Metabolic regulation of Ganoderma lucidum extracts in high sugar and fat diet-induced obese mice by regulating the gut-brain axis

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Metabolic regulation of Ganoderma lucidum extracts in high sugar and fat diet-induced obese mice by regulating the gut-brain axis Chen Dilinga,1, Guo Yinruia,1, Qi Longkaia,1, Tang Xiaocuia, Liu Yadia,b, Feng Jiaxina,c, ⁎ Zhu Xiangxianga,d, Zeng Miaoa,e, Shuai Oua, Wang Dongdonga, Xie Yizhena, , Burton B. Yanga,f, ⁎ Wu Qingpinga, a

State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Guangdong Open Laboratory of Applied Microbiology, Guangdong Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China b Guangdong Pharmaceutical University, Guangzhou 510006, China c Guangxi University of Chinese Medicine, Nanning 530023, China d Academy of Life Sciences, Jinan University, Guangdong Province, Guangzhou 510000, China e Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China f Institute of Medical Science, University of Toronto, Toronto, Canada

A R T I C LE I N FO

A B S T R A C T

Keywords: Gut-liver axis Gut-brain axis Metabolic regulation Prebiotics Ganoderma lucidum

In this study, an obese mouse model was developed by administering a high sugar and fat diet for 60 days, followed by administration of a special dietary supplement of Ganoderma lucidum extract for another 35 days. Then, the changes in histopathology of the liver, adipose, colon, intestines, spleen, renal and brain tissues; metabolism and transcription; and gut microbiota were monitored. The results showed that alcohol extracts of the G. lucidum fruit body could reduce body weight; change the serum levels of lipid; ameliorate the damage to the gut microbiota, colon, liver, brain and other organs induced by the high sugar and fat diet; and activate the leptin regulatory pathways in the hypothalamus to improve metabolism. These findings indicate that administration of the alcohol extracts of the G. lucidum fruit body has beneficial effects on the microbiome-gut-liver and microbiome-gut-brain axes, and activates leptin-mediated signaling to improve metabolic regulation.

1. Introduction

urgently needed. One study showed that the gut microbiota are involved in the pathogenesis of obesity (Sonnenburg & Bäckhed, 2016), particularly the metabolites produced by the symbiotic microbes (e.g., short-chain fatty acids, lipopolysaccharides, methane, and trimethylamine oxide). These metabolites can cause chronic inflammatory reactions by increasing energy intake, slowing down bowel movement, and accelerating the accumulation of intracellular cholesterol, thus inducing obesity and insulin resistance and promoting atherosclerosis (De Vadder et al., 2014; Perry et al., 2016). Central obesity is the typical characteristic of metabolic syndrome, and diet is the key cause of obesity. Diet is one of the main factors influencing the gut microbiota composition; therefore, dietary supplements are important for the maintenance of homeostasis of gut microbiota. Modulation of gut-brain signaling was demonstrated

Owing to changes in lifestyle and eating habits, metabolic diseases, such as obesity, diabetes, hypertension, hyperlipemia, hyperuricemia, and nonalcoholic fatty liver disease (Non-AFLD), have become a pandemic in China (Long et al., 2019; Yuan et al., 2019; Zhang et al., 2016). Reports showed that there were about more than 450 million metabolic disease patients in China today (Swinburn et al., 2019). Although some risk factors, such as smoking, drinking, exercise, meditation, and genetics, are known to be associated with metabolic disorders (Chassaing et al., 2015; Sonnenburg & Bäckhed, 2016), scholars believe these factors do not explain the higher prevalence and variety of metabolic diseases in China relative to that in developed countries. Therefore, the development of prevention and treatment strategies is

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Corresponding authors. E-mail addresses: [email protected] (C. Diling), [email protected] (G. Yinrui), [email protected] (Q. Longkai), [email protected] (T. Xiaocui), [email protected] (L. Yadi), [email protected] (F. Jiaxin), [email protected] (Z. Xiangxiang), [email protected] (Z. Miao), [email protected] (S. Ou), [email protected] (W. Dongdong), [email protected] (X. Yizhen), [email protected] (B.B. Yang), [email protected] (W. Qingping). 1 These authors have contributed equally to this work. https://doi.org/10.1016/j.jff.2019.103639 Received 22 July 2019; Received in revised form 10 October 2019; Accepted 16 October 2019 1756-4646/ © 2019 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

Please cite this article as: Chen Diling, et al., Journal of Functional Foods, https://doi.org/10.1016/j.jff.2019.103639

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and the reverse primer 5′-GGACTACHVGGGTWTCTAAT-3′, as well as the forward primer 5′-CCTAYGGGRBG CASCAG-3′ and reverse primer 5′-GGACTACNNGGGTATCTAAT-3′, for rats. Each amplified product was concentrated via solid-phase reversible immobilization and quantified by electrophoresis using an Agilent 2100 Bioanalyzer (Agilent). After quantification of DNA concentration by NanoDrop, each sample was diluted to a concentration of 1 × 109 mol/μl in TE buffer and pooled. A total of 20 μL of the pooled mixture was used for sequencing with the Illumina MiSeq sequencing system according to the manufacturer's instructions. The resulting reads were analyzed as described previously (Ling et al., 2014).

to have unprecedented potential for treating obesity (Clemmensen et al., 2017; Page, 2019; Solas, Milagro, Ramírez, & Martínez, 2017; Torres-Fuentes, Schellekens, Dinan, & Cryan, 2017) and type 2 diabetes (Grasset et al., 2017; Slyepchenko et al., 2016). Ganoderma lucidum is widely used as a medicinal and edible powder food for promoting health in Southeast Asia for a long history. Traditional Chinese medicine records that ganoderma lucidum has effects on delaying aging, while the mode of action are not very clear. In this study, an obese mouse model was prepared by administering a high sugar and fat diet for 60 days, followed by dietary supplementation of Ganoderma lucidum extracts for another 35 days. Then, changes in the histopathology of liver, adipose, colon, intestinal, spleen, renal, and brain tissues; metabolism and transcription; and gut microbiota were monitored, with the aim of elucidating the prebiotic effects of G. lucidum extracts on highsugar-and-fat-diet-induced obese mice via regulation of the gut-brain axis.

2.4. Metabolomic analysis A 40-mg feces sample was homogenized in 400 μL of deionized water containing 10 μg/mL of L-norvaline as an internal standard. Following centrifugation at 14,000g and 4 °C for 15 min, a total of 300 μL of the supernatant was transferred. The extraction was repeated by adding 600 μL of ice-cold methanol to the residue. The supernatants from the two extractions were combined. A total of 400 μL of the combined supernatants and 10 μL of the internal standard solution (50 μg/mL of L-norleucine) were combined and evaporated to dryness under a nitrogen stream. The residue was reconstituted in 30 μL of 20 mg/mL methoxyamine hydrochloride in pyridine, and the resulting mixture was incubated at 37 °C for 90 min. Then, 30 μL of BSTFA (with 1% TMCS) was added to the mixture and derivatized at 70 °C for 60 min prior to gas chromatography-mass spectrometer (GC–MS) metabolomics analysis. Metabolomics instrumental analysis was performed on an Agilent 7890A gas chromatography system coupled to an Agilent 5975C inert MSD system (Agilent Technologies Inc., CA, USA). An OPTIMA® 5 MS Accent fused-silica capillary column (30 m × 0.25 mm × 0.25 μm; MACHEREY-NAGEL, Düren, GERMAN) was utilized to separate the derivatives. Helium (> 99.999%) was used as a carrier gas at a constant flow rate of 1 mL/min through the column. The injection volume was 1 μL in split mode (2:1), and the solvent delay time was 6 min. The initial oven temperature was held at 70 °C for 2 min, ramped up to 160 °C at a rate of 6 °C/min, to 240 °C at a rate of 10 °C/min, to 300 °C at a rate of 20 °C/min, and finally held at 300 °C for 6 min. The temperatures of the injector, transfer line, and electron impact ion source were set to 250 °C, 260 °C, and 230 °C, respectively. The electron ionization (EI) energy was 70 eV, and data were collected in full scan mode (m/z 50–600). The typical total ion current (TIC) chromatograms are illustrated in Fig. S1. Information on the peak picking, alignment, deconvolution, and further processing of raw GC–MS data can be found in previously published protocols (Gao, Pujos-Guillot, & Sebedio, 2010). The final data were exported as a peak table file, including observations (sample name), variables (rt_mz), and peak areas. The data were normalized against total peak abundances before performing univariate and multivariate statistical analyses. For the multivariate statistical analysis, the normalized data were imported into SIMCA software (version 14.1, Umetrics, Umeå, Sweden), where the data were preprocessed by unit variance (UV) scaling and mean centering before performing principle component analysis (PCA), partial least squares - discriminant analysis (PLS-DA), and orthogonal filter partial least-squares discriminant analysis (OPLS-DA). The model quality is described by the R2X or R2Y and Q2 values. R2X (PCA) or R2Y (PLS-DA and OPLS-DA) is defined as the proportion of variance in the data explained by the models and indicates the goodness of fit. Q2 is defined as the proportion of variance in the data that is predictable by the model and indicates the predictability of the current model, calculated by a cross-validation procedure (Fig. S2). To avoid model overfitting, a default 7-round cross-validation in SIMCA software was performed throughout to determine the optimal number of principal components (Fig. S2).

