Polygonatum odoratum Polysaccharides Modulate Gut Microbiota and Mitigate Experimentally Induced Obesity in Rats.

Increasing evidence suggests that the gut microbiota plays vital roles in metabolic diseases. Polygonatum odoratum extract alleviates hyperglycemia and hyperlipidemia, but the underlying mechanism remains unclear. This study investigated the effects of P. odoratum polysaccharides (POPs) on high-fat diet (HFD)-induced obesity in rats and whether these effects were related to modulation of gut microbiota. POP treatment attenuated weight gain, fat accumulation, epididymal adipocyte size, liver triglycerides, and total liver cholesterol content in HFD-fed rats. POP administration also increased short-chain fatty acids (SCFAs), including isobutyric acid, butyric acid, and valeric acid. POP upregulated the expression of genes involved in adipocyte differentiation (Pparg, Cebpa, Cebpb) and lipolysis (Ppara, Atgl), and downregulated those related to lipid synthesis (Srebpf1, Fabp4, Fas), with corresponding changes in PPARγ and FABP4 protein expression. Finally, POP enhanced species richness and improved the gut microbiota community structure, reducing the relative abundances of Clostridium, Enterococcus, Coprobacillus, Lactococcus, and Sutterella. Principal coordinates analysis (PCoA) revealed a clear separation between HFD-fed rats and all other treatment groups. Correlation analysis identified negative and positive associations between obesity phenotypes and 28 POP-influenced operational taxonomic units (OTUs), including putative SCFA-producing bacteria. Our data suggest that POP supplementation may attenuate features of obesity in HFD-fed rats in association with the modulation of gut microbiota.

1. Introduction

Globally, obesity has increased at an alarming rate. In 2016, the World Health Organization found that 39% of adults worldwide were overweight, while 13% were obese [1]. This trend is a critical public health concern, given the link between obesity and chronic illnesses, including type II diabetes, cardiovascular disease, cancer, and fatty liver disease [2]. High energy intake and lack of physical activity are largely responsible for the dramatic increase in obesity rates. However, the development of obesity involves a host of complex interactions between genetics and the environment. Growing evidence has linked gut microbiota composition to changes in body weight and energy homeostasis [3,4]. Specifically, higher proportions of pathogenic Sutterella, Desulfovibrionaceae, Streptococcaceae, and Clostridium species are positively correlated with obesity development [5,6,7,8]. Furthermore, proportions of beneficial gut microbes (e.g., Bifidobacterium, Lactobacillus, Akkermansia muciniphila) decrease with weight gain associated with a high-fat diet (HFD) [8,9]. The anti-obesity effects of gut bacteria may be attributable to their generation of metabolites, such as short-chain fatty acids (SCFAs) and bile acids [10]. Both of these compounds have positive influences on defense-related processes, including immunity, inflammation, and lipid and glucose metabolism [3]. Thus, regulating intestinal microbiota dysbiosis is an effective method for preventing obesity and associated diseases [11,12].

Plant-based polysaccharide supplementation appears to protect against obesity-related metabolic syndrome [13], preventing weight gain, enhancing immunity, and improving gut microbiota composition in experimentally obese mice [11,14]. A promising source of polysaccharides is Polygonatum odoratum (Mill.) Druce, a traditional Chinese herb used as a preventative measure against diabetes, hyperlipidemia, and some cancers [15,16,17]. P. odoratum polysaccharides (POP) have been confirmed to be the primary contributor to the majority of observed benefits [18]. In experimentally induced obese mice, POP ameliorates metabolic disorders and enhances the mRNA expression of peroxisome proliferator-activated receptors (PPARs) [19], while preventing CCl4-induced liver oxidative injury [20]. Our previous in vitro study demonstrated that polysaccharides from P. odoratum rhizomes exerted antioxidant and antimicrobial activity against the pathogenic bacteria Staphylococcus aureus, Pseudomonas aeruginosa, Bacillus subtilis, and Escherichia coli [21]. Furthermore, Polygonatum kingianum polysaccharides significantly improve gut microbiota composition, decreasing the risk of type II diabetes [22]. Despite these promising findings, there is a lack of concrete data regarding whether POP modulates the gut microbiota in response to obesity.

Thus, this study aimed to investigate whether POP treatment prevents the development of obesity and modulates gut microbiota dysbiosis in obese rats. More importantly, we aimed to identify potential correlations between POP treatment and changes in the gut microbiota, providing potential insight into gut microbiota-related mechanisms underlying the anti-obesity effects of POP.

2. Results

2.1. Response of Obese Rats to POP Treatment

During establishment of the model (modeling time), total energy intake in the HFD group was higher than that in the normal control (NC) group (Figure 1A). In addition, during treatment, total energy intake in the HFD group was higher than those of all other groups [NC, HFD + simvastatin (SIM), HFD + POP] (Figure 1B). After eight weeks of HFD feeding, the average body weight of HFD rats was 22.63% higher than that of NC (Figure 1C). By week 14 (end of experiment), feeding of the HFD had significantly increased body weight, epididymal and perirenal fat accumulation, epididymal adipocyte size, and hepatic triglyceride (TG) and total cholesterol (TC) content (all p < 0.05; Figure 1D–I and Figure 2A,B). Notably, the POP and SIM treatments significantly decreased weight gain, body fat accumulation, epididymal adipocyte size, and hepatic TC content compared with those in the HFD-only group (all p < 0.05; Figure 1E–I and Figure 2B). Moreover, POP also significantly reduced liver TG content (p < 0.05; Figure 2A). Thus, POP appears to attenuate body weight gain and fat accumulation in rats with HFD-induced obesity by regulating total energy intake.

