Two unique biological response-­
modifier glucans beneficially regulatinggut microbiota and faecal metabolome
in a non-­alcoholic steatohepatitis animalmodel, with potential applications inhuman health and disease
Senthilkumar Preethy,1 Nobunao Ikewaki,2,3 Gary A Levy,4 Kadalraja Raghavan,5Vidyasagar Devaprasad Dedeepiya,6 Naoki Yamamoto,7 Subramaniam Srinivasan,6
Natarajan Ranganathan,8 Masaru Iwasaki,9 Rajappa Senthilkumar,1Samuel J K Abraham ‍ ‍6,9,10

To cite: Preethy S, Ikewaki N,
Levy GA, et al. Two uniquebiological response-­modifier
glucans beneficially regulatinggut microbiota and faecal
metabolome in a non-­alcoholic steatohepatitis
animal model, with potential
applications in human healthand disease. BMJ Open Gastro
2022;9:e000985. doi:10.1136/
bmjgast-2022-000985► Additional supplemental

material is published online
only. To view, please visit thejournal online (http://​dx.​doi.​
org/​10.​1136/​bmjgast-​2022-​
000985).
Received 5 July 2022
Accepted 4 September 2022

© Author(s) (or theiremployer(s)) 2022. Re-­use
permitted under CC BY-­NC. No
commercial re-­use. See rights
and permissions. Publishedby BMJ.
For numbered affiliations see
end of article.
Correspondence to
Dr Samuel J K Abraham;​drsam@​nichimail.​jp

ABSTRACT
Objective The gut microbiome and its metabolites are
influenced by age and stress and reflect the metabolism
and health of the immune system. We assessed the gut
microbiota and faecal metabolome in a static animalmodel of non-­alcoholic steatohepatitis (NASH).
Design This model was subjected to the following
treatments: reverse osmosis water, AFO-­202, N-­163,
AFO-­202+N-­163 and telmisartan treatment. Faecal
samples were collected at 6 and 9 weeks of age. The
gut microbiome was analysed using 16S ribosomal RNA
sequences acquired by next-­generation sequencing,
and the faecal metabolome was analysed using gas
chromatography-­mass spectrometry.
Results Gut microbial diversity increased greatly
in the AFO-­202+N-­163 group. Postintervention, theabundance of Firmicutes decreased, whereas that of
Bacteroides increased and was the highest in the AFO-­
202+N-­163 group. The decrease in the abundanceof Enterobacteriaceae and other Firmicutes and theabundance of Turicibacter and Bilophila were thehighest in the AFO-­202 and N-­163 groups, respectively.
Lactobacillus abundance was highest in the AFO-­
202+N-­163 group. The faecal metabolite spermidine,
which is beneficial against inflammation and NASH, wassignificantly decreased (p=0.012) in the N-­163 group.
Succinic acid, which is beneficial in neurodevelopmental
and neurodegenerative diseases, was increased in the
AFO-­202 group (p=0.06). The decrease in fructose was
the highest in the N-­163 group (p=0.0007). Isoleucineand Leucine decreased with statistical significance(p=0.004 and 0.012, respectively), and tryptophan also
decreased (p=0.99), whereas ornithine, which is beneficial
against chronic immune-­metabolic-­inflammatory
pathologies, increased in the AFO-­202+N-­163 group.
Conclusion AFO-­202 treatment in mice is beneficial
against neurodevelopmental and neurodegenerative
diseases, and has prophylactic potential against metabolic
conditions. N-­163 treatment exerts anti-­inflammatory

Preethy S, et al. BMJ Open Gastro 2022;9:e000985. doi:10.1136/bmjgast-2022-000985

WHAT IS ALREADY KNOWN ON THIS TOPIC⇒ The influence of the gut microbiome on the faecal

metabolome and its association with several diseases are already known.

WHAT THIS STUDY ADDS
⇒ This study demonstrates the efficacy of beta-­1,3–

1,6-­glucans with prebiotic potentials, beneficiallyinfluencing the gut microbiome and metabolome.

HOW THIS STUDY MIGHT AFFECT RESEARCH,
PRACTICE OR POLICY⇒ These results recommend an in-­depth explorationof the relationship among prebiotics, the gut microbiome and gut–multiorgan axes on the fundamentals of disease onset.
⇒ Hidden prophylactic and therapeutic solutions tonon-­contagious diseases with Aureobasidium pullulans that produces beta-­1,3–1,6-­glucans may be
unveiled.
effects against organ fibrosis and neuroinflammation. In
combination, these compounds exhibit anticancer activity.