2. Methods 2.1. Animal model preparation and treatments Adult male KM mice (18–22 g, 9 months) obtained from the Center of Laboratory Animal of Guangdong Province (SCXK [Yue] 2008–0020, SYXK [Yue] 2008–0085) were pair-housed in plastic cages in a temperature-controlled (25 ± 2 °C) colony room under a 12/12-h light/ dark cycle. Food and water were available ad libitum. All experimental protocols were approved by the Center of Laboratory Animals of the Guangdong Institute of Microbiology. All efforts were made to minimize the number of animals used. The mice in the control group were fed a standard diet (Control or Normal), and the mice in the model groups were fed a high sugar and fat diet (Model). Water was freely available, and these treatments were administered for 3 months. After the mice were fed a high sugar and fat diet for one month, various doses of G. lucidum extract, GH (high-dose of G. lucidum extract) of 200 mg/kg/d, GM (middle-dose of G. lucidum extract) of 100 mg/kg/d, GL (low-dose of G. lucidum extract) of 50 mg/ kg/d, extracted with ethanol were delivered by intragastric administration. The preparation and constituents of alcohol extracts of the G. lucidum fruit body (AGL) were indicated in our recently published paper (Lai et al., 2019). The high sugar and fat diet was continued for another two months. The components of the high sugar and fat diet included 20% sucrose, 15% fat, 1.2% cholesterol, 0.2% bile acid sodium, 10% casein, 0.6% calcium hydrogen phosphate, 0.4% stone powder, 0.4% premix, and 52.2% basic feed. The heat ratio was as follows: protein 17%, fat 17%, and carbohydrate 46%. 2.2. Obesity related-parameter measurement The appearance, behavior, and fur color of the animals were observed and documented every day. Animal weight was measured every 3 days during the drug administration period. Following the water maze testing, blood and serum samples were acquired. And cytokines were measured, and the brains of the animals were dissected. A total of 4 brains from each group were fixed in 4% paraformaldehyde solution and prepared as paraffinized sections. The sections were stained with hematoxylin-eosin (H&E) and immunohistochemistry staining and observed under light microscopy (Chen et al., 2014). 2.3. Microbiome analysis Fresh intestinal content samples were collected before the fasting of the mice and stored at −80 °C. Frozen microbial DNA isolated from mice intestinal content samples with total masses ranging from 1.2 to 20.0 ng were stored at −20 °C. The microbial 16S rRNA genes were amplified using the forward primer 5′-ACTCCTACG GGAGGCAGCA-3′ 2

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by incubation with a horseradish peroxidase-conjugated goat antimouse (Servicebio, G2211-1-A) or goat anti-rabbit (Servicebio, G22102-A) IgG secondary antibody (1:2000). The antibodies were as follows: anti- LEPR, AGPR, NPY, POMC and GABAergic (obtained from Affinity) as well as GAPDH (CST, 2118L) and β-Actin (CST, 4970S). Band intensity was quantified using ImageJ software (NIH).

For univariate statistical analysis, the normalized data were analyzed in the R platform (version 3.3.0), where parametric testing was performed on the normally distributed data by Welch’s t test, while nonparametric testing was conducted on the abnormally distributed data by the Wilcoxon Mann-Whitney test. The variables with VIP (Variable importance in the projection) values in the OPLS-DA model larger than 1 and p values from the univariate statistical analysis lower than 0.05 were identified as potential differential metabolites (Fig. S2). Fold change was calculated as the binary logarithm of the average normalized peak intensity ratio between Group 1 and Group 2, where a positive value indicates that the average mass response of Group 1 is higher than that of Group 2.

2.8. Statistical analysis All data are described as the means ± standard deviations (SD) of at least three independent experiments. Significant differences between treatments were assessed by one-way analysis of variance (ANOVA) with a significance level of p < 0.05 using the Statistical Package for the Social Sciences (SPSS; Abacus Concepts, Berkeley, CA, USA) and Prism 5 (GraphPad, San Diego, CA, USA) software.

2.5. RNA sequencing Total RNA was isolated using Trizol Reagent (Invitrogen Life Technologies), followed by determination of the concentration, quality, and integrity using a NanoDrop spectrophotometer (Thermo Scientific). Three global brain samples of RNA were used as input material for the RNA sample preparations. Sequencing libraries were generated using the TruSeq RNA Sample Preparation Kit (Illumina, San Diego, CA, USA). Briefly, mRNA was purified from total RNA using poly-T oligoattached magnetic beads. Fragmentation was carried out using divalent cations under elevated temperature in an Illumina proprietary fragmentation buffer. First-strand cDNA was synthesized using random oligonucleotides and SuperScript II. Second-strand cDNA synthesis was subsequently performed using DNA Polymerase I and RNase H. Remaining overhangs were converted into blunt ends via exonuclease/ polymerase activities, and the enzymes were removed. After adenylation of the 3′ ends of the DNA fragments, Illumina PE adapter oligonucleotides were ligated to prepare for hybridization. To select cDNA fragments of the preferred 200-bp length, the library fragments were purified using the AMPure XP system (Beckman Coulter, Beverly, CA, USA). DNA fragments with ligated adaptor molecules on both ends were selectively enriched using Illumina PCR Primer Cocktail in a 15cycle PCR reaction. Products were purified (AMPure XP system) and quantified using the Agilent high-sensitivity DNA assay on a Bioanalyzer 2100 system (Agilent). The sequencing library was then sequenced on a Hiseq platform (Illumina) by Shanghai Personal Biotechnology Co., Ltd.

3. Results 3.1. Evaluation of the protective effects of Ganoderma lucidum in nonalcoholic fatty liver disease The aging obese mouse model was prepared by feeding mice a highfat diet for 60 days. Then, 100 mg/kg/d of alcohol extracts of the G. lucidum fruit body (AGL), alcohol extracts of G. lucidum superfine powder (AGS) and alcohol extracts of G. lucidum solid fermentation (AGF) were administered by gavage. After 35 days, the body weights of all the G. lucidum extract-treated groups were reduced (Fig. 1A). The levels of total cholesterol (T-CHO, Fig. 1Bb), low-density lipoprotein cholesterol (LDL-C, Fig. 1Bc) and triglycerides (TG, Fig. 1Bd) were reduced after treatment. pathological changes of small intestine and liver tissues; and the expression of NF-κB in small intestine tissues were also reduced (Fig. 1C). The changes in microbiota in the colon tissue were assessed by the 16S rRNA technique, which showed that the relative abundances of the Bacteroidales S247 group, Lachnospiraceae UCG001, Butyricicoccus, Clostridiales, Desulfovibrio, Desulfovibrionaceae, Helicobacter, and Campylobacterales were different. All the results showed that the alcohol extracts of G. lucidum fruit body ameliorated the alter to the gut microbiota, small intestine, and liver in the mice fed a high sugar and fat diet.

2.6. Histopathology and immunostaining The liver, adipose, colon, intestinal, spleen, and renal tissues of the animals were dissected. A total of four samples from each group were fixed in 4% paraformaldehyde solution and prepared as paraffin sections. Sections were stained with hematoxylin-eosin (H&E). Nissl staining, Silver staining, and TUNEL staining were performed. Immunostaining was performed using paraffin-embedded, 3-μm-thick sections and a two-step peroxidase- conjugated polymer technique (DAKO Envision kit, DAKO, Carpinteria, CA). Slides were observed by light microscopy and immunostained (IF) for antibodies against GAFP and IBA-I.

3.2. Protective effects of AGL on the liver in high sugar and fat diet-fed mice As shown in Fig. 2A, the weights of the high sugar and fat-fed mice were higher than those of the mice fed a standard diet (control, p < 0.05); the blood glucose (Fig. 2B) and TMAO levels (Fig. 2C) were also higher (p < 0.05), which suggested that the high sugar and fat diet resulted in an increase in weight as well as blood glucose and TMAO levels. The livers and adipose tissues were removed and fixed in 4% paraformaldehyde at pH 7.4 for further pathological observation. These tissue samples were converted into paraffin sections after drawing materials, fixation, washing, dehydration, transparency, dipping in wax, and embedding. Obesity-related parameters and other related pathological changes were measured. Staining of the livers with hematoxylin and eosin (H&E, Fig. 2D), Masson (Masson's trichrome, Fig. 2F) and oil red O (ORO, Fig. 2G) revealed cell shrinkage, cell size reduction, rounded cell morphology, cytoplasmic vacuolar changes, fibrosis, and a significant increase in adipose cells, which indicated that fatty liver is associated with dysbacteriosis. Pathological changes in abdominal subcutaneous adipose tissue were also found (Fig. 2E). Fortunately, all three G. lucidum extract-treated groups exhibited a decrease in damage, which indicated that the alcohol extracts of G. lucidum protected the liver from damage induced by a long-term high sugar and fat diet.

2.7. Western bolting Briefly, global brain tissue from treated mice (purchased from the Beijing HFK Bioscience Co., LTD [Certificate No: SCXK (Jing) 20140004], was dissected, and proteins were extracted with radioimmunoprecipitation assay (RIPA) lysis buffer (Thermo ScientificTM TPERTM Tissue Protein Extraction Reagent, 78510). The proteins were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis and transferred onto polyvinylidene fluoride membranes. After blocking with 5% nonfat dry milk in Tris-buffered saline (20 mM TrisHCl, 500 mM NaCl, pH 7.4) with 0.2% Tween-20 (Aladdin, T104863), the membranes were probed with antibodies overnight at 4 °C, followed 3

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Fig. 1. The protective effects of Ganoderma lucidum extracts prepared by different methods on NAFLD. A shows the body weight changes; B shows the levels of total cholesterol (T-CHO), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C); C shows the pathological changes in the colon and liver tissues and the expression of NF-κB in the colon; and D shows a heat map of the gut microbiota. The ethanol extracts of the G. lucidum fruit body (AGL), G. lucidum superfine powder (AGS) and G. lucidum solid fermentation (AGF). Data are presented as the means ± SD of 3 independent experiments. *p < 0.05 and **p < 0.01 vs. the model group by one-way ANOVA, followed by the Holm-Sidak test.

