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OUTLINE

 1.  Highlights
 2.  Abstract
 3.  Graphical abstract
 4.  Keywords
 5.  Abbreviations
 6.  1. Introduction
 7.  2. Materials and methods
 8.  3. Results and discussion
 9.  4. Conclusion
 10. 5. Data availability
 11. Declaration of Competing Interest
 12. Acknowledgements
 13. Competing interests
 14. Appendix A. Supplementary material
 15. References

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GENE

Volume 846, 20 December 2022, 146856



INTEGRATED ANALYSIS OF EFFECT OF DAISAIKOTO, A TRADITIONAL JAPANESE MEDICINE, ON
THE METABOLOME AND GUT MICROBIOME IN A MOUSE MODEL OF NONALCOHOLIC FATTY LIVER
DISEASE

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panelShioriIshizawaaAkinoriNishiaPersonEnvelopeNorikoKaifuchiaChikaShimoboriaMiwaNahatabChihiroYamadabSeiichiIizukaaKatsuyaOhbuchiaMitsueNishiyamaaNaokiFujitsukabToruKonocd1MasahiroYamamotoa
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HIGHLIGHTS



•

Daisaikoto ameliorated the nonalcoholic fatty liver disease activity score of
STAM mice.

•

Daisaikoto decreased the levels of several liver lipid mediators such as
arachidonic acid and its derivatives.

•

Daisaikoto administration improved this imbalance in microbiome composition.

•

Daisaikoto increased ursodeoxycholic acid content and altered several amino
acids in stool.




ABSTRACT

Dysregulation of lipid metabolism and diabetes are risk factors for nonalcoholic
fatty liver disease (NAFLD), and the gut–liver axis and intestinal microbiome
are known to be highly associated with the pathogenesis of this disease. In
Japan, the traditional medicine daisaikoto (DST) is prescribed for individuals
affected by hepatic dysfunction. Herein, we evaluated the therapeutic potential
of DST for treating NAFLD through modification of the liver and stool metabolome
and microbiome by using STAM mice as a model of NAFLD. STAM mice were fed a
high-fat diet with or without 3 % DST for 3 weeks. Plasma and liver of STAM,
STAM with DST, and C57BL/6J (“Normal”) mice were collected at 9 weeks, and
stools at 4, 6, and 9 weeks of age. The liver pathology, metabolome and stool
microbiome were analyzed. DST ameliorated the NAFLD activity score of STAM mice
and decreased the levels of several liver lipid mediators such as arachidonic
acid and its derivatives. In normal mice, nine kinds of family accounted for
94.1 % of microbiome composition; the total percentage of these family was
significantly decreased in STAM mice (45.6 %), and DST administration improved
this imbalance in microbiome composition (65.2 %). In stool samples, DST
increased ursodeoxycholic acid content and altered several amino acids, which
were correlated with changes in the gut microbiome and liver metabolites. In
summary, DST ameliorates NAFLD by decreasing arachidonic acid metabolism in the
liver; this amelioration seems to be associated with crosstalk among components
of the liver, intestinal environment, and microbiome.


GRAPHICAL ABSTRACT

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KEYWORDS

Daisaikoto
Traditional Japanese medicine
Nonalcoholic fatty liver disease
Gut microbiome
Metabolome


ABBREVIATIONS

COX
cyclooxygenase
DST
daisaikoto
GC-MS/MS
gas chromatography–tandem mass spectrometry
LC–MS/MS
liquid chromatography–tandem mass spectrometry
NAFL
nonalcoholic fatty liver
NAFLD
NAFL disease
NAS
NAFLD activity score
NASH
nonalcoholic steatohepatitis
OTU
operational taxonomic unit
SCFA
short chain fatty acids
UDCA
ursodeoxycholic acid


1. INTRODUCTION

Nonalcoholic fatty liver disease (NAFLD) is a concerning modern healthcare
problem with increasing prevalence worldwide (Vernon et al., 2011). Metabolic
syndrome induced by excessive consumption of a high fat diet results in
nonalcoholic fatty liver (NAFL). Multiple factors, such as systemic
dysregulation of lipid metabolism and inflammation, are associated with the
pathogenesis of NAFLD and, for a certain number of affected individuals, NAFL
progresses to nonalcoholic steatohepatitis (NASH), which is characterized by
lobular inflammation, steatosis, and ballooning (Tilg and Moschen, 2010, Cohen
et al., 2011). Among the many factors associated with NAFLD/NASH, arachidonic
acid and its metabolites are thought to play an important role in the disease
(Yu et al., 2006, Sztolsztener et al., 2020). Furthermore, NASH involves the
development of hepatic fibrosis, cirrhosis, and hepatocellular carcinoma; thus,
improvement at the stage of NAFL is important to inhibit disease progression.
While various therapeutic approaches have been considered, medication for NAFLD
remains insufficient because of the complexity of the pathogenic mechanism.