Figure 1

Effects of POP on HFD-induced obesity in rats. (A) Energy intake during modeling time. (B) Energy intake during treatment. (C) Body weight during modeling time. (D) Body weight during treatment. (E) Body weight gain during treatment. (F) Perirenal fat. (G) Epididymal fat. (H) Epididymal adipocyte microsections. (I) Epididymal adipocyte size evaluated by ImageJ software. NC = normal control, HFD = high-fat diet, HFD + SIM = HFD with simvastatin (1.8 mg/kg), HFD + POP = HFD with P. odoratum polysaccharides (400 mg/kg). AUC = Area under curve. All data are presented as means ± SEM. ** p < 0.01 for HFD (n = 30) versus NC (n = 10) in (C). a–c Mean values not sharing a common superscript were significantly different among the groups (n = 10 for each) (p < 0.05).

Figure 2

Effects of POP on TG (A) and TC (B) content in the livers of HFD-fed rats during the first six weeks. NC = normal control, HFD = high-fat diet, HFD + SIM = HFD with simvastatin (1.8 mg/kg), HFD + POP = HFD with P. odoratum polysaccharides (400 mg/kg). All data are presented as means ± SEM. a–c Mean values not sharing a common superscript were significantly different among the groups (n = 10 for each) (p < 0.05).

2.2. Polysaccharide Treatment Altered the Expression of Lipid Metabolism-Related Genes in Adipose Tissue

Compared with those in the NC group, HFD-fed rats exhibited upregulation of mRNAs encoding fatty acid synthase (Fas), sterol regulatory element-binding transcription factor 1 (Srebf1), and fatty acid binding protein 4 (Fabp4) (Figure 3C,D,F), along with downregulation of those encoding peroxisome proliferator-activated receptor α (Ppara), PPARγ (Pparg), adipose triglyceride lipase (Atgl), CCAAT/enhancer-binding protein α (Cebpa), and Cebpb (Figure 3A,B,E,G,H). When obese rats were treated with POP, however, expression of Fas, Srebf1, and Fabp4 significantly decreased (p < 0.05) (Figure 3C,D,F), while that of Ppara, Pparg, Atgl, Cebpa, and Cebpb increased (p < 0.05) (Figure 3A,B,E,G,H). In particular, the HFD + POP and NC groups did not significantly differ in expression of Ppara, Pparg, Atgl, Fabp4, Cebpa, and Cebpb (Figure 3A,B,E,F,G,H). Besides, the former group exhibited lower Fas mRNA expression (p < 0.05) (Figure 3C).

Figure 3

POP treatment modulated the mRNA expression of lipid metabolism-related genes in HFD-fed rats. Quantitative real-time PCR was used to assess (A) Ppara, (B) Pparg, (C) Fas, (D) Srebf1, (E) Atgl, (F) Fabp4, (G) Cebpa, and (H) Cebpb mRNA expression in adipose tissue. NC = normal control, HFD = high-fat diet, HFD + SIM = HFD with simvastatin (1.8 mg/kg), HFD + POP = HFD with P. odoratum polysaccharides (400 mg/kg). All data are presented as means ± SEM. a–c Mean values not sharing a common superscript were significantly different among the groups (n = 8 for each) (p < 0.05).

Treatment with SIM significantly decreased Fas mRNA expression (p < 0.05) and increased Ppara and Cebpa mRNA expression (both p < 0.05) compared with levels in the HFD group (Figure 3A,C,G). Similarly, POP significantly increased HFD-induced inhibition of PPARγ protein expression and attenuated FABP4 protein expression compared with those in the HFD group (Figure 4). Therefore, POP may reduce lipid accumulation by regulating the expression of genes involved in lipid metabolism in rats with HFD-induced obesity.

Figure 4

Western blotting analysis of PPARγ and FABP4 relative expression in HFD-fed rats. (A) PPARγ protein expression, (B) FABP4 protein expression. NC = normal control, HFD = high-fat diet, HFD + SIM = HFD with simvastatin (1.8 mg/kg), HFD + POP = HFD with P. odoratum polysaccharides (400 mg/kg). All data are presented as means ± SEM. a,b Mean values not sharing a common superscript were significantly different among the groups (n = 5 for each) (p < 0.05).

2.3. POP Induced Structural Changes in Gut Microbiota

After removing low-quality sequences, we obtained 10,490,150 raw reads (24 samples, n = 6 for each group), with an average of 437,089 ± 14,795 reads per sample. We then generated 9,355,208 clean reads, averaging 748,416 ± 13,009 per fecal sample. Rarefaction analysis suggested that the current sequencing depth successfully captured rare and new phylotypes, as well as the majority of gut microbial diversity (Figure 5). The HFD group exhibited significantly fewer observed species and a significantly lower Chao index (i.e., species richness) (p < 0.05) compared to those in the NC group (Figure 5A,B). However, the Shannon index (species diversity) in the HFD group was lower than that in NC rats, although this difference was not statistically significant (p > 0.05, Figure 5C). Under POP treatment, the Chao index was significantly higher than that in the HFD group (p < 0.05), while both the Shannon index and observed species number tended to be higher as well (p = 0.07 and 0.08 respectively). These results support the view that POP ameliorates decrease in gut microbiota richness stemming from obesity.

Figure 5

Diversity analysis of gut microbiota from fecal samples of POP-treated obese rats. (A) Observed species. (B) Chao index. (C) Shannon’s diversity. NC = normal control, HFD = high-fat diet, HFD + SIM = HFD with simvastatin (1.8 mg/kg), HFD + POP = HFD with P. odoratum polysaccharides (400 mg/kg). All data are presented as means ± SEM. a,b Mean values not sharing a common superscript were significantly different among the groups (n = 6 for each) (p < 0.05).

