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. 2023 May 13;13(6):189. doi: 10.1007/s13205-023-03617-9

Long-term consumption of different doses of Grifola frondosa affects immunity and metabolism: correlation with intestinal mucosal microbiota and blood lipids

Jing Liu 1, Yi Wu 1, Ying Cai 1, Zhoujin Tan 1,✉, Na Deng 1,✉
PMCID: PMC10183060  PMID: 37193332

Abstract

Grifola frondosa (GF) is an edible mushroom with hypoglycemic and hypolipidemic effects. In this study, the specific pathogen-free male mice were randomized into the normal (NM), low-dose GF (LGF), medium-dose GF (MGF), and high-dose GF (HGF) groups. The LGF, MGF, and HGF groups were fed with 1.425 g/(kg d), 2.85 g/(kg d), and 5.735 g/(kg d) of GF solution for 8 weeks. After feeding with GF solution, compared with the NM group, the thymus index was significantly increased in the LGF group, and TC, TG, and LDL of mice were significantly increased in the HGF group, while HDL was significantly decreased. Compared with the NM group, the uncultured Bacteroidales bacterium, Ligilactobacillus increased in the LGF group, and Candidatus Arthromitus increased in the MGF group. The characteristic bacteria of the HGF group included Christensenellaceae R7, unclassified Clostridia UCG 014, unclassified Eubacteria coprostanoligenes, and Prevotellaceae Ga6A1. Among them, Ligilactobacillus showed a negative correlation with HDL. Unclassified Eubacterium coprostanoligenes group and Ligilactobacillus showed a positive correlation with TG. In summary, our experiments evidenced that GF improves lipid metabolism disorders by regulating the intestinal microbiota, providing a new pathway for hypolipidemic using GF dietary.

Keywords: Grifola frondosa, Intestinal microbiota, Metabolism, Immunity

Introduction

The functions of the normal human intestinal microbiota include metabolic and nutritional, antimicrobial protection, maintaining the integrity of the intestinal mucosa, and regulating the maintenance of the immune response (Sebastián et al. 2018). The abundance of intestinal microbiota and its metabolites was found to be strongly associated with diabetes, obesity, and lipid metabolism (Wu et al. 2021; Sekirov et al. 2010). In recent years, the global prevalence of metabolic diseases, including obesity, hyperlipidemia, and hyperglycemia, has increased. Studies have suggested that these diseases may be associated with increased high-calorie, low-fiber diets, and decreased physical activity (Saklayen 2018). Diet habits profoundly affect the composition and function of intestinal microbiota, especially when excessive sugar and fat intake changes intestinal microbiota. It may lead to diseases such as obesity and inflammatory bowel disease (David et al. 2014). Research has found that plant-based ingredients can improve the harmful effects of a high-fat diet by regulating intestinal microbiota. Dendrobium officinale can improve intestinal mucosal microbiota diversity and alleviate the negative effects of a high-fat diet (Li et al. 2021a). Asparagus extract exerts hypolipidemic effects in mice with high-fat diet-induced hyperlipidemia through regulating intestinal microbiota (Guo et al. 2020a). In summary, diet plays an important role in metabolic diseases by regulating intestinal microbiota.