The liver is connected to the gut via the portal vein, and these organs
functionally interact with each other along the gut–liver axis (Schnabl and
Brenner, 2014). As part of the functional connection, primary bile acids
secreted from liver are metabolized by the microbes to secondary bile acids,
which have a variety of biological activities (Yoshimoto et al., 2013, Ma and
Patti, 2014, Ramirez-Perez et al., 2017, Kuno et al., 2018). Recently,
maintaining the balance of the gut environment has been shown to be crucial for
our health, while dysbiosis dysregulates the gut–liver axis and leads to a
progression of NAFLD/NASH (Xie et al., 2016, Albillos et al., 2020).　For
example, it has been reported that transplantation of the fecal microbiome of
NASH model mice causes the development of NAFLD/NASH in normal mice (Henao-Mejia
et al., 2012, Le Roy et al., 2013). Thus, modulating dysbiotic gut microbiota is
an important therapeutic target for improving this disease.

In Japan, the Ministry of Health, Labour and Welfare has approved a
pharmaceutical-grade traditional Japanese medicine, daisaikoto (DST), as a
remedy for liver conditions such as cholecystitis, cholelithiasis, hepatic
dysfunction, and jaundice, as well as diabetes mellitus, hypertension, cerebral
hemorrhage, urticaria, hyperchylia, acute gastrointestinal catarrh, nausea,
vomiting, anorexia, hemorrhoids, neurosis, and insomnia. Administration of DST
has been shown to improve dyslipidemia and insulin resistance, and to decrease
NF-kB expression in a rat model of diabetic fatty liver induced by a high-fat
diet and streptozotocin (Qian et al., 2016). In addition, DST and its component
herbs, ginger and rhubarb, are known to alter the intestinal microbiome
(Neyrinck et al., 2017, Kawashima et al., 2019, Wang et al., 2020). Based on
those studies, it is expected that DST may be useful in the treatment of NAFLD
by modulation of lipid metabolism and maintenance of the gut–liver axis. In this
study, therefore, we investigated the effect of DST on NAFL in STAM mice, one of
the standardized mouse models of NAFL/NASH (Fujii et al., 2013). The
pharmacological effect of DST on NAFL/NASH was evaluated, and comprehensive
profiles of liver lipid mediators, the gut microbiome, and stool metabolome were
analyzed by a systems biological approach. As a result, we revealed that DST
ameliorates NAFLD by altering the content of liver lipid mediators and improving
the composition of the gut microbiome and stool metabolome.


2. MATERIALS AND METHODS


2.1. DAISAIKOTO

Daisaikoto (DST) comprises a hot water extract of the following eight crude
drugs: Bupleurum root (26.09 %), Pinellia tuber (17.39 %), Scutellaria root
(13.04 %), Peony root (13.04 %), Jujube (13.04 %), Immature orange (8.7 %),
Rhubarb (4.35 %), and Ginger (4.35 %). The dried powdered extract of DST was
supplied by Tsumura and Co. (Tokyo, Japan).


2.2. ANIMALS

Male pups delivered by female pregnant C57BL/6J mice obtained from Japan SLC
(Shizuoka, Japan) were used for the experiment. STAM mice were established by
subcutaneous administration of streptozotocin (200 μg/20 μL /head)
(Sigma-Aldrich Japan, Tokyo, Japan) at 2 days of age, followed by administration
of a high fat diet (HFD; High-fat diet 32, CLEA Japan, Tokyo, Japan) after
weaning at 4 weeks of age (STAM group, n = 8) (Fujii et al., 2013). To assess
the effect of DST, STAM mice were fed HFD containing 3 % DST from 6 weeks of age
(STAM + DST group, n = 8) for 3 weeks. Control C57BL/6J mice without any
treatment (Normal group, n = 8) were fed standard chow (CE-2, CLEA Japan, Tokyo,
Japan) after weaning at 4 weeks. The mice were kept in group housing in a cage
with paper chips maintained at a temperature of 23 ± 3 °C, relative humidity of
50 ± 20 %, and 12 h/12 h light/dark cycle (8:00–20:00). Mice in all groups were
allowed free access to food and water. During the experimental period, daily
body weight was measured, and stool samples were collected at 4, 6, and 9 weeks
of age. The mice were sacrificed for liver and plasma sampling at 9 weeks. The
experimental design is shown in Supplementary Fig. S1.

The mice were maintained in accordance with the Guidelines for the Care and Use
of Laboratory Animals of SMC Laboratories, Inc. (Tokyo, Japan). All experimental
procedures were approved by the Laboratory Animal Committee of SMC Laboratories,
Inc. and Tsumura & Co., and followed the Guidelines of the Ministry of Health,
Labour and Welfare, Japan, for the conduct of animal experiments.


2.3. LIVER PATHOLOGY

For histopathological analysis, liver tissue was fixed by formaldehyde, embedded
in a paraffin block, sectioned, and then stained with hematoxylin and eosin.
Steatosis, ballooning, and inflammation were scored, and the total score was
calculated as the NAFLD activity score (NAS) (Kleiner et al., 2005), where the
higher the score, the more the severe the findings. To evaluate hepatic
fibrosis, sections were stained with Sirius red, and the positively stained area
was measured by using ImageJ software (National Institute of Health).


