Supplementation of quinoa reduced the body weight and ovary weight

After 4 weeks of quinoa intervention, there was no significant change in body weight of PCOS rats. (Fig. 1A). Following 8 weeks of quinoa supplementation, the body weight of PCOS rats was markedly decreased compared with the PCOS group (P < 0.05) (Fig. 1B). Additionally, the intervention with quinoa reduced the ovary weight in PCOS-like rats (P < 0.05) (Fig. 1C).

Fig. 1

Quinoa improved the symptoms of PCOS rats. (A) Bodyweight at 4 weeks. (B) Bodyweight at 8 weeks. (C) Ovary weight. (D) Testosterone (T). (E) Luteinizing hormone (LH). (F) Serum follicle-stimulating hormone (FSH). (G) LH/FSH. (H) Estradiol(E2). (I) Fasting serum insulin (FINS). (J) HOMA-IR. (K) HE staining of ovary tissue sections. (L) HE staining of pancreas tissue. (M-O) Different estrous cycles in each group. The estrous cycle consisted of diestrus, proestrus, estrus, and metestrus. The estrous cycle consisted of diestrus (D), proestrus (M), estrus (E), and metestrus (P). Data are expressed as mean ± SD (n = 5). *P < 0.05.**P < 0.01. ***P < 0.001. ****P < 0.0001

Effect of quinoa on the hormone levels in the PCOS-like rats

Serum T, LH concentrations and LH/FSH ratio were obviously higher in the PCOS-like rats than in the control rats (P < 0.001, P < 0.01, P < 0.05). However, quinoa supplementation markedly decreased the serum T, LH concentration and LH/FSH ratio compared with the PCOS group (P < 0.001, P < 0.01, P < 0.05) (Fig. 1D, E and G). Additionally, the serum E2 levels decreased in the PCOS group compared with the control group, while the quinoa supplementation markedly increased the serum levels of E2 in PCOS rats (P < 0.05, Fig. 1H). FINS and HOMA-IR were significantly increased in the PCOS group compared to the control group during the experiment (P < 0.0001, P < 0.01), while the FINS and HOMA-IR levels decreased in quinoa group than PCOS group (P < 0.001, P < 0.01) (Fig. 1I-J). The estrous cycle of rats in the normal group is regular, and the diestrus of PCOS rats was prolonged; however, quinoa reversed the estrus cycle of rats to normal (Fig. 1M-O).

Effects of quinoa on ovary and pancreas morphological structure

As shown in Fig. 1K and L, the morphology of ovary and pancreas in PCOS-like rats were altered. HE staining show ovaries of the PCOS model group were disordered, with no oocytes and obvious cystic dilatation in the follicles. Furthermore, there were only two to three granular cell layers, ovarian interstitial hyperplasia, and thickening in the ovarian cortex. After treatment with quinoa, ovarian structure was partially recovered, and we found increased layers of granulosa cells and restored oocytes and corona radiata in the dominant follicles (Fig. 1K). The HE-stained sections of the pancreas showed that islet morphology appeared normal in control group. This difference is reflected in the looser and more variable organization of cells within islets, and the numbers of islets were reduced, cells exhibiting abnormal with enlarged, irregular nuclei in PCOS group. Interestingly, quinoa treated animals had an obviously greater number and morphology of total islets than the PCOS-like rats, which showed uniform size, regular shape, and clear boundaries of the pancreas (Fig. 1L).

Network pharmacology prediction

A total of 117 chemical constituents of quinoa were retrieved through the initial literature search, 72 potentially active ingredients were obtained after intestinal absorption and drug-like screening, and we have marked with different colors (supplemental Table 1).

In total, 524 targets related with quinoa were screened through SwissTargetPrediction database, but the total of 14 components of quinoa-saponin-10, quinoa-saponin-6, quinoa-saponin-7, quinoasaponin-8, Tannins, saponin, omega-6, omega-3, (epi)-gallocatechin, Catechuic acid, Zeatin, Vitamin B6, serine, Epicatechin did not predict the relating targets. At the same time, the 4688 relation targets of PCOS were screened from the Genecards database (www.genecards.org). As shown in Fig. 2A, the 261 intersecting targets were obtained by introducing the target network between quinoa and PCOS into the CytSpace software.