INTRODUCTION
The microbiome is now considered to be a
virtual organ of the body, with approximately
100 trillion micro-­organisms present in thehuman gastrointestinal tract. The microbiome encodes over three million genes that
produce thousands of metabolites comparedwith the 23 000 genes in the human genome,
thus replacing several host functions andinfluencing the host’s fitness, phenotype
and health. Gut microbiota influence several
aspects of human health, including immune,
1

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Gut microbiota

metabolic and neurobehavioural traits.1 The gut microbiota ferments non-­digestible substrates, such as dietary
fibres and endogenous intestinal mucus, which support
the growth of specialist microbes producing short-­chain
fatty acids (SCFAs) and gases. The major SCFAs produced
are acetate, propionate and butyrate. Butyrate is essential for maintaining colonic cells, apoptosis of coloniccancer cells, activation of intestinal gluconeogenesis,maintenance of oxygen balance in the gut, preventionof gut microbiota dysbiosis, and has beneficial effects on
glucose and energy homoeostasis. Propionate is transported to the liver, where it regulates gluconeogenesis, isan essential metabolite for the growth of other bacteria,
and regulates central appetite.1 Gut dysbiosis, that is,an altered state of the microbiota community, is associated with several diseases, including diabetes, metabolic disorders, obesity, cancer, rheumatoid arthritis andneurological disorders such as Parkinson’s disease (PD),
Alzheimer’s disease, multiple sclerosis (MS) and autism
spectrum disorders (ASD).2 3 The faecal metabolome
represents the functional readout of the gut microbialactivity; it can be considered an intermediate phenotype mediating the host–microbiome interactions. On
average, 67.7%±18.8% of the faecal metabolome variance represents the gut microbial composition. Thus,
faecal metabolic profiling is a novel tool for exploring
the association between microbiome composition, hostphenotypes and disease states.4 Other than faecal microbiota transplantation, probiotics and prebiotic nutritional supplements can help restore the dysbiotic gut toa healthy state.
Beta-­
glucans are among the most promising nutritional supplements with established efficacy against metabolic diseases, diabetes, cancer, cardiovascular diseases
and neurological diseases. Beta-­glucans derived from two
strains of the black yeast Aureobasidium pullulans, AFO-­202
and N-­163, have shown beneficial effects against various
diseases and conditions. In patients with diabetes, there
was a decrease in HbA1c levels from 9.1 to 7.8,5 which wasable to regulate dyslipidaemia.6 In children with ASD,
202 beta-­
glucan improved behavioural patterns
AFO-­
and sleep, apart from increased melatonin and alpha-­
synuclein levels.7 8 In Duchenne muscular dystrophy,N-­163 beta-­glucan decreased inflammatory markers andincreased plasma dystrophin levels in human subjects.9
In a study using the Stelic Animal Model (STAM) ofnon-­alcoholic steatohepatitis (NASH),10 AFO-­202 beta-­
glucan was able to regulate metabolism, while N-­
163
beta-­
glucan was able to reduce inflammation andfibrosis. Their combination decreased the non-­alcoholic
fatty liver disease (NAFLD) activity score. In COVID-­
19,11 12 the combination of AFO-­
202 beta-­
glucan andN-­163 beta-­glucan decreased markers such as IL-­6 and C
reactive protein (CRP), which are associated with cytokine storms. A study in children with ASD showed that
AFO-­202 beta-­1,3–1,6-­glucans exerts its effects primarily
by balancing the gut microbiome.13 This study was undertaken as an extension of the NASH study10 to assess the2faecal microbiome and metabolome profile before andafter administration of AFO-­202 and N-­163 beta-­glucans
individually and in combination.

METHODS
Mice
This study was conducted in accordance with the Animal
Research: Reporting of In Vivo Experiments Guidelines.
C57BL/6J mice were obtained from Japan SLC (Tokyo,
Japan). Animal care followed the following guidelines:
Act on Welfare and Management of Animals (Ministryof the Environment, Japan, Act No. 105; 1 October1973), Standards relating to the care and managementof laboratory animals and relief of pain (Notice No.
88 of the Ministry of the Environment, Japan; 28 April2006), and Guidelines for proper conduct of animal
experiments (Science Council of Japan; 1 June 2006).
Protocol approval was obtained from SMC Laboratories,
the Japanese equivalent of the Institutional Animal Careand Use Committee (study reference no.: SP_SLMN128-­
2107-­
6_1). The mice were maintained in a specificpathogen-­free facility under controlled conditions:temperature, 23°C±3°C; humidity, 50%±20%; 12 hours
artificial light and dark cycles (light from 8:00 to 20:00
hours); and adequate air exchange.
The STAM for NASH was developed as previously
described.10 A single subcutaneous streptozotocin injection of 200 µg (STZ; Sigma-­Aldrich, USA) was administered 2 days after birth. Mice were fed a high-­fat diet
(HFD, 57 kcal% fat, Cat# HFD32, CLEA Japan, Japan)
from 4 to 9 weeks of age. All mice developed liver steatosis
and diabetes, and steatohepatitis was observed histologically at 3 weeks.
Study groups
The mice were randomly allocated to five study groups(n=8 in each group) (table 1).
Vehicle group/control group: Mice in this group wereorally administered 5 mL/kg of reverse osmosis water as
the vehicle solution once daily from 6 to 9 weeks of age.
AFO-­202 beta-­glucan group: The mice in this groupwere orally administered 1 mg/kg of AFO-­
202 beta-­
glucan supplemented in 5 mL/kg of vehicle once daily
from 6 to 9 weeks of age.
N-­163 beta-­glucan group: The mice were orally administered 1 mg/kg of N-­163 beta-­glucan supplemented in
5 mL/kg of vehicle once daily from 6 to 9 weeks of age.
AFO-­
202+N-­
163 beta-­
glucan group: The mice wereorally administered 1 mg/kg of AFO-­202 supplemented
in 5 mL/kg of vehicle once daily as well as 1 mg/kg ofN-­163 supplemented in 5 mL/kg of vehicle once daily
from 6 to 9 weeks of age.
Telmisartan group: The mice were orally administered
10 mg/kg telmisartan in the vehicle once daily from 6 to
9 weeks of age.