2.4. WIDE TARGETED METABOLOMICS ANALYSIS IN LIVER USING LC–MS/MS

After the addition of ice-cold methanol at 20 mg tissue/mL, frozen liver samples
were homogenized for 30 s by using an automill (TK-AM7-24, Tokken Inc., Chiba,
Japan). The samples were spiked with internal standards (see below) and agitated
at 4 °C for 60 min before centrifugation at 13000 rpm and 4 °C for 10 min (Model
3740, Micro Refrigerated Centrifuge, KUBOTA). After centrifugation, the
supernatants were passed through solid-phase extraction (SPE) cartridges and
separated by LC–MS/MS as described for plasma samples.

The LC–MS/MS system comprised two LC-30AD pumps, SIL-30AC auto-sampler, CTO-20A
column oven, CBM-20A system controller, and LCMS-8050 triple-quadrupole mass
spectrometer (Shimadzu). The peak areas of each quantified ion were calculated
and normalized to those of the internal standards (0.5 ng/μL each of
tetranor-prostaglandin E metabolite-d6, thromboxane B2-d4, prostaglandin E2-d4,
prostaglandin D2-d4, leukotriene C4-d5, leukotriene B4-d4,
5-hydroxyeicosatetraenoic acid-d8, and 15-hydroxyeicosatetraenoic acid-d8;
0.25 ng/μL of oleoylethanolamide-d4; and 10 ng/μL each of arachidonic acid-d8,
6-keto-prostaglandin F1a-d4, prostaglandin F2a-d4, 12-hydroxyeicosatetraenoic
acid-d8, platelet-activating factor-d4, eicosapentaenoic acid-d5, and
docosahexaenoic acid-d5 in methanol). The analytical conditions, chromatogram
acquisitions, and waveform processing have been previously described (Kitagawa
et al., 2019). Data were analyzed with LC–MS solution software and LC–MS/MS
Method Package for Lipid Mediators version 2 (Shimadzu), and processed with
Excel (Microsoft Corporation, Redmond, WA, USA). Missing values were replaced
with half of the minimum positive value. Normalized values were used in further
comparisons. The lipid mediator pathways were created with reference to LimeMap
(Nishi et al., 2021) and visualized by using VANTED software
(https://www.cls.uni-konstanz.de/software/vanted/) (Rohn et al., 2012).


2.5. STOOL METABOLOME ANALYSIS

About 10–20 mg of lyophilized feces sample was added to 1.2 mL of 80 %
acetonitrile containing 400 μg/mL of 2-isopropylmalic acid as an internal
standard, and homogenized by using zirconia beads in an automill (Tokken Inc.).
After centrifugation, hydrophilic metabolites in the supernatant were analyzed
by using a previously published method (Takeo et al., 2017, Kitagawa et al.,
2019) and the SGI-M100 automated derivatization system (AiSTI SCIENCE, Wakayama,
Japan). In brief, extracted samples were loaded on an ion-exchange SPE
cartridge, where the target metabolites were retained. Derivatization was
performed by passing methoxyamine/pyridine and N-methyl-N-(trimethylsilyl)
trifluoroacetamid sequentially through the SPE cartridge. Derivatized samples
were analyzed by GC–MS/MS using a GCMS-TQ8040 (Shimadzu, Kyoto, Japan) system
with a fused silica capillary column (BPX5: 30 m × 0.25 μm; film thickness,
0.25 μm; SGE, Melbourne, Australia). The analytical conditions, chromatogram
acquisition, and waveform processing have been previously described (Kitagawa et
al., 2019).

The peak intensity of each quantified ion was calculated and normalized to that
of the internal standard (2-isopropylmalic acid). Normalized values were used in
comparisons.


2.6. 16S RRNA METAGENOME SEQUENCE OF STOOL SAMPLES

16S rRNA sequencing of stool samples was conducted in accordance with the method
of Nishiyama et al. (2020) with some modifications. First, freeze-dried stool
samples (10–30 mg) were homogenized in a Lysing matrix E tube (MP biomedicals,
LLC., Santa Ana, USA) by using a FastPrep-24 automated cell disruptor (MP
biomedicals), and then centrifuged at 10,000g for 30 min. DNA was extracted from
the lysate by using a standard phenol/chloroform/isoamyl alcohol method. The 16S
rRNA gene library for sequencing using MiSeq (Illumina, Inc., San Diego, CA,
USA) was prepared in accordance with the manufacturer’s protocol. In short,
stool sample DNA (10 ng) was amplified by using an Advantage-HF 2 PCR kit
(Takara Bio Inc., Shiga, Japan) and universal primers for the V3–V4 region of
16S rRNA (forward primer, 5′ TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG
3′; reverse primer, 5′ GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC
3′). Index sequences were added to the 5′ and the 3′ ends of each of amplicon,
and amplicon concentrations were measured by using a Quant-iT PicoGreen dsDNA
Assay kit (Thermo Fisher Scientific, Inc., Waltham, MA, USA). The prepared
amplicons were mixed in equal quantities, and the library was applied to MiSeq
Reagent kit v3 (Illumina, Inc.). Sequencing was conducted by using MiSeq
(Illumina), and the sequence data were processed with QIIME 1.8.0 (Caporaso et
al., 2010). The 5′ and 3′ sequence reads were joined, and sequences with a Phred
quality score below 20 were eliminated. Chimera (contaminated) sequences were
detected by using Usearch and removed. Using a matching criterion of 97 %,
Greengenes v.13_8 was used to pick open-reference operational taxonomic units
(OTU) and identify OTU representative sequences, and the taxonomy was
summarized. The 16S rRNA sequence data is deposited in DDBJ BioProject
(PRJDB13930).


