The anti-obesity effect of fermented tremella/blueberry and its potential mechanisms in metabolically healthy obese rats



• A metabolically healthy obesity (MHO) rat model was established in this study.
• The anti-obesity potential of fermented Tremella/blueberry (FTB) was evaluated.
• FTB reduced body weight and improved lipid profiles of MHO rats.
• FTB modulated the diversity of gut microbiota and metabolites profile.


Fermented fruits or vegetables has attracted much attention by researchers over the world due to the diverse health benefits and desirable flavor characteristics. In this work, a metabolically healthy obesity (MHO) model was established to investigate the effects of fermented tremella/blueberry (FTB) on the food intake, body weight and blood lipid profiles in MHO rats. The intestinal microbiota and short-chain fatty acids (SCFAs) compositions were also determined. FTB demonstrated significant anti-obesity activity by reducing body weight and improving blood lipid profiles in MHO rats. FTB treatment also modulated the diversity of intestinal microbiota. The bacterial abundance related to obesity prevention (AllobaculumBlautiaParabacteroides and Prevotella) were increased while the abundances of pathogenic bacteria were reduced. The amounts of specific health promoting SCFAs were also increased. This study was the first to document the anti-obesity potential of FTB in MHO model, indicating its potential as a functional food for obesity prevention.

1. Introduction

Obesity has become a global health concern in last decades (Jaacks et al., 2019). It can lead to cardiovascular disorder, diabetes, and other complications (Blüher, 2019). About 30% of the obese population are metabolically healthy and this type of obesity is called metabolically healthy obesity (MHO) (van Vliet-Ostaptchouk et al., 2014). However, MHO can progress into metabolically unhealthy obesity (MUO), which contributes to inflammation and other diseases (Iacobini et al., 2019). It is a known fact that intestinal microbiota is an important factor influencing the health outcomes of the host by regulating the immune system (Cox et al., 2015, Paone and Cani, 2020). Various evidence demonstrated that several non-communicable diseases (NCDs), such as, obesity and type-2 diabetes mellitus are closely related to the disturbances in the gut microbiota (Shen et al., 2017). Since the occurrence of MUO is related to the gut microbiota (Seo et al., 2015, Ye et al., 2021) where the relationship between MUO and gut microbiota is not fully understood, it is of great interest to discover functional food with modulating effects on gut microbiota to prevent MUO.

Fermented foods have been widely accepted by people over the world due to their desirable taste and beneficial effects on health (Tamang et al., 2020). Fermentation can improve the organoleptic properties of food and afford new flavour characteristics. Some unique bioactive compounds can be produced by fermentation, which demonstrates good bioactivities, including antioxidant, immunomodulatory and lipid-lowering activities (Marco et al., 2017). The microorganisms in the fermented food demonstrates beneficial effects on human health, such as improving the gut microbiota (Sun et al., 2019). Tremella serves as an edible and medicinal fungus in daily diets. It can stimulate blood production, and has immunomodulatory activity (Reshetnikov et al., 2000). The leading bioactive compounds, especially polysaccharides, are responsible for the bioactivities (Ge et al., 2020). Fermentation modifies the chemical structure of these bioactive chemicals and produce new bioactive compounds. The fermented tremella products have been reported to have diverse bioactivities, such as antioxidant and anti-obesity activities (Lee et al., 2019). Blueberry is a widely accepted berry with sweet taste and abundant amounts of phytochemicals, especially anthocyanins. Anthocyanins are responsible for the antioxidant activity, cardioprotective capacity, and cancer prevention properties of blueberry (Kalt et al., 2020). Fermentation facilitates the release of bound phenolics and other bioactive compounds, such as flavanols, flavonols and phenolic acids in blueberry, which brings further health benefits (Dey et al., 2016).

It is well known that gut microbiota composition is largely influenced by daily diet compositions. Various functional foods have shown to modulate the composition of gut microbiota by stimulating the growth of beneficial microorganisms due to the presence of numerous bioactive compounds, such as phenolic compounds, flavonoids and polysaccharides (Cai et al., 2020, Chang et al., 2021, Chen et al., 2016, Guo et al., 2020). Previous study has shown that fermented white jelly fungus (Tremella fuciformis Berk) demonstrated anti-obesity and anti-diabetic properties in obese mice (Lee et al., 2019). Another recent study has shown that fermented mulberry fruit polysaccharides modulated the intestinal microbiota of the diabetic mice (Chen, Huang, Fu & Liu, 2016). However, to the best of our knowledge, no studies have been conducted on the lipid-lowering properties using the combination of fermented Tremella fungus and blueberry product.