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Open access

Table 1 Study design and treatment scheduleGroup

No of mice

Test substance

Dose (mg/kg)

Volume (mL/kg)

Regimen

Sacrifice1

8

Vehicle–5

PO, QD,
6–9 weeks

9 weeks

2

8

AFO-­202 Beta
Glucan1

5

PO, QD,
6–9 weeks

9 weeks3

8

N-­163 Beta Glucan

15PO, QD,
6–9 weeks

9 weeks

4

8

AFO-­202 Beta
Glucan N-­163 Beta
Glucan1
1

5

PO, QD,
6–9 weeks

9 weeks

5

8

Telmisartan

105PO, QD,
6–9 weeks

9 weeks

PO, per os; QD, every day.

Test substances
AFO-­
202 and N-­
163 beta-­
glucans were provided by
GN Corporation, Japan. Telmisartan (micardis) was
purchased from Boehringer Ingelheim (Germany).
Randomisation
NASH model mice were randomised into the aforementioned groups (n=8 each) at 6 weeks of age based on theirbody weight on the day before starting the treatment.
Randomisation was performed by body weight-­stratified
random sampling using Excel software. They were stratified by body weight to obtain the SD, and the difference
in mean weights among the groups was negligible.
Animal monitoring and sacrifice
The viability, clinical signs (lethargy, twitching andlaboured breathing) and behaviour of the mice weremonitored daily. Body weight was recorded daily before
treatment commencement. Significant clinical signs of
toxicity, morbidity and mortality were observed before
and after solution administration. The animals weresacrificed at 9 weeks of age by exsanguination throughdirect cardiac puncture under isoflurane anaesthesia(Pfizer, USA).
Collection of faecal pellets samples
Frequency: Faecal samples were collected at 6 weeks ofage (before treatment administration) and 9 weeks ofage (before sacrifice).
Procedure: At 6 weeks of age, faecal samples werecollected from each mouse using the clean catch method.
Animals were handled using clean gloves sterilised with
70% ethanol. The abdomen was gently massaged and thebottom of the mouse was positioned over a fresh sterilised petri dish to collect 1–2 faecal pellets. At the time ofsacrifice, faecal samples were aseptically collected fromthe caecum. Tubes containing faeces were immediately
placed on ice. These tubes were snap-­frozen in liquid
nitrogen and stored at −80°C for shipping. Online supplemental figure 1 shows the groups and corresponding
faecal sample numbers allotted for microbiome andmetabolomic analyses.
Preethy S, et al. BMJ Open Gastro 2022;9:e000985. doi:10.1136/bmjgast-2022-000985

Microbiome analysis
In this analysis, 16S ribosomal RNA (rRNA) sequence
generation sequencing (NGS)
data acquired by next-­
from faecal RNA were used to perform community
analysis using the Quantitative Insights into Microbial
Ecology (QIIME2) programme for microbial community
analysis. The raw read data in the FASTQ format output
from NGS were trimmed to remove adapter sequences
and low-­QV regions that may have been included in thedata. Cutadapt was used to remove adapter sequencesfrom DNA sequencing reads. Trimmomatic was used asa read-­trimming tool for the Illumina NGS data. The
adapter sequence was trimmed using the adapter trimming programme Cutadapt if the trimming of the region
at the end of the read sequence overlapped the corresponding sequence by at least one base (mismatch tolerance: 20%). When reads containing N were present in
at least one of Read1 and Read2, both Read1 and Read2
were removed.
Illumina adapter sequence information:
Read1 3' end side
​CTGT​CTTC​TATA​CACA​TCTC​CGAG​CCCA​CGAGAC
Read2 3' end side
​CTGT​CTTC​TATA​CACA​TCTG​ACGC​TGCC​GACGAQV regions was performed
Trimming of the low-­on the read data after processing using the QV trimming programme Trimmomatic under the following
conditions.
A window of 20 bases was slid from the 5' side, and thearea with an average QV<20 was trimmed.
After trimming, only reads with >50 bases remaining inboth Read1 and Read2 were used as the outputs.
Population analysis
Microbial community analysis based on the 16S rRNA
sequence was performed on the sequence data trimmed
in the previous section using the QIIME2. The annotation
programme ‘sklearn’ of QIIME2 was used to annotate
the amplicon sequence variant (operational taxonomic
units) (ASV (OTU)) sequences.
3