2.7. STOOL AND LIVER BILE ACID ANALYSIS

Weighed stool samples were homogenized with stainless beads in about 1 mL of
ethanol. Each homogenate was heated at 60 °C for 5 min and then centrifuged at
room temperature for 5 min. The resulting supernatant was retained, while the
precipitate was washed twice with 1 mL of ethanol. The three supernatants were
combined and adjusted to exactly 5 mL with ethanol, before being stored as
aliquots in polypropylene tubes at –80 °C.

For analysis, 20 μL of extracted sample, 80 μL of ethanol, and 20 μL of Naptalam
solution (Wako Pure Chemical Industries, Osaka, Japan) as an internal standard
were mixed and then centrifuged at 15,000g and 4 °C for 3 min. Next, 80 μL of
the resulting supernatant was mixed with 80 μL of ultrapure water and then
subjected to LC-MS/MS analysis on a Prominence UFLCXR system (Shimadzu
Corporation, Kyoto, Japan) coupled with an API4000 mass spectrometer (AB Sciex,
CA, USA).


2.8. STATISTICAL ANALYSIS

The microbiome data were analyzed by using Excel, Qiime, MicrobiomeAnalyst
(https://www.microbiomeanalyst.ca/) (Chong et al., 2020), and R software
(https://www.r-project.org/). α-Diversity was estimated by Shannon–Wiener (SW)
index using Qiime and GraphPad Prism7 (GraphPad Software, San Diego, CA, USA).
The Biological Observation Matrix (biom)-formatted files, which are in a
standardized format to analyze metagenome data (https://biom-format.org.),
obtained from Qiime were used for β-diversity analysis, and principal
coordinates analysis was conducted using MicrobiomeAnalyst. T-distributed
stochastic neighbor embedding (t-SNE) was performed using the Rtsne package in R
software (https://cran.r-project.org/web/packages/Rtsne/Rtsne.pdf). The
differences in microbes at the family level among groups were compared by
Mann–Whitney U test with Bonferroni’s correction. Mann–Whitney U test and
analysis of false discovery rate were performed using R software. Clustering
analysis of stool metabolites was conducted by MetaboAnalyst
(https://www.metaboanalyst.ca) (Chong et al., 2019), using Ward's linkage as the
clustering algorithm. Differences in body weight and differences in the
metabolome were compared by Welch’s t-test or Mann–Whitney U test with
Bonferroni’s correction. Differences in NAS were evaluated by Mann–Whitney U
test with Bonferroni’s correction. Spearman’s rank correlation coefficient for
associations among the NAS, metabolome, and microbiome was calculated. The data
were visualized by using GraphPad Prism7 or Excel software. A P value of < 0.05
was considered to be statistically significant.


3. RESULTS AND DISCUSSION

Although DST is clinically used for hepatic dysfunction, details on the
ameliorative effect of DST on NAFL/NASH are lacking. A model of NAFL/NASH has
been developed in the STAM mouse, which shows fatty liver at 8 weeks of age and
progressive fibrosis at 12 weeks, and eventually develops tumors at 20 weeks of
age (Fujii et al., 2013). According to the definition of the committee of the
Clinical Research Network, a NAS (total score of steatosis, lobular
inflammation, and ballooning) score of 5 points or higher is categorized as
“NASH”; <3 points as “not NASH”; and between 3 and 5 point as borderline
(Kleiner et al., 2005). Given this background information, we used STAM mice at
9 weeks of age to evaluate the effect of DST on the early phase of NASH. As a
result, NAS (average ± S.E.M; 4.25 ± 0.16) and each sub-score at 9 weeks of age
were significantly higher in the STAM group than in the Normal group (Fig.
1a–d), and STAM mice were categorized as borderline (Fig. 1, e-2). The area of
hepatic fibrosis, and hepatic triglyceride and plasma alanine transaminase
levels were increased in STAM mice (Supplementary Table S1). During the
experimental period, body weight was significantly decreased, while liver weight
was significantly increased in STAM mice as compared with Normal mice
(Supplementary Table S1). DST administration significantly ameliorated NAS; in
particular, hepatic steatosis was improved (Fig. 1), and 87.5 % (7/8) mice did
not exhibit steatosis (Fig. 1b). In addition, DST tended to decrease ballooning
(Fig. 1c), but did not affect inflammation (Fig. 1d). These results suggested
that DST especially affected the mechanism of steatosis in the STAM model. DST
did not affect the body or liver weight of STAM mice (Supplementary Table S1),
and there were no apparent abnormalities in DST-treated mice during the
experimental period.

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Fig. 1. Analysis of NAFLD activity score (NAS) and liver pathology. The NAFLD
activity score was calculated as the total score of steatosis, lobular
ballooning, and inflammation. (a) NAS, (b) steatosis score, (c) ballooning
score, (d) inflammation score. (e) Liver pathology. (e-1) Normal mice, (e-2)
STAM mice, (e-3) STAM + DST mice. *P < 0.05, †P < 0.01 by Mann-Whitney U test
with Bonferroni’s correction. Blue, red, and green color circles show normal,
STAM, and STAM + DST groups, respectively. Individual values and mean ± S.E.M
are shown.