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Fig. 2. Protective effects of alcohol extracts from Ganoderma lucidum on body weight changes (A); glucose (B) and TMAO (C) levels in serum; pathological changes in the liver (D) and adipose tissues (E); and Masson (F) and oil red O staining (G). Doses of G. lucidum extract (GH, 200 mg/kg/d; GM, 100 mg/kg/d; GL, 50 mg/kg/d, extracted with ethanol) were provided by intragastric administration. Data are presented as the means ± SD of 3 independent experiments. #p < 0.05 vs. the control group, *p < 0.05 and **p < 0.01 vs. the model group according to one-way ANOVA, followed by the Holm-Sidak test.

recover to the control group (Fig. 3B–E). The genus-level analysis revealed that the OTU of Clostridiales, Lachnospiraceae, Oscillospira, Ruminococcaceae, Dehalobacterium, Erysipelotrichaceae, AF12, [Mogibacteriaceae], Veillonella, Bilophila, Streptococcus were increased significantly, while those of Lactobacillus, Bifidobacterium, Roseburia, Peptostreptococcaceae, Blautia, Turicibacter were decreased by the high sugar and fat diet and recover after AGL treatment (p < 0.01, Table 4). Most of these changes were recover after AGL treatment (Fig. S3 and

3.3. AGL changes the gut microbiota of high sugar and fat-fed mice The alpha diversity analysis results showed that the indexes of Chao1, ACE, and Simpson were different after the treatment with AGL (Fig. 3A), and the PCA result and PLS-DA (Partial Least Squares Discriminant Analysis) result were different between the groups (Fig. 3F and G). The figures show the change of class, order, family, genus level, the treatment groups directly show the gut microbiota composition 5

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Fig. 3. Effect of alcohol extracts from Ganoderma lucidum on the gut microbiota in high sugar and fat diet-fed mice. (A) Alpha diversity analysis results; (B) PCA results and PLS-DA (Partial Least Squares Discriminant Analysis) result of different concentrations of extracts on gut microbiota treated with AGL; Changes in the gut microbiota at the genus level (C) and genus level (D). Doses of G. lucidum extract (GH, 200 mg/kg/d; GM, 100 mg/kg/d; GL, 50 mg/kg/d, extracted with ethanol) were delivered by intragastric administration. Data are presented as the means ± SD of 3 independent experiments. *p < 0.05 and **p < 0.01 vs. the model group by one-way ANOVA, followed by the Holm-Sidak test.

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Fig. 4. Effect of alcohol extracts from Ganoderma lucidum on the gut microbiota in high sugar and fat diet-fed mice. (A) A heat map of 72 different metabolites detected using GC/MS, among which 40 were down-regulated and 32 were up-regulated; (B) The model quality is of R2X (PCA, a) and R2Y (PLS-DA, b; OPLS-DA, c), the predictability of the current model (d); (C) The enriched pathways according to KEGG analysis; (D) The combined data analysis of the microbiome and metabolites in fimo. Data are presented as the means ± SD of more than 8 independent experiments. *p < 0.05 and **p < 0.01 vs. the model group by one-way ANOVA, followed by the Holm-Sidak test.

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Fig. 5. Effect of alcohol extracts from Ganoderma lucidum on gut microbiota in high sugar and fat diet-fed mice. (A) A heat map of M vs AGL-H different metabolites detected using GC/MS; (B) A heat map of M vs AGL-L different metabolites detected using GC/MS; (C) The enriched pathways of M vs AGL-H according to KEGG analysis; (D) The enriched pathways of M vs AGL-L according to KEGG analysis. Data are presented as the means ± SD of more than 8 independent experiments. *p < 0.05 and **p < 0.01 vs. the model group by one-way ANOVA, followed by the Holm-Sidak test.

metabolites using MetaboAnalyst 4.0 (metabolome shown in Fig. 4C) showed significant differences in aminoacyl-tRNA biosynthesis (p = 1.75E−06); alanine, aspartate and glutamate metabolism (p = 7.77E−05); valine, leucine and isoleucine biosynthesis (p = 0.001242); the citrate cycle (TCA cycle) (p = 0.001941); arginine and proline metabolism (p = 0.00398); nitrogen metabolism (p = 0.007215); and glycerolipid metabolism (p = 0.008918). In the GL-treated group (low dosage of AGL), the metabolic analysis of intestinal contents showed that 14 different metabolites were detected by GC/MS, among which 8 were down-regulated and 6 were upregulated (Fig. 5A, Fig. S1). In the GM-treated group (middle dosage of AGL), there were 22 different metabolites detected in the GC/MS, among which were 15 down-regulated and 7 were up-regulated (Fig. 5B). PCA (R2X = 0.604) (Fig. S2) and (O)PLS-DA (-0.0623, Fig. S2) were carried out to visualize the metabolic alterations in the experimental groups after mean centering and unit variance scaling. VIP ranked the overall contribution of each variable to the (O)PLS-DA (Fig. S2) model, and those variables with a VIP value > 1.0 were considered relevant for group discrimination (Fig. S2). All the analysis results

Table 5), which indicated that AGL could improve the gut microbiota composition resulting from high sugar and fat diet.

3.4. AGL resulted in metabolic alterations to the intestinal contents in the high sugar and fat-fed mice The metabolic analysis of intestinal contents from the high sugar and fat-fed mice showed that 72 different metabolites were detected using GC/MS, among which 40 were down-regulated and 32 were upregulated (Fig. 4A, Fig. S2). Principle component analysis (PCA; R2X = 0.604) (Fig. 4Ba) and (orthogonal) partial least-squares discriminant analysis (O)PLS-DA (−0.0623, Fig. 4Bb) were carried out to visualize the metabolic alterations in the experimental groups after mean centering and unit variance scaling. Variable importance in the projection (VIP) ranked the overall contribution of each variable in the (O)PLS-DA (Fig. 4Bc) model, and those variables with a VIP > 1.0 were considered relevant for group discrimination (Fig. 4Bd). The results suggested that the metabolites of feces samples from the groups varied, and the heat map is shown in Fig. 4A. Pathway analysis of different 8

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Fig. 6. Effects of the high sugar and fat diet on the mRNA sequencing of the liver. (A) A heat map of differentially expressed mRNAs; (B) A total of 16430 mRNAs were detected in the liver samples of the model and normal groups, among which 1142 mRNAs were down-regulated and 833 mRNAs were up-regulated. (C) The KEGG analysis of differentially expressed mRNAs (top 10).

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Table 1 GO analysis of mRNA sequencing in the liver of the high sugar and fat fed mice. GO_Term GO:0044710 GO:0008152 GO:0044281 GO:0044237 GO:0071704 GO:0055114 GO:0006950 GO:0044238 GO:0016126 GO:0044711 GO:0051234 GO:0006629 GO:0051179 GO:0048583 GO:1902653 GO:0048518 GO:0010033 GO:0006810 GO:0044283 GO:0045766 GO:0006695 GO:1904018 GO:0006694 GO:1901564 GO:1901617 GO:0070887 GO:0046165 GO:0065008 GO:0044255 GO:0016125 GO:0008610 GO:0002376 GO:1902652 GO:0071310 GO:1901701 GO:0045765 GO:0044699 GO:1901575 GO:0044248 GO:0032879 GO:1902578 GO:0009056 GO:0023051 GO:1901342 GO:0048522 GO:0008203 GO:0006955 GO:0006082 GO:1901700 GO:0010646 GO:1901615 GO:1901135 GO:0072376 GO:0043436 GO:0002682 GO:0044765 GO:0009966 GO:0019637 GO:0002684 GO:0048584 GO:0019752 GO:0006066 GO:0006793 GO:0044712

single-organism metabolic process metabolic process small molecule metabolic process cellular metabolic process organic substance metabolic process oxidation-reduction process response to stress primary metabolic process sterol biosynthetic process single-organism biosynthetic process establishment of localization lipid metabolic process localization regulation of response to stimulus secondary alcohol biosynthetic process positive regulation of biological process response to organic substance transport small molecule biosynthetic process positive regulation of angiogenesis cholesterol biosynthetic process positive regulation of vasculature development steroid biosynthetic process organonitrogen compound metabolic process organic hydroxy compound biosynthetic process cellular response to chemical stimulus alcohol biosynthetic process regulation of biological quality cellular lipid metabolic process sterol metabolic process lipid biosynthetic process immune system process secondary alcohol metabolic process cellular response to organic substance cellular response to oxygen-containing compound regulation of angiogenesis single-organism process organic substance catabolic process cellular catabolic process regulation of localization single-organism localization catabolic process regulation of signaling regulation of vasculature development positive regulation of cellular process cholesterol metabolic process immune response organic acid metabolic process response to oxygen-containing compound regulation of cell communication organic hydroxy compound metabolic process carbohydrate derivative metabolic process protein activation cascade oxoacid metabolic process regulation of immune system process single-organism transport regulation of signal transduction organophosphate metabolic process positive regulation of immune system process positive regulation of response to stimulus carboxylic acid metabolic process alcohol metabolic process phosphorus metabolic process single-organism catabolic process

Cluster frequency

Genome frequency of use

Corrected p-value

531 out of 1955 genes, 27.2% 963 out of 1955 genes, 49.3% 225 out of 1955 genes, 11.5% 822 out of 1955 genes, 42.0% 879 out of 1955 genes, 45.0% 159 out of 1955 genes, 8.1% 358 out of 1955 genes, 18.3% 843 out of 1955 genes, 43.1% 20 out of 1955 genes, 1.0% 154 out of 1955 genes, 7.9% 418 out of 1955 genes, 21.4% 149 out of 1955 genes, 7.6% 504 out of 1955 genes, 25.8% 381 out of 1955 genes, 19.5% 17 out of 1955 genes, 0.9% 593 out of 1955 genes, 30.3% 307 out of 1955 genes, 15.7% 399 out of 1955 genes, 20.4% 70 out of 1955 genes, 3.6% 33 out of 1955 genes, 1.7% 16 out of 1955 genes, 0.8% 35 out of 1955 genes, 1.8% 28 out of 1955 genes, 1.4% 198 out of 1955 genes, 10.1% 37 out of 1955 genes, 1.9% 253 out of 1955 genes, 12.9% 28 out of 1955 genes, 1.4% 368 out of 1955 genes, 18.8% 117 out of 1955 genes, 6.0% 28 out of 1955 genes, 1.4% 72 out of 1955 genes, 3.7% 211 out of 1955 genes, 10.8% 27 out of 1955 genes, 1.4% 213 out of 1955 genes, 10.9% 113 out of 1955 genes, 5.8% 44 out of 1955 genes, 2.3% 1324 out of 1955 genes, 67.7% 166 out of 1955 genes, 8.5% 147 out of 1955 genes, 7.5% 282 out of 1955 genes, 14.4% 342 out of 1955 genes, 17.5% 175 out of 1955 genes, 9.0% 320 out of 1955 genes, 16.4% 46 out of 1955 genes, 2.4% 503 out of 1955 genes, 25.7% 25 out of 1955 genes, 1.3% 121 out of 1955 genes, 6.2% 117 out of 1955 genes, 6.0% 174 out of 1955 genes, 8.9% 322 out of 1955 genes, 16.5% 64 out of 1955 genes, 3.3% 122 out of 1955 genes, 6.2% 17 out of 1955 genes, 0.9% 114 out of 1955 genes, 5.8% 141 out of 1955 genes, 7.2% 316 out of 1955 genes, 16.2% 283 out of 1955 genes, 14.5% 105 out of 1955 genes, 5.4% 98 out of 1955 genes, 5.0% 214 out of 1955 genes, 10.9% 107 out of 1955 genes, 5.5% 48 out of 1955 genes, 2.5% 221 out of 1955 genes, 11.3% 97 out of 1955 genes, 5.0%