Animal experiments found that certain mushroom nutrients can alter intestinal microbiota’s activity and diversity by stimulating certain probiotics’ proliferation (Nguepi and Song 2020). Mushrooms such as Grifola frondosa (GF), Hericumerinaceus, and Lentinulaedodes act as prebiotics to stimulate the growth of beneficial intestinal microbiota, prevent hypocholesterolemia, and have anti-hypertensive, antioxidant, and anti-inflammatory effects, and even protect the liver (Jayachandran et al. 2017). GF is an edible mushroom of the family Polyporaceae. It not only has the nutritional effects of mushrooms but also has a variety of medicinal values due to its special chemical composition. It is one of Asia’s most widely used medicinal mushrooms and one of the most commonly studied fungi for nutritional and medicinal purposes. It contains various bioactive compounds, such as polysaccharides, proteins, peptides, fatty acids, ergosterol, flavonoids, alkaloids, ascorbic acid, tocopherols, etc. (Wu et al. 2021). Previous studies have shown that plant polysaccharides (Fructooligosaccharides, Polydextrose, and Xylooligosaccharides) can treat diarrhea by promoting the growth of beneficial intestinal bacteria and inhibiting the reproduction of pathogenic bacteria. Plant polysaccharides are an important source of probiotics, which can promote the growth of intestinal probiotics and enhance immunity (Wang and Cheong 2023; Li et al. 2022). Among them, Grifola frondosa polysaccharides (GFP) can affect the activity of intestinal microorganisms and, thus, the digestive and absorptive functions of the organism (Valdemiro Carlos 2011; Liu et al. 2019). GF has been shown to have a variety of promising biological activities, including anti-tumor (Zhao et al. 2021), antioxidant (Ding et al. 2016), immunomodulatory (Ma et al. 2015), anti-hyperglycemic (Jiang et al. 2020), anti-hyperlipidemic (Wu et al. 2022a, b), and prevent metabolic syndrome by altering intestinal microbiota (Li et al. 2019; Guo et al. 2020b). To summarize, the possible preventive effects of appropriate ingestion of GF on human metabolic syndrome.

The effects of polysaccharides from GF on hypolipidemic, anti-hyperglycemic, and anti-inflammatory mechanisms have been widely recognized. But GF is mainly eaten as food in the form of solid mycelium and GF dry matter, which have not been studied for body health. Based on previous studies, we investigated the role of different concentrations of GF in regulating the intestinal microbiota and blood total cholesterol (TC), triglyceride (TG), low-density lipoprotein (LDL), and high-density lipoprotein (HDL) from the perspective of intestinal microbiota.

Material and methods

Experimental animals

To rule out the effect of sex on intestinal microbiota (Wu et al. 2022a, b), 20 male Kunming mice (20 ± 2 g) were purchased from the Hunan Slaccas Jingda Laboratory Animal Co. The animals were bred in the Animal Experiment Center of Hunan University of Chinese Medicine at a temperature of 23–25 ℃ and a humidity of 47–53%, with a free diet and plenty of water.

Feed

Mice fed common feed; feed composition is detailed in Table 1. It is supplied by the Animal Experiment Center of the Hunan University of Chinese Medicine and produced by Jiangsu Medison Biomedical Co.

Table 1.

Common feed (per kg of feed)

Component Content
Water (g)  ≤ 100
Crude protein (g)  ≥ 200
Crude fiber (g)  ≥ 40
Crude fat (g)  ≤ 50
Crude ash (g)  ≤ 80
Calcium (g) 10–18
Phosphorus (g) 6–12
Calcium: phosphorus 1.2: 1–1.7: 1
Lysine (g)  ≥ 13.2
Methionine and cysteine (g)  ≥ 7.8
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Preparation of Grifola frondosa solution

GF was purchased from Qingyun County, Lishui City, Zhejiang Province. A certain amount of GF-dried products, with an original weight of W1, are taken and placed in an oven at a temperature of 105–115 ℃. The GF is dried until no further change in weight occurs, to obtain a constant weight of W2. The GF dry matter is then obtained by weighing the GF at its constant weight. The dry material was powdered and passed through a 120-mesh sieve to GF dry material powder. According to references (Dai et al. 2015) and the solubility of GF, the low, medium, and high doses of GF solution of 1.425 g/(kg d), 2.85 g/(kg d), and 5.735 g/(kg d) were prepared by weighing the appropriate amount of GF dry matter powder with distilled water and boiling it for 5 min and then cooling it. The prepared GF solution was stored at 4 ℃ and rewarmed to 25–30 ℃ before use.

