Key role of the gut–microbiota–brain axis via the subdiaphragmatic vagus nerve in demyelination of the cuprizone-treated mouse brain

Multiple sclerosis (MS) is the most common demyelinating disease that attacks the central nervous system. Dietary intake of cuprizone (CPZ) produces demyelination resembling that of patients with MS. Given the role of the vagus nerve in gut-microbiota-brain axis in development of MS, we performed this study to investigate whether subdiaphragmatic vagotomy (SDV) affects demyelination in CPZ-treated mice. SDV significantly ameliorated demyelination and microglial activation in the brain compared with sham-operated CPZ-treated mice. Furthermore, 16S ribosomal RNA analysis revealed that SDV significantly improved the abnormal gut microbiota composition of CPZ-treated mice. An untargeted metabolomic analysis demonstrated that SDV significantly improved abnormal blood levels of metabolites in CPZ-treated mice compared with sham-operated CPZ-treated mice. Notably, there were correlations between demyelination or microglial activation in the brain and the relative abundance of several microbiome populations, suggesting a link between gut microbiota and the brain. There were also correlations between demyelination or microglial activation in the brain and blood levels of metabolites. Together, these data suggest that CPZ produces demyelination in the brain through the gut-microbiota-brain axis via the subdiaphragmatic vagus nerve.


Introduction
Multiple sclerosis (MS) can affect any part of the central nervous system (CNS) and is characterized by chronic neuroinflammation and the destruction of myelin sheaths. The symptoms of MS cause physical and psychological problems and have an important economic burden (Dahham et al., 2021;Nicholas et al., 2021;Wang et al., 2022a). An increasing body of evidence suggests that the gut-microbiota-brain axis plays a crucial role in the pathogenesis of MS (Cantarel et al., 2015;Farshbafnadi et al., 2021;Ghezzi et al., 2021;Maghzi and Weiner, 2020;Parodi and Kerlero de Rosbo, 2021;Wang et al., 2022b). Moreover, a recent meta-analysis revealed the abnormal composition of gut microbiota in MS patients, although α-diversity was not altered (Plassais et al., 2021). A role of gut microbiota in the cognitive impairment of MS patients has also been suggested (Ghadiri et al., 2022). However, the precise mechanisms underlying the role of the gut-microbiota-brain axis in MS remain elusive.
The vagus nerve-the principal component of the parasympathetic nervous system-plays an important role in the interface between the gut microbiota and the brain (Bonaz et al., 2018;Bravo et al., 2011;Cawthon and de La Serre, 2018;Chang et al., 2022;Cryan et al., 2019;Forsythe et al., 2014;Wei et al., 2022). We have previously reported that, after lipopolysaccharide (LPS) administration, the onset of depression-like behaviors and the abnormal composition of gut microbiota in mice can be blocked by subdiaphragmatic vagotomy (SDV) . Furthermore, SDV also reportedly blocks the onset of depression-like behaviors in mice who have received fecal microbiota transplantation from mice with depression-like behaviors (Pu et al., 2021;Wang et al., 2020;Wang et al., 2021a). Collectively, it is likely that the gut-microbiota-brain axis via the subdiaphragmatic vagus Abbreviations: CNS, central nervous system; CPZ, cuprizone; EAE, experimental encephalomyelitis; LFB, luxol fast blue; LPS, lipopolysaccharide; MBP, myelin basic protein; MS, multiple sclerosis; SDV, subdiaphragmatic vagotomy. nerve plays a crucial role in depression-like behaviors in rodents Wei et al., 2022).
Two animal models of MS have been widely used: experimental encephalomyelitis (EAE) and cuprizone (CPZ; bis-cyclohexanoneoxalyldihydrazone) treatment. The EAE model is mostly relevant for relapse-remitting MS (Kipp et al., 2017;Palumbo and Pellegrini, 2017). In contrast, CPZ has been applied to study the process of demyelination in the CNS (Procaccini et al., 2015;Torkildsen et al., 2008;Zhan et al., 2020); CPZ-treated animal models may thus be useful for identifying potential therapeutic methods of blocking demyelination (Franklin and Ffrench-Constant, 2017;Torkildsen et al., 2008;Wang et al., 2022a). However, no studies have yet reported the role of the subdiaphragmatic vagus nerve in demyelination using animal models of MS.
The CPZ model mostly mimics the acute and chronic disease courses of MS; it is likely to be a useful model for developing novel therapeutic candidates that protect against demyelination and stimulate remyelination in the CNS (Lubrich et al., 2022;Palumbo and Pellegrini, 2017;Salinas Tejedor et al., 2015;Skripuletz et al., 2011). In the present study, we investigated whether SDV affects demyelination in the brains of CPZtreated mice. We also performed a 16S ribosomal RNA analysis of fecal samples and conducted an untargeted metabolomic analysis of blood samples because the gut-microbiota-brain axis may contribute to the pathogenesis of CPZ-treated mice (Moles et al., 2021;Wang et al., 2022a).