UniFrac-based principal coordinates analysis (PCoA) results revealed a distinct clustering of the gut microbiota composition for each treatment group, with the HFD group clearly separated from the remaining three groups (Figure 6A). As expected, POP treatment increased the similarity between the overall gut microbiota compositions of the HFD + POP and NC groups, suggesting that POP had substantial effects on the gut microbiota composition of HFD-fed rats. Similarity analysis (ANOSIM, Figure 6B) showed that the distances between groups were significantly larger than those within groups (R = 0.471 and p = 0.001).

Figure 6

POP treatment regulated gut microbiota structure in HFD-fed obese rats. (A) UniFrac-based PCoA plots. (B) Analysis of similarity (ANOSIM) among groups based on the Bray–Curtis distances. (C) Relative abundances of gut microbiota at the phylum level. Relative abundances of (D) major phyla Firmicutes and Bacteroidetes and (E) minor phyla Actinobacteria, Elusimicrobia, and Proteobacteria. Relative abundances of gut microbiota at the (F) genus and (H) species levels. (G) Relative abundances of minor genera Enterococcus, Coprobacillus, Lactococcus, Sutterella, and Clostridium. All data are presented as means ± SEM. a,b Mean values not sharing a common superscript were significantly different among the groups (n = 6 for each) (p < 0.05).

Taxonomic analysis suggested that the fecal microbiota was represented by four major phyla: Firmicutes, Bacteroidetes, Proteobacteria, and Spirochaetes (Figure 6C). Neither Firmicutes (F) and Bacteroidetes (B) abundances, nor the F/B ratio, significantly differed among the four treatment groups (Figure 6D). In contrast, HFD feeding significantly increased the relative abundance of Actinobacteria, compared to that in the NC group (p < 0.05; Figure 6E). However, POP treatment significantly reversed this HFD-induced increase (p < 0.05) and tended to have an inhibitory effect on the relative abundance of Proteobacteria (p = 0.08; Figure 6E).

At the genus level, HFD significantly enhanced the relative abundances of Coprobacillus, Lactococcus, and Sutterella (all p < 0.05; Figure 6F,G). The relative abundances of Coprobacillus and Sutterella were restored to NC levels upon POP and SIM treatment (both p < 0.05). Likewise, Clostridium, Enterococcus, and Lactococcus relative abundances also decreased in the HFD + POP group compared with those in the HFD group (p < 0.05; Figure 6F,G).

2.4. Key Phenotypes Responding to POP Treatment of HFD-Fed Rats

We identified 5124, 3617, 4984, and 4464 operational taxonomic units (OTUs) in the NC, HFD, HFD + SIM, and HFD + POP groups, respectively. HFD treatment significantly altered the abundances of 50 OTUs, among which 22 were enriched while the other 28 were reduced compared with those in the NC group (Figure 7, Table A1 and Table A2). Additionally, POP and SIM treatments significantly changed the abundance of 41 (21 increased and 20 decreased) and 26 (12 increased and 14 decreased) OTUs (Figure 7, Table A2) in comparison with those in the HFD group, respectively.

Figure 7

Heat map of RDA-identified key OTUs in response to POP treatment according to Spearman’s correlations between the identified OTUs and weight gain, epididymal fat, perirenal fat, and liver TG and TC levels. Colors of squares on the left indicate the average abundance of the OTU in each group. Open (○) and closed (●) circles represent lower and higher relative abundances (log10 transformed), respectively, of OTUs in NC, HFD + SIM, or HFD + POP groups compared with that in the HFD group. Stars (★) indicate OTUs in the NC group altered by HFD intervention that were restored by POP or SIM treatment. Colors of squares on the right indicate R-values of Spearman’s correlation between OTU and weight gain, epididymal fat, perirenal fat, and liver TG and TC levels. Phylum, family, and genus names of the OTUs are shown on the right. * p < 0.05 and ** p < 0.01.

Table A1

Fifty key OTUs responding to POP treatment, as identified by redundancy analysis.