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Open accessDA, values
using OriginLab Origin 2021b. For OPLS-­
from a two-­tailed Student’s t-­
test were applied to thenormalised peak areas; metabolites with VIP values>1 and p<0.05 were included. The Euclidean distance andWard’s method were used to analyse the heat map. The
mean and variance were normalised so that the mean was
0 and the variance was 1.
For other normally distributed variables, the t-­test or
analysis of variance with Tukey’s honestly significant
difference was used; values of p<0.05 were considered
statistically significant.

Using ‘sklearn,’ the ASV (OTU) sequences obtained
were annotated with taxonomy information, that is,
Kingdom/Phylum/Class/Order/Family/Genus/
Species, based on the 16S rDNA database.
The dataset of the 16S rDNA database ‘greengenes’
provided on the QIIME2 resource site was used for the
analysis. The obtained ASVs (OTUs) were aggregatedand graphed based on the taxonomy information andread counts of each specimen. Based on the compositionof the bacterial flora of each specimen compiled above,
various index values for alpha diversity were calculated.
Metabolome analysis
After lyophilisation, approximately 10 mg of the faecal
sample was separated and extracted using the Bligh-­
Dyer method, and the resulting initial aqueous layer wascollected and lyophilised. The residue was derivatisedusing 2-­
methoxyamine hydrochloride and N-­
methyl-­N-­(trimethylsilyl) trifluoroacetamide and subjected togas chromatography-­mass spectrometry as an analyticalsample. 2-­Isopropylmalic acid was used as an internal
standard. In addition, an operational blank test was
conducted.
The analytical equipment used included GCMS-­
TQ8030 (Shimadzu Corporation, Japan) and BPX5 GC
columns (film thickness, 0.25 µm; length, 30 m; innerdiameter, 0.25 mm; Shimadzu GC).
Peak detection and analysis
MS-­
DIAL V.4.7 (http://prime.psc.riken.jp/compms/​
index.html) was used to analyse and prepare the peak list
(peak height) under the conditions described in online
supplemental table 1. Thus, peaks that were detected in
the quality control (QC) samples and whose coefficientof variation was <20% and whose intensity was more than
twice that of the operational blank test were treated as
the detected peaks.
Differential abundance analysis, principal component
analysis, orthogonal projections to latent structure
discriminant analysis and clustering analysis
Principal component analysis (PCA) and orthogonal
partial least squares-­discriminant analysis (OPLS-­
DA)
were performed to visualise metabolic differences among
the experimental groups. SIMCA-­P+V.17 (Umetrics) wasused for PCA. The normalised peak heights of the sample-­derived peaks were used for PCA using all the samples
and five points (F18S-­12, F18S-­14, F18S-­16, F18S-­18 andF18S-­20). The transform was set to zero and the scaling
was set to Pareto scaling. Differential metabolites wereselected according to their statistically significant variable
importance in the projection (VIP) values obtained from
DA model. R (https://www.r-project.org/)
the OPLS-­
was used for hierarchical cluster analysis and heat map
generation.
Statistical analysis
Statistical data were analysed using Microsoft Excel statistics package analysis software. Graphs were prepared
4

