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Citation: Hrnciarova, J.; Kubelkova,
K.; Bostik, V.; Rychlik, I.; Karasova, D.;
Babak, V.; Datkova, M.; Simackova, K.;
Macela, A. Modulation of Gut
Microbiome and Autism Symptoms of
ASD Children Supplemented with
Biological Response Modifier: A
Randomized, Double-Blinded,
Placebo-Controlled Pilot Study.
Nutrients 2024,16, 1988.
https://doi.org/10.3390/
nu16131988
Academic Editors: William B. Grant
and Lorena Perrone
Received: 12 May 2024
Revised: 18 June 2024
Accepted: 19 June 2024
Published: 21 June 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
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nutrients
Article
Modulation of Gut Microbiome and Autism Symptoms of ASD
Children Supplemented with Biological Response Modifier: A
Randomized, Double-Blinded, Placebo-Controlled Pilot Study
Jela Hrnciarova 1,2 , Klara Kubelkova 3, *, Vanda Bostik 3, Ivan Rychlik 4, Daniela Karasova 4, Vladimir Babak 4,
Magdalena Datkova 1,2, Katerina Simackova 1,2 and Ales Macela 3
1Faculty of Medicine, Charles University, 500 03 Hradec Kralove, Czech Republic;
hrnciarovaj@lfhk.cuni.cz (J.H.); datkovam@lfhk.cuni.cz (M.D.); katerina.simackova@fnhk.cz (K.S.)
2Department of Psychiatry, University Hospital in Hradec Kralove, 500 03 Hradec Kralove, Czech Republic
3Military Faculty of Medicine, University of Defence, 500 03 Hradec Kralove, Czech Republic;
vanda.bostikova@unob.cz (V.B.); amacela@seznam.cz (A.M.)
4Veterinary Research Institute, 621 00 Brno, Czech Republic; ivan.rychlik@vri.cz (I.R.);
daniela.karasova@vri.cz (D.K.); vladimir.babak@vri.cz (V.B.)
*Correspondence: klara.kubelkova@unob.cz
Abstract: The etiology and mechanisms of autism and autism spectrum disorder (ASD) are not yet
fully understood. There is currently no treatment for ASD for providing significant improvement
in core symptoms. Recent studies suggest, however, that ASD is associated with gut dysbiosis,
indicating that modulation of gut microbiota in children with ASD may thus reduce the manifestation
of ASD symptoms. The aim of this pilot study (prospective randomized, double-blinded, placebo-
controlled) was to evaluate efficacy of the biological response modifier Juvenil in modulating the
microbiome of children with ASD and, in particular, whether Juvenil is able to alleviate the symptoms
of ASD. In total, 20 children with ASD and 12 neurotypical children were included in our study.
Supplementation of ASD children lasted for three months. To confirm Juvenil’s impact on the
gut microbiome, stool samples were collected from all children and the microbiome’s composition
was analyzed. This pilot study demonstrated that the gut microbiome of ASD children differed
significantly from that of healthy controls and was converted by Juvenil supplementation toward a
more neurotypical microbiome that positively modulated children’s autism symptoms.
Keywords: autism; microbiome; biological response modifier; psychobiotics
1. Introduction
Autism spectrum disorder (ASD) is a behaviorally defined neurodevelopmental dis-
order. ASD lacks specific clinical biomarkers and has seen an evolving conceptualization
through the decades since it was first described. Over the past four decades, there has been
dramatic increase in the number of individuals diagnosed with ASD [
1
]. In general, ASD is
diagnosed by 3 years of age in most of those children experiencing it, although roughly 40%
of such children are not first evaluated until 4 years of age [
2
]. A psychiatric diagnosis of
ASD, which has behavior as its basis of definition, relies heavily upon precise observation
and clinical expertise because the condition lacks standardized biomarkers [3].
ASD is one of the most common and challenging neurodevelopmental disorders in
children. Its prevalence rate worldwide now exceeds 1%. A small number of these children
appear to develop normally in their first year and then go through a period of regression
between 18 and 24 months of age. ASD is characterized by deficits in communication
and social interaction, as well as a presence of repetitive and restrictive behaviors. More-
over, ASD often manifests with a wide range of comorbidities that include morphological,
physiological, and psychiatric conditions. ASD’s most commonly proposed causes are phys-
iological and metabolic disorders involving immunity, oxidative stress, and mitochondrial
Nutrients 2024,16, 1988. https://doi.org/10.3390/nu16131988 https://www.mdpi.com/journal/nutrients
Nutrients 2024,16, 1988 2 of 14
dysfunction [
4
]. Co-occurrence of two or more disorders in the same individual has been
observed, with comorbidities including anxiety, depression, attention deficit/hyperactivity
disorder (AD/HD), epilepsy, gastrointestinal symptoms/problems, sleep disorders, learn-
ing disabilities, obsessive–compulsive disorder, intellectual disability, sensory problems,
and immune disorders. The most prevalent comorbidity, at roughly 50%, is intellectual
disability [
5
]. At least one comorbidity exists in about 70% of children with ASD, while
41% have two or more [
6
]. An estimated 20% of individuals diagnosed with ASD also have
epilepsy [7].
Although ASD’s etiology remains largely unexplained, a recent finding identifies
specific gut microbiota composition in ASD patients. Post-mortem examination of ASD
subjects’ brain tissue and small intestines has revealed that the blood–brain barrier and
gut barrier were disrupted, with significant neuroinflammation evidenced by increased
expression of genes and markers associated with brain inflammation. It has further been
inferred that the gut–brain axis disruption may be associated with non-self antigens that
trigger a neuroinflammatory reaction by crossing the damaged gut barriers, thus leading to
ASD in genetically susceptible subjects [
8
]. In a study involving 192 twins, however, genetic
factors accounted for only 38% of ASD risk, whereas the remaining 68% was attributed
to environmental factors [
9
]. A significant role was ascribed to the gut microbiota [
10
].
Microbiota is shaped by diet, lifestyle, and microbial exposure in the early developmental
phase and infection, as well as by genetic makeup, metabolites, and immunological and
hormonal aspects [11].
Analysis of gut microbiota is currently a growing area of research linked to neuropsy-
chological disorders, including depression [
12
], metabolic disorders such as obesity [
13
],
and gastrointestinal disorders, including inflammatory bowel disease or irritable bowel
syndrome [
14
,
15
]. Many studies have identified that microbiota composition in ASD pa-
tients differs significantly from that in healthy controls [
16
–
19
]. Nutritional intervention,
prebiotics, probiotics, and symbiotics, including fecal microbiota transplantation as a rem-
edy to modulate the species composition of the intestinal microbiota of patients with ASD,
already have been tested [
20
–
24
]. These studies generally have concluded, however, that
their findings should be taken with caution because there still exist only limited data from
studies examining different regimens of different remedy applications and there have not
yet been double-blind studies demonstrating clinical significance of the effects of those
remedies used. Here, therefore, we present the results of a double-blind, placebo-controlled
study utilizing the biological response modifier Juvenil for influencing the gut microbiota
of children suffering from ASD and for modulating their ASD symptoms.
2. Study Design, Materials, and Methods
2.1. Study Design
The pilot prospective double-blind randomized feasibility study enrolled 28 children
(9 girls and 19 boys) of Czech nationality aged 3 to 7 years. Of these, 16 children under
care of the Psychiatric Clinic of the Hradec Kralove University Hospital, Czech Republic
met the criteria for a diagnosis of ASD and the remaining 12 children (control group)
were neurotypical (NT), i.e., without any signs of ASD (Table 1). The ASD children were
randomly selected for the study by their attending psychiatrists. The children forming
the control (neurotypical) group were randomly included in the study on the basis of an
agreement with parents living in the geographical area of this study. These children were
never under the care of a psychiatrist.
