
This might partially explain why males show more clear ADHD symptoms than females.
ADHD gut also imports L-tryptophan by Bacteroides coprocola at a significantly higher
rate. The amount of L-tryptophan imported by ADHD samples by another Bacteroides
species, B. uniformis, is more significant compared to Control samples. D. longicatena imports
L-tryptophan, but fluxes are below 1 mmol/gDCW h. For D. longicatena, tryptophan
consumption or indole production is not mentioned previously for the gut of ADHD
subjects in the literature. Only one subject with ADHD (female) has Bacillus licheniformis
(0.12% abundance). There is L-tryptophan import under the Western Diet and export under
Atkins’ and Vegan diets by this species, but these fluxes are not significant in amount.
Tyrosine and phenylalanine are both dopamine and noradrenaline precursors [67,68].
Tyrosine is produced from phenylalanine hydrolysis and DOPA is produced from tyrosine
hydrolysis. In the end, DOPA is decarboxylated to dopamine [69]. These two metabolites
show similar behavior in our gut model. They both give insignificant exchange fluxes most
of the time. At other times, fluxes are still low, but since they are above 1 mmol/gDCW h,
they cannot be seen as insignificant. These metabolites are not produced in some species.
Bidirectional phenylalanine exchange is observed in A. putredinis, but flux values are
insignificant. Tyrosine exchange only occurs in the import direction but again flux values
are very low. Although flux rates are insignificant, phenylalanine and tyrosine import
are also observed for Bifidobacterium adolescentis, Bifidobacterium pseudocatelunatum and
Coprococcus eutactus, and that is important for model validation. Tyrosine and phenylalanine
are also consumed by Subdoligranulum variabile. Both have insignificant flux values (less
than 1 mmol/gDCW h), but their consumption information is important. While tyrosine
is only imported by this bacterium, phenylalanine shows bidirectional fluxes. S. variabile
consumes and produces phenylalanine under the influence of the Atkins’ and Vegan diets,
but there is only consumption under the Western Diet.

3.5. Elasticities
The fluxes for each reaction in the gut community model were estimated for both
ADHD and neurotypical Control cases by the computational pipeline embedded in MICOM,
and those with relatively high values are given in Figure 5a,b, respectively. Elasticity
coefficients are then used to analyze the sensitivity of bacterial abundances as well as
exchange fluxes to changes in diets at the species level. The responses of the gut microbiota
to three diets (Western, Atkins’, and Vegan) are summarized in Tables 5–7, where ADHD
means a wider range of variances of elasticity coefficients for ADHD subjects, i.e., higher
range of elasticities wrt healthy Control subjects. NA means that a bacterium is not present
in that pair; same means the same range of variances of elasticity coefficients for both
Control and ADHD subjects.
Under the influence of the Western Diet, Bacteroides uniformis, Bacteroides vulgatus,
Coprococcus catus, and Subdoligranulum variabile consistently show higher elasticity variances
in ADHD individuals (Table 5). Alistipes putredinis, Collinsella aerofaciens, Coprococcus
comes, and Subdoligranulum variabile show higher elasticity variances for ADHD subjects
under Atkins’ Diet (Table 6). Atkins’ Diet mimics the ketogenic diet. Bacteroides uniformis,
Bacteroides vulgatus, Bifidobacterium adolescentis, and Subdoligranulum variabile have higher
elasticity variances for ADHD subjects under the influence of the Vegan Diet (Table 7).
Among the three diets, Subdoligranulum variabile is the common bacterium showing high
elasticity variances, i.e., ADHD subjects’ metabolism is highly responsive to changes in this
organism’s abundance.
Microbiota elasticity coefficients of the Atkins’ Diet have wider elasticity ranges in
ADHD individuals. This diet also changes flux elasticity coefficients. The Atkins’ Diet
affects the ADHD gut more than the Western Diet.
 comes, and Subdoligranulum variabile show higher elasticity variances for ADHD subjects
under Atkins’ Diet (Table 6). Atkins’ Diet mimics the ketogenic diet. Bacteroides uniformis,
Bacteroides vulgatus, Bifidobacterium adolescentis, and Subdoligranulum variabile have higher
elasticity variances for ADHD subjects under the influence of the Vegan diet (Table 7).
Among the three diets, Subdoligranulum variabile is the common bacterium showing high
Metabolites 2023, 13, 592 16 of 31
elasticity variances, i.e., ADHD subjects’ metabolism is highly responsive to changes in
this organism’s abundance.

Diet
F lu xes R eactio n s D iet
400 EX_12dhchol(e) Atkins
200 EX_ac(e) Vegan
0 EX_acald(e) Western
−200 EX_ade(e)
EX_ala_L(e)
−400 EX_asn_L(e)
M etab o lites EX_co2(e)
12−Dehydrocholate EX_cys_L(e)
acetate EX_dad_2(e)
acetaldehyde EX_dcyt(e)
Adenine EX_for(e)
L−alanine EX_fru(e)
L−asparagine EX_fum(e)
carbon dioxide EX_gcald(e)
L−cysteine EX_gln_L(e)
2−deoxyadenosine EX_glu_L(e)
Deoxycytidine EX_gly(e)
Formate EX_glyc(e)
D−Fructose EX_glyclt(e)
Fumarate EX_gsn(e)
glycolaldehyde EX_gua(e)
L−glutamine EX_h(e)
L−glutamate(1−) EX_h2(e)
Glycine EX_h2o(e)
Glycerol EX_hxan(e)
glycolate EX_indole(e)
Guanosine EX_ins(e)
Guanine EX_lac_D(e)
proton EX_lac_L(e)
Hydrogen EX_orn(e)
Water EX_pac(e)
Hypoxanthine EX_pi(e)
Indole EX_pro_L(e)
Inosine EX_rib_D(e)
(R)−lactate EX_ser_L(e)
(S)−lactate EX_succ(e)
Ornithine EX_ura(e)
phenylacetate EX_xan(e)
hydrogenphosphate
L−proline
D−ribose
L−serine