3742 out of 21,429 genes, 17.5% 8442 out of 21,429 genes, 39.4% 1494 out of 21,429 genes, 7.0% 7312 out of 21,429 genes, 34.1% 7928 out of 21,429 genes, 37.0% 952 out of 21,429 genes, 4.4% 2749 out of 21,429 genes, 12.8% 7632 out of 21,429 genes, 35.6% 39 out of 21,429 genes, 0.2% 1004 out of 21,429 genes, 4.7% 3471 out of 21,429 genes, 16.2% 980 out of 21,429 genes, 4.6% 4334 out of 21,429 genes, 20.2% 3138 out of 21,429 genes, 14.6% 33 out of 21,429 genes, 0.2% 5264 out of 21,429 genes, 24.6% 2445 out of 21,429 genes, 11.4% 3335 out of 21,429 genes, 15.6% 364 out of 21,429 genes, 1.7% 117 out of 21,429 genes, 0.5% 32 out of 21,429 genes, 0.1% 133 out of 21,429 genes, 0.6% 94 out of 21,429 genes, 0.4% 1476 out of 21,429 genes, 6.9% 149 out of 21,429 genes, 0.7% 1994 out of 21,429 genes, 9.3% 97 out of 21,429 genes, 0.5% 3112 out of 21,429 genes, 14.5% 772 out of 21,429 genes, 3.6% 99 out of 21,429 genes, 0.5% 408 out of 21,429 genes, 1.9% 1622 out of 21,429 genes, 7.6% 95 out of 21,429 genes, 0.4% 1649 out of 21,429 genes, 7.7% 756 out of 21,429 genes, 3.5% 209 out of 21,429 genes, 1.0% 13,361 out of 21,429 genes, 62.4% 1230 out of 21,429 genes, 5.7% 1060 out of 21,429 genes, 4.9% 2325 out of 21,429 genes, 10.8% 2915 out of 21,429 genes, 13.6% 1322 out of 21,429 genes, 6.2% 2703 out of 21,429 genes, 12.6% 229 out of 21,429 genes, 1.1% 4547 out of 21,429 genes, 21.2% 91 out of 21,429 genes, 0.4% 847 out of 21,429 genes, 4.0% 813 out of 21,429 genes, 3.8% 1326 out of 21,429 genes, 6.2% 2741 out of 21,429 genes, 12.8% 371 out of 21,429 genes, 1.7% 859 out of 21,429 genes, 4.0% 48 out of 21,429 genes, 0.2% 797 out of 21,429 genes, 3.7% 1038 out of 21,429 genes, 4.8% 2703 out of 21,429 genes, 12.6% 2380 out of 21,429 genes, 11.1% 721 out of 21,429 genes, 3.4% 661 out of 21,429 genes, 3.1% 1720 out of 21,429 genes, 8.0% 741 out of 21,429 genes, 3.5% 257 out of 21,429 genes, 1.2% 1792 out of 21,429 genes, 8.4% 662 out of 21,429 genes, 3.1%

9.75E−26 5.9E−17 5.4E−11 6.54E−11 1.16E−10 1.28E−10 1.24E−09 2.07E−09 9.41E−08 0.000000313 0.00000087 0.00000124 0.0000013 0.00000268 0.00000284 0.00000349 0.00000604 0.00000616 0.00000764 0.0000128 0.0000162 0.0000318 0.0000545 0.0000626 0.0000652 0.00009 0.00011 0.00012 0.00013 0.00019 0.00019 0.00023 0.00029 0.00035 0.00049 0.00058 0.00061 0.00069 0.00074 0.00087 0.00103 0.00117 0.00119 0.0013 0.00159 0.00186 0.00208 0.00222 0.00226 0.00227 0.00248 0.00254 0.00273 0.00425 0.00443 0.00467 0.00472 0.00498 0.00512 0.00555 0.00591 0.0067 0.00693 0.00984

3.5. A high sugar and fat diet changes the mRNA sequence of liver and intestinal tissues

suggested that the metabolites of feces samples from all the treated groups varied. Pathway analysis of different metabolites using MetaboAnalyst 4.0 (metabolome view in Fig. 5C and D) showed that aminoacyl-tRNA biosynthesis; alanine, aspartate and glutamate metabolism; valine, leucine and isoleucine biosynthesis; the TCA cycle; arginine and proline metabolism; nitrogen metabolism; and glycerolipid metabolism were improved after AGL treatment.

There were 16430 mRNAs detected in the liver samples of the model and normal groups, among which 1142 mRNAs were down-regulated while 833 mRNAs were up-regulated (Fig. 6A and B). The GO analysis results of the mRNA expression differences are shown in Table 1 10

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Table 2 KEGG analysis of mRNA sequencing in the liver of the high sugar and fat fed mice. Pathway_ID

Pathway

Up_Number

Down_Number

DEG_number

Total_number

p-value

FDR

ko04610 ko00190 ko00100 ko00830 ko00900 ko00982 ko04015 ko03440 ko00140 ko04141 ko00980 ko00480 ko00640 ko00330 ko00520 ko04711 ko00860 ko04612 ko04611 ko04918 ko00220 ko04142 ko04724 ko00720

Complement and coagulation cascades Oxidative phosphorylation Steroid biosynthesis Retinol metabolism Terpenoid backbone biosynthesis Drug metabolism - cytochrome P450 Rap1 signaling pathway Homologous recombination Steroid hormone biosynthesis Protein processing in endoplasmic reticulum Metabolism of xenobiotics by cytochrome P450 Glutathione metabolism Propanoate metabolism Arginine and proline metabolism Amino sugar and nucleotide sugar metabolism Circadian rhythm - fly Porphyrin and chlorophyll metabolism Antigen processing and presentation Platelet activation Thyroid hormone synthesis Arginine biosynthesis Lysosome Glutamatergic synapse Carbon fixation pathways in prokaryotes

28 1 1 8 2 6 18 2 9 31 5 2 3 3 6 1 6 13 12 9 1 19 5 1

4 33 10 15 8 13 23 9 12 1 11 12 6 9 6 3 4 3 9 5 5 2 15 4

32 34 11 23 10 19 41 11 21 32 16 14 9 12 12 4 10 16 21 14 6 21 20 5

87 117 19 88 22 67 212 28 85 163 65 55 31 49 49 8 40 81 119 69 19 120 113 14

3.03275E−11 9.4596E−09 4.08615E−07 1.61698E−05 2.37312E−05 2.52108E−05 3.94217E−05 4.87698E−05 9.10558E−05 0.00020076 0.000633154 0.000952169 0.002899416 0.003062499 0.003062499 0.005347419 0.005672101 0.006948177 0.008504402 0.008801082 0.009312701 0.009348599 0.009750159 0.009908942

7.24827E−09 1.13042E−06 3.2553E−05 0.000966145 0.001004231 0.001004231 0.001345971 0.001456997 0.002418037 0.004798162 0.013756712 0.018964031 0.048795817 0.048795817 0.048795817 0.079743067 0.079743067 0.088405606 0.088405606 0.088405606 0.088405606 0.088405606 0.088405606 0.088405606

States and 15–30% in China in 2016, with an increasing trend (Estes et al., 2018; Fan, Kim, & Wong, 2017). Approximately 10–20% of cases may degenerate into nonalcoholic steatohepatitis (NASH) and even cirrhotic nodules or hepatocellular carcinoma, making NAFLD one of the most common chronic liver diseases worldwide (Wu et al., 2019; Zhang et al., 2017). A reasonable diet and regular exercise are widely recognized as effective management interventions for the prevention and control of NAFLD; however, the results are not as good as expected owing to lack of awareness of appropriate diet. To further understand the interactions involved in the microbiotagut-liver axis, a combined data analysis was performed. First, the results of the combined data analysis of the microbiome and metabolites in fimo are shown in Fig. 4D, which demonstrates that Bifidobacteriaceae, Lactobacillaceae, Clostridiales, Streptococcaceae, RF39, Veillonella, Ruminococcaceae, Peptostreptococcaceae, Desulfovibrionaceae and Mogibacteriaceae may be the key bacteria in changing the metabolic imbalance (Figs. 3D and 4D). The untargeted metabolites of 2-oxo-4-methylthiobutanoic acid, 2-ketoisovaleric acid, 2-ketoisocaproic acid, phosphate, and proline [+CO2] were the main differential metabolites (Fig. 4A and D). Second, conjoint analysis of the microbiome, intestinal content metabolites, and mRNA sequencing of the intestine was carried out. The results, which are shown in Fig. 8A, demonstrate that there were complex interactions between the bacteria of Bifidobacterium, Streptococcus, Lactobacillus, Veilloanella, Peptococcaceae, Roseburia, and untargeted metabolites of phosphate, 2-ketoisocaproic acid, 2-oxo-4-methylthiobutanoic acid, proline [+CO2], and genes of Hspa1b, Tmc7, Dedd2, Hsph1, AA986860, Aldh4al. Third, the results of the conjoint analysis of the microbiome, intestinal content metabolites and mRNA sequence of the liver shown in Fig. 8B (|correlation| > 0.9) suggested that Lactobacillus, Bifidobacterium, Oscillospira, Peptococcaceae, Roseburia, Ruminococcaceae, RF39, Streptococcus, Turicibacter and Veillonella were the primary bacteria; the genes Abcg2, Acat2, AI182371, Apoc4, Atg13, C6, C9, Canx, Colec12, Fastkd3, Gstm2, Hspa5, Hyou1, Lhpp, Lsm7, Mmab, mt-Nd1, Nr2f6, Rpl36, Rpl38, Rps6kb2, Serpine2, Sirpa, Spcs1, and Sugct may be the key genes; and 1,3-dihydroxyacetone, 1-propanamine, 2,3-dihydroxypyridine, 2-deoxyguanosine, 2-deoxyribose, 2-hydroxybutyric acid, 2-ketoglutaric acid, 2-ketoisocaproic acid, 2-oxo-4-methylthiobutanoic acid, 3-methyl-2-ketovaleric acid, 4-hydroxybutyric acid, asparagine, citrulline, deoxycholic acid, galacturonic acid, glucuronic acid, guanine, histidine, isoleucine,