Nutrients 2024,16, 1988 3 of 14
Table 1. Demographic characteristics of the subjects involved in this study.
Characteristic Value
ASD Group Neurotypical Group
Average age at enrollment (years) 6 ±3 * 5 ±2 *
Age range (years) 3–9 3–9
Male/Female, (number) 13/3 8/4
Ethnicity/Location
White/East Bohemia, CZE White/East Bohemia, CZE
Note: * mean ±SD.
The group of autistic children was randomly divided into two groups of 8 children
each. The first group was administered Juvenil, while the second group of 8 autistic
children was given a placebo throughout the study. Juvenil or placebo capsules were
administered orally to the children by their parents at home once a day for 3 months. The
inclusion of children with autism into the study did not affect their existing treatments,
education, or rehabilitation. To evaluate the effect of Juvenil on the gut microbiome, stool
samples were collected once from healthy children and from autistic children before and
after providing Juvenil or placebo. Autistic children were evaluated using the Childhood
Autism Rating Scale in its standard version (CARS2-ST) by a clinical psychiatrist and
subjective information based on observations of the children’s behavior was obtained
by conducting interviews with the children’s parents. The study was approved by the
hospital’s ethics committee and informed consent was signed by the parents of all study
participants (see Figure 1).
Nutrients 2024, 16, x FOR PEER REVIEW 3 of 14
Table 1. Demographic characteristics of the subjects involved in this study.
Characteristic Value
ASD Group Neurotypical Group
Average age at enrollment (years) 6 ± 3 * 5 ± 2 *
Age range (years) 3–9 3–9
Male/Female, (number) 13/3 8/4
Ethnicity/Location White/East Bohemia, CZE White/East Bohemia, CZE
Note: * mean ± SD.
The group of autistic children was randomly divided into two groups of 8 children
each. The first group was administered Juvenil, while the second group of 8 autistic
children was given a placebo throughout the study. Juvenil or placebo capsules were
administered orally to the children by their parents at home once a day for 3 months. The
inclusion of children with autism into the study did not affect their existing treatments,
education, or rehabilitation. To evaluate the effect of Juvenil on the gut microbiome, stool
samples were collected once from healthy children and from autistic children before and
after providing Juvenil or placebo. Autistic children were evaluated using the Childhood
Autism Rating Scale in its standard version (CARS2-ST) by a clinical psychiatrist and
subjective information based on observations of the children’s behavior was obtained by
conducting interviews with the children’s parents. The study was approved by the hos-
pital’s ethics committee and informed consent was signed by the parents of all study
participants (see Figure 1).
Figure 1. Flow diagram of the participant progress from recruitment to the end of the experiment.
Include exclusions/dropouts.
Figure 1. Flow diagram of the participant progress from recruitment to the end of the experiment.
Include exclusions/dropouts.
Nutrients 2024,16, 1988 4 of 14
2.2. Juvenil and Placebo
The Juvenil and placebo capsules were prepared by Uniregen, Ltd., Nachod, Czech
Republic. Capsules of Juvenil contained 1.0 mg of Juvenil crude substance while placebo
capsules contained redistilled water. Both capsule types looked identical and were stored
at room temperature until use.
Juvenil is a nontoxic alcohol–ether extract of bovine tissue registered as a dietary
supplement with modulatory activity on the immunity, dominantly on cells of the innate
immune system, and on the organism’s regeneration [
25
,
26
]. Juvenil is a complex mixture of
peptides, nucleotides, free amino acids, and some other components of animal origin [27].
2.3. Stool Samples Procedure
The following recommendations were given to parents concerning fecal sample collec-
tion: (1) sample a stool from a clean container (e.g., potty) or from a piece of stool on toilet
paper; (2) the stool must not be diluted with water or urine; (3) collect the stool sample
using the scoop placed in the lid of the collection container; (4) a small amount of stool
is sufficient for the examination (i.e., not larger than the size of a hazelnut or 1–2 mL in
the case of a liquid stool sample); (5) return the sampling scoop to the container and screw
on the cap; (6) place the collection container in a plastic bag, close the bag, and label it
with the child’s name and surname, not marking the sample container in any other way;
(7) place the container with the stool sample in a closed plastic bag and keep it at 4
◦
C
(in the refrigerator); (8) personally deliver the collection container with the sample to the
doctor no later than 24 h after taking the sample (preferably the same day, but no later than
the following day); and (9) stool samples should be collected one day before the start of
supplementation and on the last day of supplementation (i.e., after 12 weeks from the start
of supplementation).
The collection container and its contents were labeled by the attending physician and
the samples were stored at −20 ◦C until subsequent examination.
2.4. Microbiota Analysis
Microbiota composition was determined as described previously [
28
]. The samples were
homogenized in a MagNALyzer (Roche, Basel, Switzerland). Following homogenization, the
DNA was extracted using a QIAamp DNA Stool Mini Kit (Qiagen, Hilden, Germany) accord-
ing to the manufacturer’s instructions and the DNA concentration was determined spectropho-
tometrically. DNA samples were diluted to 5 ng/mL and were used as template in polymerase
chain reaction (PCR) with forward primer 5
′
-TCGTCGGCAGCGTCAGATGTGTATAAGAG
ACAG-MID-GTCCTACGGGNGGC WGCAG-3
′
and reverse primer 5
′
-GTCTCGTGGGCTC
GGAGATGTGTATAAGAGACA G-MIDGTGACTACHVGGGTATCTAATCC-3
′
. MIDs shown
above represent different sequences 5, 6, 7, or 9 base pairs in length that were used to identify
individual samples within the sequencing groups. PCR amplification was performed using
a HotStarTaq Plus Master Mix kit (Qiagen) and the resulting PCR products were purified
using AMPure beads (Beckman Coulter, Prague, Czech Republic). In the next steps, the
concentration of PCR products was determined spectrophotometrically, the DNA was diluted
to 100 ng/
µ
L, and groups of 14 PCR products with different MID sequences were indexed
with the same indices using a Nextera XT Index Kit (Illumina, San Diego, CA, USA). Prior
to sequencing, the concentrations of differently indexed samples were determined using
a KAPA Library Quantification Complete kit (Kapa Biosystems, Wilmington, MA, USA),
all indexed samples were diluted to 4 ng/
µ
L, and 20 pM phiX DNA was added to final
concentration of 5% (v/v). Sequencing was performed using a MiSeq Reagent Kit v3 and
MiSeq apparatus (Illumina).
Sequencing data were analyzed using QIIME 2 [
29
]. Raw sequence data were de-
multiplexed and quality filtered; sequencing primers were then clipped using Je [
30
] and
Fastp [
31
]. The resulting sequences were denoised with DADA2 [
32
]. Taxonomy was
assigned to ASVs using the q2-feature-classifier [
33
] classify-sklearn naïve Bayes taxonomy
classifier against the Silva 138 [34]. All the software tools were used with default settings.