Animals
Adult male C57BL/6 J mice (8-9 weeks old, body weight 20-25 g, Japan SLC, Inc., Hamamatsu, Japan) were used. Mice were housed under controlled temperatures and 12 h light/dark cycles (lights on between 07:00 and 19:00) with access to food (CE-2; CLEA Japan, Inc., Tokyo, Japan) and water ad libitum . The experimental protocol was approved by the Chiba University Institutional Animal Care and Use Committee (permission number: 3-042). This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health, USA. Animals were deeply anesthetized with isoflurane before being killed by cervical dislocation. All efforts were made to minimize suffering.

SDV procedure
Bilateral SDV or sham surgery was performed under continuous inhalation anesthesia with 2%-3.5% isoflurane using an inhalation small animal anesthesia apparatus (KN-1071 NARCOBIT-E; Natsume Seisakusho, Tokyo, Japan), as previously reported (Pu et al., 2021;Wang et al., 2020;Wang et al., 2021a;Zhang et al., 2020). Briefly, each mouse was placed in the right-side decubitus position and the skin was disinfected with iodophor disinfectant. Starting from the midline alba of the abdomen, an incision of approximately 1 cm was made parallel to the costal arch at 0.5 cm below the left costal arch. A mini incision spreader was then used to expose the underlying liver tissue. The liver tissue was carefully pushed upward using a small sterilized cotton ball moistened with physiological saline solution. With the aid of an animal surgical microscope (Leica, Heidelberg, Germany), the fascia between the caudate lobe and the left lobe of the liver was cut to fully expose the esophagus and the surrounding surgical field of view. The dorsal and ventral branches of the vagus nerve, which run along the esophagus under the diaphragm, were then able to be identified, carefully separated, and severed. If no bleeding was detected and no additional injury of the esophagus, liver, or other organs had occurred, the liver tissue was then returned to its original position and 0.5 mL physiological saline solution was injected into the abdominal cavity. Next, 5-0 surgical silk sutures were used to suture the muscle and skin layers of the abdominal incision layer by layer, ensuring an aseptic operation. The successful implementation of SDV was confirmed by a significant increase in stomach volume on postoperative day 14, caused by the loss of vagus nerve innervation.
For the sham operation, an abdominal wall incision of the same size as that in the SDV procedure was made in the same way at the same site. After the dorsal and ventral branches of the subdiaphragmatic vagus nerve were gently exposed but not severed, the animals were checked to ensure no bleeding and no additional damage to any other organs. Once the abdominal organs were restored to their original positions, 0.5 mL normal saline was injected into the abdominal cavity and the incision was sutured layer by layer using the same method as for the SDV surgery.

CPZ model
After 1 week of recovery from the SDV or sham operation, mice received 0.2% weight/weight CPZ (Cat# B0476; Tokyo Chemical Industry Co., Ltd., Tokyo, Japan) or control (CON) food pellets for 6 weeks, as previously reported . Chow was replaced three times per week. Based on the operation (SDV or sham) that the mice underwent and the presence or absence of CPZ in their food pellets, the mice were divided into four groups: sham + CON group (n = 9), sham + CPZ group (n = 9), SDV + CON group (n = 10), and SDV + CPZ group (n = 10). The body weight of each mouse was measured every week.

Fecal sample collection
Fresh fecal samples of mice were collected at around 9:00 a.m. and placed into sterilized screw-cap microtubes, as previously reported . They were then immediately frozen in liquid nitrogen and stored at − 80 • C until use.

Plasma and brain sample collection
Mice were anesthetized under continuous inhalation anesthesia with 5% isoflurane. Blood was collected using a 1 mL syringe and centrifuged at 4 • C before the supernatant was collected and stored at − 80 • C. The mice were then transcardially perfused with isotonic saline and ice-cold 4% paraformaldehyde in 0.1 mM phosphate buffer (30 mL per mouse, pH 7.4). Next, the brain was collected and post-fixed overnight at 4 • C. The brain from one mouse in the SDV + CON group was not used because it was abnormal (it had a hole in it).