RESULTS
There were no significant differences in mean bodyweight on any day during the treatment period between
the control and other treatment groups. There were no
significant differences in mean body weight on the day ofsacrifice between the treatment groups.
Effects on NASH
The effects of AFO-­202 and N-­163 beta-­glucans on NASH
were reported in our earlier paper10 in a different set ofanimals subjected to the same interventions. Briefly, AFO-­
202 beta-­
glucan significantly decreased inflammation-­associated hepatic cell ballooning and steatosis, whereasN-­
163 beta-­
glucan significantly decreased fibrosis andinflammation. The combination of AFO-­202 and N-­163
significantly decreased NAS.10
Gut microbiome analysis
The alpha-­
diversity indices, Simpson and Shannon
indices, showed that the postintervention gut microbial diversity was highest in the AFO-­202+N-­163 group(figure 1A,B).
Regarding taxonomic profiling, Firmicutes was themost abundant phylum, followed by Bacteroides (figure 2
and online supplemental figure 2).
However, postintervention, the abundance of Firmicutes decreased, whereas that of Bacteroides increased.
This decrease and increase in the abundance of Firmicutes and Bacteroides, respectively, were highest in the
AFO-­202+N-­163 and telmisartan groups when comparedwith the other groups (figure 2 and online supplemental
figure 2).
When individual taxa were analysed in each of the beta-­
glucan groups compared with the telmisartan group,
the decrease in the abundance of Enterobacteriaceae
and Firmicutes was the highest in the AFO-­202 group(figure 3A,B). Turicibacter abundance decrease was thehighest in the N-­163 group (figure 3C). Bilophila abundance increased in all groups, but decreased to 0 in theN-­163 group (figure 3D). The increase in Lactobacillus
abundance was the highest in the AFO-­202+N-­163 group(figure 3E). The abundance of Proteobacteria decreasedin the AFO-­202+N-­163 group but increased in the telmisartan group (figure 3F). The decrease in Akkermansia
abundance was the highest in the AFO-­202+N-­163 group

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Figure 1 Alpha-­diversity indices. (A) Simpson index; (B) Shannon index reflecting the diversity of operational taxonomic units
in samples. Both indices showed that the AFO-­202+N-­163 group had the highest bacterial abundance postintervention.

(figure 3G). Prevotella decreased in AFO-­
202+N-­
163
group (figure 3H)
Faecal metabolome analysis
The resulting score plot of PCA using the normalised
peak heights of the 10 samples (preintervention andpostintervention of the five groups) is shown in online
supplemental figure 3. The contributions of the firstand second principal components were 55 and 20%,
respectively. PCA of the five postintervention samples(F18S-­12, F18S-­14, F18S-­16, F18S-­18 and F18S-­20) using
the peak heights after normalisation; the obtained scoreplot is shown in online supplemental figure 4A,B and theloading plot in online supplemental figure 4C,D. The
contributions of the first and second principal components were 49 and 34%, respectively.
The peak heights of all the detected metabolite
compounds after normalisation are shown in online
supplemental table 2. The number of peaks detected in
the QC samples was 108, of which 53 samples were qualitatively determined and 55 samples remained unknown.
Differential abundance analysis and log2 fold change
results are shown in online supplemental figure 5.
Score plots of PCA and compounds with VIP values≥1in the OPLS-­DA are shown in online supplemental figure
4. Online supplemental table 3 shows the compoundswith VIP values ≥1 and their coefficients in the OPLS-­
DA. PCA of the control group showed that the contribution of the first principal component axis (PC1) was
96.7% and that of the second principal component axis
(PC2) was 1.5%. PCA revealed that the contributions of
PC1 and PC2 in the AFO-­202, N-­163, AFO-­202+N-­163,
Preethy S, et al. BMJ Open Gastro 2022;9:e000985. doi:10.1136/bmjgast-2022-000985

and telmisartan groups were 90.4% and 4.9%, 94.8%
and 2.1%, 96.5% and 1.4%, and 95.1% and 1.9%,
respectively.
In all groups, except for the telmisartan group, phosphoric acid showed the highest log2 fold increase,
whereas putrescine showed the highest decrease. With
respect to specific compounds, the increase in succinicacid was highest in the AFO-­202 group, with statisticalsignificance (p=0.06) (figure 4A). The increase in phosphoric acid was highest in the N-­163 group, followed by
the AFO-­202+N-­163 and AFO-­202 groups (figure 4B), but
the difference was not statistically significant (p=0.21).
The decrease in fructose was highest in the N-­163 group(p=0.0007) (figure 4C). Tryptophan decreased in the
AFO-­
202+N-­
163 group but not significantly (p=0.99);
however, it increased in the other groups (figure 4D).
The decrease in isoleucine and leucine was highest in the
AFO-­202 group, with statistical significance (p=0.004 and0.012, respectively) (figure 4E,F). The changes in phenylalanine can be observed from Figure 4G; however, thedifference was not statistically significant (p=0.18)(figure 4G). Methionine levels increased in all groups but
were not statistically significant (p=0.14) (figure 4H). The
decrease in spermidine was highest in the N-­163 group,with statistical significance (p=0.012) (figure 4I). The
increase in ornithine was highest in the AFO-­202+N-­163
group (figure 4J). Euclidean distance hierarchical clustering analysis demonstrated that the different intensity
levels of the characteristic metabolites also matched theaforementioned findings (figure 5 and online supplemental figure 6).
5

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Open accessFigure 2

Index of the most abundant taxa across species levels.