Nutrients 2024,16, 1988 5 of 14
2.5. Statistical Methods
Microbiota composition in different groups of patients was compared using permuta-
tional multivariate analysis of variance (PERMANOVA, R project, package vegan, function
adonis2; Bray–Curtis dissimilarity, 9999 permutations). If PERMANOVA rejected the null
hypothesis, then pairwise comparisons were made of all groups. Statistical significance was
established at p< 0.05. LEfSe (linear discriminant analysis effect size) was used to determine
taxa which most likely explained the differences between the compared groups [
35
]. To
correlate the individual categories of ASD patient symptoms with bacterial taxa that may
characterize the gut dysbiosis of children with ASD, the Covariance S tool in Microsoft
Excel 2021 (v16.0) was used.
3. Results
3.1. Gut Microbiota Composition in Autistic and Neurotypical Children
Comparison of beta diversity using principal coordinate analysis (PCoA) confirmed
differences in the clustering of samples from control and ASD children (Figure 2). All
groups of ASD patients (NT, ASD Placebo, and ASD before and after Juvenil treatment)
harbored microbiota different from those of the NT group (p= 0.001). At the bacterial
phylum level, there were significant differences in the abundance of Actinobacteriota,
Firmicutes, and Proteobacteria. While Actinobacteriota and Proteobacteria dominated in
autistic children, Firmicutes were more abundant in neurotypical controls (Tables 2and S1).
Nutrients 2024, 16, x FOR PEER REVIEW 5 of 14
Fastp [31]. The resulting sequences were denoised with DADA2 [32]. Taxonomy was as-
signed to ASVs using the q2-feature-classifier [33] classify-sklearn naïve Bayes taxonomy
classifier against the Silva 138 [34]. All the software tools were used with default settings.
2.5. Statistical Methods
Microbiota composition in different groups of patients was compared using per-
mutational multivariate analysis of variance (PERMANOVA, R project, package vegan,
function adonis2; Bray–Curtis dissimilarity, 9999 permutations). If PERMANOVA re-
jected the null hypothesis, then pairwise comparisons were made of all groups. Statistical
significance was established at p < 0.05. LEfSe (linear discriminant analysis effect size)
was used to determine taxa which most likely explained the differences between the
compared groups [35]. To correlate the individual categories of ASD patient symptoms
with bacterial taxa that may characterize the gut dysbiosis of children with ASD, the
Covariance S tool in Microsoft Excel 2021 (v16.0) was used.
3. Results
3.1. Gut Microbiota Composition in Autistic and Neurotypical Children
Comparison of beta diversity using principal coordinate analysis (PCoA) confirmed
differences in the clustering of samples from control and ASD children (Figure 2). All
groups of ASD patients (NT, ASD Placebo, and ASD before and after Juvenil treatment)
harbored microbiota different from those of the NT group (p = 0.001). At the bacterial
phylum level, there were significant differences in the abundance of Actinobacteriota,
Firmicutes, and Proteobacteria. While Actinobacteriota and Proteobacteria dominated in
autistic children, Firmicutes were more abundant in neurotypical controls (Tables 2 and
S1).
Figure 2. Fecal microbiota analysis of ASD and control children. Principal coordinate analysis
(PCoA) using Bray–Curtis distance matrix separated samples of control and ASD children. Blue
dots indicate samples from neurotypical control children; yellow dots indicate samples from ASD
children.
Table 2. Relative abundance of gut microbiota between ASD group (without any treatment) and
neurotypical control group (NT) at phylum levels.
Phylum ASD NT p-Value
Actinobacteriota 3.04 1.18 0.03
Bacteroidota 49.02 49.48 0.60
Campylobacterota 0.03 0 0.35
Figure 2. Fecal microbiota analysis of ASD and control children. Principal coordinate analysis (PCoA)
using Bray–Curtis distance matrix separated samples of control and ASD children. Blue dots indicate
samples from neurotypical control children; yellow dots indicate samples from ASD children.
LEfSe analysis (linear discriminant analysis effect size) identified bacterial taxa dis-
criminating the ASD groups prior to supplementation from the NT group (Figure 3).
Bifidobacterium longum, Ruminococcus torque, Faecalibacterium, Flavonifractor,Pseudomonadas,
and Clostridia vadinBB60 were characteristic for the gut microbiota of autistic children
while Streptococcus parasanguinis, Monoglobus,Terrisporobacter, or Bacteroides cellulosilyticus
were more abundant in the microbiota of healthy controls.
Nutrients 2024,16, 1988 6 of 14
Table 2. Relative abundance of gut microbiota between ASD group (without any treatment) and
neurotypical control group (NT) at phylum levels.
Phylum ASD NT p-Value
Actinobacteriota 3.04 1.18 0.03
Bacteroidota 49.02 49.48 0.60
Campylobacterota 0.03 0 0.35
Cyanobacteria 0.09 0.12 0.92
Desulfobacterota 0.29 0.17 0.39
Euryarchaeota 0.006 0.21 0.03
Firmicutes 41 46 0.004
Fusobacteriota 0.001 0.003 0.17
Patescibacteria 0.02 0.002 0.08
Proteobacteria 6 2 0.01
Synergistota 0.005 0 0.28
Verrucomicrobiota 0.84 0.69 0.80
Nutrients 2024, 16, x FOR PEER REVIEW 6 of 14
Cyanobacteria 0.09 0.12 0.92
Desulfobacterota 0.29 0.17 0.39
Euryarchaeota 0.006 0.21 0.03
Firmicutes 41 46 0.004
Fusobacteriota 0.001 0.003 0.17
Patescibacteria 0.02 0.002 0.08
Proteobacteria 6 2 0.01
Synergistota 0.005 0 0.28
Verrucomicrobiota 0.84 0.69 0.80
LEfSe analysis (linear discriminant analysis effect size) identified bacterial taxa dis-
criminating the ASD groups prior to supplementation from the NT group (Figure 3).
Bifidobacterium longum, Ruminococcus torque, Faecalibacterium, Flavonifractor, Pseudomona-
das, and Clostridia vadinBB60 were characteristic for the gut microbiota of autistic chil-
dren while Streptococcus parasanguinis, Monoglobus, Terrisporobacter, or Bacteroides cellu-
losilyticus were more abundant in the microbiota of healthy controls.
Figure 3. Taxa characteristic for ASD and control children. LEfSe analysis identified bacterial taxa
in stool samples typical for ASD (green) and neurotypical control children (red).
3.2. Gut Microbiome Modulation by Juvenil
Pairwise comparison of NT, ASD patients before treatment, and ASD patients after
Juvenil supplementation demonstrated there to be a significant difference in the micro-
biota composition between the NT and ASD patients before supplementation, but there
was no significant difference in microbiota composition in a comparison of ASD patients
after Juvenil either with NT healthy controls or with ASD patients before treatment. Ju-
venil administration thus shifted the profile of the gut microbiota composition of autistic
children toward that of the neurotypical children, although it did not result in the resto-
ration of a completely healthy type of microbiota. In addition, there were no significant
differences in microbiota composition at the phylum level when comparing ASD patients
Figure 3. Taxa characteristic for ASD and control children. LEfSe analysis identified bacterial taxa in
stool samples typical for ASD (green) and neurotypical control children (red).
3.2. Gut Microbiome Modulation by Juvenil
Pairwise comparison of NT, ASD patients before treatment, and ASD patients after
Juvenil supplementation demonstrated there to be a significant difference in the microbiota
composition between the NT and ASD patients before supplementation, but there was
no significant difference in microbiota composition in a comparison of ASD patients after
Juvenil either with NT healthy controls or with ASD patients before treatment. Juvenil
administration thus shifted the profile of the gut microbiota composition of autistic children
toward that of the neurotypical children, although it did not result in the restoration of a
completely healthy type of microbiota. In addition, there were no significant differences in
microbiota composition at the phylum level when comparing ASD patients either before
and after placebo treatment or before and after Juvenil administration (Table 3).