Histopathology and immunofluorescence
Post-fixed brains were serially sectioned at 30 μm using a vibratome (VT1000S, Leica Microsystems AG, Wetzlar, Germany). Sections from bregma 1.10 to − 0.58 were selected. For LFB (luxol fast blue) staining, four sections were collected per mouse (except for one mouse in the sham + CON group, for which just three sections were used); every third section was selected. The LFB staining was performed using an LFB staining kit (Cat# LBC-1; ScyTek Laboratories, Inc., USA). Immunofluorescence was performed as reported previously (Wang et al., 2021b;Wang et al., 2022a). Briefly, sections were washed three times for 15 min with 0.1 mM phosphate buffer before being blocked in 3% bovine serum albumin with 0.3% Triton X-100 for 2 h. Incubation with primary antibody (mouse, anti-myelin basic protein [MBP], Cat# sc-271,524, Santa Cruz Biotechnology, Inc., CA, USA, 1:100; rabbit, anti-ionized calcium-binding adapter molecule 1 [IBA1], Cat# 019-19,741, Fuji-Film Wako Pure Chemical Corporation, Tokyo, Japan, 1:250) was conducted overnight at 4 • C; the sections were then incubated with secondary antibody (Alexa Fluor 546 goat anti-mouse IgG 1 , 1:1000; Alexa Fluor 488 donkey anti-rabbit IgG, 1:1000) for 2 h at room temperature. Next, the sections were washed three times for 15 mins with 0.1 mM phosphate buffer with 0.1% Tween-20, and analyzed using a Keyence BZ-900 microscope (Tokyo, Japan) and ImageJ software, in a blind manner. Furthermore, we did not apply the threshold for data analysis. The percentage area of demyelination was determined using both LFB and MBP staining as follows: (corpus callosum area − MBP/ LFB-positive area)/corpus callosum area × 100% . The percentage of IBA1-positive area was determined as follows: (IBA1positive area/corpus callosum area) × 100% .
The α-diversity was measured using Chao1, observed operational taxonomic units (OTUs), and Shannon. In contrast, β-diversity was analyzed using principal component analysis (PCA) and principal coordinates analysis (PCoA). Significance was evaluated using analysis of similarities (ANOSIM). Linear discriminant analysis effect size (LEfSe) (Segata et al., 2011) was performed based on bacterial abundance to explore significant differential biomarkers between groups with different taxonomic levels (http://huttenhower.sph.harvard.ed u/galaxy/). Only taxa with linear discriminant analysis scores >4.0 and P-values <0.05 were considered significantly enriched. The results were visualized using taxonomic bar charts and cladograms.

Untargeted metabolomic analysis of plasma samples
The untargeted metabolomic analysis of plasma samples was performed using ultra-performance liquid chromatography-tandem quadruple time-of-flight mass spectrometry, as previously reported (Wan et al., , 2022bYang et al., 2023). Acquisition was performed using an ExionLC™ AD system (SCIEX, Tokyo, Japan) coupled to a X500R QTOF system (SCIEX). Metabolomic data were analyzed using R statistical software version 4.0.5 and MS-DIAL version 4.60 (Tsugawa et al., 2015). Metabolites were detected from at least 50% of the analyzed samples and the coefficient of variation values of 30% of metabolites in the pooled quality control samples; annotation level 2, proposed by Schymanski et al. (2014), was used for the data analysis.
Orthogonal partial least squares discriminant analysis (OPLS-DA), which is a multivariate analysis model, was implemented in SIMCA-P version 14.0. Significant peaks were determined by the combination of variable importance in projection values >1 and Wilcox signed-rank test P-values <0.05.

