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. 2022 Nov 22;32(3):361–369. doi: 10.1007/s10068-022-01192-y

Effects of Flammulina velutipes polysaccharides on gut microbiota composition and metabolism in vitro fermentation

Anxiang Su 1, Gaoxing Ma 1, Ning Ma 1, Fei Pei 1, Wenjian Yang 1, Qiuhui Hu 1,✉
PMCID: PMC9905359  PMID: 36778094

Abstract

Flammulina velutipes polysaccharides (FVP) exhibit many biological activities, but the effects on gut microflora and metabolism were still unclear. Here, we explored the composition of FVP, their influence on human gut microflora composition and metabolites. FVP were used to vitro fermentation through human fecal inoculums. In addition, 16S rRNA sequencing were used to assess the effects of FVP on the gut microbiota. The metabolic profiles were investigated using untargeted metabolomics approaches in the LC-MS platform. The results showed that FVP was mainly consisted of glucose, mannose, xylose, fucose and galactose. FVP is shown to increase the relative abundances of Bifidobacteriaceae, as well as Bacteroidaceae and remarkably decrease the numbers of genera Lachnospiraceae coupled with Enterococcaceae. The differential metabolites were identified and mainly involved the metabolism of glycerophospholipid, linoleic acid and synthesis of unsaturated fatty acids. FVP may exhibit biological activity function by regulating gut microflora composition and metabolites.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10068-022-01192-y.

Keywords: Flammulina velutipes polysaccharides, Gut microflora, Metabolite, Antibiotic treatment, Vitro fermentation

Introduction

Flammulina velutipes is a widely accepted edible mushroom(Pan et al., 2022). F. velutipes polysaccharides (FVP) have been widely accepted as one of the main ingredient among many identified beneficial compounds such as polysaccharides, proteins and polyphenols (Yang et al., 2015). FVP have been reported to have a wide range of bioactivities, for instance prebiotic, immune modulatory, antioxidant, anti-osteoporosis, along with learning, as well as memory improving activities (Dong et al., 2017; Liang et al., 2019; Su et al., 2018; Wang et al., 2019). However, most of polysaccharides have low bioavailability and cannot be digested when administered orally and largely arrives unmetabolized in the colon (Muniyandi et al., 2022). It is likely that polysaccharides can be degraded, then utilized by the gut microflora (Guijie Chen et al., 2018). Thus, the interactive influences of polysaccharides along with gut microflora have attracted the attention of many researchers.

Accumulating evidence has shown that the change of gut microflora composition and metabolites are closely linked to some serious chronic diseases, including obesity (Sung et al., 2016), Parkinson’s disease (Lionnet et al., 2018; Sampson et al., 2016), colon cancer and Alzheimer disease (Kadish et al., 2016). Many studies have reported that polysaccharides can regulate intestinal flora and that this change is related to health. Some beneficial bacteria and metabolites, for instance Bifidobacterium, Lactobacillus along with SCFAs(short chain fatty acids) are positively correlated with the health of the body and the immune response of the intestines (Ding et al., 2017; Routy et al., 2018). For example, polysaccharides from Pleurotus eryngii possess the potential to decrease gut dysbiosis via altering the composition of intestinal flora and adjust the SCFAs concentrations (Ma et al., 2017). Ganoderma lucidum polysaccharides can attenuate intestinal disorders, as well reduce obesity-linked metabolic disorders (Chang et al., 2015; Khan et al., 2018).

Metabolite changes can be investigated by metabolomics. The metabolomics approach is a new tool for food nutrition assessment. The metabolic profiling method can simultaneously measure multiple parameters. We can find differential metabolites after nutritional interventions, and subsequently analyze the metabolic pathways of these metabolites involved (Lim et al., 2022). For example, in order to analyze the effect of Lycium barbarum polysaccharides in young healthy men, Xia et al. investigated the changes of metabolites in the serum and urine (Xia et al., 2018). An obvious decline of TG/HDL index was observed after intervention. Five differential metabolites were found in the urine and three differential metabolites were found in the serum. The main metabolic pathways involved were related to glycerophospholipid metabolism and tyrosine metabolism. Jin et al. (2019) studied the effect of polysaccharides from mycelia of Ganoderma lucidum on the response of intestine. All metabolites were tested by non-targeted metabolomics method. Twelve different metabolites were found. The main metabolic pathways involved were vitamin B6 metabolism, pyrimidine metabolism, fructose along with mannose metabolism, as well as alanine, aspartate and glutamate metabolism.