(p < 0.01), and KEGG analysis results are shown in Fig. 6C and Table 2 (p < 0.01). The GO analysis showed significant differences in the GO: 0044710 of the single-organism metabolic process (p = 9.75E−26), GO: 0008152 of the metabolic process (p = 5.9E−17), GO: 0044281 of the small molecule metabolic process (p = 5.4E−11), GO: 0044237 of the cellular metabolic process (p = 6.54E−11), and GO: 0071704 of the organic substance metabolic process (p = 1.16E−10). KEGG analysis showed that the pathways of complement and coagulation cascades (p = 3.03E−11), oxidative phosphorylation (p = 9.46E−09), steroid biosynthesis (p = 4.09E−07), retinol metabolism (p = 1.62E−05), terpenoid backbone biosynthesis (p = 2.37E−05), drug metabolism - cytochrome P450 (p = 2.52E−05), Rap1 signaling pathway (p = 3.94E−05), homologous recombination (p = 4.88E−05), and steroid hormone biosynthesis (p = 9.11E−05) were significantly influenced. The RNA-seq of small intestinal tissues showed that there were 42 RNAs up-regulated and 68 RNAs down-regulated (|Log2FC| > 1.0, FDR < 0.05 vs the normal, Table 3). Additionally, the pathological changes in the small intestine and colon of the high sugar and fat dietfed mice were different from those of the mice fed a standard diet, as cell shrinkage, cell size reduction and turn around, cytoplasmic vacuolar changes, cell boundary blurring, severe intestinal villi shedding, and damaged tissue blocks were scattered (Fig. 7A) in the model group (CT denote rip cutting of the colon, IL denote transection of the colon, IT denote rip cutting of the small intestinal, IL denote transection of the small intestine). At the same time, the pathological changes in the spleen (Fig. 7B), renal tissue (Fig. 7B), and spinal marrow (Fig. 7B, H& E, Luxol Fast Blue, LFB), which showed that cells of the dysbacteriosis mice were smaller and cytoplasmic vacuoles decreased in number in the model group, were improved in all the AGL-treated groups (Fig. 7). This result indicated that the high sugar and fat diet caused damage to all the organs, and that the alcohol extracts of G. lucidum are beneficial as a dietary supplement and/or potential drug for injury recovery. 3.6. Effects of AGL on the microbiota-gut-liver axis Non-alcoholic fatty liver disease (NAFLD) is a metabolic syndrome of the liver, accompanying obesity, dyslipidemia, and insulin resistance. In recent years, the global incidence of NAFLD has increased; the incidence in adults was approximately 20–30% in Europe and the United 11

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Table 3 Different expression of mRNA in the intestine of the high sugar and fat fed mice. Symbol

Model_count

Control_count

Model_fpkm

Control_fpkm

log2(FC)

P-value

FDR

Significant

Zbed6 – Hoxb13 Wfdc2 Hspa1a Hspa1b TCONS_00053457 B430305J03Rik TCONS_00102194 Oas1e P2ry4 Enpp7 Ano3 Hsph1 Nos1ap Slc5a12 Slc5a4a TCONS_00003927 Fosb Mmp28 Xpnpep2 Lama3 Cubn TCONS_00030165 Aldh4a1 Tppp AA986860 Slc7a8 Slc25a37 Per1 Fam46a Npc1l1 Slc13a2 Clca4b Slc15a1 Slc26a2 Fos Ace Sgpl1 Trim65 Dedd2 Tnfaip3 Pxmp4 Aldob Plin2 Capg Pcp4l1 Rnaset2b Lamb3 Dgkh Acot2 Srebf1 TCONS_00045808 Pdpn Me1 Kctd14 Ugt1a9 Apoc2 Akr1e1 Fabp1 Kdelr3 Akr1b8 Acaa1b Dnah2 Mfsd2a G6pc Trf Pmm1 Fbp1 Leap2 TCONS_00104968 Aqp8 Cyp4a10 –

0.0000 0.0000 0.0000 1.0000 112.5600 105.5233 4.6667 1.3867 12.3333 2.6667 5.6667 658.6667 5.6667 573.3333 40.0000 1581.6667 352.6667 38.6667 35.3333 20.0633 844.0000 291.0000 2478.0000 42.3333 713.1667 197.3333 585.3333 2777.6667 1629.9633 361.3333 1852.6667 8629.0000 2533.0000 14008.7567 4650.3333 1838.6933 1254.0000 32553.3333 6076.6667 238.3333 578.6667 339.0000 1019.0000 136563.3333 3756.0000 345.3333 288.0000 888.7933 1074.3333 79.3333 378.2067 6791.6667 870.3333 59.6667 6322.3333 321.3333 5504.0233 21952.2567 336.3333 47933.3333 676.0000 184.6667 2768.4900 722.6667 2640.0000 2082.0000 1084.7700 426.0000 3959.3333 1448.0000 314.0000 156.3333 1395.7300 15.9967

33.3333 27.7200 9.6667 52.3333 4094.0700 3694.2167 98.3333 23.8267 204.0000 29.3333 45.6667 4973.0000 35.0000 3506.3333 222.1367 8163.6667 1823.6667 190.6667 161.0000 92.0300 3634.6800 1197.3333 10122.6667 164.6667 2711.4733 659.3333 2001.6667 9312.6667 5125.4100 1100.0000 5498.0000 24776.0000 7321.0000 39716.3133 12899.6667 5039.6667 3448.6667 83294.3333 15008.6667 586.6667 1368.0000 809.0000 564.3567 73734.6667 2004.3333 177.0000 139.3333 421.0767 456.6667 33.6667 156.9833 2679.6667 355.3333 23.3333 2330.3333 120.6667 2027.3867 8115.0567 121.0000 16737.0000 229.3333 60.3333 823.8267 220.6667 756.6667 607.6667 305.8333 116.3333 1068.0000 386.0000 71.3333 36.6667 274.6667 3.0033

0.0010 0.0010 0.0010 0.1133 2.5967 2.5600 0.0733 0.0267 14.7600 0.1367 0.0867 40.0333 0.0567 15.1067 3.0133 31.3833 12.6200 1.7033 0.7967 0.4767 17.7767 3.0633 13.9833 8.3067 14.5300 2.9333 17.9733 46.7533 21.9900 9.8067 22.0867 130.5667 75.1800 328.7233 103.5433 15.3500 45.4767 499.7600 107.1333 4.5900 16.2267 5.2100 34.3133 5138.7467 163.3700 23.2200 14.4400 83.2133 20.3600 1.3667 12.8133 144.7033 516.0067 2.8367 153.8467 10.4633 183.2700 4760.8433 14.6933 14284.5167 39.3467 11.3067 129.6400 3.4600 123.0733 62.2000 57.3967 28.8700 219.8000 468.9667 7.5533 8.7733 49.0500 0.7967

0.3900 0.7100 0.3100 5.9067 85.1200 82.5600 1.3300 0.3900 248.5100 1.2600 0.6200 273.0033 0.3033 87.1267 15.3133 143.6400 58.1500 7.7133 3.5200 1.7633 66.4233 11.4667 50.2700 28.7467 48.4100 7.7933 53.0667 137.4567 56.2000 30.9333 57.1433 328.1433 194.9700 816.7700 254.2500 36.8833 111.2400 1098.5467 225.5467 9.9967 33.6700 11.8600 16.6867 2433.1467 77.0733 10.9567 6.2733 34.0900 7.5967 0.5033 4.6567 52.0200 217.8200 0.8667 52.7267 3.4567 60.1433 1662.1300 4.4867 4917.8400 11.8667 3.3200 35.6433 1.0467 31.6900 16.0133 9.5733 7.2333 52.0733 126.2167 1.4967 1.7633 8.9100 0.1467

7.9415 7.7788 6.2941 5.2181 4.9333 4.8740 4.1298 3.8570 3.8182 3.2381 2.8199 2.7396 2.4158 2.4093 2.3148 2.1894 2.1254 2.0730 1.9812 1.9405 1.8739 1.8647 1.8097 1.7609 1.7031 1.5830 1.5702 1.5373 1.4318 1.4046 1.3773 1.3235 1.3098 1.2981 1.2598 1.2524 1.2335 1.1645 1.1075 1.0977 1.0471 1.0431 −1.0504 −1.0974 −1.1275 −1.1923 −1.2646 −1.3175 −1.4253 −1.4445 −1.4673 −1.5298 −1.5371 −1.5602 −1.6520 −1.6684 −1.6753 −1.6793 −1.6869 −1.7484 −1.7655 −1.8170 −1.9517 −1.9554 −1.9850 −2.0056 −2.0611 −2.0774 −2.1018 −2.1701 −2.2860 −2.3078 −2.5144 −2.5430