Statistical analysis
Data are presented as the mean ± standard error of the mean. Body weight data were analyzed using repeated-measures two-way analysis of variance (ANOVA) followed by the Bonferroni post-hoc test. We used the log-transformation for data of LFB and IBA1, since these data were not normally distributed (Feng et al., 2014). The demyelination area in LFB staining/MBP immunofluorescence and the IBA1-positive area data were analyzed using two-way ANOVA followed by the Bonferroni posthoc test.
The Kruskal-Wallis test was used to analyze the α-diversity of gut microbiota and the relative bacterial abundance at different levels. For the β-diversity of gut microbiota, the PCA of OTU levels, PCoA, and unweighted or weighted UniFrac phylogenetic distance were analyzed using ANOSIM with the vegan package in R (2.5.4) (Xia and Sun, 2017).
For the plasma metabolite analysis, we used orthogonal partial least squares discriminant analysis as the multivariate analysis model, implemented in SIMCA-P (version 14.0). Significant peaks were determined by the combination of variable importance in projection values >1, Wilcox signed-rank test P-values <0.05, and false discovery rate < 0.076 between the sham + CPZ and SDV + CPZ groups. Two-way ANOVA followed by the Bonferroni post-hoc test was used to analyze differences among the four groups.
Correlations among the demyelination area, IBA1-positive area, plasma metabolites, and the relative abundances of bacteria were analyzed using Spearman's rank test. The integrative network of associations between differentially abundant taxa, plasma metabolites, IBA1-positive areas, and demyelination areas were assessed using Spearman's analysis and visualized with Cytoscape (version 3.8.1). For all analyses, the level of significance was set as P < 0.05.

Effects of SDV on demyelination and body weight in CPZ-treated mice
One week after the SDV or sham operation, the mice received chow with or without CPZ for 6 weeks (Fig. 1A). Body weight gain compared with baseline (week − 1) was significantly higher in the SDV + CPZ group than in the sham + CPZ group (Fig. 1B). The demyelination area in the brain was assessed by LFB staining and MBP immunostaining. Six weeks of CPZ treatment produced demyelination in the corpus callosum of mice; SDV significantly alleviated this CPZ-induced demyelination ( Fig. 1C, D, F, G). Collectively, these findings indicate that SDV can ameliorate CPZ-induced body weight gain and demyelination in mice.

Effects of SDV on microglial activation in CPZ-treated mice
Multiple evidence suggests that microglial activation contributes to MS development (Deng and Sriram, 2005;Kalafatakis and Karagogeos, 2021;Mayrhofer et al., 2021). We therefore investigated microglial activation in the corpus callosum of CPZ-treated mice by immunostaining for the microglial marker IBA1. In the sham + CON group, microglia were distributed broadly and evenly throughout the corpus callosum of mice; they were small and ramified, which is the typical morphology of resting microglia . In the sham + CPZ group, there was a robust increase in the IBA1-positive area of the corpus callosum compared with the sham + CON group. SDV significantly alleviated this CPZ-induced increase in IBA1-positive area ( Fig. 2A-C). Together, these results suggest that SDV can ameliorate CPZ-induced microglial activation in the mouse corpus callosum.

Gut microbiota composition
The αand β-diversity were used to analyze gut microbiota composition in the four groups. Regarding α-diversity, there were no differences in Chao1, observed OTUs, or Shannon among the four groups ( Fig. 3A-C). To determine the similarity (i.e., β-diversity) between microbiota communities in the four groups, PCA and PCoA were performed. The compositions of microbiota communities were significantly separated using PCA as evaluated by ANOSIM (R = 0.5259, P = 0.001) (Fig. 3D) based on OTU levels. Furthermore, the PCoA with unweighted and weighted UniFrac distance showed significant differences in the four groups using ANOSIM (R = 0.4157, P = 0.001) (Fig. 3E, F). These findings indicate that SDV can restore CPZ-induced β-diversity abnormalities in the gut microbiota.
At the species level, the relative abundances of the following 23 microbiota were significantly different in the four groups:  (Fig. 4).
The correlations among demyelination and IBA1-positive areas and these six plasma metabolites were then examined. Both the demyelination area (with LFB staining) and the IBA1-positive area were negatively correlated with all six compounds (Fig. 6D). The demyelination area (with MBP staining) was negatively correlated with glucosereductone and monoethyl carbonate only (Fig. 6D).

Correlations among bacterial relative abundance, plasma metabolites, and demyelination and IBA1-positive areas
Heat maps were used to demonstrate the correlations among demyelination and IBA1-positive areas, plasma metabolites, and relative   Table 1. The data are the median and interquartile range (n = 9 or 10). CPZ, cuprizone; SDV, subdiaphragmatic vagotomy. bacterial abundances that differed significantly at the species level (Fig. 7A). Spearman's correlations between the microbiome and plasma metabolites were also analyzed. Twenty-three bacteria were significantly associated with 27 plasma metabolites, which were further related to demyelination and IBA1-positive areas (Fig. 7A).
A correlation network revealed that the correlations (Spearman's analysis, R > 0.5, P < 0.05) among plasma metabolites, demyelination area, IBA1-positive area, and bacterial relative abundances differed significantly between any two groups (Fig. 7B). There were also correlations among the relative abundances of significant microbiomes at the species level and significantly different plasma metabolites between the sham + CPZ and SDV + CPZ groups (Fig. 7 B). A: Cladogram (LDA score > 4.0, P < 0.05) showed the taxonomic distribution difference among sham + CON, sham + CPZ, SDV + CON, and SDV + CPZ groups, indicating with different colour region. Each successive circle represents a differentially abundant taxonomic clades at phylum, class, order, family, genus and species level from the inner to outer rings. B: Histograms of the different abundant taxa based on the cutoff value of LDA score (log 10 ) > 4.0 and P < 0.05 among the four groups. p, phylum; c, class; o, order; f, family; g, genus; s, species. CPZ, cuprizone; SDV, subdiaphragmatic vagotomy.
(caption on next page) X. Wang et al.