Above all researchers indicated that diet can regulate gut microbiota composition and metabolite profiles; however, little attention focused on simultaneous investigation of intestinal flora and metabolites, which can obtain useful information relating to the biological function. The biological activity mechanism of FVP is still imperfect, especially from the perspective of gut microbiota and metabolism. Therefore, our study is aim to analyze the composition of FVP, as well as investigate the effect on intestinal flora and metabolic profile after FVP supplementation using in vitro fermentation by human fecal inoculums. The structural characteristics of FVP were investigated by infrared (IR) spectrum and NMR spectroscopy. Changes in gut microbiota composition were detected by 16 s rRNA sequencing technology. The metabolic profiles were investigated using untargeted metabolomics approaches in the LC-MS platform.

Materials and methods

Materials and reagents

Fresh mushrooms were supplied by Jiangnan Bio-technology (Nanjing, China). Bile salts were commercially provided by Aladdin company (Shanghai, China). 3-Methyl-1-phenyl-2-pyrazolin-5-one (PMP), 3,5-dinitrosalicylic acid, monosaccharide standards were commercially acquired from Sigma (United States).

Preparation of FVP

FVP preparation was done as documented previously (Su et al., 2018). Briefly, fresh F. velutipes was used to generate a powder after drying at 60 °C, and subsequently filtered with a No. 300 mesh. The powder was dissolved in distilled water at the ratio of 25 mL/g at 80 °C for two hours twice. Afterwards, we precipitated solubilized extract by using absolute ethanol at 4 °C. The sevag method was used to deproteinize from the precipitation collected by centrifugation. Then the precipitate was dialyzed for three days, then freeze-dried to generate FVP powder. As documented previously (Yang et al., 2012), further purification was implemented.

Preparation of fecal homogenates

We acquired fresh stool samples feces from one female, as well as three male volunteers (all healthy) who were not taking probiotics or antibiotics medication three months from the day of fecal collection. Besides, they did not previously suffer from any aggressive bowel disease, as well as had been on normal diets. Homogenization of randomly-chosen fresh stool specimens was done, then equal amounts from each volunteer mixed, followed by blending well with 0.1 mol/L autoclaved PBS (pH 7.2) to generate a 10% (w/v) mixture. Spinning of the stool slurry was done for five minutes at 500 rpm to remove food residues.

Fermentation by fecal microbiota

FVP in vitro fermentation was done as documented previously (Chen et al., 2018). First, we prepared culture medium. Basic Composition of culture medium in supplementary data. FVP were introduced to basic medium (10:1 (v/w)) and autoclaved. Fecal suspension (1 mL) were introduced to the 9.0 mL of the mixture medium and cultured in an anaerobic incubator equipment (Coy Laboratory Products, United States) at 37 °C. We established a control in FVP absence. After 48 h fermentation, samples were collected for tests. Each test was conducted three times.

Chemical characterization of FVP

The quantification of overall carbohydrate contents was done with the phenol–sulfuric acid method, whereas that of reducing sugar of FVP was done using 3,5-dinitrosalicylic acid approach. Mannose, arabinose, galactose, galacturonic acid, glucose, ribose, glucuronic acid, xylose, rhamnose and fucose were employed as the standard. Monosaccharides were analyzed on HPLC (Agilent Technologies, USA). The digesting solution (100 μL) containing 100 μL 0.6 M NaOH was reacted with a 0.5 M PMP in methanol (200 μL) for 100 min at 70 °C and then neutralized with 0.3 M HCl (100 μL). The residue was redissolved in 1.0 mL water and subsequently in chloroform for removal of PMP and filtration with a 0.45 μm film prior to HPLC. The monosaccharide standards were derivatized similarly. The HPLC equipment consisted of diode-array detector along with Eclipse Plus C18 column (4.6 × 250 mm, 5 μm, Agilent). Temperature inside the column oven was set at 35 °C. Mobile phase consisted of PBS (0.1 mol/L, pH 6.7) with acetonitrile mixture at a ratio of 83: 17 (v/v) and rate of flow of 1.0 mL/min.