Therefore, in this work, tremella and blueberry were fermented by probiotics complex. The anti-obesity effect of fermented tremella/blueberry was determined using MHO rats in vivo. This MHO rat model was established by monitoring changes of food intake, food utilization, body weight, blood lipid profiles and glucose levels daily before and after the animal modeling period (28 days). MHO rats are obese without serological abnormality. Besides, the abundance of gut microbiota and SCFAs were investigated to reveal the anti-obesity mechanisms of the fermented tremella/blueberry (FTB) product. These results will provide useful information to understand the health benefits of fermented foods in obesity prevention among people with MHO condition.

2. Materials and methods

2.1. Materials

Fresh tremella and blueberry were purchased from Huaxin Fungus (Jilin, China). Complex-probiotics were purchased from Taiwan Sub-core Biotechnology (Taiwan, China). Glycerol detection kit, total cholesterol kit, high-density lipoprotein cholesterol kit, low-density lipoprotein cholesterol kit were all purchased from Nanjing Jiancheng Bioengineering Institute (Nanjing, China). The other chemicals used in this study were of analytical grade. Non-fat diet (ND) and high-fat diet (HFD) was purchased from Yisi Experimental Animal Technology (Jilin, China).

2.2. Tremella/blueberry fermentation

The tremella/blueberry fermented probiotic product was prepared according to the method established by our laboratory (Luo et al., 2019). First, Tremella (188 g) and blueberry (75 g) were pulverized. Next, distilled water (563 g), sucrose (41 g) and trehalose (124 g) were added and mixed thoroughly. The slurry was heated at 90 °C for 40 min. After cooling to ambient temperature, 0.01% (w/w) probiotics complex (including 0.25 mg/g Lactobacillus acidophilus, 0.25 mg/g Lactobacillus rhamnosus, 0.25 mg/g Lactobacillus casei, 0.041 mg/g Lactobacillus plantarum, 0.041 mg/g Bifidobacterium longum, 0.042 mg/g Bifidobacterium lactis, 0.042 mg/g Bifidobacterium breve, 0.042 mg/g Lactobacillus paracasei, 0.042 mg/g Streptococcus thermophilus) were added under sterile conditions. After incubation at 40 °C for 17 d, the mixture was sterilized at 80 °C for 30 min, followed by concentration at 45 °C under vacuum until 1/10 of its original volume, the FTB product was obtained. The primary composition and enzyme activities of FTB

2.3. Animals and diets

All procedures involving animals and their care were conducted in accordance with the Guidelines for Care and Use of Laboratory Animals and approved by the Animal Ethics Committee of Jilin Agricultural University (Jilin, China). Seventy-two male Sprague-Dawley (SD) rats (180 ± 20 g, 42 d of age) were purchased from Yisi Experimental Animal Technology (Jilin, China), and randomly divided into six groups (twelve rats each group). A high-fat diet (45% of energy from fat) was prepared by mixing with 10 mL/kg of cod liver oil, 100 g/kg of lard, 100 g/kg of whole milk powder, and 790 g/kg of standard chow diet (purchased from Yisi Experimental Animal Technology, Jilin, China) while the non-fat diet (ND) consisted of standard chow diet (10% of energy from fat). The rats were housed in individual ventilation cages with 12 h alternating light/dark cycles at a temperature of 23 °C ± 3 °C and were fed with water ad libitum. After one week of adaptation period, rats were randomly divided into five treatment groups (n = 12 per group) before they were fed with a high-fat diet once a day by intragastric gavage for four weeks. The first group fed with standard-chow diet and water was classified as normal group (NG). Body weights of the rats were measured and the blood samples were collected once a week without fasting (epicanthus or tail). Blood pressure was not measured since MHO is a condition with normal blood pressure (Phillips et al., 2013). The HFD group was divided into five groups: a control group fed with HFD only (CON), a low-dose group (L) fed HFD plus FTB at 25 mL/kg/day, a medium-dose group (M) fed HFD plus FTB at 50 mL/kg/day, a high-dose group (H) fed HFD plus FTB at 75 mL/kg/day and simvastatin group (SIM) fed at 10 mg/kg/day as the positive control for 30 days.