Discussion
The major findings of the present study are as follows. First, compared with sham surgery, SDV ameliorated both demyelination and microglial activation in the corpus callosum of CPZ-treated mice. Second, SDV partially restored the abnormal β-diversity of gut microbiota in CPZ-treated mice. Two phylum-Firmicutes and Candidatus Saccharibacteria-were different between the sham + CPZ and SDV + CPZ groups. Furthermore, several genera and species were altered among the four groups. The LEfSe algorithm identified two species, Lactoba-cillus_sp_NBRC14512 and Turicibacter_sp_LA62, as specific microbial biomarkers for the SDV + CPZ group. Third, 27 metabolites were identified as having significant differences in abundance between the sham + CPZ and SDV + CPZ groups. Of these 27 metabolites, SDV significantly improved the reduced levels of six metabolites (DL-arginine, DL-malic acid, maleic acid, isocitric acid, glucosereductone, and monoethyl carbonate) in CPZ-treated mice. Notably, there were negative correlations between demyelination or microglial activation and plasma metabolites. Fourth, the relative abundances of some species of bacteria were correlated with demyelination or microglial activation in the brain as well as with plasma metabolites. Taken together, our findings indicate that the gut-microbiota-brain axis might play a role in the demyelination of CPZ-treated mice via the subdiaphragmatic vagus nerve.
Several lines of evidence suggest that microglial activation plays a crucial role in MS development (Chu et al., 2018;Deng and Sriram, 2005;Gao and Tsirka, 2011;Guerrero and Sicotte, 2020;Rawji and Yong, 2013;Voet et al., 2019). Microglial activation can lead to neuroinflammation and myelin and axonal damage in both CPZ-treated mice and MS patients (Clarner et al., 2012). Recently, we reported that microglial activation is positively correlated with demyelination in the corpus callosum of CPZ-treated mice, suggesting a link between demyelination and microglial activation . In the present study, we found that SDV alleviated demyelination and microglial activation in the corpus callosum of CPZ-treated mice. Collectively, it is therefore possible that the subdiaphragmatic vagus nerve contributes to microglial activation and demyelination in the corpus callosum of CPZ-treated mice.
Accumulating evidence has highlighted the essential role of abnormal gut microbiota in the pathogenesis of MS (Cantarel et al., 2015;Chen et al., 2019;Farshbafnadi et al., 2021;Ghezzi et al., 2021;Maghzi and Weiner, 2020;Parodi and Kerlero de Rosbo, 2021). In the current study, many species of bacteria were altered between the sham + CPZ and SDV + CPZ groups. Among these species, there was a strong positive correlation between the relative abundance of Bacteroides sp. Smarlab 3,302,398 and demyelination in the brain. Although the precise functions of this bacterial species remain unclear, it is possible that Bacteroides sp. Smarlab 3,302,398 is involved in demyelination via neuroinflammation. Furthermore, there was a positive correlation between the relative abundances of both Bacteroides caecimuris and Gabonia massiliensis and microglial markers in the brain. These two bacteria might play a role in inflammation (Behary et al., 2021;Kishikawa et al., 2020;Osaka et al., 2017;Sanchis-Artero et al., 2021), and may therefore contribute to neuroinflammation in CPZ-treated mice. Nonetheless, it is noteworthy that SDV was able to alleviate the CPZ-induced increased relative abundances of both Gabonia massiliensis and Bacteroides caecimuris, as well as those of Lactobacillus johnsonii and Provotella sp. CA17. We have recently reported that SDV can block depression-like behaviors in mice after LPS administration  or after fecal microbiota transplantation from mice with depression-like behaviors (Pu et al., 2021;Wang et al., 2020;Wang et al., 2021a;Wang et al., 2021b). In addition, it was reported that SDV can abolish the increased hippocampal expression of IBA1 and the cognitive deficits that occur after LPS (5 mg/kg) treatment in mice, thus indicating a role of the subdiaphragmatic vagus nerve in LPS-induced neuroinflammation (Wu et al., 2021). Together with the present results, these findings suggest that the subdiaphragmatic vagus nerve contributes to demyelination in CPZ-treated mice.