Next, to reveal the pharmacological properties of DST, we carried out a wide,
targeted analysis of lipid mediators in the liver. The metabolism of arachidonic
acid plays an important role in NAFL/NASH, and arachidonic acid is increased in
the liver of rats fed a high fat diet (Sztolsztener et al., 2020). In addition,
metabolites in the cyclooxygenase (COX) pathway are increased in a mouse model
of fatty liver (Yu et al., 2006). It is known that arachidonic acid is decreased
in the STAM model as compared with a high fat diet model, but the relative
amount of metabolites of COX is increased and the metabolic pathway related to
COX is enhanced (Saito et al., 2015). In this study, therefore, we measured
lipid mediators mainly derived from omega-3 fatty acids (α-linoleic acid,
docosahexaenoic acid, and eicosapentaenoic acid) and omega-6 fatty acids
(arachidonic acid, linoleic acid, eicosadienoic acid, and dihomo-γ-linolenic
acid).

The profile of lipid mediators is presented in Supplementary Table S2, and the
metabolic pathway of arachidonic acid-derived lipid mediators detected in the
liver is presented in Fig. 2. Arachidonic acid-derived lipid mediators were
broadly affected in STAM mice as compared with Normal mice. Arachidonic acid was
significantly higher in STAM mice than in Normal mice. In the arachidonic acid
pathway, several prostaglandins such as prostaglandin D2, prostaglandin E2, and
13,14-dihydro-15-keto prostaglandin J2 were significantly increased in STAM
mice. Several mouse models of NAFLD have shown an increase in liver COX-2, the
key enzyme for producing prostaglandins from arachidonic acid (Yu et al., 2006,
Henkel et al., 2012). In particular, prostaglandin E2 is directly associated
with lipid accumulation and hepatic steatosis in NAFLD (Henkel et al., 2012).
Puri et al. (2009) have shown that several hydroxy-eicosatetraenoic acids
(HETEs) metabolized by lipoxygenase, such as 5-HETE, 8-HETE, and 15-HETE, are
increased in NAFL and NASH. Furthermore, 13,14-dihydro-15-keto prostaglandin D2
has been reported as a candidate biomarker for NASH (Loomba et al., 2015). In
this study, prostaglandin D2 and 13,14-dihydro-15-keto-prostaglanidn J2 were
significantly increased in STAM mice, while 13,14-dihydro-15-keto prostaglandin
D2 per se was not significantly altered. Furthermore, HETEs tended to be higher
in STAM mice, although the differences were not significant (Fig. 2 and
Supplementary Table S2).

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Fig. 2. Profile of lipid mediators in the liver. The arachidonic acid
(AA)-derived lipid mediators detected are mapped on the AA metabolic pathway.
The ratio AA-derived lipid mediators that were increased or decreased in Normal
relative to STAM mice (STAM/Normal), and in STAM mice relative to STAM + DST
mice (STAM + DST/STAM) are summarized in the table below. Abbreviations: AA,
arachidonic acid; DHET, dihydroxy-eicosatrienoic acid; DiHETE,
dihydroxy-eicosatetraenoic acid; EET, epoxy-eicosatrienoic acid; HETE,
hydroxy-eicosatetraenoic acid; HHT, hydroxy-heptadecatrienoic acid; HpETE,
hydroperoxy-eicosatetraenoic acid; iP, iso-prostaglandin; KETE,
keto-eicosatetraenoic acid; LT, leukotriene; LX, lipoxin; PG, prostaglandin; TX,
thromboxane; 6,15-dh-13,14-k-PGF1a, 6,15-dihydro-15-keto-PGF2a;
13,14-dh-15-k-PGF2a, 13,14-dihydro-15-keto-PGF2a; 13,14-dh-15-k-PGD2,
13,14-dihydro-15-keto-PGD2; 13,14-dh-15-k-PGE2, 13,14-dihydro-15-keto-PGE2;
13,14-dh-15-k-PGJ2, 13,14-dihydro-15-keto-PGJ2; 13,14-dh-15-k-tetranor-PGE2,
13,14-dihydro-15-keto-tetranor-PGE2; 13,14-dh-15-k-tetranor-PGF1a,
13,14-dihydro-15-keto-tetranor-PGF1a.

DST administration significantly decreased arachidonic acid,
13,14-dihydro-15-keto prostaglandin J2, and prostaglandin F2α, which tended to
be increased in STAM mice (Fig. 2 and Supplementary Table S2). Overall, 92 %
(36/39) of the detected lipid mediators were decreased in STAM mice given DST as
compared with STAM mice. Although prostaglandin E2, which is known to affect
lipid accumulation, was not clearly decreased by DST, modulation of omega-6
fatty acid-derived lipid mediators may play an important role in improving
steatosis. We propose that DST modulates the balance of arachidonic acid
metabolism in liver to have an ameliorative effect on NAFLD. Overall, however,
omega-3 fatty acid-derived lipid mediators tended to be decreased in STAM mice
as compared with Normal mice. This broad decrease might be caused by differences
in the source of fat in the diet between standard chow (CE-2) and High-fat diet
32. As a result, we focused on arachidonic acid-derived lipid mediators. As
candidates for the effect of DST on arachidonic acid, it has been reported that
compounds in scutellaria root, baicalin, baicalein, and wogonin inhibit
phospholipase, which is associated with arachidonic acid metabolism (Ku et al.,
2015). Thus, these constituents might be involved in the effects of DST on
arachidonic acid metabolism. Overall, we propose that the amelioration of NAFL
by DST is due to the effects of DST on arachidonic acid; however, further
studies will be needed to reveal the details of the mechanism.