Nutrients 2024,16, 1988 7 of 14
Table 3. Comparison of microbiota composition (%) at the phylum level before and after Juvenil or
placebo treatment.
Phylum Juvenil Placebo
Before After p-Value Before After p-Value
Actinobacteriota
3.78 2.25 0.26 2.78 2.54 0.84
Bacteroidota 51.05 58.62 0.57 50.83 57.22 0.53
Campylobacterota
0.07 0.06 0.91 0.0006 0 0.32
Cyanobacteria 0.07 0.06 0.83 0.05 0.11 0.36
Desulfobacterota
0.29 0.13 0.48 0.33 0.36 0.79
Euryarcheota 0.005 0.02 0.37 0.01 0.02 0.54
Firmicutes 36.37 34.05 0.60 41.83 34.29 0.12
Fusobacteriota 0.0008 0 0.32 0.0009 0 0.32
Patescibacteria 0.006 0.006 0.95 0.02 0.01 0.44
Proteobacteria 6.97 4.11 0.22 3.48 4.38 0.59
Synergistota 0.005 0.002 0.56 0.007 0 0.32
Verrucomicrobiota
1.28 0.61 0.48 0.66 1.07 0.51
A moderate effect of Juvenil administration in comparison to placebo can be seen
also from the comparison of operational taxonomic units (OTUs) completely lost or newly
acquired during treatment (Table S2). The greatest losses or acquisition were recorded
in OTUs belonging to families Lachnospiraceae,Ruminococcaceae,Oscilospiraceae, and Chris-
tenellaceae, all comprising spore-forming bacterial species. While children after placebo
treatment lost 305 OTUs and newly acquired 132 OTUs, microbiota in Juvenil-treated ASD
patients were more stable as there were only 200 OTUs lost and 152 newly appearing
(Table S2).
3.3. Behavioral Status of Autistic Children and Juvenil
The behavioral status of autistic children of both groups was assessed using CARS2-ST
at the time of entry into the study and after completion of supplementation. A comparison
of the group supplemented with Juvenil and the group supplemented with placebo showed
a positive shift in the values of the rating scale parameters during 3-month supplementation
with Juvenil (Table 4and Table S3). Nevertheless, the changes associated with Juvenil as
well as placebo supplementation did not reach statistical significance (ASD placebo group
p= 0.62, ASD Juvenil group p= 0.19). There were also no significant differences in the effect
of supplementation in children with mild symptoms of ASD and severe symptoms of ASD
in either group (Juvenil group p= 0.95, placebo group p= 0.82). Numerically, however,
using the percentile parameter, there was a 12.4% reduction in autism symptoms associated
with Juvenil supplementation, which was approximately double that associated with the
placebo (6.6% reduction). In comparing the difference between Juvenil supplementation
versus placebo using the T-score parameter, we can observe shifts of two or more points in
favor of Juvenil for categories 4 (motor manifestations), 7 (visual reactions), 10 (fear and
nervousness), 12 (nonverbal communication), and 13 (activity level). In addition, Juvenil
showed a significant positive effect (p= 0.009) when comparing the index of individual
CARS-2 ST categories modulation by Juvenil and placebo, respectively (Table 4).
Nutrients 2024,16, 1988 8 of 14
Table 4. Comparison of individual CARS2-ST * categories in Juvenil- and placebo-supplemented
groups. Data were collected before and after 3-months supplementation. Before and after numbers
are sums of the CARS2-ST category values from all members of the given group (Juvenil, placebo).
Childhood Autism Rating
Scale (CARS2-ST) ΣJuvenil ΣPlacebo Shift Index Index
Category Before After Before After
Juvenil/Placebo
Juvenil Placebo
1 Relationship to people 26.5 24.5 21.5 19.5 2/2 0.916 0.921
2 Imitation 23.0 21.5 17.5 16.0 1.5/1.5 0.928 0.933
3 Emotional response 21.5 19.5 22.5 22.0 2/0.5 0.902 1
4 Body 21.0 19.0 20.0 20.0 2/0 0.900 1
5 Object use 20.5 19.0 15.5 15.0 1/0.5 0.921 1
6 Adaptation to change 19.5 18.5 21.5 19.5 1/2 0.944 0.916
7 Visual response 19.5 17.5 18.5 18.5 2/0 0.892 1
8 Listening response 22.0 21.0 17.5 17.5 1/0 1 1
9Taste–smell–touch response
and use 18.0 18.0 18.0 17.0 0/1 1 1
10 Fear and nervousness 20.0 17.0 20.0 19.5 3/0.5 0.823 0.972
11 Verbal communication 26.5 26.5 24.5 23.0 0/1.5 1 0.952
12 Nonverbal communication 24.0 22.5 16.5 18.0 1.5/−1.5 0.930 1
13 Activity level 24.0 22.0 21.5 21.5 2/0 0.904 1
14 Level and consistency of
intellectual response 21.5 21.5 24.5 24.5 0/0 1 1
15 General impressions 25.0 24.5 24.0 24.0 0.5/0 0.978 1
t-test p= 0.0095
Note: * Eric Schopler, Mary E. Van Bourgondien, Glenna Janette Wellman, Steven R. Love. (CARS™2) Childhood
Autism Rating Scale™, Second Edition (https://www.wpspublish.com/cars-2-childhood-autism-rating-scale-
second-edition.html, accessed on 12 May 2020).
3.4. Correlation between Abundance of Key Bacterial Genera and ASD Symptoms
To further explore whether specific microbiota composition can be associated with
ASD symptoms, the abundance of 22 genera reported by other authors as associated with
ASD symptoms were compared with results from CARS2-ST testing using a covariance
S test (Figure 4). Prevotella,Escherichia/Shigella,Veillonella,Streptococcus,Alistipes, and
Bifidobacterium had the highest positive correlation coefficients in relation to the total
CARS2-ST score (i.e., the greater the abundance of these genera, the more severe were the
autism symptoms). On the other hand, a negative correlation was observed for Bacteroides,
Faecalibacterium,Barnesiella, and Blautia (Figure 4A), indicating that an increase in the
abundance of these genera was associated with relief in autistic symptoms. Of the five
tested CARS2-ST categories, the nonverbal communication category was characterized by
a positive correlation with Blautia (Figure 4E). For all categories tested, including the total
score, Veillonella,Streptococcus, and Clostridium repeatedly exhibited a positive correlation.
Nutrients 2024,16, 1988 9 of 14
Nutrients 2024, 16, x FOR PEER REVIEW 9 of 14
Figure 4. Comparison of microbiome data with total scores of CARS2-ST and its individual cate-
gories in which the total score shifted by at least two points after Juvenil supplementation.
(A)—total score, (B)—body, (C)—visual response, (D)—fear and nervousness, (E)—nonverbal
communication, (F)—activity level. ** only Ruminococcus torques group and/or Ruminococcus
gauvreauii_group.
4. Discussion
The gut microbiota is a complex ecosystem that, through its metabolites or entero-
endocrine cell products induced by those metabolites, affects the microbiota–gut–brain
axis and thus homeostasis of the entire organism. Consistent with previously published
studies [36–41], a different composition of gut microbiota was recorded for children with
ASD compared to unrelated neurotypical controls. The significant changes in represen-
tation were demonstrated in the phyla Actinobacteriota, Firmicutes, and Proteobacteria.