Animal grouping and intervention

After 3 days of acclimatization, 20 mice were randomly divided into the normal (NM), low-dose GF (LGF), medium-dose GF (MGF), and high-dose GF (HGF) groups. The mice in the NM group were given sterile saline, and the LGF, MGF, and HGF groups were given 1.425 g/(kg d), 2.85 g/(kg d), and 5.735 g/(kg d) GF aqueous decoction, 0.4 mL each time, twice daily for 8 weeks. All experimental animal procedures were performed in the experimental animal protocol approved by the Institutional Animal Care and Use Committee of the Hunan University of Chinese Medicine. Figure 1 shows the experimental design and the specific experimental procedure.

Fig. 1.

Fig. 1

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Experimental design and general conditions of the animals (NM the normal group, LGF the low-dose GF group, MGF the medium-dose GF group, HGF the high-dose GF group)

TC, TG, LDL, and HDL testing

At the end of the 8 weeks intervention, blood was collected through the eyes, stood at 4 °C for 1–2 h, centrifuged at 3000 r for 15 min and the upper serum was collected. TC and TG were measured using the biochemical kit (Quanzhou Konodi Biotechnology Co) according to the instructions divided into control tubes, standard tubes, sample tubes, add the corresponding reagents incubation. LDL and HDL were measured using the Rayto RT-6100 ELISA analyzer according to the instructions of the ELISA test kit (Quanzhou Konodi Biotechnology Co). Standard curve of HDL: y = 63.213x–1.15, R2 = 0.9942. Standard curve of LDL: y = 4.4985x + 0.0282. R2 = 0.9972.

Calculation of organ indexes

Each mouse was weighed before blood collection, and after blood collection, the mice were executed by cervical dislocation. The spleen, thymus, and liver were immediately dissected and removed, and the blood was drained with filter paper and weighed. The organ indexes were calculated using the formula: Organ index = organ weight (mg)/body weight (g).

Intestinal mucosa samples collection

After 8 weeks, mice were neck-dislocated and executed, and intestinal mucosal samples were taken and collected, referring to the previous method (Li et al. 2021b). Under sterile conditions, the intestinal tissue from the pylorus to the ileocecal region was incised longitudinally with sterile scissors. After rinsing the intestinal contents with saline, the intestinal mucosa was scraped separately with a coverslip. Intestinal mucosa from each mouse was collected in an EP tube and stored at – 80 ℃.

Total DNA extraction, PCR amplification, and 16S rRNA gene sequencing

All samples were processed by Beijing Bemac Biotechnology Co (Beijing, China). The total microbial genomic DNA of intestinal mucosal microbiota in each test tube was extracted according to the instructions of the DNA extraction kit (MN NucleoSpin 96 So). Total DNA was extracted by lysing the sample, precipitation to remove impurities, filtration to remove inhibitors, DNA binding, membrane washing, drying, and elution. The V3 + V4 variable region of bacterial 16S rRNA was amplified using primers 338F (5'-ACTCCTACGGGAGGCAGCA-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3'). PCR is performed using extracted DNA as a template, primers with connectors are designed, and then the products are purified, quantified, and homogenized to form a sequencing library. Each sample was mixed in the appropriate proportion according to the sequencing volume requirements, mixed, and purified using OMEGA DNA purification columns, detected by 1.8% agarose gel electrophoresis. The PCR products were recovered by cutting the gel using the Monarch DNA Recovery Kit.

The amplification reaction system is as follows: 50 ng genomic DNA, 0.3 μL Vn F, 0.3 μL Vn R, 5 μL KOD FX Neo Buffer, 2 μL dNTP (2 mM each), 0.2 μL KOD FX Neo, and finally 10 μL made up with ddH2O. The amplification conditions were as follows: Denaturation at 95 ℃ for 5 min, the reaction was rapidly cooled to 50 ℃ for 30 s, then warmed to 72 ℃ for 40 s, then reacted at 72 ℃ for 7 min and stored at 4 ℃ after the end for a total of 25 cycles. Finally, the PCR products were sequenced by Illumina Novaseq 6000 sequencing platform.