Dysregulation of the gut–liver axis associated with dysbiotic microbiome leads
to the progression of NAFL/NASH (Xie et al., 2016, Albillos et al., 2020). We
therefore focused on the effects of DST on STAM-induced changes in the gut
metabolome and microbiome. We collected stool samples in mice at 4, 6, and
9 weeks of age, and analyzed stool microbiota by 16S rRNA metagenome sequencing.
The microbiome composition before treatment with DST was similar between the
STAM and STAM + DST groups at 4 and 6 weeks of age. The number of trimmed
qualified reads across the samples at 9 weeks of age was 29025 ± 14496
(average ± S.D.) and the number of detected OTU was 773 ± 227. To analyze
differences in the richness and evenness of the microbiome among the Normal,
STAM, and STAM + DST groups, we conducted α-diversity analysis. As a result,
α-Diversity, as assessed by SW index, was significantly decreased in STAM mice
as compared with Normal mice, and administration of DST recovered the SW index
in STAM mice (Fig. 3a). Because a decrease in SW index indicates a low richness
and evenness of microbiome, this finding suggests that DST improved the richness
and evenness. In β-diversity analysis using principal coordinates analysis with
unweighted unifrac distance, the three groups were clearly separated (Fig. 3b).
The Normal and STAM groups were separated in PC1, and STAM and STAM + DST tended
to be separated in PC2. This tendency was also observed in t-SNE (Supplementary
Figure S2). These data suggest that STAM affected the composition of the
microbiome, and STAM + DST further modulated this composition.

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Fig. 3. Analysis of the stool microbiome. (a) Shannon–Wiener index for the
Normal, STAM, and STAM + DST groups. * P < 0.05 by Bonferroni’s multiple
comparisons test. Blue, red, and green color circles show normal, STAM, and
STAM + DST groups, respectively. Individual values and mean ± S.E.M are shown.
(b) Principal coordinate analysis using unweighted unifrac distance for the
microbiome community of the Normal, STAM, and STAM + DST groups. (c) Relative
abundance of family in the microbiome. Family that accounted for more than 1 %
in Normal mice were defined as the dominant family. Others family that accounted
for<1 % in Normal mice were defined as minor family.

We also analyzed changes in the microbiome composition at the family level. We
defined the family accounting for more than 1 % in Normal mice as the dominant
family (Shibagaki et al., 2017). The dominant family in Normal mice comprised
nine kinds of family with a cumulative percentage of 94.1 ± 0.6 %
(average ± S.E.M) in Normal mice. This percentage was significantly decreased in
STAM mice (45.6 ± 3.8 %), but significantly improved in STAM mice given DST
(65.2 ± 4.3 %) (Fig, 3c, and Supplementary Table S3), indicating that DST
administration reconstituted the dysbiotic microbiome in the STAM model of
NAFLD. Furthermore, we detected 13 family present at<1 % in Normal mice that
specifically increased to more than 1 % in STAM mice. The total percentage of
these family was significantly increased in STAM mice (51.9 ± 3.8 %) as compared
with Normal mice, but significantly decreased by DST administration in STAM mice
(32.5 ± 4.1 %) (Supplementary Table S4). In particular, Corynebacteriaceae,
Staphylococcaceae, Rikenellaceae, and Porphylomonadaceae, which were
specifically increased in the STAM group, were tended to be decreased in the
STAM + DST group. These alterations in gut family in STAM mice are similar to
those in inflammasome-deficient mice, which show liver steatosis, intestinal
inflammation, and impairment of intestinal barrier function (Henao-Mejia et al.,
2012). In addition, transplantation of the fecal microbiota from the NASH model
mouse to normal mouse causes NAFLD/NASH (Henao-Mejia et al., 2012, Le Roy et
al., 2013). Collectively, these observations suggest that the decrease in
bacteria is critically important to the improvement of NASH. These results lead
us to presume that DST acts as a modulator for the dysbiotic microbiome in
NAFLD. Some effects of the constituent herbs in DST on the intestinal microbiome
have been reported. For example, ginger modulates the microbiome in the
prevention of obesity (Wang et al., 2020), while rhubarb alters the microbiome
composition and improves pathology in acholic-induced hepatic injury. (Neyrinck
et al., 2017, Wang et al., 2020). Thus, it can be presumed that individual
constituents of DST are involved in its effects on the microbiome.