2.25E−14 1.34E−05 0.00028 0.00013 2.07E−24 1.95E−15 5.50E−14 2.25E−06 1.09E−17 5.61E−05 6.06E−06 3.80E−08 8.57E−05 9.01E−16 1.06E−05 4.53E−07 8.02E−10 5.71E−05 8.94E−08 8.28E−05 7.06E−06 1.21E−05 5.23E−09 7.59E−05 8.70E−07 1.88E−05 2.30E−08 6.52E−07 1.96E−06 0.000186 9.89E−06 3.30E−05 0.000171 1.78E−05 1.31E−05 0.000255 0.000132 0.000207 0.000263 0.000184 0.000164 0.000193 0.000227 0.000258 0.000137 0.00016 0.000138 1.89E−05 1.05E−05 0.000297 6.75E−06 1.29E−06 2.27E−05 0.000175 3.99E−06 1.09E−05 1.89E−06 7.97E−06 9.83E−07 1.35E−05 2.92E−06 6.07E−06 6.08E−07 1.05E−06 7.16E−10 4.64E−10 1.66E−08 2.08E−06 2.07E−11 1.74E−05 2.37E−07 5.19E−05 1.53E−07 0.000255

5.69E−11 0.003786 0.045544 0.027373 3.67E−20 5.77E−12 1.22E−10 0.000928 4.81E−14 0.013613 0.00211 2.69E−05 0.018749 3.19E−12 0.003247 0.000251 8.36E−07 0.013665 5.46E−05 0.018345 0.002358 0.003558 4.63E−06 0.017032 0.00044 0.004931 1.85E−05 0.00034 0.000847 0.033326 0.003128 0.008244 0.032548 0.004769 0.003786 0.043068 0.027478 0.03601 0.043464 0.033326 0.031633 0.033905 0.03901 0.043136 0.027737 0.03121 0.027737 0.004931 0.003247 0.047869 0.002301 0.0006 0.005841 0.032555 0.001503 0.003263 0.000836 0.002615 0.000484 0.003786 0.001176 0.00211 0.000327 0.000503 7.93E−07 5.48E−07 1.40E−05 0.000877 2.62E−08 0.004729 0.000135 0.012764 9.02E−05 0.043068

up up up up up up up up up up up up up up up up up up up up up up up up up up up up up up up up up up up up up up up up up up down down down down down down down down down down down down down down down down down down down down down down down down down down down down down down down down

(continued on next page) 12

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Table 3 (continued) Symbol

Model_count

Control_count

Model_fpkm

Control_fpkm

log2(FC)

P-value

FDR

Significant

Ren1 TCONS_00126047 Notum BC035947 Acot3 Adad2 Slc16a9 TCONS_00030167 Eno1b Acot1 Akr1d1 C8b S100g Nr0b2 Gm27029 Fam83a Gsdmc3 Gsdmc2 Hmgcs2 TCONS_00057909 St6galnac1 Gm5286 Paqr9 G0s2 Trim50 Lep Apoa2 Retnlb Gsdmc TCONS_00019039 Fam205a4 TCONS_00030454 Gm20708 Exosc6 Pde2a Gm10184

109.6667 98.6667 46.3333 10.0000 48.5067 30.3333 530.6667 35.0000 4143.2800 892.7933 19.6667 45.3333 362.0000 92.6667 24.9467 76.3333 390.6900 1869.0267 5609.3333 47.3333 394.0000 105.6667 1086.6667 662.6667 20.0000 24.6667 230.0000 76.6667 27.4400 9.3333 10.5467 14.6000 36.5533 76.5900 108.6267 836.9567

21.6667 19.3333 8.0000 1.6667 8.3333 5.0000 83.6667 5.3333 649.9900 134.6833 2.6667 5.6667 44.3333 10.6667 2.6633 7.3333 35.2200 163.0233 471.6667 3.6667 31.0000 6.6667 64.6667 39.0000 1.0000 1.0000 6.6667 1.3333 0.3333 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

5.4233 7.6867 1.9867 0.2267 1.6667 1.2167 11.3267 31.6833 178.5900 46.0400 1.3533 1.5233 185.6433 7.2800 0.9700 4.8567 20.6900 65.6367 121.8100 1.0933 12.0667 5.3000 8.5333 76.4900 0.9633 0.6867 91.3700 14.9267 0.7900 6.7500 0.1700 1.4200 1.5233 4.5167 1.8267 31.8733

0.9200 1.2833 0.3667 0.0333 0.2533 0.1767 1.6433 4.4567 25.2400 6.1767 0.3800 0.1600 19.8433 0.7533 0.0900 0.3100 1.1867 5.0133 8.8200 0.0733 0.7867 0.2967 0.4333 4.2433 0.0533 0.0200 2.6767 0.2033 0.0100 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010 0.0010

−2.5931 −2.6168 −2.7005 −2.7138 −2.7275 −2.7601 −2.8816 −2.8983 −2.9092 −2.9276 −3.0563 −3.1791 −3.2682 −3.3316 −3.4531 −3.5464 −3.6983 −3.7841 −3.7861 −3.8326 −3.9218 −4.1562 −4.2698 −4.3272 −4.3360 −4.6335 −5.3038 −5.9442 −6.1629 −6.4006 −6.5609 −6.9735 −8.3514 −9.4614 −9.8408 −12.8656

5.95E−05 0.000191 5.43E−06 0.000271 3.29E−08 6.32E−05 0.000137 7.22E−05 2.75E−08 5.70E−21 4.59E−06 2.79E−05 8.31E−06 1.95E−11 0.000183 0.000146 1.63E−05 3.52E−06 5.90E−18 8.72E−08 1.65E−09 8.34E−13 3.04E−13 1.00E−13 1.89E−06 2.04E−09 4.38E−08 6.62E−05 9.02E−05 0.000173 0.000179 0.000122 3.01E−06 0.000143 4.72E−08 3.35E−12

0.014058 0.033877 0.001962 0.04439 2.43E−05 0.014741 0.027737 0.016394 2.12E−05 5.05E−17 0.001693 0.007056 0.002678 2.62E−08 0.033326 0.028806 0.004525 0.001355 3.48E−14 5.46E−05 1.62E−06 1.34E−09 5.38E−10 1.98E−10 0.000836 1.90E−06 2.99E−05 0.015223 0.019489 0.032555 0.032991 0.026074 0.001184 0.028562 3.10E−05 4.95E−09

down down down down down down down down down down down down down down down down down down down down down down down down down down down down down down down down down down down down

(Fig. 10A, p < 0.05). The TUNEL assay showed that the number of apoptotic cells in the hypothalamus of the model group was greater than that in the control group (Fig. 10B, p < 0.05). IBA-1 (green fluorescence) and GFAP (red fluorescence) expressions using IF were altered in the model group, while all these indexes in all the G. lucidum extract-treated groups were improved (Fig. 10, p < 0.05). The results indicated that the G. lucidum extracts could influence the gut-brain axis. Fig. 11A shows that the AGL influences the mRNA sequence in the brains of aged mice, demonstrating that the AGL could maintain most of the brain mRNAs at near-normal levels. The IPA analysis revealed that several mRNAs were enriched in the obesity-related pathways, such as AGPAT2, KAT2B, FGF1, FZD2, FZD7, SMAD9, KAT6A, LPIN1, SLC2A4, FZD1, CTBP2, SAP30, EBF1, LPL, RPS6KA1, and KLF5 mRNAs in the adipogenesis pathway; SLC6A12, GABRA4, ADCY5, GABRG3, MRAS, GABRA6, GABRA5, ADCY2, GABRQ, GABRA2, and KCNH2 mRNAs in GABA receptor signaling; TNFRSF11B, FOS, IL18, MRAS, NR1H3, IL1RL2, PDGFB, NFKBIE, and IKBKE mRNAs in PPAR signaling; and ADCY5, PRKAR2B, PLCB4, MRAS, IRS1, PLCE1, NFKBIE, MEF2C, IL1RL2, LPL, PRKCB, ADCY2, PLCZ1, NOTUM, and IKBKE mRNAs in PPARα/RXRα activation. Accordingly, we reanalyzed the interactions of some bacteria and metabolites, as shown in Fig. 11B. We selected 32 genera bacteria that were changed at the genus level by the aging intervention and reversed by AGL and 12 fecal metabolites that were changed by the aging intervention and reversed by AGL (Fig. S3). Then, classical Spearman correlation analysis was performed (Fig. 11B). Based on the correlation analysis, construct correlation networks were generated by selecting p < 0.05 and a correlation coefficient > 0.1, as shown in Fig. 11C. From the network, it can be seen that the metabolism of tryptophan is significantly associated with the gut microbiota (AF12, Arthromitus, Bifidobacterium, Bilophila, Blautia, Clostridiales, Clostridiums,

lyxose, methionine, methylphosphate, phenylalanine, proline[+CO2], pyroglutamic acid, spermidine, threonine, and uridine are the main metabolites that influence the liver. Although the concept of the ‘gut-liver axis’ was formally introduced by Marshall in 1998 (Marshall, 1998), several aspects of the enterohepatic circulation remain poorly understood. Many studies have demonstrated that the gut microbiome and endoxemia and/or damage induced by their harmful metabolites play an important role in the pathogenesis of NAFLD. However, this has been neglected as a ‘forgotten organ’ in the past. The gut and liver share a common embryonic origin, and there are numerous interrelationships between them with respect to various anatomical and biological functions. Approximately 70% of the liver blood flow is from the portal vein; therefore, the liver is constantly exposed to sources of antigens, microorganisms, and their metabolic products from the intestine. In this study, based on an integrated analysis of the results of the microbiome, intestinal content metabolites, and mRNA sequence of the intestine and liver (Fig. 8), together with the KEGG pathway analysis of gut microbiota (Fig. 9) and metabolites (Figs. 4–6) of the AGL-treated groups, we speculated that multiple key factors (bacteria, metabolites and mRNA) may participate in the beneficial effects of G. lucidum in high sugar and fat diet-induced obese mice. Thus, a nutrition-based prevention strategy focusing on gut microbiota may represent an effective solution. 3.7. AGL improves injuries induced by a high sugar and fat diet in the brains of obese mice To elucidate the benefits of G. lucidum supplementation in high sugar and fat diet-induced obese mice, we assessed the pathological changes in brain tissue samples. Nissl staining showed that significantly fewer neurons (blue points) were observed in the model group 13