Metabolites
Reactions
Succinate
uracil
Metabolites 2023, 13, x FOR PEER REVIEW Xanthine 17 of 30

(a)
Diet
F lu xes R eactio n s D iet
400 EX_12dhchol(e) Atkins
200 EX_ac(e) Vegan
0 EX_acald(e) Western
−200 EX_ade(e)
EX_ala_L(e)
−400 EX_asn_L(e)
M etab o lites EX_co2(e)
12−Dehydrocholate EX_cys_L(e)
acetate EX_dad_2(e)
acetaldehyde EX_dcyt(e)
Adenine EX_for(e)
L−alanine EX_fru(e)
L−asparagine EX_fum(e)
carbon dioxide EX_gcald(e)
L−cysteine EX_gln_L(e)
2−deoxyadenosine EX_glu_L(e)
Deoxycytidine EX_gly(e)
Formate EX_glyc(e)
D−Fructose EX_glyclt(e)
Fumarate EX_gsn(e)
glycolaldehyde EX_gua(e)
L−glutamine EX_h(e)
L−glutamate(1−) EX_h2(e)
Glycine EX_h2o(e)
Glycerol EX_hxan(e)
glycolate EX_indole(e)
Guanosine EX_ins(e)
Guanine EX_lac_D(e)
proton EX_lac_L(e)
Hydrogen EX_orn(e)
Water EX_pac(e)
Hypoxanthine EX_pi(e)
Indole EX_pro_L(e)
Inosine EX_rib_D(e)
(R)−lactate EX_ser_L(e)
(S)−lactate EX_succ(e)
Ornithine EX_ura(e)
phenylacetate EX_xan(e)
hydrogenphosphate
L−proline
D−ribose
L−serine
Metabolites
Reactions

Succinate
uracil
Xanthine

(b)
Figure 5.
Figure 5. Metabolite
Metabolite exchange
exchange fluxes
fluxes for
for (a)
(a) Control
Control samples
samples and
and (b)
(b) ADHD
ADHD samples
samples under
under Western,
Western,
Atkins’ and Vegan diets.
Atkins’ and Vegan diets.

Table 5. Elasticity comparison for Western Diet.

Organism Pair 1 Pair 2 Pair 3 Pair 4 Pair 5 Pair 6 Pair 7 Pair 8 Pair 9 Pair 10
Alistipes putredinis ADHD Control Control Control NA NA ADHD Control Control Control
Bacteroides coprocola NA NA NA NA NA NA NA ADHD NA NA
Bacteroides uniformis Control ADHD ADHD ADHD ADHD NA ADHD Same Same Control
Bacteroides vulgatus ADHD Same ADHD Control NA Control ADHD ADHD Control ADHD
Bifidobacterium adolescentis Control Control Same Control Same NA Control ADHD Control Control
Bifidobacterium angulatum ADHD NA NA NA NA NA NA ADHD NA Control
Metabolites 2023, 13, 592 17 of 31

Table 5. Elasticity comparison for Western Diet.

Organism Pair 1 Pair 2 Pair 3 Pair 4 Pair 5 Pair 6 Pair 7 Pair 8 Pair 9 Pair 10
Alistipes putredinis ADHD Control Control Control NA NA ADHD Control Control Control
Bacteroides coprocola NA NA NA NA NA NA NA ADHD NA NA
Bacteroides uniformis Control ADHD ADHD ADHD ADHD NA ADHD Same Same Control
Bacteroides vulgatus ADHD Same ADHD Control NA Control ADHD ADHD Control ADHD
Bifidobacterium adolescentis Control Control Same Control Same NA Control ADHD Control Control
Bifidobacterium angulatum ADHD NA NA NA NA NA NA ADHD NA Control
Bifidobacterium pseudocatenulatum ADHD NA NA NA NA NA NA ADHD Control Same
Collinsella aerofaciens Control Control Same ADHD ADHD NA Control Control Control ADHD
Coprococcus catus Same ADHD ADHD ADHD NA NA ADHD ADHD NA Control
Coprococcus comes Control ADHD Control Control ADHD Control ADHD ADHD Control Control
Coprococcus eutactus NA NA ADHD ADHD NA NA NA Control NA NA
Dialister invisus Control NA NA ADHD NA NA NA NA NA Control
Dorea formicigenerans Control NA Same Control Control ADHD ADHD Control ADHD Control
Dorea longicatena Control ADHD ADHD Control ADHD Control Same Same ADHD
Holdemania filiformis NA NA NA ADHD NA NA NA NA NA NA
Parabacteroides merdae Control ADHD ADHD NA NA NA Control NA NA NA
Ruminococcus albus Control NA NA ADHD NA NA NA ADHD NA NA
Ruminococcus bromii Control ADHD NA ADHD NA ADHD ADHD NA Control NA
Streptococcus mitis Control NA NA NA NA NA NA NA NA NA
Streptococcus parasanguinis Same NA NA NA NA NA NA NA NA NA
Streptococcus thermophilus Control Same NA NA Same NA NA NA Control NA
Subdoligranulum variabile ADHD ADHD Control ADHD ADHD ADHD Control Same Control ADHD
Metabolites 2023, 13, 592 18 of 31

Table 6. Elasticity comparison for Atkins’ Diet.