Bioinformatics

To improve the quality of the analysis results, it is important to ensure sequence adequacy and sequence quality. Therefore, the raw data was spliced [FLASH (Magoč and Salzberg. 2011), version 1.2.11], filtered [Trimmomatic (Bolger et al. 2014), version 0.33], and chimeras were removed [UCHIME (Edgar et al. 2011), version 8.1] to obtain valid sequences before analysis. The sequences were clustered using USEARCH (Edgar 2013) (version 10.0) software to cluster the valid sequences to OTUs with 97% similarity, and the representative sequences of OTUs were defined by taxonomy. The community structure was statistically analyzed at different taxonomic levels according to the taxonomy. For linear discriminant analysis effect size (LEfSe), the Kruskal-Walli’s rank sum test and Wilcoxon rank sum test were performed. Then, linear discriminant analysis (LDA) was used to assess the effect size of each differential abundance taxon. Based on the above analysis, in-depth statistical and visual analysis of community structure and system development can be carried out.

Correlation analysis

Further correlation analysis was performed on intestinal mucosal microbiota, TG, TC, LDL, HDL, and metabolic pathways. The correlation coefficient is used to indicate the correlation between two variables. The closer the correlation coefficient is to 1 or -1, the more correlated the two elements are, and the closer the correlation coefficient is to 0, the less correlated the two elements are. This study used the R language R version 3.6.3 (https://www.omicstudio.cn/tool) to calculate Spearman's rank (Spearman) correlation coefficient and plot the heat map.

Statistical analysis

Experimental data were expressed in mean ± standard deviation. All data were statistically analyzed using the SPSS 21.0 statistical software package. Comparisons were made between multiple groups, satisfying normality and homogeneity of variance, using one-way ANOVA, and multiple groups were compared using LSD assays. The rank sum test was used for non-conforming normality or variance homogeneity, and Tamhane’s T2 (M) test was used for multigroup comparison. P < 0.05 indicated a statistically significant difference.

Results

Effect of Grifola frondosa on organ indexes in mice

As important immune organs, the thymus, spleen, and liver indexes can reflect the body’s immune function. Compared with the NM group, the thymic index was significantly increased in the LGF (P < 0.01, Fig. 2A). In contrast, the spleen and liver indexes had insignificant differences (P > 0.05, Fig. 2B and C).

Fig. 2.

Fig. 2

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Organ indexes of mice under the intervention of different concentrations of GF (Organ index = organ weight (mg)/body weight (g)). A Thymic index; B Spleen index; C Liver index (**P < 0.01, NM the normal group, LGF the low-dose GF group, MGF the medium-dose GF group, HGF the high-dose GF group)

Effect of Grifola frondosa on TG, TC, LDL, and HDL in mice

Compared with the NM group, LDL, TG, and TC all increased in the LGF, MGF, and HGF groups, and the HGF group significantly increased (P < 0.05, Fig. 3A to D). While compared with the NM group, the HDL significantly decreased in the MGF and HGF groups (P < 0.05, Fig. 3B).

Fig. 3.

Fig. 3

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Blood lipid in mice with different concentrations of GF. A TC levels in serum; B HDL levels in serum; C TG levels in serum; D LDL levels in serum (*P < 0.05, **P < 0.01, ***P < 0.001, NM the normal group, LGF the low-dose GF group, MGF the medium-dose GF group, HGF the high-dose GF group)

Effect of Grifola frondosa on intestinal mucosal microbiota in mice

Effect of different concentrations of Grifola frondosa on the OTU number in mice

The growth rate of OTUs decreases as the number of sequencing increases, indicating that the amount of sequencing data is sufficient for this analysis (Fig. 4A). There were 754, 702, 674, and 699 OTUs found in the NM, LGF, MGF, and HGF groups, and 207 OTUs in the four experimental groups, which accounted for about 15.18% of the total number of OTUs in the four groups (Fig. 4B). The above results indicate that GF decreases the OTU count (Number: PRJNA883061. https://www.ncbi.nlm.nih.gov/).

Fig. 4.