OTU ID	Phylum	Class	Order	Family	Genus
OTU0012	Bacteroidetes	Bacteroidia	Bacteroidales	Paraprevotellaceae	Paraprevotella
OTU1365	Bacteroidetes	Bacteroidia	Bacteroidales	Paraprevotellaceae	Paraprevotella
OTU0005	Bacteroidetes	Bacteroidia	Bacteroidales	Paraprevotellaceae	Prevotella
OTU0911	Bacteroidetes	Bacteroidia	Bacteroidales	Prevotellaceae	Prevotella
OTU0994	Bacteroidetes	Bacteroidia	Bacteroidales	Prevotellaceae	Prevotella
OTU1497	Bacteroidetes	Bacteroidia	Bacteroidales	S24-7
OTU0024	Bacteroidetes	Bacteroidia	Bacteroidales	S24-7
OTU0241	Bacteroidetes	Bacteroidia	Bacteroidales	S24-7
OTU0030	Bacteroidetes	Bacteroidia	Bacteroidales	S24-7
OTU0051	Bacteroidetes	Bacteroidia	Bacteroidales	S24-7
OTU0056	Bacteroidetes	Bacteroidia	Bacteroidales	S24-7
OTU0080	Bacteroidetes	Bacteroidia	Bacteroidales	S24-7
OTU0105	Bacteroidetes	Bacteroidia	Bacteroidales	S24-7
OTU0170	Firmicutes	Clostridia	Clostridiales	Clostridiaceae
OTU0003	Spirochaetes	Spirochaetes	Spirochaetales	Spirochaetaceae	Treponema
OTU0359	Spirochaetes	Spirochaetes	Spirochaetales	Spirochaetaceae	Treponema
OTU0998	Spirochaetes	Spirochaetes	Spirochaetales	Spirochaetaceae	Treponema
OTU1044	Firmicutes	Clostridia	Clostridiales	Lachnospiraceae	Clostridium
OTU0194	Firmicutes	Clostridia	Clostridiales	Ruminococcaceae	Ruminococcus
OTU0283	Firmicutes	Clostridia	Clostridiales	Ruminococcaceae	Ruminococcus
OTU0295	Firmicutes	Clostridia	Clostridiales	Ruminococcaceae	Clostridium
OTU0391	Firmicutes	Clostridia	Clostridiales	Ruminococcaceae
OTU0442	Firmicutes	Clostridia	Clostridiales	Ruminococcaceae	Ruminococcus
OTU0873	Firmicutes	Clostridia	Clostridiales	Ruminococcaceae	Ruminococcus
OTU0885	Bacteroidetes	Bacteroidia	Bacteroidales	S24-7
OTU0076	Firmicutes	Clostridia	Clostridiales	Ruminococcaceae	Ruminococcus
OTU0352	Firmicutes	Clostridia	Clostridiales	Clostridiaceae	Clostridium
OTU0717	Firmicutes	Clostridia	Clostridiales
OTU0149	Proteobacteria	Deltaproteobacteria	Desulfovibrionales	Desulfovibrionaceae	Bilophila
OTU0151	Bacteroidetes	Bacteroidia	Bacteroidales	Porphyromonadaceae	Parabacteroides
OTU0061	Bacteroidetes	Bacteroidia	Bacteroidales	Porphyromonadaceae	Parabacteroides
OTU0055	Bacteroidetes	Bacteroidia	Bacteroidales	Paraprevotellaceae	Prevotella
OTU0161	Bacteroidetes	Bacteroidia	Bacteroidales	S24-7
OTU0017	Bacteroidetes	Bacteroidia	Bacteroidales	S24-7
OTU0173	Bacteroidetes	Bacteroidia	Bacteroidales	S24-7
OTU0036	Bacteroidetes	Bacteroidia	Bacteroidales	S24-7
OTU0009	Bacteroidetes	Bacteroidia	Bacteroidales	S24-7
OTU0010	Bacteroidetes	Bacteroidia	Bacteroidales	S24-7
OTU0648	Firmicutes	Bacteroidia	Bacteroidales	Lachnospiraceae;	Clostridium
OTU0776	Firmicutes	Clostridia	Clostridiales	Lachnospiraceae	Clostridium
OTU0046	Firmicutes	Clostridia	Clostridiales	Lachnospiraceae	Roseburia
OTU0137	Firmicutes	Clostridia	Clostridiales	Lachnospiraceae	Ruminococcus
OTU0141	Firmicutes	Clostridia	Clostridiales	Ruminococcaceae	Ruminococcus
OTU0215	Firmicutes	Clostridia	Clostridiales	Lachnospiraceae	Ruminococcus
OTU0102	Firmicutes	Clostridia	Clostridiales	Ruminococcaceae	Ruminococcus
OTU0404	Proteobacteria	Gammaproteobacteria	Pseudomonadales	Moraxellaceae	Psychrobacter
OTU0028	Proteobacteria	Betaproteobacteria	Burkholderiales	Alcaligenaceae	Sutterella
OTU0914	Proteobacteria	Betaproteobacteria	Burkholderiales	Alcaligenaceae	Sutterella
OTU0348	Proteobacteria	Betaproteobacteria	Burkholderiales	Alcaligenaceae	Sutterella
OTU0225	Actinobacteria	Actinobacteria	Actinomycetales	Corynebacteriaceae	Corynebacterium

Red: OTUs with enhanced abundance compared with that in the HFD group. Green: OTUs with reduced abundance compared with that in the HFD group.

Table A2

Fifty key OTUs responding to POP, as identified by redundancy analysis.

OTU ID	NC (%)	HFD (%)	HFD + SIM (%)	HFD + POP (%)
OTU0012	5.372650324	1.141401927	4.725000916	8.790443736
OTU1365	0.349016159	0.061375545	0.320618519	0.659558096
OTU0005	47.49459529	6.268550071	22.91139936	13.10230479
OTU0911	0.28947272	0.14473636	0.137407937	0.398483016
OTU0994	0.211608222	0.076948445	0.076948445	0.165805577
OTU1497	0.277564032	0.05679528	0.422300392	0.184126635
OTU0024	5.928694441	1.177127991	5.130812356	5.421201129
OTU0241	0.641237038	0.000005496	0.000916053	0.373749588
OTU0030	9.535194753	3.48008501	5.360741636	7.637133121
OTU0051	3.276721263	0.20153164	2.815946649	2.203107251
OTU0056	2.630903961	0.660474149	2.618995273	2.900223517
OTU0080	0.278480085	0.000005496	0.013740794	0.421384339
OTU0105	2.143563812	0.207944011	1.014070573	0.714521271
OTU0170	40.04800117	11.96639918	43.27250742	24.93679235
OTU0003	0.935290022	0.485508043	1.000329779	0.786889451
OTU0359	0.543219376	0.330695101	0.732842329	0.522150159
OTU0998	0.483675937	0.126415302	0.275731926	0.246418233
OTU1044	0.181378476	0.000005496	0.045802646	0.061375545
OTU0194	0.603678868	0.003664212	0.170385841	0.080612656
OTU0283	0.137407937	0.025649482	0.327946942	0.179546371
OTU0295	0.057711333	0.004580265	0.250082445	0.284892455
OTU0391	0.07236818	0.0000054963	0.025649482	0.146568466
OTU0442	0.184126635	0.10076582	0.205195852	0.215272434
OTU0873	0.517569895	0.000054963	0.280312191	0.282144297
OTU0885	0.691619948	0.341687736	1.119416658	0.753911546
OTU0076	0.360008794	0.159393207	3.259316258	0.124583196
OTU0352	0.174966106	0.010076582	0.12916346	0.11633872
OTU0717	0.124583196	0.000916053	0.029313693	0.005496317
OTU0149	0.603678868	0.222600857	0.377413799	0.331611154
OTU0141	0.956359239	0.000916053	0.000916053	0.447949874
OTU0151	0.000916053	0.198783482	0.07878055	0.020153164
OTU0061	0.240921916	2.157304606	0.580777546	0.473599355
OTU0055	0.000916053	5.28012898	0.002748159	1.152394562
OTU0161	0.402147228	0.560624382	0.298633249	0.272983768
OTU0017	4.220255762	10.22864681	5.834340992	4.274302884
OTU0173	0.250998498	0.575281228	0.296801143	0.137407937
OTU0036	1.261404859	10.95874098	1.946612436	0.84002052
OTU0009	4.281631307	11.87662599	2.474258913	5.92503023
OTU0010	5.066688652	9.421604192	4.130482577	3.033967242
OTU0648	0.261991133	2.5173134	1.014986626	0.085192921
OTU0776	0.003664212	0.030229746	0.014656847	0.010076582
OTU0046	0.112674508	16.8352204	0.555128064	0.272067715
OTU0137	0.0000054963	0.387490381	0.0000054963	0.001832106
OTU0215	0.000005.49632	0.22351691	0.070536074	0.043054487
OTU0102	0.000916053	0.551463853	0.246898245	0.263823238
OTU0404	0.02839764	0.294052985	0.030229746	0.053131069
OTU0028	2.104173537	13.70598366	1.250412224	1.134073504
OTU0914	0.058627386	0.455278297	0.032977905	0.030229746
OTU0348	0.02839764	0.162141365	0.040306328	0.023817376
OTU0225	0.110842402	0.838188414	0.199699535	0.21069217