Organism Pair 1 Pair 2 Pair 3 Pair 4 Pair 5 Pair 6 Pair 7 Pair 8 Pair 9 Pair 10
Alistipes putredinis ADHD ADHD Control ADHD NA NA ADHD Control Control ADHD
Bacteroides coprocola NA NA NA NA NA NA NA Control NA NA
Bacteroides uniformis Control ADHD Control ADHD Control NA ADHD Control Control Control
Bacteroides vulgatus ADHD Control Control ADHD NA Control Control Control Control ADHD
Bifidobacterium adolescentis Control ADHD Control Control ADHD NA ADHD Control Control ADHD
Bifidobacterium angulatum Control NA NA NA NA NA NA Control NA ADHD
Bifidobacterium pseudocatenulatum Same NA NA NA NA NA NA Control Control ADHD
Collinsella aerofaciens ADHD ADHD ADHD Same ADHD NA ADHD Control ADHD ADHD
Coprococcus catus Same Same Same Control NA NA ADHD Control NA ADHD
Coprococcus comes ADHD Control ADHD ADHD ADHD Control ADHD Control Control ADHD
Coprococcus eutactus NA NA ADHD Control NA NA NA Control NA NA
Dialister invisus Control NA NA ADHD NA NA NA NA NA Control
Dorea formicigenerans ADHD NA Same Control ADHD ADHD ADHD Control Same Control
Dorea longicatena ADHD ADHD Control Control ADHD NA Control Control Control ADHD
Holdemania filiformis NA NA NA Control NA NA NA NA NA NA
Parabacteroides merdae Same ADHD ADHD NA NA NA ADHD NA NA NA
Ruminococcus albus Control NA NA Control NA NA NA Control NA NA
Ruminococcus bromii Control ADHD NA Control NA Control ADHD NA Control NA
Streptococcus mitis ADHD NA NA NA NA NA NA NA NA NA
Streptococcus parasanguinis ADHD NA NA NA NA NA NA NA NA NA
Streptococcus thermophilus Control ADHD NA NA ADHD NA NA NA Control NA
Subdoligranulum variabile ADHD ADHD ADHD Same ADHD ADHD ADHD Control Control Control
Metabolites 2023, 13, 592 19 of 31

Table 7. Elasticity comparison for Vegan Diet.

Organism Pair 1 Pair 2 Pair 3 Pair 4 Pair 5 Pair 6 Pair 7 Pair 8 Pair 9 Pair 10
Alistipes putredinis Control Control ADHD ADHD NA NA Control Same ADHD ADHD
Bacteroides coprocola NA NA NA NA NA NA NA Control NA NA
Bacteroides uniformis Control Control ADHD ADHD Control NA Same ADHD ADHD ADHD
Bacteroides vulgatus Control Control ADHD ADHD NA ADHD Control ADHD Control ADHD
Bifidobacterium adolescentis ADHD ADHD ADHD Control Control NA Control ADHD Control ADHD
Bifidobacterium angulatum Control NA NA NA NA NA NA ADHD NA ADHD
Bifidobacterium pseudocatenulatum Control NA NA NA NA NA NA Control Control Control
Collinsella aerofaciens Same ADHD Control Control ADHD NA Control Control Control Control
Coprococcus catus Control Control ADHD ADHD NA NA Control ADHD NA Control
Coprococcus comes Control Same ADHD ADHD ADHD ADHD Control Control Control Same
Coprococcus eutactus NA NA ADHD Control NA NA NA Control NA Control
Dialister invisus Control NA NA ADHD NA NA NA NA NA NA
Dorea formicigenerans Control NA Control Control ADHD ADHD Control Control Control ADHD
Dorea longicatena Control Control Control Control Control NA Control Same Control Control
Holdemania filiformis NA NA NA ADHD NA NA NA NA NA NA
Parabacteroides merdae Control ADHD Control NA NA NA ADHD NA NA NA
Ruminococcus albus Control NA NA Control NA NA NA ADHD NA NA
Ruminococcus bromii Control ADHD NA Control NA Same Control Control NA
Streptococcus mitis Control NA NA NA NA NA NA NA NA NA
Streptococcus parasanguinis Control NA NA NA NA NA NA NA NA NA
Streptococcus thermophilus Control Control NA NA Control NA NA NA Control NA
Subdoligranulum variabile ADHD ADHD ADHD Control ADHD Control Control Control Same ADHD
Metabolites 2023, 13, 592 20 of 31

When the elasticity coefficients (and their variances) of exchange fluxes under all
the diets are examined, lower elasticity coefficients are obtained in healthy individuals.
It has been observed that the elasticity coefficients of the exchange fluxes of amino acid
metabolism, minerals and metals such as zinc, cobalt, copper, oxygen and starch (carbon
source of the microbiota) are high in ADHD individuals. The slightest change in these
will seriously affect the intestinal system of ADHD individuals. In addition, the elasticity
coefficients of chloride and proline in healthy individuals are found to be considerably
lower than those in ADHD individuals, indicating a flux control. This result shows that the
changes in the amounts of chloride and proline have less effect in healthy individuals, but
there is no such trend in ADHD individuals.
In general, similar elasticity coefficients are obtained between healthy individuals and
ADHD cases, but when the standard deviations are examined, lower values are calculated
in healthy individuals, which is interpreted as healthy individuals being more resistant to
changes and standing firm.