Fig. 4

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Alpha diversity and Beta diversity of intestinal mucosal microbiota in mice with different concentrations of GF. A Shannon–Wiener curves of intestinal mucosal microbiota for each group of samples; B Venn diagram: distribution of the number of OTUs of intestinal mucosal microbiota in each group of samples. C ACE index; D Chaol index; EShannon index; (F) Simpson index; G Principal component analysis (*P < 0.05,**P < 0.01, NM the normal group, LGF the low-dose GF group, MGF the medium-dose GF group, HGF the high-dose GF group)

Effect of different concentrations of Grifola frondosa on the intestinal mucosal microbiota structure in mice

Compared with the NM group, the Chao1 index and ACE index decreased in the different doses of the GF group (P > 0.05, Fig. 4C and D), and the Shannon and Simpson were significantly decreased in the LGF and MGF groups (P < 0.05, Fig. 4E and F). When the principal component variable 1 is 36.91% and the principal component variable 2 is 23.10%, the LGF and MGF groups are scattered in the distribution. In comparison, the NM group and HGF groups are relatively concentrated (Fig. 4G). In conclusion, the results indicate the richness and diversity of the intestinal mucosal microbiota changed after GF intervention (Number: PRJNA883061. https://www.ncbi.nlm.nih.gov/).

Effect of different concentrations of Grifola frondosa on the structural composition of the intestinal mucosal microbiota in mice

The phylum-level clustering analysis showed that the HGF groups were all better clustered in one category, while the NM, LGF, and MGF groups had more dispersed samples (Fig. 5A). Analysis of the composition of the phylum level intestinal mucosal microbiota showed that Firmicutes, Bacteroidetes, and Proteobacteria accounted for 67.79%, 26.91%, and 1.05%, respectively, in the NM group (Fig. 5B). Compared with the NM group, as shown in Fig. 5C to F, the relative abundance of Firmicutes and Bacteroidetes decreased and Proteobacteria and the Firmicutes/Bacteroidetes (F/B) ratio increased in the LGF and MGF groups (P > 0.05). The relative abundance of Firmicutes, Proteobacteria, and F/B ratio decreased, and Bacteroidetes increased in the HGF and MGF groups (P > 0.05).

Fig. 5.

Fig. 5

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A Heat map of relative abundance at the phylum level; B the composition and relative abundance of species at the phylum level; C–E the relative abundance of Firmicutes, Bacteroidetes, and Proteobacteria in each group; F Firmicutes/Bacteroidetes ratio (NM the normal group, LGF the low-dose GF group, MGF the medium-dose GF group, HGF the high-dose GF group)

As shown in Fig. 6B to K, analysis of the composition of the phylum level intestinal mucosal microbiota showed that compared with the NM group, the LGF group uncultured Bacteroidales bacterium, Candidatus Arthromitus were significantly increased (P < 0.05). Compared with the NM group, Ligilactobacillus significantly increased in the LGF and HGF groups (P < 0.05), and unclassified Prevotellaceae, and unclassified Oscillospiraceae significantly decreased in the LGF group (P < 0.05). Alloprevotella and unclassified Oscillospiraceae significantly decreased in the MGF group (P < 0.05). Compared with the NM group, the relative abundance of Christensenellaceae R7 group, unclassified Clostridia UCG 014 decreased (P < 0.05) in the MGF group (Number: PRJNA883061. https://www.ncbi.nlm.nih.gov/).

Fig. 6.

Fig. 6

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A the composition and relative abundance of species at the genus level; B–K genus level significantly different bacteria (*P < 0.05, **P < 0.01, ***P < 0.001, NM the normal group, LGF the low-dose GF group, MGF the medium-dose GF group, HGF the high-dose GF group)

Distinguishing the effects of different doses of Grifola frondosa on mice based on characteristic intestinal mucosal microbiota

We identified the characteristic microbial taxa of GF interventions by LEfSe analysis (Fig. 7A and B). Alloprevotella is the characteristic microbiota of the NM group. Uncultured Bacteroidales bacterium and Ligilactobacillus are the characteristic microbiota of the LGF group. Candidatus Arthromitus is the characteristic microbiota of the MGF group. Christensenellaceae R7 group, unclassified Clostridia UCG 014, unclassified Eubacterium coprostanoligenes group, and Prevotellaceae Ga6A1 group are the characteristic microbiota of the HGF group (Number: PRJNA883061. https://www.ncbi.nlm.nih.gov/).