Figure 4. Comparison of microbiome data with total scores of CARS2-ST and its individual categories
in which the total score shifted by at least two points after Juvenil supplementation. (A)—total
score, (B)—body, (C)—visual response, (D)—fear and nervousness, (E)—nonverbal communication,
(F)—activity level. ** only Ruminococcus torques group and/or Ruminococcus gauvreauii_group.
4. Discussion
The gut microbiota is a complex ecosystem that, through its metabolites or enteroen-
docrine cell products induced by those metabolites, affects the microbiota–gut–brain axis
and thus homeostasis of the entire organism. Consistent with previously published stud-
ies [
36
–
41
], a different composition of gut microbiota was recorded for children with ASD
compared to unrelated neurotypical controls. The significant changes in representation
were demonstrated in the phyla Actinobacteriota,Firmicutes, and Proteobacteria. LEfSe analy-
sis revealed an increased abundance of Bifidobacterium longum,Ruminococcus torques group,
Faecalibacterium,Flavonifractor, and several taxa of Clostiridiae and Pseudomonodaceae as
characteristic for the gut microbiota of autistic children. On the other hand, autistic children
Nutrients 2024,16, 1988 10 of 14
were characterized also by lower abundance of taxa from the Prevotellaceae,Peptostrepto-
coccaceae, and Monoglobaceae families. Faecalibacterium prausnitzii (F. prausnitzii) is one of
the main producers of butyrate in the intestine and, because butyrate is an inhibitor of
NF-
κ
B and IFN-
γ
[
42
], F. prausnitzii may interfere with the body’s inflammatory responses.
Moreover, F. prausnitzii is an of IL-10 inducer and may, therefore, be referred to as an
anti-inflammatory gut bacterium [
43
]. Aside from Faecalibacterium, the remaining positively
scored bacterial taxa (e.g., Ruminococcus,Flavonifractor, and Bifidobacterium species have
been suggested as predictors of more adverse post-traumatic neuropsychiatric sequelae
outcomes [
44
]. Flavonifractor prevalence has also been associated with another psychiatric
diagnosis of affective disorder [
45
]. On the other hand, the bacterial taxa with decreased
abundance in the gut microbiota of autistic children (e.g., Prevotella) have high genetic di-
versity and, therefore, it is difficult to predict their functional relationships to autism [
46
,
47
].
Whether Prevotella is or is not beneficial to health depends on many factors [
48
], so it cannot
be used unambiguously as a predictive factor of gut dysbiosis in autism [
49
]. A lower
abundance of the families Monoglobaceae and Peptostreptococcaceae in the gut microbiome
has been associated with maternal prenatal stress or anxiety symptoms [50,51].
In this pilot study, the genuses Bacteroides and Prevotella were found to have the highest
negative and positive correlation coefficients, respectively, in relation to total CAR2-ST score.
Bacteroides and Prevotella, two quite closely related genera, were frequently associated with
extreme opposite autism symptoms (Figure 4). Interestingly, Prevotella is usually enriched
in African ethnics with a high proportion of plants in their food (enterotype 2) while
Bacteroides enrichment is associated with a Western diet (enterotype 1) [
52
,
53
]. Another
interesting observation for Prevotella and Bacteroides is that Bacteroides dominates the gut
microbiota of piglets or humans under lactation and is replaced within a short time after
weaning by the related Prevotella [
54
–
56
]. This may point to the importance of weaning, diet,
and associated changes in the gut microbiota for the development of autism in children. In
this respect, moreover, the mother’s diet during pregnancy and lactation, especially from
the viewpoint of consuming a Western diet with an unbalanced ratio of polyunsaturated
fatty acids, may adversely affect brain development [57,58].
Microbiota transfer therapy or probiotic supplementation of ASD individuals has been
tested as means to alleviate ASD symptoms by modulation of gut microbiota [
59
–
66
]. Micro-
biota transfer therapy treatment protocol, consisting of the application of vancomycin, the
laxative MoviPrep, SHGM (Standardized Human Gut Microbiota), and Prilosec (Omepra-
zole), reduced the rates of core ASD symptoms [
5
,
64
]. The administration of probiotics,
either alone or in combination with other biologically active substances such as colostrum
or oxytocin, led to minor reductions in autism symptoms but without reaching statistical
significance [
62
,
63
,
67
]. Only a study that was based on the application of four probiotic
bacteria in combination with fructooligosaccharides provided a significant reduction in
the severity of autism and gastrointestinal symptoms [
65
]. Although our results are in
agreement with these studies, in that a biologically active substance may alleviate ASD
symptoms, whether this is a direct immunostimulating effect or rather is caused by modifi-
cation of the gut microbiota composition, remains uncertain.
Data from this pilot study presented here demonstrate significant changes in the
microbiome, and in parallel, the effects on some categories of CARS2-ST. However, these
results must be approached with caution, as children in home care were included in this
study, and confounding variables such as diet in the family, passive smoking, the influence
of complementary medicines used by children, or the physical and psychological family
environment could have impacted microbiota profiles.
5. Conclusions
This pilot study confirms, albeit in a small number of children, that children with
ASD have altered composition of the gut microbiota. A high abundance of Bacteroides was
associated with weaker ASD symptoms while Prevotella,Escherichia/Shigella,Veillonella,
Streptococcus,Alistipes, and Bifidobacterium were enriched in the gut microbiota of autistic
Nutrients 2024,16, 1988 11 of 14
children with strongest symptoms. An altered composition of ASD children’s gut mi-
crobiota was shifted toward a neurotypical profile by Juvenil supplementation. Juvenil
also positively modulated children’s autism symptoms, namely in the categories of motor
manifestations, visual reactions, fear and nervousness, nonverbal communication, and
activity level. Juvenil supplementation of ASD children was safe, well-tolerated, and had
no side effects.
Supplementary Materials: The following supporting information can be downloaded at:
https://www.mdpi.com/article/10.3390/nu16131988/s1, Table S1: All basic data on the composi-
tion of gut microbiota of children with ASD and neurotypical children expressed in percentages;
Table S2: Number of lost or acquired OTUs within individual bacterial families before and after
supplementation of ASD children; Table S3: The results of behavioral status of autistic children of
both groups assessed by the CARS2-ST at the time of entry into the study and after completion of
supplementation. For the purposes of this study, Juvenil was designated as the moon and placebo as
the sun.
Author Contributions: J.H.; participated in conceptualization, decisions on methodology, and writing
of the original draft, K.K.; participated in conceptualization, decisions on methodology, investigation,
and writing of the original draft, V.B. (Vanda Bostik); writing—review and editing, I.R.; participated
in decisions on methodology, formal analysis, validation, and writing of the original draft, D.K.;
participated in microbiome analysis, V.B. (Vladimir Babak); participated in microbiome analysis, M.D.;
Methodology, Validation, K.S.; Methodology, Validation, A.M.; participated in conceptualization,
decisions on methodology, investigation, data curation, supervision, and writing of the original draft.
All authors have read and agreed to the published version of the manuscript.
Funding: This study was supported by the Ministry of Defence of the Czech Republic—long-term
organization development plan Medical Aspects of Weapons of Mass Destruction of the Faculty of
Military Health Sciences, University of Defence (DZRO-FVZ-ZHN-II).
Institutional Review Board Statement: The study was conducted according to the guidelines of the
Declaration of Helsinki, and approved by the Institutional Review Board and Ethics Commission of
the University Hospital Hradec Kralove, Czech Republic, No.: 202005 S09P, on 12 May 2020.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: All data are available within the manuscript.