3.6. Intervention by a Probiotic (L. rhamnosus)

Probiotics are described as live microorganisms that provide the host with health
benefits when given in sufficient doses. Lactobacillus rhamnosus (L. rhamnosus), which is sold
as a nutritional supplement and added to a number of foods, including dairy products, is
one of the most extensively researched friendly bacteria. L. rhamnosus promotes the growth
of good bacteria, such as Bacteroides, Clostridia, and Bifidobacteria, in addition to preventing
the colonization of harmful bacteria.
The exchange (import and export) behaviors of the short chain fatty acids such as
acetate, formate, propionate and butyrate are investigated in detail by the gut commu-
nity model, as L. rhamnosus helps increase their production. For example, upon probiotic
addition to the community model, the ADHD sample starts to consume (import) bu-
tyrate at 4.90652 mmole/g DCW/h in Subdoligranulum variabile metabolism. Before the
intervention, this particular male ADHD subject was exporting butyrate at a flux rate of
0.95877 mmole/g DCW/h under the Atkins’ Diet. Import rates of acetate and formate in
S. variabile increase for most samples (ADHD and Control) for all diets after the L. rhamnosus
addition. For Bacteroides uniformis, formate exchange fluxes show minor changes after the
intervention. The pairs that export acetate show higher flux values for the sibling with
ADHD. Moreover, the number of male samples importing acetate is higher than that
of females. Similarly, L. rhamnosus addition to the environment affects the isobutyrate
and isovalerate production ability of B. vulgatus. The acetate exchange carried out by
Bifidobacterium dentium for ADHD samples is also impacted by the addition of L. rhamnosus.
For B. pseudocatelunatum, after L. rhamnosus addition, acetate export flux values of ADHD
subjects decrease by 5–15% under the Western Diet. The short-chain fatty acids produced
by Coprococcus catus, acetate, butyrate, formate, and propionate, are affected significantly
by the addition of L. rhamnosus. These changes in short-chain fatty acid fluxes varies by
around 5–10% under the Atkins’ and Vegan diets for ADHD samples, but by 0–5% for
Control samples under the Atkins’ diet and by 10–20% under the Vegan diet (mainly for
acetate fluxes). Both Control and ADHD samples under the Western Diet show few cases
with extreme changes. The other species under the genus Coprococcus is C. comes, which is
also affected by probiotic intervention in terms of short-chain fatty acids exchange fluxes.
Acetate shows the highest changes in flux values, which are decreased in both ADHD and
control cases. All propionate flux values are increased but are still insignificant. On the
other hand, L. rhamnosus’s addition to the system had no significant effect on C. eutactus.
Neurotransmitter precursors phenylalanine, glutamate, tryptophan, and tyrosine
showed up, but the intervention did not result in any significant changes for these metabo-
lites for any of the microbiota.
Metabolites 2023, 13, 592 21 of 31