Fig. 2

Network pharmacology analysis of quinoa on PCOS. (A) Venn analysis diagram of quinoa with PCOS. (B) KEGG pathway enrichment analysis. (C) GO pathway enrichment analysis. (D) Component-target-pathway diagram of quinoa in the treatment of polycystic ovary syndrome. (Q stands for quinoa)

To further find the possible mechanism of quinoa on PCOS, 170 pathways were acquired by KEGG enrichment analyses. As shown in Fig. 2B, the top 20 pathways were shown by bubble diagram, such as Pathways in cancer, PI3K-Akt signaling pathway, Chemical carcinogenesis - receptor activation, Lipid and atherosclerosis, Chemical carcinogenesis - reactive oxygen species, Neuroactive ligand-receptor interaction, Ras signaling pathway, MAPK signaling pathway, Proteoglycans in cancer, Rap1 signaling pathway, cAMP signaling pathway, EGFR tyrosine kinase inhibitor resistance, Endocrine resistance, and et al. It is noteworthy that PI3K/AKT signal pathway is the most obviously enriched except in tumor signal pathway.

Analogously, we used the DAVID 6.8 database did the GO enrichment analysis for 261 common targets. As Fig. 2C shown the top 30 key targets between quinoa and PCOS, which mainly involved in the plasma membrane, cytosol, cytoplasm, nucleus, signal transduction, protein binding, ATP binding, protein phosphorylation, enzyme binding, positive regulation of cell proliferation, and et al.

Using Cytoscape 3.9.1 to construct the quinoa component-target-pathway network diagram, and through its built-in tools to analyze the topologic parameters of quinoa intervention in polycystic ovary syndrome target network, get the core components. As shown in Fig. 2D, this network is composed of 181 nodes and 974 edges. The red nodes represent potential targets, the blue nodes represent quinoa active components, the green nodes represent potential signal pathways, and the connecting lines represent the interaction between the three. The larger the node area and the darker the color in the figure, the greater the impact on polycystic ovary syndrome. In cytoscape network analysis, the degree value of Q5 (Flavonol) is 32, the betweenness centrality is 0.0315, and the closeness centrality is 0.419. It is predicted that Flavonol is the main component of quinoa in the intervention of polycystic ovary syndrome, followed by Q10 (apigenin-7-methylether) with the degree value of 31, the betweenness centrality of 0.0389, and the closeness centrality is 0.417, and Q11 (Acacetin) with the degree value of 30, the betweenness centrality of 0.027, and the closeness centrality is 0.415.

Molecular docking

Seven core potential compounds were docking with eight core targets EGFR (PDBID:3IKA), MET (PDBID:4R1V), MAPK3(PDBID:), AKT1(PDBID:1UNQ), IGF1R (PDBID:1IGR), PIK3R1 (PDBID:2IUG), MAPK1 (PDBID:6SLG), GSK3B (PDBID:1GNG) on the PI3K-AKT signaling pathway, and finally 56 groups of receptor ligand docking results were obtained. Among the 56 groups of receptor ligand results, 30 groups with affinity < -5 kcal · mol− 1 and affinity < -7.9 kcal · mol− 1 accounted for more than 50%. The highest docking score is MET-Quercetin, with a score of -9.2 kcal · mol− 1, and the lowest docking score is AKT1-apigenin-7-methyl, with a score of -5.7 kcal · mol− 1, indicating that the screened core compounds may have good binding activity with the core targets on the PI3K-AKT signaling pathway. See Fig. 3 for molecular docking results.