A number of previous studies have indicated that gut microbiota is linked to microglial activation in the brain (Abdel-Haq et al., 2019;Cryan et al., 2019;Erny and Prinz, 2020;Lynch et al., 2021;Ma et al., 2019;Mossad and Erny, 2020;Wang et al., 2018;Yang et al., 2022). Similarly, we identified correlations between IBA1 expression in the corpus callosum and the relative abundances of several bacteria; this finding indicates that microglial activation may be regulated by gut microbiota. Furthermore, there was a positive correlation between the relative abundance of Lactobacillus johnsonii and microglial activation, suggesting that this bacterial species may contribute to inflammation in CPZ-treated mice. Taken together, microbiome-microglia crosstalk might play a crucial role in demyelination in the brains of CPZ-treated mice through the gut-microbiota-brain axis.
Six plasma metabolites (malic acid, maleic acid, arginine, glucosereductone, isocitric acid, and monoethyl carbonate) were increased in the SDV + CPZ group compared with the sham + CPZ group. Notably, all six of these plasma metabolites were decreased in the sham + CPZ group compared with the sham + CON group. Malic acid is a saturated dicarboxylic acid, whereas maleic acid is an unsaturated dicarboxylic acid. Interestingly, we identified negative correlations between plasma levels of malic acid or maleic acid and demyelination or microglial activation in the corpus callosum of CPZ-treated mice. Furthermore, the plasma levels of the other four metabolites (arginine, glucosereductone, isocitric acid, and monoethyl carbonate) were negatively correlated with demyelination or microglial activation in the brain. Although the detailed functions of these six metabolites are unknown, they may contribute to the beneficial effects of SDV on demyelination in the brains of CPZ-treated mice. However, further study of these six metabolites is needed to confirm their roles in the protective effects of SDV on CPZtreated mice. Furthermore, there were positive correlations between the relative abundance of Lactobacillus hominis and many plasma metabolites in the current study, suggesting that this bacterial species may have a role in the production of these metabolites.
The present study has some limitations. First, we did not identify the specific species of bacteria and metabolites that contribute to the beneficial effects of SDV in CPZ-treated mice. Further research is therefore needed to confirm the specific microbiome and microbederived metabolites that underlie the beneficial effects of SDV. Second, because the EAE model reproduces different patterns of MS from the CDZ-treated model (Palumbo and Pellegrini, 2017), we need to investigate the effects of SDV on demyelination in other animal models-such as the EAE model-of MS (Wang et al., 2021b).
In conclusion, the findings of the current study suggest that SDV can ameliorate demyelination and microglial activation in the brains of CPZtreated mice through the gut-microbiota-brain axis. Given the crucial role of the vagus nerve in the gut-microbiota-brain axis, vagus nerve stimulation may be a promising therapeutic option for MS patients.

Data and code availability
The 16 s rRNA sequencing data have been deposited to the NCBI Sequence Read Archive and are available at the accession number PRJNA868498.

Declaration of Competing Interest
Dr. Hashimoto is the inventor of filed patent applications on "The use of R-Ketamine in the treatment of psychiatric diseases", "(S)-norketamine and salt thereof as pharmaceutical", "R-Ketamine and derivative thereof as prophylactic or therapeutic agent for neurodegeneration disease or recognition function disorder", "Preventive or therapeutic agent and pharmaceutical composition for inflammatory diseases or bone diseases", "R-Ketamine and its derivatives as a preventive or therapeutic agent for a neurodevelopmental disorder", and "Preventive or therapeutic agent and pharmaceutical composition for inflammatory diseases" by the Chiba University. Dr. K. Hashimoto has also received speakers' honoraria, consultant fee, or research support from Abbott, Boehringer Ingelheim, Daiichi-Sankyo, Meiji Seika Pharma, Seikagaku Corporation, Sumitomo-Pharma, Taisho, Otsuka, Murakami Farm and Perception Neuroscience. Other authors declare no conflict of interest.

Data availability
Data will be made available on request.