4. Discussion
The fact that people with ADHD frequently experience gastrointestinal (GI) dysfunc-
tion, such as constipation, low-grade inflammation, and childhood digestive problems,
serves to highlight the possibility that the gut microbiome may play a role in this condition.
Children with ADHD have been shown to have adverse reactions to particular meals and
food additives, exposure to harmful dietary pollutants, and suboptimal levels of micronu-
trients such as vital fatty acids, zinc, magnesium, and iron. Food allergies can take the
shape of hyperactivity. Given that ADHD children frequently exhibit a behavioral response
to food, including real allergies, these two conditions may have similar pathophysiological
routes [70]. Although it is likely that the gut microbiota plays a substantial role, there is
rising evidence to show the role of nutrition in the regulation of ADHD behavior. Diet may
thus contribute to the improvement of behavioral problems, probably through different
unidentified complex mechanisms. It has long been known that the ketogenic diet has
therapeutic benefits for children with autism spectrum disorder. Additionally, studies
on animals have demonstrated that the ketogenic diet affects activity levels and trainabil-
ity [71]. The effects of the Vegan Diet on ADHD or specific ADHD symptoms, such as
inattention, hyperactivity, and impulsivity, have not been studied yet. However, there are
some clinical studies [72].
Dietary interventions can be beneficial for children with ADHD. Thus, in the present
study, the effects of the Western, Atkins’ (ketogenic) and Vegan diets on the prevalence of
gut microbiota and their metabolites are investigated. In most of the cases examined in
the present study, no strong correlation is found between the exchange direction of key
metabolites (SCFAs and amino acids etc.) and health status, diets, gender, or age. Inferring
with certainty whether a particular microbial profile is linked to ADHD is difficult given
the results’ high degree of heterogeneity. The main findings on the key metabolites are
summarized in Table 8.
In the present study, the species contributors to the key SCFAs mainly come from
the phyla of Actinobacteria (genus Bifidobacterium and Collinsella), Bacteroidetes (genus
Bacteroides and Prevotella), Bacillota (genus Coprococcus and Subdoligranulum), Bacteroidota
(genus Alistipes), and Firmicutes (genus Ruminococcus, Dialister, and Dorea). Alistipes is
a bile-resistant anaerobic acid-producing indole-positive genus [73]. The major product
of Alistipes putredinis is succinate, as well as acetic, isobutyric, isovaleric, and propionic
acids, which are produced in smaller amounts. Alistipes population found in the human
gastrointestinal tract is correlated with fiber and processed food consumption in the diet [62].
Studies showed that the consumption of food of animal origin for a limited time increases
the abundance of bile-tolerant and putrofactive microorganisms such as A. putredinis [74].
Alistipes genus is also related to fatigue and stress, which are also seen in mood disorders
such as depression [75]. This might be related to Alistipes being an indole-positive organism
that effects serotonin availability in a negative way [75].
One of the most dominant bacterial genera in the human gut is Bacteroides, with almost
25% of the bacteria in the human gut belonging to this genus [76]. Bacteroides species might
pass from mother to child during birth and take their place in the gut microbiome from
birth [77]. Bacteroides coprocola, Bacteroides plebeius, Bacteroides uniformis, and Bacteroides
vulgatus are the species with a significant effect on samples studied here. B. coprocola is one
of the L-tryptophan consumers and has been suggested to be an ADHD biomarker, since
it showed lower abundance in the ADHD gut. Bacteroides vulgatus is another propionate-
producing bacterium, and uses succinate as its main carbon source [78]. There is also
evidence about the effects of B. vulgatus on cognitive function in Alzheimer’s patients via
Glu metabolism [79]. Bacteroides uniformis is a probiotic used to modulate depression and
anxiety in clinical studies done on rodents [80,81]. B. uniformis supplementation to mice
affects reward response in the brain in a positive way and reduces anxiety [80].
Metabolites 2023, 13, 592 22 of 31

Table 8. The relationship between the taxa, diets, and key metabolites *.

Key Metabolite Western Diet Atkins’ Diet Vegan Diet

A. putredinis ↓
C. comes ↓
C. comes ↑ A. putredinis ↑
P. goldsteinii ↑
Acetate P. copri ↓ P. copri ↑
R. albus ↑
R. albus ↓ R. albus ↑
P. goldsteinii ↓
P. buccae ↑
C. catus ↓
C. catus ↓
Formate P. goldsteinii ↑
P. goldsteinii ↓
P. buccae ↑
P. merdae ↑
Propionate P. ruminicola ↑ P. goldsteinii ↑
P. merdae ↑
A. Putredinis ↑
B. licheniformis ↑
B. vulgatus ↑
B. hydrogenotrophica ↑
C. catus ↑
P. ruminicola ↑ P. ruminicola ↑
P. goldsteinii ↑
Glutamate F. magna ↓ B. hydrogenotrophica ↓
P. copri ↑
M. curtisii ↓ F. magna ↑
P. distasonis ↓
P. goldsteinii ↓
F. magna ↓
P. distasonis ↓
P. coprii ↓
M. curtisii ↑
L-Phenylalanine P. distasonis ↓ D. formigeneras D. formigeneras
D. formigeneras
B. uniformis ↓
L-Tryptophan B. licheniformis B. licheniformis
B. licheniformis
M. curtisii ↑
L-Tyrosine D. formigeneras D. formigeneras
D. formigeneras
* Export fluxes are shown in bold font. The upwards arrow represents high flux values provided by the taxon and
the downwards arrow represents the opposite. Some bacteria show bidirectional metabolic fluxes (export and
import), where the diet achieves the same effect in both directions. This is why some bacteria are shown twice
under the same diet. Additionally, for some species, changes in the diet directly affected the flux direction (cases
B. licheniformis and D. formigeneras); these species are shown in the table without an arrow.

Bifidobacteria is an immunoregulating bacteria genus [82]. Bifidobacterium species

export low amounts of ethanol, formate, and succinate, but no butyrate or propionate
production is possible. Tetrose and hexose phosphates are used to produce acetate. Some
of the pyruvates can be used in acetate and formate production, which might alter the
fermentation balance in the gut. Acetate import by Bifidobacteria is also possible, and
acetate can be reduced to ethanol. There are four Bifidobacterium species with significant
import and export fluxes, which are Bifidobacterium adolescentis, Bifidobacterium angulaum,
Bifidobacterium dentium, and Bifidobacterium pseudocatelunatum. B. adolescentis is defined as a
possible ADHD biomarker by a previous clinical study [57]. Bifidobacterium adolescentis in
the human intestine has antidepressant effects. In the present study, there are nine pairs
containing this species B. adolescentis, and ADHD abundance is higher for seven of them
which contradicts the previous literature findings. Bifidobacterium adolescentis is another
GABA-producing probiotic. An animal study proved that it has antidepressant effects and
also reduces inflammation in the hippocampus [83]. In addition, ω − 3 fatty acid intake
has an effect on the amount of Bifidobacterium, with the standard Western Diet having a 25:1
ratio of ω − 6 to ω − 3 fatty acids [84].
Metabolites 2023, 13, 592 23 of 31