DISCUSSION
This is the first study to investigate the influence ofbeta-­glucans on the profiles of the faecal gut microbiome and metabolome in a NASH murine model. This
study assessed two different beta-­glucans produced bydifferent strains of the same species of black yeast, A.
pullulans. Beta-­
glucans are obtained from different
sources, and their functionality depends on the sourceand extraction/purification processes.14 The beta-­
glucans described in this study, AFO-­202 and N-­163
strains of A. pullulans black yeast, are unique as they
are produced as exopolysaccharides without the needfor extraction/purification; hence, their biologicalactions are superior to those of the other strains.15
6Furthermore, both beta-­glucans have the same chemical
formula but different structural formulas, and henceexert diverse biological actions. AFO-­202 beta-­glucan
has beneficial metabolic benefits as it regulates blood
glucose levels5 and enhances immunity in immune-­related infections, such as COVID-­19.11 12 Moreover, it
has positive effects on melatonin and alpha-­synuclein
neurotransmitters and sleep and behaviour in neurodevelopmental disorders, such as ASD.7 8 In a previous
NASH animal study, AFO-­202 beta-­glucan significantly
decreased the inflammation-­associated hepatic cellballooning and steatosis.10 The N-­163 beta-­glucan has
immunomodulatory benefits in terms of regulating
dyslipidaemia, which is evident from the balance inPreethy S, et al. BMJ Open Gastro 2022;9:e000985. doi:10.1136/bmjgast-2022-000985

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Open access

Figure 3 Differences between the read count of selected bacteria before and after intervention. (A) Enterobacteriaceae; (B)Firmicutes; (C) Turicibacter; (D) Bilophila; (E) Lactobacillus; (F) Proteobacteria (G) Akkermansia; (H) Prevotella.

Figure 4 Peak heights of the detected compounds after normalisation. (A) Succinic acid; (B) phosphoric acid; (C) Fructose;(D) Tryptophan; (E) Isoleucine; (F) Leucine; (G) Phenylalanine; (H) Methionine; (I) Spermidine; (J) Ornithine. (** significant;* notsignificant; p<0.05). TMS, trimethylsilylation.
Preethy S, et al. BMJ Open Gastro 2022;9:e000985. doi:10.1136/bmjgast-2022-000985

7

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Open accessFigure 5 Euclidean distance hierarchical clustering analysis demonstrating the different intensity levels of characteristic
metabolites in the treatment groups. (A) AFO-­202 group; (B) N-­163 group; (C) AFO-­202+N-­163 (Comparison images of thetelmisartan and control groups are shown in online supplemental figure 6).

the levels of non-­esterified fatty acids16 and a decreasein fibrosis and inflammation in NASH.10 The combination of AFO-­202 and N-­163 decreased proinflammatory markers and increased anti-­inflammatory markers
in healthy human volunteers,17 decreased NAS in the
NASH model,10 and significantly controlled immune-­mediated dysregulated levels of interleukin-­
6, CRP,and ferritin in COVID-­19 patients.11 12 In a study on
gut microbiome analysis in ASD patients, AFO-­
202
showed efficient control of Enterobacteriaceae as well
as beneficial reconstitution of the gut microbiome withpositive effects on ASD.13 This study aimed to evaluate
the benefits of AFO-­202 and N-­163, individually and incombination, in an animal model of NASH.
We used STAM to treat NASH.10 18 19 In this model,
HFD-­fed mice were allowed to develop liver steatosis byadministering streptozotocin solution 2 days after birth.
This model recapitulates most of the features of themetabolic syndrome of NASH that occurs in humans,
and the HFD leads to diabetes, dyslipidaemia and liver
steatosis. Therefore, the gut microbiome and faecal
metabolite profiles present at baseline recapitulate thoseof metabolic syndrome.20 21 This leads to pathophysiological problems in different organ systems of the body,
including the heart, liver, and kidneys, as well as immune-­metabolic interactions, leading to a decline in the
immune system with ageing and its associated complications. Therefore, this study could serve as a forerunner tostudy the effects of beta-­glucans on the different aspectsof metabolic syndrome-­associated pathologies as well as
conditions associated with immune-­metabolic interactions, including neurological disorders wherein immune-­metabolic interactions have profound implications.20
8