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Fig. 7. Amelioration of organ damage induced by a high sugar and fat diet in mice following administration of Ganoderma lucidum alcohol extracts. A shows the changes in small intestinal and colon tissues; B shows the pathological changes in abdominal subcutaneous adipose tissue, spleen tissue, renal tissue, and spinal marrow (H&E, Luxol Fast Blue, LFB). Data are presented as the means ± SD of more than 8 independent experiments, and the histopathology data are the results of more than 3 independent experiments.

and methionine, and is inferred to be a harmful bacterium. Blautia has a negative correlation with pyroglutamic acid, putrescine, tryptophan and methionine and, simultaneously, a positive correlation with GABA, and is inferred to be a beneficial bacterium. Bilophila has a positive correlation with putrescine, tryptophan, and methionine, and is inferred to be a harmful bacterium. Lactobacillus has a negative correlation with putrescine, tryptophan, cholic acid, and methionine, and is

Lactobacillus, Peptostreptococcaceae), and this association is reduced by the alcohol extracts of G. lucidum. The relative abundance of Clostridiales is significantly associated with the levels of pyroglutamic acid, putrescine, tryptophan, cholic acid, methionine, malic acid and a-hydroxybutyric acid. Clostridiales has a negative correlation with malic acid and a-hydroxybutyric acid and, simultaneously, has a positive correlation with pyroglutamic acid, putrescine, tryptophan, cholic acid 14

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Fig. 8. Effects of AGL on the microbiota-gut-liver axis. (A) Conjoint analysis of the microbiome, intestinal content metabolites, and mRNA sequence of the intestine; (B) Conjoint analysis results of the microbiome, intestinal content metabolites and mRNA sequence of the liver. Data are presented as the means ± SD of more than 8 independent experiments, and the transcriptome sequencing data are the result of more than 3 independent experiments. *p < 0.05 and **p < 0.01 vs. the model group by one-way ANOVA, followed by the Holm-Sidak test.

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Fig. 9. The KEGG pathway analysis of gut microbiota using PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States).

effects.

inferred to be a beneficial bacterium. For more information please refer to Fig. 11C. These findings collectively suggest that AGL influences the central nervous system (CNS) by targeting the gut-brain axis. In particular, the hypothalamus, which is the regulatory center for appetite (Dagostino et al., 2016; Dalvi et al., 2017), may play an important role in these

3.8. AGL regulates the leptin-mediated signaling molecules Next, we measured the expressions of LEPR, POMC, and GABAergic neurons (GAD 65) in the hypothalamus, which are all associated with 16

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Fig. 10. AGL improves the injuries in brain tissue induced by a high sugar and fat diet in obese mice. (A) The pathological changes in brain samples (H&E), Nissl staining; (B) Silver staining; (C) The TUNEL assay; (D) IBA-1 (green fluorescence) and GFAP (red fluorescence) expression using IF. Data are presented as the results of more than 3 experiments. *p < 0.05 and **p < 0.01 vs. the model group by one-way ANOVA, followed by the Holm-Sidak test. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

the CNS, and in this cycle, gut microbiota play an important role. In the last few decades, the role of the gut microbiota has been implicated in many diseases, such as inflammatory bowel diseases (IBDs) (Aleksandrova & Romero-Mosquera, 2017; Lopetuso et al., 2018), metabolic syndrome and associated complications (diabetes, obesity, NAFLD, insulin resistance, and atherosclerosis) (Li et al., 2017), neuropsychiatric traits (notably, anxiety, depression, and autism) (Mayer & Hsiao, 2017), and cancer (deterioration, prognosis, complications, effects of radiotherapy and chemotherapy) (Roy & Trinchieri, 2017). In this study, the OTU of Clostridiales, Lachnospiraceae, Oscillospira, Ruminococcaceae, Dehalobacterium, Erysipelotrichaceae, AF12, [Mogibacteriaceae], Veillonella, Bilophila, Streptococcus were increased significantly, while those of Lactobacillus, Bifidobacterium, Roseburia, Peptostreptococcaceae, Blautia, Turicibacter were decreased by the high sugar and fat diet and recover after AGL treatment (p < 0.01, Table 4), which indicated that the high sugar and fat diet induced dysbacteriosis. Furthermore, most of these changes were reduced in all the AGL-treated groups (Fig. S3), which indicated that AGL may represent a potential prebiotic (see Table 6). It is increasingly obvious that loss of the fragile equilibrium within this complex ecosystem, termed dysbiosis, is implicated in numerous human diseases. Any perturbation in host-microbiota crosstalk may serve as an initiating or reinforcing factor in disease pathogenesis. A

leptin, using western blotting. As shown in Fig. 12, the levels of these proteins were activated in all the AGL-treated groups, especially in the high-dose-treated group (*p < 0.05, **p < 0.01). This result demonstrates that AGL can influence the CNS, and negative feedback then regulates appetite and metabolism.

4. Discussion Numerous studies have confirmed a dual-direction regulation between the CNS and gastrointestinal system, which plays an important role in intestinal motility, absorption, endocrine and immune functions, and maintaining the stability of the gastrointestinal environment (Browning & Travagli, 2014; Yoo & Mazmanian, 2017). The gut microbiota play a very important role in the brain-gut axis, central nervous system, autonomic nervous system, enteric nervous system, and digestive tract; this dense and diverse microbial community acts as a large ecosystem, namely, the brain-gut-microbiome axis (Wang & Kasper, 2014). In this system, signal transduction plays a regulatory role and is bidirectional. In the bidirectional process, the gut microbiota not only regulate the CNS by secreting metabolites and/or directly stimulating the digestive tract epithelial tissue, but also elicit a series of changes according to the regulation of the intestinal endocrine system, motility and intestinal mucosal permeability under the downstream signal transduction of the CNS. These processes also trigger changes in 17

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Fig. 11. Effects of AGL on the microbiota-gut-brain axis. (A) AGL influences the mRNA sequence of the brain in aged mice; (B) The interactions between 32 bacteria at the genus level and metabolites; (C) The correlation analysis results and correlation networks generated by selecting p < 0.05 and a correlation coefficient > 0.1 for bacteria and metabolites. Data are presented as the means ± SD of more than 8 independent experiments. *p < 0.05 and **p < 0.01 vs. the model group by one-way ANOVA, followed by the Holm-Sidak test.

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Fig. 12. AGL regulates the leptin-mediated related signaling molecules. The expression of LEPR, AGPR, NPY, POMC and GAD65 in the hypothalamus assessed by western blotting. Data are presented as the means ± SD of more than 3 independent experiments. *p < 0.05 and **p < 0.01 vs. the model group by one-way ANOVA, followed by the Holm-Sidak test.

regulates circadian rhythm and improves sleep, and a drug treatment for deficiency of nicotinic acid (pellagra), is one of the essential amino acids in the human body. Many diseases are affected by end products of Trp metabolism (Agus, Planchais, & Sokol, 2018), which follows three major pathways in the gastrointestinal tract: (1) the serotonin (5-hydroxytryptamine [5-HT]) production pathway in enterochromaffin cells via Trp hydroxylase 1 (TpH1) (O'Mahony, Clarke, Borre, Dinan, & Cryan, 2015); (2) the kynurenine pathway (KP) in both immune and epithelial cells via indoleamine 2,3-dioxygenase 1 (Kennedy, Cryan, Dinan, & Clarke, 2017); and (3) the direct transformation of Trp into several molecules, including ligands of the aryl hydrocarbon receptor (AhR), by the gut microbiota (Rothhammer et al., 2016). In this study, we found that nine bacteria, namely, Clostridiales, Blautia, Peptostreptococcaceae, Lactobacillus, Bilophila, Coprobacillus, AF12, Bifidobacterium, and Arthromitus, may be correlated with Trp (Fig. 11C), and the AGL-treated groups could reverse most of these changes through the alcohol extracts of G. lucidum (Fig. S3). Methionine (Met) is a precursor of S-adenosylmethionine (SAM), the methyl donor for DNA methylation, and its metabolism influences histone modification, which is involved in many activities, including inflammatory responses, immunity, aging and diseases (Ji et al., 2019; Klein Geltink & Pearce, 2019; Liu & Pile, 2017). In this study, we found that the level of methionine in fimo was up-regulated in the model group (Fig. 5, 1.76 of FC(M/N), p < 0.05 vs control) and was improved in the AGL-treated groups (Fig. 5, p > 0.05 vs control). The classical Spearman correlation analysis showed that methionine is influenced by Streptococcus and then influences the mRNA expression of Abcg2, Pdia3,

Table 4 17 bacterial at genus level in normal group changed by high sugar and fat diet (p < 0.01). Genus (M VS. N)

Modify

Clostridiales Lachnospiraceae Oscillospira Ruminococcaceae Dehalobacterium Erysipelotrichaceae AF12 [Mogibacteriaceae] Veillonella Bilophila Streptococcus Lactobacillus Bifidobacterium Roseburia Peptostreptococcaceae Blautia Turicibacter

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large array of metabolites drives the crosstalk between the host and its microbiome. By performing Spearman correlation analysis, we found that tryptophan (Trp), putrescine, glucaric acid, methionine (Met), GABA, a-hydroxybutyric acid, and malic acid are the key metabolites. Tryptophan, which is used as a nutritional supplement for pregnant women and in infant milk additives, an antioxidant, as well as a precursor of the neurotransmitter serotonin, a sedative medicine that 19