The genus Coprococcus is an anaerobic whose species are isolated from human feces [85].
Coprococcus uses carbohydrate fermentation as the main source of energy and growth [85].
Short-chain fatty acids—butyrate, acetate, formate, and propionate—are the main products
of this genus. Coprococcus catus, Coprococcus comes, and Coprococcus eutactus are the species
with significant metabolite fluxes. These species play a significant role in carbohydrate
metabolism. Coprococcus catus is a known propionate producer which uses lactate as its main
carbon source, and a study shows 3,4-dehydroxyphenylacetaldehyde to DOPAC conversion
can be related to this species [86]. The conversion of 3,4-dehydroxyphenylacetaldehyde to
DOPAC can be related to Coprococcus comes, in addition to C. catus [86].
Dorea uses glucose and some other sugars for acetate, formate, ethanol, H2 , and
CO2 production by fermentation. In addition to those metabolites, lactate production
may be observed, but butyrate is not an expected output from this genus [87]. In this
study, the species Dorea formigeneras and Dorea longicatena are observed. D. formigeneras
is a formate-producing bacterium found in the human gastrointestinal tract. In order to
produce formate, D. formigeneras follows a carbohydrate fermentation pathway. NaCl
concentration in the environment may limit cell growth. As a result of glucose metabolism,
D. formigeneras mainly produces acetate, formate, and lactate. The bacterium also utilizes
pyruvate for acetate, formate, and ethanol production. Pyruvate utilization may result
in lactate and succinate to a lesser extent. Propionate production from threonine is also
possible. D. longicatena uses several carbon sources and mainly produces acetate, formate,
and ethanol. Coexisting bacteria in the gut flora are able to use acetate, formate, and H2
produced by D. longicatena. In fact, this species is responsible for producing 10–30 g of
acetate in the human gastrointestinal tract on a daily basis [87]. D. longicatena is a possible
autism biomarker and the neurotransmitter precursor metabolites are not exchanged
by this bacterium. Moreover, this species, Dorea longicatena, is only expressed in three
ADHD samples.
Prevotella is one of the predominant genera in human gastrointestinal tracts. Some
species of the genus are found in oral cavities [88]. In this study, Prevotella copri, Prevotella
ruminicola, and Prevotella buccae were expressed in gut samples. P. copri is a predominant
bacterium in human feces and isolated from it [89]. The bacterium uses several different
sugars for acid production. The main products are acetate and succinate, but bigger fatty
acid products also exist. Bacteroides in the growth medium might inhibit the growth of this
species. Another species under the Prevotella genus is P. ruminicola which is mostly found
in cow rumen [88]. Similar to P. copri, propionate, formate and acetate are produced by
this species. P. buccae is a species isolated from human periodontal flora, human feces, and
some other human tissues, and is not a bile-resistant bacterium [90]. The main metabolites
produced by this species are acetate and succinate, but formate production is also observed
for some strains. In terms of minor products, low isovalerate and isobutyrate exports occur.
Ruminococcus is a cellulolytic genus that lives in the mammalian gastrointestinal tract.
Bacteria belonging to this genus require carbohydrates for growth. The main products
of carbohydrate fermentation are acetate and formate; sometimes, succinate and ethanol
might appear in the system [78]. In this study, Ruminococcus bromii and Ruminococcus albus
were expressed in some samples. R. bromii is usually isolated from human feces and differs
from other Ruminococcus species in terms of being non-cellulolytic. Instead of cellulose,
R. bromii ferments starch [91].
Subdoligranulum species produce butyrate and lactate as major products of glucose
and some other carbohydrates. Subdoligranulum variabile is a known butyrate producer
living in human intestine and represents 1% of total Subdoligranulum population [92].
Subdoligranulum variabile also produces small amounts of acetate and succinate in addition
to its main products [93].
Among the sample-specific species, Akkermansia muciniphila is an anaerobic bacteria
found in the gut lumen of different mammals, including humans and rats, which uses the
epital mucus layer as its carbon and nitrogen source [94]. A. municiphila is related to obesity,
inflammation, Crohn’s disease, and insulin resistance [95], and is also found in gut samples
Metabolites 2023, 13, 592 24 of 31