2.4. Sample collection

At the end of the intervention period, samples were collected according to the methods reported by Meng et al. (2019) with some modifications. All the rats were anesthetized by urethane, and killed with CO2. All the experimental procedures adhered strictly to the Guide of Care and Use of Laboratory Animals (ISBN-10: 0-309-15396-4). The serum was collected to determine the total cholesterol (TC), total triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and glucose levels. The intestinal contents, liver, and abdominal venous blood samples were collected in sterilized centrifuge tube immediately after dissection, stored at −80 °C for biochemical analyses, histopathological examination and measurement of gut microbiota diversity.

2.5. Biochemical assays

The body weight and fat (including abdominal, peritesticular and perirenal fats) of rats were measured. In relation, food intake, total calorie intake (food intake × calories in unit weight), body weight changes, food utilization, and the ratio of fat/body weight were calculated (Neto et al., 2020).

TC, TG, LDL-C, HDL-C and glucose levels were quantified by commercial kits (Nanjing Jiancheng, China) in accordance with the manufacturers’ protocols. The venous blood sample were placed at 25 °C for 30 min and separated by a high-speed refrigerated centrifuge (Guangzhou, China) at 3000 rpm for 15 min, then stored in a refrigerator at 4 °C. Each sample was measured in triplicate (Zhang et al., 2020).

2.6. Histopathological examination of liver tissues
The histopathological examination was performed by following the methods of Guo et al. (2020) with minor modifications. The liver was flushed by physiological saline to remove surface blood at 4 °C, dried with absorbent paper, put in a 4% paraformaldehyde solution for 12 h, and then paraffin-embedded. The liver tissues were cut into 3–5 µm thickness and were stained with hematoxylin-eosin. The pathological changes of the stained samples were evaluated by a light microscope (Carl Zeiss Inc., Germany) at 400× magnification.

Microbiota genomic DNA extraction and high-throughput sequencing of the bacterial 16S rRNA were determined according to the protocols reported by Zheng et al. (2017) with minor modifications. The microbial genomic DNA was extracted from the intestinal content samples using Fast DNA SPIN extraction kits (MP Biomedicals, Santa Ana, CA, USA). The quantitative and qualitative analysis of the extracted DNAs were measured using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, United States) and agarose gel electrophoresis, respectively. Polymerase chain reaction (PCR) amplification of the V4-V5 hypervariable regions of bacterial 16S rRNA gene, with a length of approximately 500 bp, was performed using general bacterial primers 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and the reverse primer 907R (5′-CCGTCAATTCMTTTRAGTTT-3′). PCR conditions were as follows: initial denaturation at 98 °C for two min, followed by 25 cycles consisting of denaturation at 98 °C for 15 s, annealing at 55 °C for 30 s, and extension at 72 °C for 30 s, with a final extension of 5 min at 72 °C. The PCR amplicons were purified with Agencourt AMPure beads (Beckman Coulter, Indianapolis, IN, United States) and quantified using the PicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, United States). After that, the individual quantification steps pooled in equal amounts paired-end 2 × 300 bp sequencing performed with Illumina MiSeq platform using MiSeq Reagent Kit v3 (Illumina) at Tiny Gene Bio-Tech Co. Ltd. (Shanghai, China).

Sequence analysis was performed using Quantitative Insights into Microbial Ecology (QIIME) (v1.8.0) and R packages (v3.2.0). Bacterial operation taxonomic units (OTUs) were generated using QIIME

OTU taxonomic classification was conducted by BLAST searching the representative sequences set against the Greengenes Database. OTU-level alpha diversity indices, such as Chao1 richness estimator, ACE metric (Abundance-based Coverage Estimator), Shannon diversity index, and Simpson index, were calculated using the OTU table in QIIME. Beta diversity analysis was performed to investigate the structural variation of microbial communities across samples using UniFrac distance metrics and visualized via principal coordinate analysis (PCoA), nonmetric multidimensional scaling (NMDS) and unweighted pair-group method with arithmetic means (UPGMA) hierarchical clustering. The significance of differentiation of microbiota structure among groups was determined using PERMANOVA (Permutational multivariate analysis of variance) and ANOSIM (analysis of similarities). Taxa abundances at the phylum, class, order, family, genus and species levels were statistically compared among groups by Metastats, and visualized as violin plots. LEfSe (Linear discriminant analysis effect size) was performed to detect differentially abundant taxa across groups using default parameters. Microbial functions were predicted by PICRUSt (Phylogenetic investigation of communities by reconstruction of unobserved states) from the 16S rRNA sequencing data of the gut microbial samples