Relevance of metabolome and microbiome
An abundance of bacterial species such as Proteobacteria, Enterobacteriaceae and Escherichia coli has beenreported in humans with NAFLD. A greater abundanceof Prevotella species has been reported in children withobesity and NAFLD.21 22 In this study, there was a decreasein the Enterobacteriaceae abundance with AFO-­202 anda significant decrease in the Prevotella abundance with
AFO-­
202+N-­
163 (Figure 3). In terms of faecal metabolites, an increase in tryptophan was observed in theN-­163 group, but not more than that in the telmisartangroup. In NAFLD, tryptophan metabolism is disturbed,
and supplementation with tryptophan is reportedly
beneficial because it increases intestinal integrity andimproves liver steatosis and function in an NAFLD mousemodel.22 Decreased butyrate production increases intestinal inflammation, gut permeability, endotoxaemia
and systemic inflammation. An increased abundanceof 2-­hydroxyisobutyric acid was observed in the AFO-­
202+N-­163 group (online supplemental table 2). Isobutyrate is a precursor of n-­butyrate23 and further researchon whether there is conversion of isobutryate to butyrate
in the gut in a positive manner by the action of beneficial
microbiota is warranted.
Potential in neurological illnesses
Neurodevelopmental and neurodegenerative disorders
In addition to the influence of beta-­
glucans on the
profiles of the faecal gut microbiome and metabolome ina NASH murine model, we previously reported a decreasein the abundance of Enterobacteriaceae, E. coli, Akkermansia muciniphila CAG:154, Blautia spp, Coprobacillus
spp and Clostridium bolteae CAG:59. Furthermore,
Preethy S, et al. BMJ Open Gastro 2022;9:e000985. doi:10.1136/bmjgast-2022-000985

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Open accessthere was an increase in the abundance of Faecalibacterium prausnitzii and Prevotella copri following AFO-­202
administration in children with ASD.13 In this study, thedecrease in the abundance of Enterobacteriaceae was thehighest in the AFO-­202 group (figure 3). Succinic acid,
which is reportedly low in individuals,24 was found to be
the highest in the AFO-­202 group. In several neurodegenerative diseases, such as PD, the levels of amino acids,
such as isoleucine, leucine and phenylalanine, have beenfound to be high in the faecal metabolome.25 In thisstudy, the decrease in the levels of these amino acids was
the highest in the AFO-­202 group (figure 4).
Neuroinflammatory disorders
Turicibacter has been associated with inflammatory
conditions, such as inflammatory bowel disease andother chronic immune-­mediated inflammatory diseases,
such as MS, owing to its correlation with the expressionof tumour necrosis factor.26 In this study, the N-­163 andtelmisartan groups showed a decrease in Turicibacter
abundance (figure 3). Similarly, a significant increasein the relative abundance of Desulfovibrionaceae (Bilophila) was reported in early-­onset paediatric MS.27 In
this study, the N-­163 group showed a reduction in theabundance of Bilophila, whereas the telmisartan group
showed an increase after intervention (figure 3). Corroborating this finding, inflammatory conditions presented
high amounts of sulfur-­containing metabolites, such as
methionine,28 whose increase was the lowest in the N-­163
group (figure 4H). A. muciniphila, a mucosal-­dwelling
anaerobe, is a double-­edged sword.29 It was previously
reported that its abundance is reduced in various
metabolic disorders, including obesity, dyslipidaemia
and type 2 diabetes, which fuelled the development of
Akkermansia-­based probiotic therapies to combat metabolic disorders.30 However, recent evidence suggests thatan increase in the abundance of Akkermansia has beenreported in patients with PD and MS.31 In this study, there
was a significant decrease in Akkermansia abundance in
the AFO-­202+N-­163 group (figure 3G).
Other implications
In overweight/obese humans, low faecal bacterial diversity is reportedly associated with a marked increase in fat
tissue dyslipidaemia, impaired glucose homoeostasis andan increased incidence of low-­grade inflammation.32 In
this study, bacterial diversity increased after the intervention, especially in the AFO-­202+N-­163 group, whichshowed the highest diversity in the Shannon and Simpson
indices (figure 1). Most studies have reported that anincrease in Firmicutes and a decrease in Bacteroides abundance is directly proportional to body weight gain.32 33
In this study, there was a clear decrease in Firmicutes
and increase in Bacteroides abundance in all the groups
post-­
intervention; however, the highest change was in
the AFO-­202+N-­163 and telmisartan groups (figure 3).
Spermidine is a metabolite that is associated with inflammation and cancer.34 The decrease in spermidine level
Preethy S, et al. BMJ Open Gastro 2022;9:e000985. doi:10.1136/bmjgast-2022-000985