Acknowledgments: The authors wish to thank Juvenil Products, a.s. Prezletice, Czech Republic for
providing extract from bovine tissue for our experiments.
Conflicts of Interest: The authors declare no conflicts of interest. There are, therefore, no financial or
material benefits for author/co-authors. The purchase of extract were conducted by the standard way.
Abbreviations
ASD autism spectrum disorder
ADHD attention deficit/hyperactivity disorder
CARS2-ST Childhood Autism Rating Scale in its standard version
DNA deoxyribonucleic acid
F. prausnitzii Faecalibacterium prausnitzii
IFN-γtype II interferon
LEfSe linear discriminant analysis effect size
NF-κB nuclear factor Kappa-light-chain-enhancer of activated B-cells
NT neurotypical
OTU operational taxonomic units
PCoA principal component analysis
IL-10 interleukin 10
Nutrients 2024,16, 1988 12 of 14
References
1.
Elsabbagh, M.; Divan, G.; Koh, Y.J.; Kim, Y.S.; Kauchali, S.; Marcin, C.; Montiel-Nava, C.; Patel, V.; Paula, C.S.; Wang, C.; et al.
Global prevalence of autism and other pervasive developmental disorders. Autism Res. 2012,5, 160–179. [CrossRef]
2.
Baio, J.; Wiggins, L.; Christensen, D.L.; Maenner, M.J.; Daniels, J.; Warren, Z.; Kurzius-Spencer, M.; Zahorodny, W.; Rosenberg,
C.R.; White, T. Prevalence of autism spectrum disorder among children aged 8 years-Autism and developmental disabilities
monitoring network, 11 sites, United States, 2014. MMWR Surveill. Summ. 2018,67, 1. [CrossRef] [PubMed]
3.
Venigalla, H.; Mekala, H.M.; Hassan, M.; Ahmed, R.; Zain, H.; Dar, S.; Veliz, S. An update on biomarkers in psychiatric
disorders–are we aware use in our clinical practice. Ment. Health Fam. Med. 2017,13, 471–479.
4.
Styles, M.; Alsharshani, D.; Samara, M.; Alsharshani, M.; Khattab, A.; Walid, M.; Al-Dewik, N.I. Risk factors, diagnosis, prognosis
and treatment of autism. Front. Biosci. 2020,25, 1682–1717.
5.
Kang, D.W.; Adams, J.B.; Gregory, A.C.; Borody, T.; Chittick, L.; Fasano, A.; Khoruts, A.; Geis, E.; Maldonado, J.; McDonough-
Means, S. Microbiota Transfer Therapy alters gut ecosystem and improves gastrointestinal and autism symptoms: An open-label
study. Microbiome 2017,5, 10. [CrossRef] [PubMed]
6.
Mannion, A.; Leader, G. Comorbidity in autism spectrum disorder: A literature review. Res. Autism Spectr. Disord. 2013,
7, 1595–1616. [CrossRef]
7.
Doshi-Velez, F.; Ge, Y.; Kohane, I. Comorbidity clusters in autism spectrum disorders: An electronic health record time-series
analysis. Pediatrics 2014,133, e54–e63. [CrossRef]
8.
Fiorentino, M.; Sapone, A.; Senger, S.; Camhi, S.S.; Kadzielski, S.M.; Buie, T.M.; Kelly, D.L.; Cascella, N.; Fasano, A. Blood–brain
barrier and intestinal epithelial barrier alterations in autism spectrum disorders. Mol. Autism. 2016,7, 49. [CrossRef] [PubMed]
9.
Hallmayer, J.; Cleveland, S.; Torres, A.; Phillips, J.; Cohen, B.; Torigoe, T.; Miller, J.; Fedele, A.; Collins, J.; Smith, K. Genetic
heritability and shared environmental factors among twin pairs with autism. Arch. Gen. Psychiatry 2011,68, 1095–1102. [CrossRef]
10.
Ogunrinola, G.A.; Oyewale, J.O.; Oshamika, O.O.; Olasehinde, G.I. The Human Microbiome and Its Impacts on Health. Int. J.
Microbiol. 2020,2020, 8045646. [CrossRef]
11.
Zhu, S.; Jiang, Y.; Xu, K.; Cui, M.; Ye, W.; Zhao, G.; Jin, L.; Chen, X. The progress of gut microbiome research related to brain
disorders. J. Neuroinflamm. 2020,17, 25. [CrossRef]
12.
Liu, L.; Wang, H.; Chen, X.; Zhang, Y.; Zhang, H.; Xie, P. Gut microbiota and its metabolites in depression: From pathogenesis to
treatment. EBioMedicine 2023,90, 104527. [CrossRef]
13.
Asadi, A.; Shadab, M.N.; Mohamadi, M.H.; Shokri, F.; Heidary, M.; Sadeghifard, N.; Khoshnood, S. Obesity and gut-microbiota-
brain axis: A narrative review. J. Clin. Lab. Anal. 2022,36, e24420. [CrossRef] [PubMed]
14.
Quaglio, A.E.V.; Grillo, T.G.; De Oliveira, E.C.S.; Di Stasi, L.C.; Sassaki, L.Y. Gut microbiota, inflammatory bowel disease and
colorectal cancer. World J. Gastroenterol. 2022,28, 4053–4060. [CrossRef]
15.
Canakis, A.; Haroon, M.; Weber, H.C. Irritable bowel syndrome and gut microbiota. Curr. Opin. Endocrinol. Diabetes Obes. 2020,
27, 28–35. [CrossRef]
16.
Louis, P. Does the human gut microbiota contribute to the etiology of autism spectrum disorders? Dig. Dis. Sci. 2012,57, 1987–1989.
[CrossRef]
17.
Sivamaruthi, B.S.; Suganthy, N.; Kesika, P.; Chaiyasut, C. The Role of Microbiome, Dietary Supplements, and Probiotics in Autism
Spectrum Disorder. Int. J. Environ. Res. Public Health 2020,17, 2647. [CrossRef] [PubMed]
18.
Saurman, V.; Margolis, K.G.; Luna, R.A. Autism Spectrum Disorder as a Brain-Gut-Microbiome Axis Disorder. Dig. Dis. Sci. 2020,
65, 818–828. [CrossRef] [PubMed]
19. Alamoudi, M.U.; Hosie, S.; Shindler, A.E.; Wood, J.L.; Franks, A.E.; Hill-Yardin, E.L. Comparing the Gut Microbiome in Autism
and Preclinical Models: A Systematic Review. Front. Cell. Infect. Microbiol. 2022,12, 905841. [CrossRef]
20.
Ristori, M.V.; Quagliariello, A.; Reddel, S.; Ianiro, G.; Vicari, S.; Gasbarrini, A.; Putignani, L. Autism, Gastrointestinal Symptoms
and Modulation of Gut Microbiota by Nutritional Interventions. Nutrients 2019,11, 2812. [CrossRef]
21.
Martínez-González, A.E.; Andreo-Martínez, P. Prebiotics, probiotics and fecal microbiota transplantation in autism: A systematic
review. Rev. Psiquiatr. Salud Ment. 2020,13, 150–164. [CrossRef] [PubMed]
22.
Ansari, F.; Pourjafar, H.; Tabrizi, A.; Homayouni, A. The Effects of Probiotics and Prebiotics on Mental Disorders: A Review
on Depression, Anxiety, Alzheimer, and Autism Spectrum Disorders. Curr. Pharm. Biotechnol. 2020,21, 555–565. [CrossRef]
[PubMed]
23.