of ASD children [25]. Bacillus licheniformis is a bacterium mostly found in bird feathers and
soil [96]. It is responsible for β -keratin degradase in bird feathers [97]. Additionally, it is
used as a probiotic for animals as an additive to animal feed [98,99]. Recently, B. licheniformis
was isolated as a human probiotic [100,101], but there are not enough studies done in terms
of probiotic effects on humans. B. licheniformis is mostly known for its effects on food. This
bacterium is responsible for bread ropiness, food contamination, and the spoilage of food
such as dairy products or bread [102,103]. Blautia hydrogenotrophica is a bacterium isolated
from human and animal feces [104]. Its main purpose is H2 /CO2 utilization for acetate
production. In addition to acetate, B. hydrogenotrophica produces ethanol, lactate, and lower
amounts of isobutyrate and isovalerate. Glucose and fructose are the main carbon sources
for fermentation. Clostridium paraputrificum is a pathogen that causes several diseases in
humans, such as bacteremia and myonecrosis [105]. A study showed that C. paraputrificum
causes necrotizing cellulitis of the abdominal wall [106]. The bacterium utilizes glucose
to produce H2 and CO2 [107]. The amount and composition of the end products are
affected by the pH of the environment. C. paraputrificum also produces propionate and
acetate as the main organic acid products, and the production of lactate and butyrate was
also observed, but at lower flux values. The amount and type of organic acid produced
affects the H2 yield rate. Collinsella aerofacians is a bacterium isolated from human feces.
Collinsella genus produces ethanol, formate, H2 , and lactate with fermentation. The main
fermentation product of C. aerofacians is acetate and comes from sucrose and/or cellobiose,
depending on which strain is isolated [108]. Finegoldia magna is an important pathogen
found in the blood [109]. However, it can also be found in the gastrointestinal tract, female
genito-urinary tract, and skin [110]. In fact, most of the F. magna cells used in in-vivo studies
are isolated from skin, soft tissue, bone, or joint infections [110]. Holdemania filiformis is an
acid-producing bacteria isolated from human feces [111]. It uses glucose as the main carbon
source during acid production. Acetate, lactate, and succinate are major acid products of the
bacterium. Formate might also show up as an additional acid product. Mobiluncus species
are acid-producing bacteria. The main fermentation products of the genus are succinate and
acetate, and lactate production may also occur. M. curtisii hydrolyses starch. Some strains of
the bacterium were isolated from vaginal fluids taken from women with bacterial vaginosis
diagnoses [112]. Parabacteroides is a bile-resistant genus commonly found in the human
gastrointestinal tract [113]. Acetate and succinate are the major acids produced by this
genus and other acids can be produced to a lower extent [113]. In this study, P. distasonis,
P. merdae, and P. goldsteinii are expressed species of this genus. P. distasonis is a species
isolated from human feces and uses several carbon sources to produce organic acids [114].
P. merdae also uses several carbon sources to produce organic acids. P. goldsteinii is another
species isolated from human feces, whuch also uses several different sources of carbon
for acid production [113]. In the presence of yeast and glucose, the bacterium is able to
produce acetate and succinate. In addition to them, isovalerate, propionate, and formate
can be seen to a lesser amount. As one can deduce from the abovementioned information,
these sample-specific species might have been encountered in ADHD subjects as a result of
a co-existing disease condition.
SCFAs affect numerous bodily functions, such as epithelial cell transport, metabolism,
growth, and differentiation, as well as lipid and carbohydrate control in the liver. In
addition to these, SCFAs also provide energy for organs such as kidneys, the heart, and the
brain, tissues such as muscles, and epithelial cells at the end of the colon. Another function
of SCFAs is to act as signaling molecules. For example, three SCFAs, mainly propionate and
acetate, are ligands for G protein-coupled receptors of Gpr41 and Gpr43. These receptors
are expressed at the end part of the small intestine, large intestine, and adipocytes.
SCFAs produced in the gut can enter the central nervous system by passing the blood–
brain barrier, and glia and neurons consume these molecules. Consequently, SCFAs are a
major energy source for glia and neurons. This cellular energy process is important for brain
development, especially in the early years of life. In addition, SCFAs are used in the central
nervous system for cell signaling, neurotransmitter synthesis, and release. SCFAs induce
Metabolites 2023, 13, 592 25 of 31

tyrosine hydroxylase, a catecholamine synthesis enzyme, and, as a result of this, dopamine