We analyzed the profile of the stool metabolome, including amino acids and bile
acids, as effectors of intestinal homeostasis. The metabolome in stool samples
was altered in STAM mice as compared with Normal mice (Fig. 4a and Supplementary
Table S5). For instance, several amino acids, including asparagine, proline, and
3-aminoisobutyric acid, which acts on PPARα (Roberts et al., 2014), tended to
decrease in STAM mice and DST significantly ameliorated the alteration of amino
acid content in STAM mice (Fig. 4a and Supplementary Table S5). Clustering
analysis revealed the broad alteration of metabolites in stool samples in both
the STAM and STAM + DST groups. We observed five different types of cluster.
Metabolites in Cluster A were decreased in STAM mice as compared with Normal
mice, and the decreases were not affected by DST administration. Cluster B
included metabolites that were decreased in STAM mice, but recovered by DST.
Cluster C corresponded to alterations unique to DST, including metabolites
increased by DST administration. The metabolites in Cluster D were increased in
the STAM group and decreased in the STAM + DST group. Lastly, the metabolites in
Cluster E were increased in STAM mice, and not affected by DST administration.
Of these alterations, in particular threonine in Cluster C was increased by DST
treatment. Threonine is known to affect epithelial goblet cells, which
synthesize mucin in damaged intestine (Faure et al., 2006, He et al., 2018). In
addition, the amino acids in Clusters B and C that were increased by DST, such
as asparagine, proline, and glycine, may have functional effects on maintaining
gut homeostasis. For example, supplementation of asparagine ameliorates
LPS-induced intestinal impairment in pig, and that of glycine prevents
experimental colitis induced by 2,4,6-trinitrobenzene sulfonic acid and dextran
sulfate sodium in rat (Tsune et al., 2003, Zhu et al., 2017). Therefore, we
propose that the intestinal microbiota and amino acids altered by DST have
beneficial effects on the intestinal environment.

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Fig. 4. Analysis of primary metabolite and ursodeoxycholic acid in stool
samples. (a) Clustering analysis of primary metabolites in stool samples.
Metabolites that were significantly altered in STAM relative to Normal mice, or
in STAM + DST relative to STAM mice at P < 0.05 by Mann-Whitney U test with
Bonferroni’s correction were selected. The values of selected metabolites were
used for clustering analysis. (b) Amount of ursodeoxycholic acid (UDCA) in stool
samples. *P < 0.05, †P < 0.01 by Mann-Whitney U test with Bonferroni’s
correction. Blue, red, and green color circles show normal, STAM, and STAM + DST
groups, respectively. Individual values and mean ± S.E.M are shown.

The microbiome is deeply associated with bile acid metabolism, and the bile
acids produced may act both beneficially and harmfully, depending on their
character. The profile of major stool bile acids such as deoxycholic acid and
muricholic acids is summarized in Supplementary Table S6; however, the
concentration of cholic acid, while a key primary bile acid, was relatively low
in stool and not quantified. The concentrations of several major bile acids,
such as deoxycholic acid, were significantly increased in STAM mice as compared
with Normal mice. In addition, the functional bile acid ursodeoxycholic acid
(UDCA) was significantly increased in the STAM + DST group as compared with the
STAM group, while several other major bile acids tended to increase in the
STAM + DST group (Fig. 4b and Supplementary Table S6). UDCA is a functional
hydrophilic bile acid in both liver and gut, and has protective effects on
epithelial cells. UDCA treatment has an anti-inflammatory effect in experimental
colitis and improves LPS-induced intestinal barrier impairment (Martinez-Moya et
al., 2013, Golden et al., 2018). In addition, UDCA has a protective effect on
the liver in cholestatic liver diseases, and the concentration of UDCA is
inversely correlated with the level of intestinal permeability in obese mice
(Beuers et al., 1998, Lazaridis et al., 2001, Stenman et al., 2012). Whereas
stool bile acids were altered in STAM mice, liver bile acids, except for TCDCA,
did not differ between the STAM and Normal groups, and DST did not affect bile
acid content in STAM mice (Supplementary Table S7). Furthermore, the level of
UDCA in liver was not increased at 9 weeks [Normal: 0.024 ± 0.008 nmol/g liver
(mean ± S.E.M), STAM: 0.16 ± 0.05, STAM + DST; 0.12 ± 0.02]. These results
suggest that DST broadly affects the gut metabolome and the gut environment in a
STAM model of NAFLD. As an important limitation regarding the effects of DST in
human, it should be noted that the metabolic pathways of several bile acids
differ between mouse and human. For instance, chenodeoxycholic acid (CDCA) and
cholic acid are primary bile acids in human, whereas CDCA is metabolized to
muricholic acids in mouse (Wahlstrom et al., 2016). Therefore, we need to
further studies to reveal the metabolomic mechanisms associated with human. We
attempted to reveal the comprehensive interactions among alterations in the
microbiome, metabolome, and NAS in the STAM model (Fig. 5). Specifically,
Spearman’s rank correlation coefficient was assessed for interactions between
bacteria [dominant family in Normal (Supplementary Table S3) and specifically
increased family in STAM (Supplementary Table S4)], and metabolites in stool
(Supplementary Table S5 and S6) and liver (Supplementary Table S2) that were
significantly altered by DST administration. Individual values of the STAM and
STAM + DST groups were used for this analysis. As a result, mainly bacteria of
dominant family in Normal mice were negatively correlated with NAS, while family
specific to STAM were positively correlated with NAS. On the one hand, dominant
family in Normal – in particular, family S24-7, Bifidobacteriaceae,
Lachnospiraceae, Ruminococcaceae, and Deferribacteriaceae – tended to be
positively correlated with stool UDCA and amino acids including proline,
asparagine, and 3-aminoisobutyric acid. On the other hand, these bacteria were
negatively correlated with lipid mediators in liver. Furthermore, family
specific to STAM mice – namely, Corynebacteriaceae, Staphylococcaceae,
Planococcaceae, Rikenellaceae, Porphyromonadaceae, Peptostreptococcaceae, and
Erysipelotrichaceae – were negatively correlated with these factors. Moreover,
these family were positively correlated with NAS and lipid mediators in liver.
These results suggest that alteration of the microbiome in the STAM model of
NAFLD is deeply associated with metabolites and hepatic pathology.