Fig. 7.

Fig. 7

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Characteristic bacteria in intestinal mucosal microbiota with different concentrations of GF and functional analysis based on PICRUSt2. A The cladogram generated from the LEfSe analysis indicates the phylogenetic distribution from phylum to species of the microbiota; B Histogram of LDA to identify bacterial species with different levels. C The top 22 predicted KEGG pathways of intestinal mucosal function; D Histogram for analysis of major differences in metabolic pathways (*P < 0.05, **P < 0.01, ***P < 0.001, NM the normal group, LGF the low-dose GF group, MGF the medium-dose GF group, HGF the high-dose GF group)

Effect of Grifola frondosa on the function of the intestinal mucosal microbiota in mice

The functional genes obtained from the four treatment groups belong to the 6 known Level 1 KEGG lanes of Metabolism, Genetic Information Processing, Human Diseases, Cellular Processes, Environmental Information Processing, and Organismal Systems. Figure 7C shows the top 22 predicted pathways of intestinal mucosal function, with the metabolic pathway being the main pathway. As shown in Fig. 7D, further analysis of metabolic pathways showed that mouse intestinal mucosal microbiota mainly assumed the functions of amino acid metabolism, carbohydrate metabolism, and energy metabolism. Compared to the MGF group, the relative abundance of Peptidoglycan biosynthesis, One Carbon Pool by Folate, and Thiamine metabolic pathway was significantly increased in the HGF group (P < 0.05). Compared to the NM group, the relative abundance of the One Carbon pool by Folate, Thiamine metabolic pathway was significantly decreased in the MGF group (P < 0.05).

Correlation between intestinal mucosal microbiota, metabolic pathways, TC, TG, LDL, and HDL

Correlation analysis of intestinal mucosal microbiota and TC, TG, LDL, and HDL showed (Fig. 8A and B) that HDL was negatively correlated with Ligilactobacillus (R < − 0.6, P < 0.01), while TG was positively correlated with unclassified Eubacterium coprostanoligenes group, Ligilactobacillus (R > 0.4, P < 0.05). Correlation analysis of intestinal mucosal microbiota and metabolic pathways showed that Biosynthesis of amino acids, Peptidoglycan biosynthesis, Thiamine metabolism, One carbon pool by folate, and Lysine biosynthesis were positively associated with Alloprevotella and Christensenellaceae R7 group, unclassified Clostridia UCG 014, unclassified Eubacteria coprostanoligenes group, Prevotellaceae Ga6A1 group, and negatively associated with Candidatus Arthromitus. Peptidoglycan biosynthesis and thiamine metabolism were negatively associated with uncultured Bacteroidales bacterium. Valine, leucine, and isoleucine biosynthesis were negatively associated with Ligilactobacillus.

Fig. 8.

Fig. 8

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Correlation Heatmap: Red represents positive correlation and the blue color represents negative correlation; the closer the red color, the closer the R value is to 1; the closer the blue color, the closer the R value is to − 1. A Correlation Heatmap of the correlation between TC, TG, LDL, and HDL and intestinal mucosal microbiota; B Correlation Heatmap of the correlation between metabolism and intestinal mucosal microbiota (*P < 0.05, **P < 0.01, ***P < 0.001)

Discussion

The thymus and spleen are important immune organs, and their immune regulatory activity is closely related to the change in the immune organ indexes (Ma et al. 2021). In this study, the thymus index was elevated in the LGF group after GF intervention, while spleen and liver indices did not change significantly. The results showed that the effect of GF on immune function in mice was modulated by raising the thymus index. However, the effect of GF on immune function should be studied in combination with the proliferative activity of immune cells (CD4+ T cells, CD8+ T cells, NK cells, and macrophages) and increased secretion of immune factors (TNF-α and INF-γ) in mice (Zhang et al. 2019).