and dopamine-related catecholamines production increase. Dopamine, serotonin, and
glutamate systems in neurodevelopmental diseases suchas ASD are altered [115] and
propionate has a similar effect on those systems.
The gut-brain axis is affected by microbially produced neurotransmitters like GABA
or their precursors. Gamma-Aminobutyric Acid (GABA) is an inhibitory neurotransmitter
and controls various physiological and psychological processes in brain. Anxiety and
depression are related to GABA signalling problems. Glutamate decarboxylase (GAD) and
vitamin co-factor pyridoxal phosphate are used to convert glutamate to GABA. Studies
show that strains of Lactobacilli and Bifidobacteria can produce GABA from monosodium
glutamate (MSG). Lactobacillus brevis and Bifidobacterium dentium (also present in our com-
munity model) are the most effective species for GABA production in MSG culture [32].
Fear and mood reactions are made to function properly via GABAA and GABAB receptors.
Serotonin regulates bodily functions such as mood, and is a product of tryptophan,
an essential amino acid. In the present study, a comparison based on gender indicates
some differences between the healthy and ADHD sample fluxes of tryptophan, which is
more significant for male pairs than for female pairs (in the case of Alistipes putredinis).
This might explain why males show more clear ADHD symptoms than females. Most
commercial antidepressants focus on raising serotonin levels. Recent studies have shown
that the serotonin level in regular mice is significantly high compared to that of germ-
free mice, and this might be a result of a host–microbe interaction [116]. The effects of
Bifidobacterium infantis on serotonin levels of germ-free mice were also observed and an
increase in tryptophan in plasma was reported [117]. Tryptophan is a serotonin precursor
and its increase shows that the bacterium has effects on tryptophan metabolism. In addition,
lactic acid bacteria such as Lactococcus lactis, Lactobacillus plantarum, and Streptococcus
thermophilus can also increase serotonin levels in plasma. However, these organisms are not
observed in our gut community model due to their very low abundance in ADHD samples.
Dopamine and norepinephrine affect motor control, cognition, memory processing,
emotion and endocrine regulation. Catecholamine neurotransmission dysregulation results
in neurological and neuropsychiatric disorders such as Parkinson’s disease, Alzheimer’s
disease, and major depression. Recent studies show that some bacteria can produce cat-
echolamines. Dopamine is detected in Bacillus cereus, Bacillus mycoides, Bacillus subtilis,
Proteus vulgaris, Serratia marcescens, Serratia aureus, and Escherichia coli biomass. In addition
to the aforementioned species, lactic acid bacteria such as Lactococcus lactis, Lactobacillus plan-
tarum, and Streptococcus thermophilus can produce dopamine. Norepinephrine was found in
Bacillus mycoides, Bacillus subtilis, Proteus vulgaris, and Serratia marcescens biomass [36]. These
organisms are lacking in our ADHD samples.
Some gut species show a direct relationship between the diet and metabolite exchange
direction. For most species, Western Diet conditions cause an import behavior for dopamine
precursors in the gut community model. In ADHD, the main problem with dopamine comes
from the low number of dopamine receptors in the hippocampus. ADHD medications aim
to produce more dopamine in the body to increase the number of molecules that find and
bind to these receptors. The Western Diet restricts the precursor flux by forcing the bacteria
to consume the precursors. The Atkins’ and Vegan diets, on the other hand, encourage the
gut flora to produce these precursors.
Our computational study successfully revealed that the probiotic strain Lactobacillus
rhamnosus (LGG) caused either increases in short chain fatty acid production fluxes or
reversed their exchange direction (import/export) for some of the strains in the gut com-
munity model, in agreement with the available literature [54,65,118]. However, no effect of
the probiotic intervention has been computationally observed on any neurotransmitter pre-
cursors. There are some recent reports that Lactobacillus rhamnosus GG significantly reduces
the signs and symptoms of anxiety and sadness, with a preventive effect on ADHD, and
also has a beneficial effect on cognitive function. The children and teenagers who received
LGG supplements reported higher health-related quality of life [54,118]. Nevertheless, in
Metabolites 2023, 13, 592 26 of 31

light of the information gained from our computational study, as well as the very limited
information in the literature, there is not enough evidence to strongly suggest the usage of
probiotic supplements to treat ADHD today.
Each bacterium’s distinct genetic makeup results in the production of distinct sets of
metabolites, which interact with host metabolites and additional compounds downstream
to form a complex network of host–microbiome interactions. Our findings and those found
in the literature suggest that medications that target the gut microbiota specifically may be
effective in the treatment of ADHD. As the gut microbiota is an ecosystem, any alterations
to one component would probably influence other parts. When designing such treatments,
personalized medicine should be used due to the huge individual variation in the human
gut microbiome.
By altering either the metabolic pathways or the expression of the genes encoding
the neurotransmitter transporters, the gut microbiome may affect the catecholaminergic
neurotransmission system [23]. Early in life, Bifidobacterium predominates in the gut and,
as people mature, its relative abundance slowly declines. Thus, the fact that its abundance
was lower in early infancy and rose in early adulthood may be a reflection of delayed
gut microbiota maturation in ADHD. An increase in Bifidobacterium species was linked to
significantly higher projected dopamine precursor (phenylalanine) biosynthesis potential
in the gut microbiota of ADHD patients compared to controls [44,119,120].
The presence of Bacillota (genus Coprococcus and Subdoligranulum), Actinobacteria
(genus Collinsella), Bacteroidetes (genus Bacteroides), Bacillota (genus Coprococcus and
Subdoligranulum), and Bacteroidota (genus Alistipes) may be possible gut microbiota indica-
tors of ADHD, as our case-control study is shown. Alistipes putredinis is especially associated
with depression in the literature. Its consumption of high amounts of L-tryptophan is an
unexpected behavior for ADHD subjects. In fact, L-tryptophan is a necessary metabolite
for serotonin production, and higher consumption in people diagnosed with ADHD may
help explain the relationship between depression and ADHD as well as ADHD and low
serotonin. However, surely, a single bacterial genus cannot be directly responsible for any
neuropsychiatric disorder, but rather they are the product of a complex interaction between
numerous bacterial genera. Furthermore, the gut microbiome is not constant, and both
internal and external factors can affect how the host responds. Researchers try to find
explanations for the inconsistent findings already reported in the literature, and variations
in the categorization and diagnosis of these neuropsychiatric illnesses also add to this
problem, being another source of confusion in much research. Numerous investigations
are likely to be complicated by the heterogeneity in these neuropsychiatric illnesses.

Supplementary Materials: The following supporting information can be downloaded at: https://
www.mdpi.com/article/10.3390/metabo13050592/s1, Supplementary Material File S1. Figure S1.
Taxa prevalence at the Species level.
Author Contributions: Conceptualization, E.T. and K.O.Ü.; Formal analysis, E.T.; Funding acquisi-
tion, K.O.Ü.; Methodology, E.T. and K.O.Ü.; Writing—original draft, E.T. and K.O.Ü.; Writing—review
editing, K.O.Ü. All authors have read and agreed to the published version of the manuscript.
Funding: This work was supported by the Bogazici University Research Funds Project No: 19761.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data used in this study are publicly available in the article of [44].
Conflicts of Interest: The authors declare no conflict of interest.
Metabolites 2023, 13, 592 27 of 31

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