Red: OTUs with enhanced abundance compared with that in the HFD group. Green: OTUs with reduced abundance compared with that in the HFD group.

Spearman’s correlation analysis identified 28 key variables that were significantly altered after POP treatment and negatively or positively correlated with alterations in body weight gain, epididymal fat, perirenal fat, and hepatic TG and TC levels (Figure 7). Among them, five families were negatively correlated with obesity parameters: Paraprevotellaceae (OTU0012, OTU1365, and OTU0005 from Bacteroidetes), Prevotellaceae (OTU0994 from Bacteroidetes), S24-7 (OTU1497, OTU0024, OTU0030, OTU0051, and OTU0080 from Bacteroidetes), Spirochaetaceae (OTU0359 and OTU0998 from Spirochaetes), and Ruminococcaceae (OTU0442 from Firmicutes). In contrast, seven families were positively correlated with obesity: Paraprevotellaceae (OTU0055), Porphyromonadacea (OTU0151 and OTU0061), S24-7 (OTU0173, OTU0036, and OTU0010), Lachnospiraceae (OTU0648, OTU0776, OTU0137, and OTU0215), Ruminococcaceae (OTU0873), Moraxellaceae (OTU0404), Alcaligenaceae (OUT0028, OTU0914, and OTU0348), and Corynebacteriaceae (OTU0225). Taken together, the results demonstrate that POP treatment modulates HFD-induced gut microbiota dysbiosis, resulting in the restoration of a microbiota community similar to that in controls.

2.5. POP Regulated SCFAs in Feces

Apart from a reduction in total SCFAs in the HFD + SIM group, the remaining groups (NC, HFD, HFD + POP) did not significantly differ in SCFA levels (Table 1). When comparing the HFD group to the NC group, however, propionic acid, butyrate acid, and valeric acid concentrations in the former were reduced by 18.41%, 77.15%, and 27.84%, respectively. Furthermore, POP treatment increased concentrations of isobutyric acid (by 47.03%), butyrate acid (587.41%), and valeric acid (93.42%) compared to those in the HFD group. In contrast, the HFD and HFD + SIM groups did not significantly differ in the production of butyric acid or valeric acid.

Table 1

Effects of POP on SCFA production in HFD-fed obese rats.

SCFA (mg/g)	NC	HFD	HFD + SIM	HFD + POP
Acetic acid	1.29 ± 0.185a	1.01 ± 0.134ab	0.79 ± 0.069b	0.65 ± 0.156b
Propionic acid	0.32 ± 0.024a	0.26 ± 0.061b	0.13 ± 0.012bc	0.27 ± 0.081b
Isobutyric acid	0.018 ± 0.002ab	0.015 ± 0.002b	0.0078 ± 0.0004c	0.022 ± 0.003a
Butyric acid	0.88 ± 0.088b	0.199 ± 0.049c	0.29 ± 0.034c	1.37 ± 0.196a
Isovaleric acid	0.022 ± 0.001a	0.025 ± 0.003a	0.0097 ± 0.001b	0.028 ± 0.006a
Valeric acid	0.032 ± 0.004a	0.024 ± 0.003b	0.013 ± 0.001b	0.046 ± 0.009a
Total	2.21 ± 0.169a	2.26 ± 0.402a	1.17 ± 0.075b	2.9 ± 0.548a

All data are presented as means ± SEM. a–c Mean values not sharing a common superscript were significantly different among the groups (n = 6 for each) (p < 0.05).

3. Discussion

In this study, we found that POP supplementation ameliorated negative effects of obesity, including weight gain, increased adipocyte size, and elevated liver TG/TC levels, in line with several previous studies [17,23].