Analysis of the FT-IR spectra

FVP (2 mg) powder was mixed and ground with 200 mg KBr powder. Subsequently, the mixtures were pressed into 1 mm slice to be used for analysis (tensor27, Bruker, Germany). Within a wave range of 400–4000 cm−1, FVP’s IR spectrum was reported.

Analysis of the NMR spectra

FVP (30 mg) was fully dissolved in 0.6 mL D2O. The test was performed on a Bruker 600 MHz NMR apparatus. The 1H NMR spectra were recorded.

Determination of molecular weights

HPLC (1200 Agilent Corp., USA) fitted with an evaporative light scattering detector along with a TSK G4000PWXL column (7.8 × 300 mm2, Tosoh Crop., Japan) was adopted to explore molecular weights. The rate of flow was 0.5 mL/min, with the oven temperature being 35 °C. 20 μL sample solution was injected through the inlet.

DNA isolation and high-throughput sequencing

DNA isolation from the fermented samples was done with the TIANamp stool DNA kit (Tiangen Biotech Co., Ltd., Beijing, China). The 16S rRNA gene comprising V3 and V4 regions was amplified by PCR. High-throughput sequencing analysis of bacterial rDNA genes was performed on the purified, pooled sample using the Illumina Hiseq 2500 platform (2 × 250 paired ends) (Illumina, Inc., San Diego, CA, United States). The raw data was purified via elimination of low quality coupled with adapter pollution. The taxonomy of each 16S rDNA gene sequence was flatten by the minimum number of sample sequences and analyzed by Biomarker analysis cloud platform (https://international.biocloud.net/).

Profiling of metabolites by LC-MS during fermentation

Agilent 1290 UHPLC system fitted to an Agilent 6545 UHD along with Accurate-Mass Q-TOF/MS was adopted for LC-MS assessment. Waters XSelect HSS T3 (2.5 µm 100 × 2.1 mm) served as the chromatographic column. Mobile phase constituted A: aqueous solution with 0.1% formic acid. B: acetonitrile solution with 0.1% formic acid. Rate pf flow: 0.35 ml/min. Temperature of column: 25 °C. Injection volume: 2 μL.

Statistical analysis

All data are given as mean ± standard deviation (SD). The data were examined on SPSS 20.0 (IBM Inc, Chicago, IL, USA) with the significant level at P < 0.05. Independent sample t test was used for comparison between the two groups, and one-way analysis of variance (ANOVA) with the Tukey test was used for multi-group data comparison. Multivariate analysis included principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were analyzed by SIMCA software (V14.1, Umetricus, Sweden). The metabolic pathways were analyzed on MetaboAnalyst (https://www.metaboanalyst.ca/).

Results and discussion

Chemical composition and molecule weight of FVP

The phenol-sulphate acid approach was used to assess the content of polysaccharides. The results showed that FVP contains 95.39% total polysaccharides. The content of uronic acid was 3.40% and was tested using a colorimetric assay. We detected a small quantity of protein in FVP (1.32 ± 0.15%). The profile of monosaccharide composition of FVP is given in Fig. 1A. FVP were composed of 49.21% glucose, 16.32% mannose, 12.41% xylose, 11.76% fucose and 10.32% galactose.

Fig. 1.

Fig. 1

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Monosaccharide composition profile (A) and molecular weight distribution (B) of FVP

FVP’s average molecular weight was identified through HPGPC, as illustrated in Fig. 1B. FVP was determined as 24 kDa. FVP demonstrated a symmetrical single peak. Zhang et al. (2018) purified a new polysaccharide from F. velutipes, having molecular weight of 54.78 kDa. Therefore, this comparison shows that FVP is a polysaccharide with lower molecular weight. Molecular weight was related to biological activity and physical properties. Hu et al. (2017) obtained a lower molecular weight polysaccharide by ultrasonic irradiation treatment. Hu et al. studied the probiotic characteristics in vitro fermentation by inoculating human feces bacteria. The results showed that polysaccharide decomposition causes sizeable escalation of SCFA production as well as in population sizes of Lactobacillus and Bacteroidaceae in fermentation cultures. High molecular weight polysaccharides exhibited high viscosity and poor solubility, which was harmful on application (Zhang et al., 2017). Thus, FVP with lower molecular weights are potentially beneficial for their utilization.