Blood lipids are influenced by habitual diet, physical activity, fasting, and genetic and pathological disorders, reflecting disease-associated metabolic disorders, dietary levels, or damage to specific organs (Eichelmann et al. 2022). Higher TC, TG, and LDL levels were associated with an increased relative risk of stroke (Yaghi and Elkind. 2015). HDL levels prevent or reduce inflammation leading to atherosclerosis and have significant atherosclerosis protective potential, which is negatively associated with cardiovascular event risk (Uehara and Saku. 2014). In this study, LDL, TC, and TG were increased and HDL was decreased in GF-treated mice compared with the NM group. Therefore, long-term consumption of GF may increase the risk of cardiovascular and cerebrovascular diseases. The results may be related to the type of mice GF interfered with and how active ingredients were extracted. Secondly, the research on the effect of reducing LDL, TC, and TG and raising HDL is focused on purified polysaccharides, and the effects of solid mycelium and GF dry matter on TC, TG, LDL, and HDL have not been deeply studied. Finally, the mycelium GF contains high crude fat and protein (Wu et al. 2021), which may explain the increase in TC, TG, LDL, and decrease in HDL in normal mice.

Compared with intestinal content microbiota, the diversity and composition of microbiota colonized in intestinal mucosa are more sensitive to intestinal diseases (Zhang et al. 2021). Dietary patterns and different dietary components directly influence intestinal microbiota composition (Zhang et al. 2018; Wang et al. 2020; Zhou et al. 2022b). Generally, the diversity of intestinal microbiota is essential for the proper functioning of the gut and its associated systems (Cryan et al. 2020). In this study, the species richness and diversity of the intestinal mucosal microbiota in mice decreased following GF interventions. This result may be due to differences in intestinal ecological regions or the expansion of probiotic dominance. Proteobacteria and Bacteroides are the two most abundant phylum in intestinal microbiota, an increase in the number of Proteobacteria or F/B ratio can lead to an imbalance or instability in the structure of the intestinal microbiota (Shin et al. 2015; Riva et al. 2017; Pammi et al. 2017). This study showed that the relative abundance of Proteobacteria and the F/B ratio increased in the LGF and MGF groups compared with the NM group. The relative abundance of Proteobacteria and the F/B ratio decreased in the HGF group. Thus, high doses of GF resulted in the increased structural stability of the intestinal microbiota compared with the NM group.

Lactobacilli have been widely used as beneficial bacteria with anti-microbial activity, immune effects, and the ability to regulate intestinal microbiota and can be used to treat various metabolic diseases (Palade et al. 2022; Guerrero Sanchez et al. 2022). Unclassified Muribaculaceae and Alloprevotella were negatively correlated with inflammatory factors and positively correlated with colonic tight junction proteins and SCFAs, which have a protective effect on the colon (Wang et al. 2021). Lactobacillus strains significantly reduced hepatic steatosis and hyperlipidemia and increased the relative abundance of Muribaculaceae (Ye et al. 2022). Combining the results of this experiment, we conclude that low and high doses of GF exerted anti-inflammatory and immunomodulatory effects by increasing some beneficial microbiota and improving intestinal mucosal immune function, which may also account for the decrease in the diversity index. Therefore, the increase in thymus index in the LGF group may be related to the increase of Ligilactobacillus. By correlation analysis, we know that elevated HDL and TG in this experiment may be associated with elevated unclassified Eubacterium coprostanoligenes group, Ligilactobacillus.