2.8. Short-chain fatty acids (SCFAs) in the intestinal contents

The SCFAs contents in the fermentation cultures were determined by gas chromatography according to the methods described by Chen et al. (2016). First, twenty milligrams of fresh intestinal content samples and 1 mL of phosphoric acid solution were transferred into a 2-mL tube (0.5%, v/v). After vortexing for 10 min and ultrasound treatment for 5 min, 0.1 mL of the supernatant was collected and added with 0.5 mL of methyl tert-butyl ether (MTBE: with internal standard) solution. After vortexing for 3 min, ultrasound treatment for 5 min and centrifugation at 12,000g at 4 °C for 10 min, 0.2 mL of the supernatant was collected, and subjected to analysis by gas chromatography-mass spectrometer (GC–MS/MS 7890B-7000D; Agilent Technologies Inc., CA, USA) on a silica capillary column (DB-FFAP, 30 m × 0.25 mm × 0.25 μm). Helium gas was used as the carrier. The initial oven temperature of 95 °C was kept constant for 1 min, raised to 100 °C at a rate of 25 °C/min and raised to 130 °C at a rate of 17 °C/min, kept for 0.4 min at 130 °C and then raised to 200 °C at a rate of 25 °C/min, hold for 0.5 min. The temperature of the injector and detector were set at 200 and 230 °C, respectively.

2.9. Statistical analyses

All results were expressed as the means of three independent determinations. Data were analyzed by Statistical Package for Social Sciences (SPSS) 23.0 software (SPSS Inc., Chicago, IL, USA). One-way analysis of variance (one-way ANOVA) was applied to determine statistical differences where Duncan test was used as the post-hoc test to compare data significance between groups. Man-Whitney U test and Spearman’s correlation coefficient were conducted to verify the correlations between SCFAs and gut microbiota alteration. p-value <0.05 was regarded as significantly different.

3. Results and discussions

3.1. Evaluation of the MHO rat model

MHO is a condition where individuals with obesity have a lower risk for cardiometabolic abnormalities. Normal glucose and lipid metabolism without hypertension is the criteria to diagnose MHO (Smith, Mittendorfer & Klein, 2019). Biological mechanisms underlying MHO include lower visceral and liver and higher leg fat deposition, expandability of subcutaneous adipose tissue, normal insulin sensitivity and beta-cell function as well as better cardiorespiratory fitness compared to MUO. MHO has been proposed as a model to study mechanisms between obesity and cardiometabolic complications. However, MHO is not considered a safe condition without any intervention. Hence, this MHO rat model may provide guidance for a personalized and risk-stratified obesity treatment (Iacobini et al., 2019, Jung et al., 2017). Establishment of this animal model was monitored by examining changes of food intake, food utilization, body weight and biochemical parameters daily before and after the animal modeling period (28 days). There was no significant difference (p > 0.05) in terms of their food intake (in grams) between the non-fat diet (ND) and HFD group. This suggests that FTB attenuated body weight gain without affecting the food intake of MHO rats. However, the food utilization rate was much higher in the HFD group (Fig. 1A and B). After the animal modeling, the body weight of the HFD group was significantly (p < 0.05) higher (417.67 ± 17.98 g) than that of the non-fat diet (ND) group (253.00 ± 5.99 g), where this was not significantly different (p > 0.05) before animal modeling (Fig. 1C and Table 2). Rats in the MHO model were obese without serological abnormality. The serological indicators, such as TC, TG, LDLC, HDLC and glucose levels were not significantly (p > 0.05) different between ND and HFD groups after 28 days of successful animal modeling (Fig. 1D–F). Conversely, serological indicators are usually higher in common obese rats as compared to MHO rats. Hence, we assumed that the MHO rat model was established successfully.
The prevalence of MHO varied under different criteria (Velho et al., 2010). People who are classified as MHO are not metabolically healthy, but they demonstrate fewer metabolic abnormalities compared to people who are MUO. An MHO model is normally designed by recruiting healthy volunteers (without diabetes, hypertension, and cardiovascular diseases) with BMI ≥ 30 kg/m2 with normal serology. It can intuitively show the impact of MHO on human, but the model has individual differences and can be influenced by uncontrollable factors such as age and gender (Phillips et al., 2013). More importantly, such model is not easily available. In this work, a relatively convenient MHO rat model was established. It was built based on the normal process of MHO. A rigorous and widely acceptable definition of MHO is needed to determine the true prevalence and long-term consequences of MHO