was the highest in the N-­163 group (figure 4I). Ornithinelevels are usually low in colorectal cancer patients.35 The
increase in ornithine levels was the highest in the AFO-­
202+N-­163 group in this study. Lactobacillus is a common
probiotic used as prophylaxis and treatment in chronic
conditions, such as cancer,36 as well as for promoting
better health. The increase in Lactobacillus abundance
was the highest in the AFO-­202+N-­163 group (figure 3).
Steroids are common immunosuppressants used to treat
chronic autoimmune conditions as well as organ transplant recipients. Steroid use has been reported to cause
an increase in E. coli and Enterococcus and a decreasein Bacteroides abundance.37 In this study, the control ofEnterobacteriaceae and an increase in Bacteroides abundance with AFO-­202, N-­163, and their combination makethem worthy adjuncts to medications such as steroids.
CONCLUSION
Two strains of black yeast A. pullulans, AFO-­
202 andN-­
163, produce beta-­
glucans that increase gut microbial diversity, control harmful bacteria, promote healthy
bacteria and induce beneficial changes in faecal metabolites, all indicative of a healthy profile, both individually and in combination in the NASH animal model. The
results of this study support the use of AFO-­202 beta-­
glucan as an agent for metabolic regulation, N-­163 beta-­
glucan as an immune-­modulator, and together they are
to be considered as potentially effective and safe adjuncts
in the management of NASH and as prophylaxis inother chronic inflammatory and immune-­dysregulated
conditions.
Author affiliations
1
Fujio-­Eiji Academic Terrain (FEAT), Nichi-­In Centre for Regenerative Medicine(NCRM), Chennai, India
2
Dept. of Medical Life Science, Kyushu University of Health and Welfare, Nobeoka,
Japan
3
Institute of Immunology, Junsei Educational Institute, Nobeoka, Japan
4
Medicine and Immunology, University of Toronto, Toronto, Ontario, Canada
5
Dept of Paediatric Neurology, Jesuit Antonyraj memorial Inter-­disciplinary Centre
for Advanced Recovery and Education (JAICARE), Madurai, India
6
Mary-­Yoshio Translational Hexagon (MYTH), Nichi-­In Centre for Regenerative
Medicine (NCRM), Chennai, India
7
Genome Medical Sciences Project, National Center for Global Health and Medicine(NCGM), Kohnodai, Chiba, Japan
8
R & D, Kibow Biotech Inc, Pennsylvania, Newtown Square, USA
9Centre for Advancing Clinical Research (CACR), University of Yamanashi - Schoolof Medicine, Chuo, Japan
10
Antony- Xavier Interdisciplinary Scholastics (AXIS), GN Corporation Co. Ltd, Kofu,
Japan
Acknowledgements The authors would like to dedicate this paper to the memoryof Mr. Takashi Onaka, who passed away on the 1 June 2022 at the age of 90 years,
who played an instrumental role in successfully culturing and industrial scale up
of AFO-­202 and N-­163 strains of Aureobasidium pullulans after their isolation byProfessor Noboru Fujii, producing the novel beta glucans described in this study.
They thank, Mr. Yasushi Onaka, Mr Masato Onaka, Mr. Yasunori Ikeue, Dr Mitsuru
Nagataki and Mr Ken Sakanishi of Sophy, Ms. Eiko Amemiya of II Dept. of Surgery,
University of Yamanashi, Mr Yoshio Morozumi and Ms Yoshiko Amikura of GN
Corporation, Japan and Loyola-­ICAM College of Engineering and Technology (LICET)
for their support to our research work.
Contributors SJKA and NI contributed to conception and design of the study. RS
performed the literature search. SJKA, GAL, NY and SP drafted the manuscript. MI,

9

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Open accessKR, SS, NR and VDD performed critical revision of the manuscript. All the authors
read, and approved the submitted version. SJKA is the guarantor who accepts full
responsibility for the finished work and/or the conduct of the study, had access tothe data, and controlled the decision to publish.
Funding The authors have not declared a specific grant for this research from any
funding agency in the public, commercial or not-­for-­profit sectors.
Competing interests SJKA is a shareholder in GN, Japan which holds sharesof Sophy, Japan., the manufacturers of novel beta glucans using different strainsof Aureobasidium pullulans; a board member in both the companies and also anapplicant to several patents of relevance to these beta glucans.
Patient consent for publication Not applicable.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement All data relevant to the study are included in thearticle or uploaded as online supplemental information.
Supplemental material This content has been supplied by the author(s). It has
not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been
peer-­reviewed. Any opinions or recommendations discussed are solely thoseof the author(s) and are not endorsed by BMJ. BMJ disclaims all liability andresponsibility arising from any reliance placed on the content. Where the content
includes any translated material, BMJ does not warrant the accuracy and reliabilityof the translations (including but not limited to local regulations, clinical guidelines,
terminology, drug names and drug dosages), and is not responsible for any error
and/or omissions arising from translation and adaptation or otherwise.
Open access This is an open access article distributed in accordance with theCreative Commons Attribution Non Commercial (CC BY-­NC 4.0) license, which
permits others to distribute, remix, adapt, build upon this work non-­commercially,and license their derivative works on different terms, provided the original work is
properly cited, appropriate credit is given, any changes made indicated, and theuse is non-­commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
ORCID iD
Samuel J K Abraham http://orcid.org/0000-0003-2646-2687

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Open access