Tan, Q.; Orsso, C.E.; Deehan, E.C.; Kung, J.Y.; Tun, H.M.; Wine, E.; Madsen, K.L.; Zwaigenbaum, L.; Haqq, A.M. Probiotics,
prebiotics, synbiotics, and fecal microbiota transplantation in the treatment of behavioral symptoms of autism spectrum disorder:
A systematic review. Autism Res. 2021,14, 1820–1836. [CrossRef] [PubMed]
24.
Taniya, M.A.; Chung, H.J.; Al Mamun, A.; Alam, S.; Aziz, M.A.; Emon, N.U.; Islam, M.M.; Hong, S.S.; Podder, B.R. Role of
Gut Microbiome in Autism Spectrum Disorder and Its Therapeutic Regulation. Front. Cell. Infect. Microbiol. 2022,12, 915701.
[CrossRef] [PubMed]
25. Kubelkova, K.; Macela, A. A short history of Imuregne—An original tissue extract. MMSL 2019,88, 115–120. [CrossRef]
26.
Bostik, V.; Kubelková, K.; Macela, A. Juvenil, a natural immune booster affecting biological responses through modulation of gut
microbiota composition. MMSL, 2023; in press. [CrossRef]
27.
Kubelkova, K.; Hubalek, M.; Rehulka, P.; Rehulkova, H.; Friedecky, D.; Zakova, J.; Macela, A. Molecular characterization of
alcohol/ether extract from bovine tissue. MMSL 2021,90, 120–136. [CrossRef]
Nutrients 2024,16, 1988 13 of 14
28.
Kubelkova, K.; Rychlik, I.; Crhanova, M.; Karasova, D.; Slizova, D.; Zakova, J.; Luksikova, L.; Macela, A. Gut microbiota
alterations by nutritional supplement Imuregen. MMSL 2020,89, 114–125. [CrossRef]
29.
Bolyen, E.; Rideout, J.R.; Dillon, M.R.; Bokulich, N.A.; Abnet, C.C.; Al-Ghalith, G.A.; Alexander, H.; Alm, E.J.; Arumugam, M.;
Asnicar, F.; et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 2019,
37, 852–857. [CrossRef]
30.
Girardot, C.; Scholtalbers, J.; Sauer, S.; Su, S.Y.; Furlong, E.E. Je, a versatile suite to handle multiplexed NGS libraries with unique
molecular identifiers. BMC Bioinform. 2016,17, 419. [CrossRef]
31. Chen, S.; Zhou, Y.; Chen, Y.; Gu, J. An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 2018,34, i884–i890. [CrossRef]
32.
Callahan, B.J.; McMurdie, P.J.; Rosen, M.J.; Han, A.W.; Johnson, A.J.; Holmes, S.P. DADA2: High-resolution sample inference
from Illumina amplicon data. Nat. Methods 2016,13, 581–583. [CrossRef] [PubMed]
33.
Bokulich, N.A.; Kaehler, B.D.; Rideout, J.R.; Dillon, M.; Bolyen, E.; Knight, R.; Huttley, G.A.; Gregory Caporaso, J. Optimizing
taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 2018,6, 90.
[CrossRef] [PubMed]
34.
Quast, C.; Pruesse, E.; Yilmaz, P.; Gerken, J.; Schweer, T.; Yarza, P.; Peplies, J.; Glockner, F.O. The SILVA ribosomal RNA gene
database project: Improved data processing and web-based tools. Nucleic Acids Res. 2013,41, D590–D596. [CrossRef] [PubMed]
35.
Segata, N.; Izard, J.; Waldron, L.; Gevers, D.; Miropolsky, L.; Garrett, W.S.; Huttenhower, C. Metagenomic biomarker discovery
and explanation. Genome Biol. 2011,12, R60. [CrossRef] [PubMed]
36.
Williams, B.L.; Hornig, M.; Parekh, T.; Lipkin, W.I. Application of novel PCR-based methods for detection, quantification, and
phylogenetic characterization of Sutterella species in intestinal biopsy samples from children with autism and gastrointestinal
disturbances. MBio 2012,3, e00261-11. [CrossRef] [PubMed]
37.
Song, Y.L.; Liu, C.X.; Finegold, S.A. Real-time PCR quantitation of Clostridia in feces of autistic children. Appl. Environ. Microbiol.
2004,70, 6459–6465. [CrossRef] [PubMed]
38.
De Angelis, M.; Piccolo, M.; Vannini, L.; Siragusa, S.; De Giacomo, A.; Serrazzanetti, D.I.; Cristofori, F.; Guerzoni, M.E.; Gobbetti,
M.; Francavilla, R. Fecal microbiota and metabolome of children with autism and pervasive developmental disorder not otherwise
specified. PLoS ONE 2013,8, 18. [CrossRef] [PubMed]
39.
Wang, L.; Christophersen, C.T.; Sorich, M.J.; Gerber, J.P.; Angley, M.T.; Conlon, M.A. Increased abundance of Sutterella spp. and
Ruminococcus torques in feces of children with autism spectrum disorder. Mol. Autism. 2013,4, 42. [CrossRef]
40.
Tomova, A.; Husarova, V.; Lakatosova, S.; Bakos, J.; Vlkova, B.; Babinska, K.; Ostatnikova, D. Gastrointestinal microbiota in
children with autism in Slovakia. Physiol. Behav. 2015,138, 179–187. [CrossRef]
41.
Yuan, J.; Zhu, L.; Liu, X.; Li, T.; Zhang, Y.; Ying, T.; Wang, B.; Wang, J.; Dong, H.; Feng, E. A Proteome Reference Map and
Proteomic Analysis of Bifidobacterium longum NCC2705. Mol. Cell. Proteom. 2006,5, 11. [CrossRef]
42.
Sokol, H.; Pigneur, B.; Watterlot, L.; Lakhdari, O.; Bermúdez-Humarán, L.G.; Gratadoux, J.J.; Blugeon, S.; Bridonneau, C.; Furet,
J.P.; Corthier, G. Faecalibacterium prausnitzii is an anti-inflammatory commensal bacterium identified by gut microbiota analysis of
Cron disease patients. Proc. Natl. Acad. Sci. USA 2008,105, 16731–16736. [CrossRef] [PubMed]
43.
Rossi, O.; van Berkel, L.A.; Chain, F.; Tanweer Khan, M.; Taverne, N.; Sokol, H.; Duncan, S.H.; Flint, H.J.; Harmsen, H.J.; Langella,
P. Faecalibacterium prausnitzii A2-165 has a high capacity to induce IL-10 in human and murine dendritic cells and modulates T
cell responses. Sci. Rep. 2016,6, 18507. [CrossRef] [PubMed]
44.
Zeamer, A.L.; Salive, M.C.; An, X.; Beaudoin, F.L.; House, S.L.; Stevens, J.S.; Zeng, D.; Neylan, T.C.; Clifford, G.D.; Linnstaedt, S.D.
Association between microbiome and the development of adverse posttraumatic neuropsychiatric sequelae after traumatic stress
exposure. Transl. Psychiatry 2023,13, 354. [CrossRef] [PubMed]
45.
Coello, K.; Hansen, T.H.; Sørensen, N.; Ottesen, N.M.; Miskowiak, K.W.; Pedersen, O.; Kessing, L.V.; Vinberg, M. Affective disor-
ders impact prevalence of Flavonifractor and abundance of Christensenellaceae in gut microbiota. Prog. Neuropsychopharmacol.
Biol. Psychiatry 2021,110, 110300. [CrossRef] [PubMed]
46.