3.2. Effects of FTB on the body weight and biochemical parameters

Body weight and fat levels are parameters directly related to obesity, while serological parameters are directly related to their metabolic status. As shown in Fig. 2A, CON group had more round lipid droplets in its liver cells where hepatic indusium was narrowed. Compared with CON, the FTB group had less lipid droplets and the liver cells were arranged regularly. The MHO rats were treated by three doses of FTB and simvastatin, respectively. After FTB treatment, the food intake and food utilization rate were not significantly changed (p > 0.05) as compared to before FTB treatment. The body weights in FTB-treated groups were significantly (p < 0.05) lower than that of CON group. Moreover, the fat level (includes testosterone, total fat, and abdominal fat) and ratio of lipid/body weight in M and H groups were significantly (p < 0.05) lower than those of CON group. There were no significant effects (p > 0.05) on the fat level (includes testosterone fat, total fat, and abdominal fat) and weight changes in both CON and simvastatin-treated (SIM) groups. The TG level in the H group was significantly (p < 0.05) lower than the CON group (Fig. 2G), while TC, LDLC, and HDLC levels were not significantly (p > 0.05) different between all groups, except for SIM group. Lowering the serum TG and body fat levels can significantly reduce the risk of CVD and diabetes by reducing fat accumulation in the liver

3.3. Effects of FTB on the diversity of intestinal microbiota

The gut microbiota is an important organ that generates numerous metabolites that produce signals which in turn regulates human metabolism. The complex interplay between gut microbiota and diet contribute to the overall health of individuals (Gowd et al., 2019). Firmicutes and Bacteroidetes were the two most abundant phyla in all groups. There were no significant changes for the OTU and microbiota abundance at the phylum level in each group after FTB treatment (Fig. 3A and 3B), but the composition of the gut microbiota was significantly changed at the class, order, family, genus and species levels (Fig. 3D and Table 3) (p < 0.05). FTB supplementation decreased the abundance in phylum Firmicutes while increased in Bacteroidetes and Tenericutes (Fig. 3B). At the order level, FTB supplementation significantly increased the bacterial abundance in Clostridiales. At the familial level, FTB supplementation increased the bacterial abundance in Ruminococcaceae, Enterobacteriaceae and Erysipelotrichaceae while reduced in Lachnospiraceae and Peptostreptococcaceae. At the generic level, the bacterial abundance was significantly increased in three genera (Allobaculum, Blautia and Coprococcus) but significantly reduced in other five genera (Bacteroides, Faecalibacterium, Oscillospira, Prevotella and Proteus). At the species level, FTB supplementation increased the abundance of S24-7, RF39 and Clostridium sp. while reduced the abundance of rc4-4. Ratio of Proteobacteria/Bacteroidetes was higher in FTB-treated groups than in CON and NG groups (Fig. 3C). There were only two phyla in NG group, namely Elusimicrobia and TM7 (Fig. 3E). Comparing with CON group, FTB-treated groups had lower abundances of Firmicutes, Bacteroidetes, Verrucomicrobia, Cyanobacteria, YS2, [Prevotella], Akkermansia, Roseburia and a higher abundance of Proteobacteria. M group had a significantly higher (p < 0.05) abundance of Allobaculum than CON group, and H group had significantly (p < 0.05) higher abundances of Coprococcus, Faecalibacterium and Blautia than CON group. Comparing with NG group, FTB-treated group had significantly (p < 0.05) lower abundances of Ruminococcus and Christensenellaceae. Meanwhile, L had significantly (p < 0.05) lower abundance of Prevotella and significantly (p < 0.05) higher abundance of Parabacteroides than that in NG. H group had significantly (p < 0.05) lower abundances of Clostridiales and Ruminococcaceae than that in NG, and significantly (p < 0.05) higher abundances of Coprococcus, Faecalibacterium and Blautia than that in NG and CON groups. For the three FTB-treated groups, M group had a significantly (p < 0.05) lower abundance of Lachnospiraceae than L group, and H had a significantly (p < 0.05) higher abundance of [Mogibacteriaceae] than L group. SIM group had more Rothia than all other groups. As shown in Fig. 4B and C, the abundance of Clostridiales was lower in FTB-treated groups while its diversity was higher. Fig. 4D showed that there were only three genera in both L and H groups, namely RF32, [Eubacterium] and Planococcaceaeas (Fig. 4E–G). The only genus detected in both M and H groups was Lachnospira (Fig. 4H). The only genus detected in FTB-treated groups was Anaerofustis