Dietary polysaccharides generally escape digestion in the stomach and the small intestine, reaching the large intestine, where they are available for fermentation and thereby enhance the growth of beneficial bacteria and improve intestinal microbial diversity [24]. In addition, the gut microbiota may regulate energy harvest, which is related to lipid metabolism and the development of obesity [25]. Furthermore, numerous reports have indicated that dietary polysaccharides from plants reduce physiological risk factors (e.g., obesity, hyperlipidemia, and inflammation) by modulating the gut microbiota [11,13,26]. Therefore, we speculated that the inhibitory effect of POP on body weight gain and fat accumulation in our study may be owing to the modification of the HFD-fed obese rat gut microflora. We found that while POP had no significant effect on the relative abundances of Firmicutes (F) or Bacteroides (B), the F/B ratio, or species diversity (Shannon’s diversity), POP supplementation enhanced overall species richness (Chao index). Notably, there was also no significant difference in the Firmicutes and Bacteroides relative abundances or species diversity between the HFD and NC groups. This result is similar to the findings of Zhen et al. [14], who reported that Fuzhuan brick-tea polysaccharides increased species richness but had limited effects on the relative abundances of Firmicutes and Bacteroides. Shannon’s diversity is an indicator of species evenness in samples [27]. Although POP significantly increased the abundances of certain taxa, the species evenness of the HFD + POP group was not increased, as no significant difference was observed in Shannon’s index between the HFD and HFD + POP groups. Moreover, POP treatment reduced levels of some Firmicutes subgroups but not others, while only one Bacteroidetes subgroup experienced a decline. Thus, supplementation-related weight loss may affect these subgroups differently [28]. We also observed a significant decrease in Actinobacteria abundance after POP supplementation, suggesting an anti-obesity effect, as this bacterial phylum has been linked to obesity [29,30]. Likewise, POP tended to reduce levels of Proteobacteria, a major phylum of pathogenic gram-negative bacteria [31,32]. Our results showed that POP intake also reduced the abundance of Sutterella, corresponding to a previous report by Tang et al. showing that polysaccharides derived from purple sweet potato reduced the abundance of Sutterella (a member of the Proteobacteria) compared with that in cyclophosphamide-treated mice [33].

Our correlation analyses revealed 28 key OTUs that were significantly associated with at least one obesity parameter. Treatment with POP significantly enhanced the relative abundances of 12 OTUs that may represent beneficial gut flora. These 12 OTUs were negatively correlated with obesity phenotypes and produce SCFAs through fermenting undigested polysaccharides [34,35]. Several of these OTUs correspond to bacteria that have previously been associated with obesity-mitigating effects, such as Lachnospiraceae, S24-7, and Ruminococcaceae [14,22].

Treatment with POP also significantly reduced the proportions of 16 OTUs that were positively correlated with obesity phenotypes. Four of these genera (Corynebacterium, Clostridium, Parabacteroides, and Sutterella) have been implicated in obesity-related metabolic disorders [36,37,38]. Furthermore, OTU_0225 and OTU_0776 were significantly correlated with epididymal fat, perirenal fat, and liver TC, indicating that they may be central to the mechanism by which POP ameliorates obesity. Gene sequencing data revealed that OTU_0776 corresponds to Clostridium aldenense, a common pathogen [39]. The present results further illustrate that the anti-obesity activity of POP may be associated with modulation of the gut microbiota.

Polysaccharides are not digestible by gastrointestinal digestive enzymes but can be fermented by gut microbiota. This process produces beneficial metabolites, most notably SCFAs (acetate, propionate, butyrate, and valerate). High SCFA levels are unfavorable to several potentially pathogenic bacteria [40] but promote the proliferation of Bacteroides, Ruminococcus, Lactobacillus, Prevotella, and Butyricicoccus [41,42]. Our results showed that POP upregulated isobutyric acid, butyric acid, and valeric acid in HFD + POP-fed rats, resulting in changes to Ruminococcus and Prevotella abundance. Similar findings have been reported for seaweed polysaccharides, which enhance SCFA production by stimulating the growth of beneficial microbes (Ruminococcaceae, Bacteroidetes, and Lactobacillus) [43]. These results indicate that POP enhances the growth of SCFA-producing bacteria in the gut, such as Ruminococcus and Prevotella.

The present study revealed that POP supplementation reduced the expression of Fas, Srebf1, and Fabp4 in adipose tissues of HFD-fed rats, while increasing the expression of Ppara, Pparg, Atgl, Cebpa, and Cebpb. All of the corresponding proteins are involved in lipid metabolism. PPARα modulates the transcription of genes involved in fatty acid oxidation and energy expenditure [44], as well as increasing fatty acid combustion, which leads to a reduction in hepatic TG content [45]. Moreover, SREBP-1c activates the transcription of genes involved in fatty acid and TG synthesis, such as Fas [46]. PPARγ plays an important role in maintaining lipid homeostasis by controlling the expression of genes functioning in lipid and carbohydrate metabolism [47,48]. PPARγ activation may promote adipocyte differentiation and fat accumulation [49,50,51] but also promotes fatty acid oxidation; increases small, relatively insulin-sensitive adipocytes [44]; and attenuates inflammation [52,53]. Our findings are supported by previous studies demonstrating that Rhizoma Polygonati Odorati extracts from the root of P. odoratum upregulate PPARγ expression and downregulate FABP4 expression in the adipocyte tissues of HFD-fed obese rats [19]. In contrast, other studies have shown that HFD feeding increases Pparg expression, whereas resveratrol and germacrone decrease Pparg mRNA expression compared with that in HFD-fed mice [54,55]. These inconsistent results may be partially due to differences in treatment conditions, diet ingredients, experimental duration, and species. Moreover, because SCFAs are PPARγ modulators [36], dietary changes may alter SCFA profiles and, in turn, PPARγ signaling. Previous research has indicated that butyrate supplementation upregulates PPARγ expression in pig adipocytes [56] and prevents diet-induced obesity by increasing energy expenditures [57]. Therefore, our results suggest that POP may induce butyrate production and thus alter Pparg, Ppara, and adipogenesis-related gene expression. As a result, obesity-related symptoms are suppressed.

4. Materials and Methods

4.1. Animals

Forty male Sprague–Dawley (SD) rats (average weight, 280 ± 20 g, 8 weeks old) with certificate no. HNASLKJ2016-0002 were purchased from Hunan Sileike Jingda Co. (Changsha, China). Subjects were housed in plastic-bottomed cages with reticulate stainless-steel covers under controlled light conditions (12-h light-dark cycle). The study was approved by the Animal Care Committee on the Ethics of Animal Experiments of Institute of Subtropical Agriculture (15 May 2017, Changsha, China). All animal protocols were in accordance with the Regulation on Management of Experimental Animals (Hunan Province, No. 259, 2012).