Chemical structure information of FVP

Figure 2A illustrated FVP’s FT-IR spectrum. Spectrum of FVP possesses a wide-spanning and heightened peak at 3400.4 cm−1 for hydroxyl stretching (O–H) vibration, as well as a weak span of C-H vibration for CH2 groups at 2926.8 cm−1. The stretching peaks at 1424.4 cm−1 and 1632.1 cm−1 coincide with asymmetric, as well as symmetric stretching vibrations of COO− and free carboxyl groups. The result implied the presence of uronic acid. This was consistent with composition of FVP (the content of uronic acid was 3.40%). Particularly, the strong absorption peak that was observed at 1036.7 cm−1 demonstrated a stretching vibration of the pyranose ring. Additionally, β-glycosidic linkage for glucosyl residue is depicted by the characteristic absorption at 890 cm−1. Moreover, FVP’s α-mannopyranose causes absorption at 820 cm−1.

Fig. 2.

Fig. 2

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Structure features of Flammulina velutipes polysaccharides: FT-IR spectrum (A), 1H NMR (B) of F. velutipes polysaccharides

The 1H NMR spectra of FVP illustrated in Fig. 2B showed that signals for 1H NMR spectra are primarily from 3.0 to 5.5 ppm. The signal at 5.4 ppm which was the typical signal of hydrogen chemical shift of furanose anomeric carbon was not observed. This indicated that FVP was pyranose. Moreover, the signal at 1.12 ppm was designated as the H-6 of α-fucose, and a generic acetyl group hydrogen signal (1.95 ppm) was observed.

Impacts of FVP on gut microbiota composition

As documented, some indigestible polysaccharides, dietary fiber, can be decomposed and utilized by the gut microflora. Thus, the composition of gut microflora was detected by 16S rRNAs sequencing technology after fecal fermentation.

Significant difference in gut microflora composition was observed between the FVP inoculated group and the control group (P < 0.05). The addition of FVP lead to changes in the gut microflora structure, especially the growth of some beneficial bacteria. At the family level, FVP treatment elevated the Bifidobacteriaceae (2% to 22%) along with Bacteroidaceae (16% to 44%) level, however decreased the Lcchnospiraceae (8% to 2%) coupled with Enterococcaceae (55% to 6%) levels (Fig. 3A). As reported (Mariat et al., 2009), these bacteria were linked to aging. Bifidobacteriaceae and Bacteroidaceae were rich in younger people’s bowels compare to older people. However, Enterococcaceae was richer in older people’s gut. This suggested that FVP might be active in anti-aging.

Fig. 3.

Fig. 3

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Heatmap analysis of the relative abundance at the family level (A) and the relative abundance at the genus level (B)

Fecal microflora community composition at the genus level was showed in Fig. 3B. Escherichia coli and Bacteroides were the main bacteria with abundance of about 65% and 20%, respectively. Treatment with FVP treatment at the genus level elevated the Bifidobacterium (3% to 20%), Bacteroides (16% to 44%) level. Bifidobacterium and Bacteroides can positively predict brain N-acetylaspartate, a biomarker of neuronal health (Mudd et al., 2017). Bacteroides is similarly directly related with 5-hydroxytryptamine. All the changes to the microflora suggest FVP can regulate neurodegenerative diseases. The results are entirely consistent with a previous research (Su et al., 2018). The previous investigation reported that FVP improves scopolamine-triggered learning along with memory impairment in mice via regulating gut microflora composition (Su et al., 2018).

The explicit bacteria for each treatment were characterized by LEfSe analysis (Fig. 4A), a precise statistical way to compare microbial flora based on their 16S rRNA level. Results that confirmed many of these trends are highly associated with the addition of FVP. Bacteroides were more abundant after FVP treatment, while Proteobacteria was richer in the control group. Specifically, alterations in microflora composition related to different treatments were subjected to LDA (Fig. 4B). Significant imparity was found in the FVP group, with the highest abundance of Bacteroides than other two groups, indicating that FVP can increase the bacterial abundance. These results generally confirmed FVP could efficiently regulate the gut microorganisms.

Fig. 4.