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Fig. 5. Correlation of intestinal bacteria with NAS and metabolites in liver and
stool. Correlations were analyzed by using Spearman’s rank correlation
coefficient.

We realize the limitations of our study, and acknowledge that more
pharmacological and pathological investigations will be needed to reveal the
mechanism of DST effects. While we focused on the disease traits of STAM mice as
compared with normal mice, it will be important to reveal the distinct
differences between the STAM model and a high fat diet to understand the
progression of disease from NAFL to NASH. Furthermore, will be is important to
assess how DST affects various factors to bring about differences. It will be
necessary to reveal which constituents in DST affect the arachidonic acid
metabolism. In order to clarify the effect of DST via the microbiome, studies on
microbiome intervention, such as fecal microbiota transplantation and use of
antibiotics, will be needed. It will be important to integrate these data on the
comprehensive function of DST and reveal its specific mechanism of action.


4. CONCLUSION

We have investigated the effect of DST on NAFL by using a systems biological
approach, which has revealed that its mechanism, in part, might involve
modulation of the intestinal environment. We will further clarify the mechanisms
of DST action on improvement of the gut environment in NAFLD in a future study.


5. DATA AVAILABILITY

Experimental microbiome and metabolome data are available from the authors.


CREDIT AUTHORSHIP CONTRIBUTION STATEMENT

Shiori Ishizawa: Conceptualization, Methodology, Validation, Formal analysis,
Investigation, Data curation, Writing – original draft. Akinori Nishi:
Conceptualization, Supervision, Project administration, Methodology, Formal
analysis, Investigation, Data curation, Writing – original draft. Noriko
Kaifuchi: Investigation, Formal analysis, Data curation, Writing – review &
editing. Chika Shimobori: Investigation, Formal analysis, Data curation, Writing
– review & editing. Miwa Nahata: Conceptualization, Methodology, Investigation,
Data curation, Writing – review & editing. Chihiro Yamada: Conceptualization,
Methodology, Investigation, Data curation, Writing – review & editing. Seiichi
Iizuka: Investigation, Writing – review & editing. Katsuya Ohbuchi:
Conceptualization, Methodology, Investigation, Data curation, Writing – review &
editing. Mitsue Nishiyama: Conceptualization, Supervision, Methodology,
Investigation, Data curation, Writing – review & editing. Naoki Fujitsuka:
Conceptualization, Supervision, Writing – review & editing. Toru Kono:
Conceptualization, Writing – review & editing. Masahiro Yamamoto:
Conceptualization, Supervision, Writing – review & editing.


DECLARATION OF COMPETING INTEREST

The authors declare the following financial interests/personal relationships
which may be considered as potential competing interests: Shiori Ishizawa
reports financial support was provided by Tsumura and Co. Akinori Nishi reports
financial support was provided by Tsumura and Co. Noriko Kaifuchi reports
financial support was provided by Tsumura and Co. Chika Shimobori reports
financial support was provided by Tsumura and Co. Miwa Nahata reports financial
support was provided by Tsumura and Co. Chihiro Yamada reports financial support
was provided by Tsumura and Co. Seiichi Iizuka reports financial support was
provided by Tsumura and Co. Katsuya Ohbuchi reports financial support was
provided by Tsumura and Co. Mitsue Nishiyama reports financial support was
provided by Tsumura and Co. Naoki Fujitsuka reports financial support was
provided by Tsumura and Co. Toru Kono reports financial support was provided by
Tsumura and Co. Masahiro Yamamoto reports financial support was provided by
Tsumura and Co.


ACKNOWLEDGEMENTS

The animal study was performed at SMC Laboratories, Inc. Analysis of bile acids
was conducted at LSI Medience Corporation (Tokyo, Japan). The study was funded
by a grant from Tsumura & Co.


COMPETING INTERESTS

S. Ishizawa, A.N., N.K., C.S., M. Nahata, C.Y., S. Iizuka, K.O., M. Nishiyama,
F.N. and M.Y are employed by Tsumura & Co.; K.T. has financial interests in
Tsumura & Co. relevant to this research. The authors declare no conflicts of
interest except for financial interest. The study was funded by a grant from
Tsumura & Co.


APPENDIX A. SUPPLEMENTARY MATERIAL

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