4.2. Animal Diets

Subjects had ad libitum access to food and water. The control chow diet (total energy content: 3.6 kcal/g) consisted of 72.3%, 19.7%, and 10% of kilocalories from carbohydrates, protein, and fat, respectively. The HFD (total energy content: 4.6 kcal/g) contained 46.4%, 19.6%, and 34.0% of kilocalories from carbohydrates, proteins, and fat, respectively. Chow was ordered from Botai Hongda Biotechnology Co. (Beijing, China).

4.3. Reagents

POP of 98% purity was purchased from Shaanxi Ciyuan Biotechnology Co. (Xi’an, China). SIM from Mo Sha Dong (Hangzhou, China) was used as the reference hypolipidemic drug. High-purity SCFA standards—including acetic acid, propionic acid, isobutyric acid, butyric acid, isovaleric acid, and valeric acid—were purchased from Sigma Aldrich Chemical, Co. (St. Louis, MO, USA). All other chemicals used were of analytical grade.

4.4. Experimental Design

After one week of acclimatization, rats were randomly assigned to two groups fed different chows: (1) NC (n = 10) and (2) HFD (n = 30). The HFD rats were considered obese after eight weeks (HFD rats exhibited 20% higher body weight than NC rats [58]) and were randomly divided into three groups of 10 rats each: (1) HFD-only; (2) HFD + SIM, fed an HFD and intragastrically administered 1.8 mg/kg body weight SIM once daily [59]; and (3) HFD + POP, fed an HFD and intragastrically administered 400 mg/kg body weight POP once daily [60]. From week 9, the NC and HFD groups were given 400 mg/kg sterile saline intragastrically.

During the experiment, rats were weighed once weekly. The experiment ended on week 14, when fecal samples of all rats were collected, immediately transferred to pre-labeled microcentrifuge tubes, and frozen in liquid nitrogen until further processing. Subsequently, after 12 h of food deprivation, all animals were anesthetized with pentobarbital sodium (35 mg/kg) and euthanized through cervical dislocation. Perirenal fat and epididymal fat were excised, weighed, and immediately frozen in liquid nitrogen. Figure 8 provides a schematic of the treatment schedule.

Figure 8

A schematic diagram of the experimental treatment schedule. All rats except normal control (NC) rats were raised on an HFD for 8 weeks. Obese rats (body weight > 20% higher than in NC rats) were then divided into three groups: HFD (high-fat diet), HFD + SIM (high-fat diet with simvastatin), and HFD + POP (high-fat diet with Polygonatum odoratum polysaccharides). All rats were gavage-dosed once per day for six consecutive weeks (weeks 8–14).

4.5. Biochemical Analysis

TG and TC contents in liver were measured with a BS-200 full automatic biochemical analyzer (Shenzhen Mindray Co., Shenzhen, China) using corresponding reagent kits and following published protocols [61].

4.6. SCFA Analysis in Feces

Fecal SCFAs were measured using gas chromatography, following a previously described method [61], with some modification. Fresh fecal samples (0.5 g) were diluted in 1 mL of 10% perchloric acid. After shaking for 30 min, the mixture was centrifuged multiple times at 12,000× g and 4 °C for 15 min, until the supernatant was clear. After filtering through syringe filters (45 µm diameter), the supernatant was injected into an Agilent 6890 N gas chromatography equipped with a flame ionization detector (FID), high-resolution gas chromatography column (DB-FFAP, 30 m × 0.25 mm × 0.25 μm), and N10149 automatic liquid sampler (Agilent, Santa Clara, CA, USA). The amount of acetic acid, propionic aid, isobutyric acid, butyric acid, isovaleric acid, and valeric acid in each sample was quantified using external calibration curves.

4.7. RNA Extraction and Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR)

Total RNA from adipose tissues was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, USA). An ND-1000 UV-vis spectrophotometer (NanoDrop Ltd., Waltham, MA, USA) was then used to evaluate RNA concentration and integrity. First-strand cDNA synthesis from RNA was performed using a PrimeScriptTM RT reagent kit (TaKaRa, Kyoto, Japan). Next, qRT-PCR was performed in triplicate using SYBR® Premix EX Taq (TaKaRa), following the manufacturer’s protocol. Relative quantification was calculated using the 2−∆∆Ct method. Primer sequences (Table A3) targeted the following rat genes: Ppara, Pparg, Fas, Srebf1, Atgl, Fabp4, Cebpa, and Cebpb.

Table A3

Primer sequences for amplification of target genes.

Target Gene 1	Primer Sequence (5′→3′)	Product Length, bp	Accession No. 2
Ppara	Forward (F): TGAAAGATTCGGAAACTGC	111	NM_013196
	Reverse (R): TGAAAGATTCGGAAACTGC
Pparg	F: TCTCACAATGCCATCAGGTTTG	86	NM_013124
	R: AGCTGGTCGATATCACTGGAG
Fas	F: ACCTCAGCAGCACATCTCAC	99	NM_017332
	R: CGTCCCTGTACACGTTCATC
Srebf1	F: CGCTACCGTTCCTCTATCAATGAC	140	NM_001276707
	R: AGTTTCTGGTTGCTGTGCTGTAAG
Atgl	F: ACCTGTG CCTTACCGTTCAC	144	NM_012859
	R: GGCAAGAGTGACAT GCAGAA
Fabp4	F: ATGAAAGAAGTGGGAGTTGGC	139	NM_053365
	R: GTTTGAAGGAAATCTCGGTGTT
Cebpa	F: CGTGGAGACGCAGCAGAAGGTGTT	127	NM_012524
	R: CAGAATCTCCTAGTCCTGGCTTGCA
Cebpb	F: GGGTTTCGGGACTTGATGCA	270	NM_024125
	R: ATGCTCGAAACGGAAAAGGT
β-actin	F: AAGGCCAACCGTGAAAAGAT	109	NM_031144
	R: TGGTACGACCAGAGGCATAC