Fig. 4

Fig. 4

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Linear discriminant analysis effect size (LEfSe) of the microbiota composition during in vitro fermentation (A) and Linear discriminant analysis (LDA) of the microbiota composition during in vitro fecal fermentation (B)

Effects of FVP on metabolic profiles

In order to analyze the effects of FVP on metabolism, a non-targeted metabolomics approach was adopted. The unsupervised PCA analysis was conducted to obtain an overall trend of all samples. An obvious distinction between control and FVP groups can be seen in the PCA scores plot (Fig. 5A). The first principal components accounted for 80.7% (X-axis). The second principal components accounted for 8.1% (Y-axis). The distinction was mainly on the Y-axis. Thus, OPLS-DA is necessary to be implemented to find the differential metabolites between groups. OPLS-DA is a supervised model that allowed the discovery of the significant biomarkers responsible for the separation between control and FVP group. The scores plot of OPLS-DA model was shown in Fig. 5B. There was a clear distinction on the X axis (R2X = 0.84). It suggested that the fermented samples from control and FVP groups had obvious different metabolomic composition. Stable cumulative R2 and Q2 of permutation test charts for OPLS-DA model are shown in Fig. 5C. The result showed that a reliable OPLS-DA model was established.

Fig. 5.

Fig. 5

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Change of metabolite. Principal component analysis (PCA) (A), OPLS-DA analysis (B), permutation test analysis for orthogonal partial least squares discriminant analysis (OPLS-DA) model (C) and variable importance for predictive components (VIP) analysis (D)

Afterwards, the variable importance for predictive components (VIP) analysis were shown in Fig. 5D. The mainly contributing metabolites were selected under the criteria: (a) VIP values > 1; (b): P values < 0.05. According to the criteria, 23 metabolites were considered as potential biomarkers. The altered metabolites in FVP treatment were mainly involved l-tyrosine, PC (16:0/20:4), PC (16:0/18:2), Citrulline, PE (16:0/P-18:1), PA (16:0/18:1), PI (18:0/22:4), PI (16:0/22:3), sphinganine, N-acetylaspartate, 2-oxo-4-methylthiobutanoic acid, linoleic acid, phenylglyoxylic acid, chenodeoxycholic acid, PS (18:0/22:6), (2S,3S)-2,3-dihydro-2,3-dihydroxybenzoate, cobinamide, α-linolenic acid, Succimer, cis-9-palmitoleic acid, 4,7,10,13,16-docosapentaenoic acid, 2-oxo-8-methylthiooctanoic acid. Most metabolites are related to neurodevelopment. Tyrosine is an essential amino acid that is needed for the production of neurotransmitters (Guo et al., 2019). N-Acetylaspartate a marker of neuronal health, was negatively predicted by Ruminococcus but positively predicted by Butyricimonas (Mudd et al., 2017). Sphingosine, also known as sphingosine belongs to sphingolipids and is one of the constituents of cell membranes (Lee et al., 2019). These results are consistent with our previous research, which supported the view that FVP can improve learning and memory (Su et al., 2018).

To further investigate the metabolic pathways and networks involved in FVP treatment, the potential biomarkers were submitted to the MetaboAnalyst (https://www.metaboanalyst.ca/). Figure 6 showed the major metabolic pathways perturbation that caused by FVP intervention. Top 5 metabolic pathways of importance included glycerophospholipid metabolism, Linoleic acid metabolism, alpha-Linolenic acid metabolism, synthesis of unsaturated fatty acids, ubiquinone as well as other terpenoid-quinone biosynthesis. FVP intervention most related to glycerophospholipid metabolism.

Fig. 6.

Fig. 6

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Pathway impact prediction between Flammulina velutipes polysaccharides intervention and control group

Altogether, this study prepared a novel polysaccharide FVP from F. velutipes and investigated the effect on gut microflora and metabolomic profile after fermentation in vitro. The results indicated that FVP may exert biological activities that function by regulating gut microflora composition and metabolites.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 15 kb) (15.7KB, docx)

Acknowledgements

This work is financially supported by the National Natural Science Foundation of China (No. 31872898) and the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

Declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Anxiang Su, Email: xiangansu@126.com.

Gaoxing Ma, Email: magaoxing90@163.com.

Ning Ma, Email: maning@nufe.edu.cn.

Fei Pei, Email: feipei87@163.com.

Wenjian Yang, Email: lingwentt@163.com.

Qiuhui Hu, Email: qiuhuihu@nufe.edu.cn.

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