Vinod, K.; Narendrakumar, M.; Suchismitha, I.; Chitra, D. Divergences in gene repertoire among the reference Prevotella genomes
derived from distinct human body sites. BMC Genom. 2015,16, 153.
47.
Ley, R.E. Gut microbiota in 2015: Prevotella in the gut: Choose carefully. Nature Reviews. Gastroenterol. Hepatol. 2016,13, 69–70.
48.
Tett, A.; Pasolli, E.; Masetti, G.; Ercolini, D.; Segata, N. Prevotella diversity, niches and interactions with the human host. Nat. Rev.
Microbiol. 2021,19, 585–599. [CrossRef] [PubMed]
49.
Agarwala, S.; Naik, B.; Ramachandra, N.B. Mucosa-associated specific bacterial species disrupt the intestinal epithelial barrier in
the autism phenome. Brain Behav. Immun. Health 2021,15, 100269. [CrossRef] [PubMed]
50. Abdelsalam, N.A.; Hegazy, S.M.; Aziz, R.K. The curious case of Prevotella copri.Gut Microbes 2023,15, 2249152. [CrossRef]
51.
Chang, C.J.; Lin, T.L.; Tsai, Y.L.; Wu, T.R.; Lai, W.F.; Lu, C.C.; Lai, H.C. Next generation probiotics in disease amelioration. J. Food
Drug Anal. 2019,27, 615–622. [CrossRef]
52.
Tremaroli, V.; Bäckhed, F. Functional interactions between the gut microbiota and host metabolism. Nature 2012,489, 242–249.
[CrossRef] [PubMed]
53.
García-Montero, C.; Fraile-Martínez, O.; Gómez-Lahoz, A.M.; Pekarek, L.; Castellanos, A.J.; Noguerales-Fraguas, F.; Coca, S.;
Guijarro, L.G.; García-Honduvilla, N.; Asúnsolo, A. Nutritional Components in Western Diet Versus Mediterranean Diet at the
Gut Microbiota-Immune System Interplay. Implications for Health and Disease. Nutrients 2021,13, 699. [CrossRef] [PubMed]
Nutrients 2024,16, 1988 14 of 14
54.
Khine, W.W.T.; Rahayu, E.S.; See, T.Y.; Kuah, S.; Salminen, S.; Nakayama, J.; Lee, Y.K. Indonesian children fecal microbiome from
birth until weaning was different from microbiomes of their mothers. Gut Microbes 2020,12, 1761240. [CrossRef] [PubMed]
55.
Kubasova, T.; Davidova-Gerzova, L.; Babak, V.; Cejkova, D.; Montagne, L.; Le-Floc’h, N.; Rychlik, I. Effects of host genetics and
environmental conditions on fecal microbiota composition of pigs. PLoS ONE 2018,13, e0201901. [CrossRef] [PubMed]
56.
Karasova, D.; Crhanova, M.; Babak, V.; Jerabek, M.; Brzobohaty, L.; Matesova, Z.; Rychlik, I. Development of piglet gut microbiota
at the time of weaning influences development of postweaning diarrhea—A field study. Res. Vet. Sci. 2021,135, 59–65. [CrossRef]
[PubMed]
57.
Simopoulos, A.P. Importance of the Omega-6/Omega-3 Balance in Health and Disease: Evolutionary Aspects of Diet, Healthy Agriculture,
Healthy Nutrition, Healthy People; Karger Publishers: Basel, Switzerland, 2011; pp. 10–21.
58.
Yau, S.Y.; Yip, Y.S.L.; Formolo, D.A.; He, S.; Lee, T.H.Y.; Wen, C.; Hryciw, D.H. Chronic consumption of a high linoleic acid diet
during pregnancy, lactation and post-weaning period increases depression-like behavior in male, but not female offspring. Behav.
Brain Res. 2022,416, 113538. [CrossRef] [PubMed]
59.
Liu, Y.W.; Liong, M.T.; Chung, Y.E.; Huang, H.Y.; Peng, W.S.; Cheng, Y.F.; Lin, Y.S.; Wu, Y.Y.; Tsai, Y.C. Effects of Lactobacillus
plantarum PS128 on Children with Autism Spectrum Disorder in Taiwan: A Randomized, Double-Blind, Placebo-Controlled Trial.
Nutrients 2019,11, 820. [CrossRef] [PubMed]
60.
Critchfield, J.W.; van Hemert, S.; Ash, M.; Mulder, L.; Ashwood, P. The potential role of probiotics in the management of childhood
autism spectrum disorders. Gastroenterol. Res. Pract. 2011,2011, 161358. [CrossRef]
61.
Santocchi, E.; Guiducci, L.; Fulceri, F.; Billeci, L.; Buzzigoli, E.; Apicella, F.; Calderoni, S.; Grossi, E.; Morales, M.A.; Muratori,
F. Gut to brain interaction in Autism Spectrum Disorders: A randomized controlled trial on the role of probiotics on clinical,
biochemical and neurophysiological parameters. BMC Psychiatry 2016,16, 183. [CrossRef] [PubMed]
62. Arnold, L.E.; Luna, R.A.; Williams, K.; Chan, J.; Parker, R.A.; Wu, Q.; Hollway, J.A.; Jeffs, A.; Lu, F.; Coury, D.L.; et al. Probiotics
for Gastrointestinal Symptoms and Quality of Life in Autism: A Placebo-Controlled Pilot Trial. J. Child Adolesc. Psychopharmacol.
2019,29, 659–669. [CrossRef]
63.
Sanctuary, M.R.; Kain, J.N.; Chen, S.Y.; Kalanetra, K.; Lemay, D.G.; Rose, D.R.; Yang, H.T.; Tancredi, D.J.; German, J.B.; Slupsky,
C.M. Pilot study of probiotic/colostrum supplementation on gut function in children with autism and gastrointestinal symptoms.
PLoS ONE 2019,14, e0210064. [CrossRef] [PubMed]
64.
Kang, D.W.; Adams, J.B.; Coleman, D.M.; Pollard, E.L.; Maldonado, J.; McDonough-Means, S.; Caporaso, J.G.; Krajmalnik-Brown,
R. Long-term benefit of Microbiota Transfer Therapy on autism symptoms and gut microbiota. Sci. Rep. 2019,9, 5821. [CrossRef]
[PubMed]
65.
Wang, Y.; Li, N.; Yang, J.J.; Zhao, D.M.; Chen, B.; Zhang, G.Q.; Chen, S.; Cao, R.F.; Yu, H.; Zhao, C.Y.; et al. Probiotics and
fructo-oligosaccharide intervention modulate the microbiota-gut brain axis to improve autism spectrum reducing also the
hyper-serotonergic state and the dopamine metabolism disorder. Pharmacol. Res. 2020,157, 104784. [CrossRef] [PubMed]
66.
Sarkar, A.; Lehto, S.M.; Harty, S.; Dinan, T.G.; Cryan, J.F.; Burnet, P.W.J. Psychobiotics and the Manipulation of Bacteria-Gut-Brain
Signals. Trends Neurosci. 2016,39, 763–781. [CrossRef]
67.
Kong, X.J.; Liu, J.; Liu, K.; Koh, M.; Sherman, H.; Liu, S.; Tian, R.; Sukijthamapan, P.; Wang, J.; Fong, M. Probiotic and Oxytocin
Combination Therapy in Patients with Autism Spectrum Disorder: A Randomized, Double-Blinded, Placebo-Controlled Pilot
Trial. Nutrients 2021,13, 1552. [CrossRef]
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