1 Ppara = peroxisome proliferator-activated receptor α; Pparg = peroxisome proliferator-activated receptor γ; Fas = fatty acid synthase; Srebf1 = sterol regulatory element-binding transcription factor 1; Atgl = adipose triglyceride lipase; Fabp4 = fatty acid binding protein 4; Cebpa = CCAAT/enhancer-binding protein alpha; Cebpb = CCAAT/enhancer-binding protein beta. 2 The reference sequence number is given for primers whose source is the National Center for Biotechnology Information (NCBI) GenBank database (http://www.ncbi.nlm.nih.gov/genbank/).

4.8. Western Blotting

Adipose tissues (100 mg) were homogenized in 1 mL RIPA buffer containing the protease inhibitor cocktail Complete EDTA free (Roche, Penzberg, Germany) and phosphatase inhibitor Cocktail PhosSTOP (Roche). Protein concentration was determined with an enhanced BCA protein assay kit (P0010, Beyotime Biotechnology, Shanghai, China). Samples were denatured at 100 °C for 5 min and stored at −80 °C until needed for western blotting. To detect target proteins, the following primary antibodies were used: PPARγ (sc-271392, Santa Cruz Biotechnology, Santa Cruz, CA, USA, diluted 1:200), FABP4 (sc-374588, Santa Cruz Biotechnology, diluted 1:200), and β-actin (60008-1-Ig, Proteintech, Chicago, IL, USA, diluted 1:10,000). PPARγ and FABP4 protein levels in experimental groups are presented as fold changes relative to the average NC value.

4.9. Histology Analysis

Epididymal fat tissues isolated from rats in the four experimental groups were washed with saline, fixed in 4% paraformaldehyde, embedded in paraffin (SVA, Uppsala, Sweden), and sectioned (5 μm). Sections were then stained with hematoxylin and eosin for observation under an optical microscope. ImageJ (National Institutes of Health, Bethesda, MD, USA) was used to estimate adipose cell size.

4.10. Gut Microbiota Analysis

Total DNA was isolated from fecal samples using a DNA Stool Mini Kit (Qiagen, Hilden, Germany) following the manufacturer’s protocol. The 515F-806R primer set was used to amplify the 16S rRNA gene containing the V4 variable region. The reaction mixture (50 µL) contained 25 µL NEB Phusion High-Fidelity PCR Master Mix (New England Biolabs, Ipswich, MA, USA), 0.5 µM of each primer, and 30 ng of template. Thermocycling procedures were as follows: pre-denaturation at 98 °C for 3 min; followed by 30 cycles of 98 °C for 45 s, 55 °C for 45 s, and 72 °C for 45 s; and a final extension for 7 min at 72 °C. Amplicons were purified with AmpureXPbeads (Agencourt, Beckman Coulter, Brea, CA, USA). The final library was quantified using the Agilent 2100 Bioanalyzer (Agilent, Palo Alto, CA, USA), then sequenced on an Illumina HiSeq 2500 to generate 250-bp paired-end reads. The raw reads were quality-filtered and merged using the following criteria [62]: (1) Raw reads were truncated at any site that gave rise to an average quality score <20. (2) Reads contaminated by adapter, ambiguous bases (N base) and more than 10bp bases of homopolymer were removed by BGI’s internal process. The remaining reads are overlapped to tags by Fast Length Adjustment of Short reads (FLASH, v1.2.11) (Baltimore, MD, USA), which were clustered into OTUs based on Ribosomal Database Project (RDP) [63] by UPARSE (Tiburon, CA, USA) with a 97% similarity cutoff [64]. Alpha diversity (Shannon’s and Simpson’s indexes), richness (Ace, observed species, and Chao1), and rarefaction curve analyses were performed by Mothur (v1.31.2) (Ann Arbor, MI, USA). UniFrac-based PCoA was performed by QIIME (v1.8.0) (Boulder, CO, USA).

4.11. Statistical Analysis

Unless otherwise stated, all data are presented as means ± SEM. The statistical significance of differences between groups was evaluated by one-way analysis of variance (ANOVA) followed by Tukey’s test using SPSS 21 software (IBM, Armonk, NY, USA). The statistical significance of the separation among treatment groups in the PCoA score plots was assessed using multivariate analysis of variance (MANOVA) according to UniFrac matrix differences. Differences in the relative abundances of OTUs were assessed using the Mann–Whitney test by the R package. (v3.1.1) (University of Auckland, Auckland, New Zealand). Spearman’s correlation analysis was used to determine the relationship between OTUs and obesity parameters. Furthermore, adjusted p-values obtained based on the false discovery rate (FDR) were used to evaluate differences in differential abundance. A value of p < 0.05 was considered to be significant in this work.

4.12. Accession Number

The sequences have been deposited in the NCBI Sequence Read Archive Database under accession number SUB4662722.

5. Conclusions

In conclusion, POP supplementation attenuated body weight gain, epididymal and perirenal fat accumulation, and liver TG and TC levels in HFD-fed obese rats. These effects were associated with the regulation of lipid metabolism-related gene expression in adipocytes. Furthermore, structural alterations of the gut microbiota induced by HFD treatment were modulated by POP and may be associated with the POP’s anti-obesity activity. The present study further identified 28 key OTUs that were negatively or positively associated with obesity parameters and that may play a key role in preventing the occurrence and development of obesity. Taken together, our results demonstrate that the anti-obesity effects of POP may be related to modulation of the gut microbiota.