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receptor α5 and δ subunits in the hippocampus (Liang, Zhou, Zhang, Yuan, & Wu, 2017). Furthermore, GABA supplementation modulated intestinal functions, including intestinal immunity, intestinal amino acid profiles and gut microbiota (Chen et al., 2019). Previous studies also demonstrated that GABA could improve sleep (Hong, Park, & Suh, 2016; Kim, Jo, Hong, Han, & Suh, 2019), and it is well known that a short sleep duration has been associated with obesity in numerous epidemiological studies (Ogilvie & Patel, 2017; St-Onge, 2017). In this study, through the combined analysis of the microbiology and metabolomics data, it was found that GABA, which can promote brain metabolism and improve cerebral vascular dysfunction, was contained in the different metabolites. Additionally, the SLC6A12, GABRA4, ADCY5, GABRG3, MRAS, GABRA6, GABRA5, ADCY2, GABRQ, GABRA2, and KCNH2 mRNAs in brain tissues (Fig. 12A); the expression of GABAergic neurons (Fig. 12); and the GABA-related bacteria were changed in the AGL-treated groups, as we found that Blautia, Desulfovibrio, and Odoribacter were positively correlated with GABA and [Prevotella] was negatively correlated with GABA (Fig. 11C). These results suggest that G. lucidum changes the structure of the gut microbiota to promote generation of the metabolite GABA (Fig. 5), and negative feedback regulates the appetite (body weights in Fig. 2A), metabolism (Fig. 5) and even sleep in the host. We also found that the level of 4-hydroxybutyric acid was corrected with the Lactobacillus and Peptostreptococcaceae bacteria and Cyp2j9, mtNd5, and mt-Nd6 mRNAs, and the level of 2-hydroxybutyric acid was corrected with the Lactobacillus and Ruminococcaceae bacteria and Acox2, AI182371, Atg13, Colec12, Hspa5, Ifi47, Prpf39, Prpf40b, and Usp28 mRNAs. α-hydroxybutyric acid, a selective metabolite biomarker of impaired glucose tolerance (Cobb et al., 2016), was corrected with the Clostridiales, Enterobacteriaceae, Sutterella, and Coprobacillus bacteria. Butyrate has been demonstrated to participate in many activities, including gene regulation, immune regulation, cell differentiation, cancer inhibition, oxidative stress reduction, intestinal barrier function adjustment, diarrhea, visceral sensitivity and the intestinal peristalsis mechanism; these features make butyrate an important factor in maintaining intestinal health (Lee et al., 2019; Lin et al., 2012; Louis & Flint, 2017; Stilling et al., 2016). In particular, the level of 4-hydroxybutyric acid, which is used as a common sedative to treat insomnia, depression, narcolepsy and alcoholism, to improve athletic performance (Gibson, Hoffmann, Hodson, Bottiglieri, & Jakobs, 1998; Raposo Pereira and McMaster, 2018a, 2018b), and improve the crosstalk of GABA (El-Habr et al., 2017), was significantly increased in the middledose AGL-treated group [43399.11 ± 19328.24 in the control group, 27291.89 ± 9062.63 in the model group (p = 0.0462 vs control, unpaired t test), 50235.40 ± 19639.07 in the GM (p < 0.0001 vs model, unpaired t test) and 41870.70 ± 18614.64 in the GL (p = 0.0174 vs model, unpaired t test), which indicated that G. lucidum has tranquilizing effects. As these results and those of previous studies demonstrate the improvement in sleep resulting from G. lucidum (Chu et al., 2007; Cui et al., 2012), we conclude that G. lucidum can improve metabolic syndrome through the microbiome-gut-brain axis, and these effects could be due to its tranquilizing activity, perhaps through the regulation of an inhibitory neurotransmitter such as GABA and 4-hydroxybutyric acid. The hypothalamus is critical for feeding and body weight regulation. Several studies have focused on hypothalamic neurons that are defined by selectively expressing transcription factors or neuropeptides, including those expressing POMC and AgRP (Krashes et al., 2014). Hypothalamic AgRP neurons can regulate food ingestion in adult mice, facilitate offspring-to-caregiver bonding during mouse development (Zimmer, Fonseca, Iyilikci, Pra, & Dietrich, 2019), and mediate the transcriptional response to leptin (Cedernaes et al., 2019). Leptin, a hormone produced in white adipose tissue, acts in the brain to communicate fuel status, suppress appetite following a meal, promote energy expenditure and maintain blood glucose stability (Zhang, Chen, Heiman, & Dimarchi, 2005). In these processes, AGRP neurons are

Table 5 32 bacterial at genus level which in normal group changed by high sugar and fat diet was reversed by AGL (alcohol extracts of Ganoderma lucidum). Genus

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Clostridiales Ruminococcaceae Desulfovibrionaceae Ruminococcus [Prevotella] Parabacteroides Mucispirillum AF12 Clostridium Veillonella Paraprevotella Arthromitus Streptophyta Bilophila Christensenellaceae Prevotellaceae Butyricimonas Lactobacillus Odoribacter Roseburia RF39 Sutterella Flexispira Desulfovibrio Bifidobacterium Enterobacteriaceae Coprobacillus Peptostreptococcaceae Allobaculum Akkermansia Peptococcaceae Blautia

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Table 6 12 fecal metabolites which in normal group changed by high sugar and fat diet was reversed by AGL (alcohol extracts of Ganoderma lucidum). Metabolite

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pyroglutamic acid putrescine tryptophan cholic acid methionine GABA (4-aminobutyric acid) glyceric acid malic acid 3-methyl-2-oxovaleric acid α-hydroxybutyric acid 2-aminobutyric acid glucaric acid

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Serpine2, Yipf2, and Lamb3. This may be one of the mechanisms by which alcohol extracts of G. lucidum delayed the progression of Alzheimer’s disease via the regulation of DNA methylation in rodents, as reported in our previous study (Lai et al., 2019). Studies have demonstrated that GABA production and gadB/gadC genes ae found in numerous bacteria, including Bacteroidetes (Bacteroides, Parabacteroides, Alistipes, Odoribacter, Prevotella), Proteobacterium (Esherichia), Firmicutes (Enterococcus), and Actinobacteria (Bifidobacterium), as well as among lactobacilli and bifidobacteria (mainly in L. plantarum, L. brevis, Bifidobacterium. adolescentis, B. angulatum, and B. dentium) and other gut-derived bacterial species (Strandwitz et al., 2019; Yunes et al., 2016). Findings also showed that juvenile gut microbiota disturbances induced chronic depression and memory loss and reduced the expression of GABA-A 20

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required for the primary action of leptin to regulate both energy balance and glucose homeostasis (Xu et al., 2018). Some studies also revealed that the neuropeptide Y (NPY) is uniquely required for the longlasting effects of AgRP neurons on feeding behavior (Chen et al., 2019; Kim et al., 2015). Another study also revealed that an analogue of the gut-produced hormone peptide YY (PYY3-36) could attenuate the activity of NPY/AgRP neurons and reduce food intake (Jones et al., 2019). The GABAergic neurons were demonstrated to participate in the regulation of food behavior; for example, one study demonstrated dynamic GAB Aergic afferent modulation of AgRP neurons and feeding behavior (Garfield et al., 2016). In this study, we found that the expression of leptin, LEPR, POMC, and GABAergic neurons (GAD 65) was activated after the administration of G. lucidum extracts, which indicated that G. lucidum could improve metabolic syndrome symptoms in high sugar and fat diet-fed mice. Additionally, 9 months old mice were used in the study. Because this age range is approximately the same as middle-aged in humans and the proportion of high sugar and high fat diet in the middle-aged is high, expected to get a results be closer to those in humans. In conclusion, this study shows that (a) a high sugar and fat diet affects multiple systems and organs; (b) the alcohol extracts of the G. lucidum fruit body can improve the microbiome-gut-liver axis; (c) the alcohol extracts of the G. lucidum fruit body can improve the microbiome-gut-brain axis; and (d) the alcohol extracts of the G. lucidum fruit body can regulate the CNS and improve metabolic regulation.

The animal protocols used in this study were approved by the Institutional Animal Care and Use committee of the Center of Laboratory Animals of the Guangdong Institute of Microbiology. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jff.2019.103639. References Aleksandrova, K., & Romero-Mosquera, B. (2017). Hernandez V. Diet, gut microbiome and epigenetics: Emerging links with inflammatory bowel diseases and prospects for management and prevention. Nutrients, 9(9), E962. Agus, A., Planchais, J., & Sokol, H. (2018). Gut microbiota regulation of tryptophan metabolism in health and disease. Cell Host & Microbe, 23(6), 716–724. Browning, K. N., & Travagli, R. A. (2014). Central nervous system control of gastrointestinal motility and secretion and modulation of gastrointestinal functions. Comprehensive Physiology, 4(4), 1339–1368. Clemmensen, C., Müller, T. D., Woods, S. C., Berthoud, H. R., Seeley, R. J., & Tschöp, M. 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Funding The present work was supported by the financial support from the National Natural Science Foundation of China (81701086), the Guangzhou Science and Technology Plan Projects (201707020022), GDAS' Project of Science and Technology Development (2019GDASYL0104007), Guangdong Province Agricultural Technology Innovation and Promotion Project (2019KJ103), and the Technology transfer project of Zhongshan & Guangdong Academy of Sciences (2016G1FC0019). Acknowledgments We would like to thank Zhang Maolei for helpful advises in the preparation of this manuscript. Sequencing services were provided by Guangzhou Geneseed Biotech Co.,Ltd. Guangzhou and Personal Biotechnology Co., Ltd. Shanghai, China. Authors’ contributions All the authors designed this study; Chen DL wrote the manuscript; Chen DL and Guo YR carried out the computational analyses; Chen DL, Guo YR, Qi LK and Liu YD collected animal physiological data and fecal samples, extracted ruminal DNA, did the physiologial and biochemical indexes measurement; Tang XC, Guo YR and Zhu XX did the western bolting analysis; Qi LK, Zeng M and Feng JX did the metabolomic analysis; Chen DL, Guo YR, Qi LK and Shuai O collected data regarding the microbial metabolic networks and transcriptome analysis; Xie YZ, BB. Yang and Wu QP helped to design the study and to develop the multi-omics analysis methods, reviewed this manuscript and offer all the necessary research start-up fund, experimental platform. All authors read and approved the final manuscript. Declaration of Competing Interest The authors declare that they have no competing interests. Consent for publication No human data are reported in this manuscript. 21

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