Candidatus Arthromitus was positively correlated with body weight (Fu et al. 2022), and Candidatus Arthromitus is an important microbiota species during diabetes and may serve as a potential therapeutic target (Zhao et al. 2022). Therefore, medium doses of GF are potentially significant for weight regulation, treatment of diabetes, and Candidatus Arthromitus as its characteristic microbiota distinguishing other concentrations of GF. It was found that increased fiber intake increased the diversity of fecal microorganisms, increased the proportion of Prevotellaceae, promoted fiber digestion, and short-chain fatty acid production (Pu et al. 2022). Eubacterium rectale suppresses lymphoid tumor formation by inhibiting CD83 from reducing inflammation (Islam et al. 2021; Lu et al. 2022). Decreased F/B and increased Muribaculaceae and the Eubacterium coprostanoligenes group were associated with improved hyperglycemia, hyperlipidemia, insulin resistance, and decreased pro-inflammatory cytokines (Yin et al. 2022). Decreased intestinal microbiota diversity and Christensenellaceae R7 group, Ruminococcaceae UCG 014, Akkermansia, and Eubacterium eligens group abundance can lead to elevated lipids (López-Montoya et al. 2022; Zhou et al. 2022a). Combining the results of this experiment, we conclude that high doses of GF are important in promoting short-chain fatty acid production, improving inflammation, and regulating lipid metabolism.

Peptidoglycan is the main structural component of the bacterial cell wall, maintaining cell shape, integrity, and survival, protecting cells from rupture due to high osmotic pressure within the cytoplasmic lysis and participating in host interactions (Xu et al. 2022; Turner et al. 2014). Thiamine is an essential nutrient for humans, and the intake of adequate amounts of thiamine increases the relative abundance of Lactococcaceae, which synthesize butyrate, and thiamine is involved in butyrate synthesis as a cofactor (Park et al. 2022). Essential amino acids maintain intestinal integrity, have a similar structure and similar functions in the body for improved health status, and the dietary arginine: lysine ratio is critical (Konieczka et al. 2022). Lysine is one of the essential amino acids that synthesize proteins, regulate fat metabolism, promote the release of endocrine hormones, promote human development, and enhance immunity (Hu et al. 2022). Ligilactobacillus had a significant negative association with Valine, leucine, and isoleucine biosynthesis. In our study, we found that consumption of GF significantly affected Metabolic pathways. GF significantly regulates metabolism and affects enhances the body’s immune function by changing characteristic bacterium.

Conclusion

GF can improve thymic index and increase beneficial intestinal microbiota, such as Ligilactobacillus, Candidatus Arthromitus, Christensenellaceae R7 group, unclassified Clostridia UCG 014, unclassified Eubacterium coprostanoligenes group, Prevotellaceae Ga6A1 group. However, GF may increase TC, TG, and LDL, while decreasing HDL. The unclassified Eubacterium coprostanoligenes group and Ligilactobacillus may be the main factors contributing to changes in HDL and TG. GF can also affect Peptidoglycan biosynthesis, Thiamine metabolism, and One carbon pool by folate, thereby impacting overall health. Therefore, patients with metabolic disorders, cardiovascular diseases, and high blood lipid levels should use GF with caution.

Acknowledgements

We thank the editors and the reviewers of this paper for their constructive.

Author contributions

JL: performed the experiments, analyzed the data and wrote the original manuscript. YW: performed the experiments and analyzed the data. YC and ZJT: further revised the manuscript. ND: reviewed the manuscript and funded acquisition. All authors contributed to the article and approved the submitted version.

Funding

This research was funded by Provincial Education Department Project (21C0221).

Data availability

The data presented in the study are deposited in the NCBI repository, accession number PRJNA883061 (https://www.ncbi.nlm.nih.gov/).

Declarations

Conflict of interest

We declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declare that there is no conflict of interest regarding the publication of this paper.

Ethical approval

This study was approved by Animal Ethics and Welfare Committee of Hunan University of Chinese Medicine. All authors knew and approved of this animal experiment.

Contributor Information

Zhoujin Tan, Email: tanzhjin@sohu.com.

Na Deng, Email: 243671178@qq.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data presented in the study are deposited in the NCBI repository, accession number PRJNA883061 (https://www.ncbi.nlm.nih.gov/).

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