Fig. 3

Molecular docking of some core compounds in quinoa. (A) MET interacts with Quercetin. (B) MAPK3 interacts with Flavonol. (C) IGF1R interacts with Quercetin. (D) Molecular docking results. Unit: Affinity (kcal · mol-1)

Quinoa may regulate PI3K/AKT/mTOR signaling pathway and autophagy in ovary of PCOS

In order to gain insights into the underlying molecular mechanism of PCOS, we conducted further validation in animal model according to results of network pharmacology. We detected the expression proteins associated with PI3K/AKT/mTOR pathway and autophagy in the ovary by immunohistochemistry and western blot. Results indicated that there was a significantly decrease of PI3K, AKT, mTOR, Bcl-2 and p62 in the PCOS-like rats than the control group. Following quinoa supplementation, importantly increasing trends were shown in the quinoa group compared with PCOS group (Fig. 4A, B, C, D, I, J, K, L, M, O and Q). Meanwhile, there was an obvious increase of Beclin 1, ULK1 and LC3B in the PCOS group than the control group, while there were importantly upregulating trends in the quinoa group versus the PCOS group (Fig. 4A, E, F, J, N, P and R). The results demonstrated that quinoa had the effect on PCOS, which may associate with PI3K/AKT/mTOR signaling pathway and autophagy.

Fig. 4

Quinoa may regulate PI3K pathway and autophagy in the ovary of PCOS rats. (A) IHC of mTOR, AKT, Bcl-2, Beclin 1, ULK1 and p62 in the ovary. (B-I) Quantification average optical density (AOD) values of mTOR, AKT, Bcl-2, Beclin 1, ULK1 and p62 in the ovary by Image-Pro Plus. (J) Representative Western blot of PI3K, AKT, mTOR, ULK1, p62, Beclin 1, Bcl-2 and LC3B in the ovary. (K-R) Quantitative assessment of the Western blot analysis results of PI3K, AKT, mTOR, ULK1, p62, Beclin 1, Bcl-2 and LC3B by Image J. Values are expressed as mean ± SD (n = 3). *P < 0.05.** P < 0.01. *** P < 0.001. **** P < 0.0001

Quinoa improved intestinal permeability and autophagy in colon of PCOS rats

To explore the influence of quinoa on duodenal and colonic pathological changes, we examined duodenum and colon tissue sections by HE staining. As shown in Fig. 5A, in the duodenum of PCOS rats, the submucosa was involved, and the villi became blunt. Following the quinoa supplementation, the duodenum was improved compared with the PCOS group. Analogously, the results of colon tissues (Fig. 5B) indicated that the structure of lamina propria, mucous layer and muscle layer of colon in PCOS group were seriously damaged, while that in quinoa group were alleviated, with only slight epithelial damage.

Fig. 5

Quinoa may improve intestinal permeability and regulate autophagy in the colon of PCOS rats. (A) HE staining of duodenum tissue. (B) HE staining of colon tissue. (C-L) IHC and quantification average optical density (AOD) values by Image-Pro Plus of Claudin 5, Occludin, Beclin 1, ULK1 and p62 in the colon Values are expressed as mean ± SD (n = 3). *P < 0.05.** P < 0.01. *** P < 0.001. **** P < 0. 0001

As shown in Fig. 5C-F, a significant reduction in the expression of tight junction protein such as Claudin 5 and Occludin, was observed in the colon of the PCOS group compared to the control group (P < 0.0001, P < 0.01). After supplementation with quinoa, Claudin 5 showed an increase compared with the expression in the PCOS group.

In addition, the results of IHC (Fig. 5G-L) showed that the expression of Beclin 1 and ULK1 increased, and the expression of p62 decreased in the PCOS group compared with control group (P < 0.0001), while Quinoa reversed these trends compared with PCOS group (P < 0.001, P < 0.001, P < 0.01).

Quinoa regulated the intestinal microflora of PCOS-like rats

We performed 16s rRNA sequence analysis to explore the role of intestinal microflora in PCOS and the therapeutic mechanism of quinoa. The α-diversity was used to examine the differences in intestinal microflora richness and diversity among the control group, PCOS group and quinoa group (Fig. 6A). Although Chao1 index, Shannon index and Simpson index indicated that the gut microbial diversity of PCOS-like rats was lower than control group, the quinoa intervention didn’t change the richness and diversity of the microbial community significantly. The β-diversity analysis was provided to assess the differences between microbial communities. As shown in Fig. 6B, principal coordinates analysis (PCoA) indicated that a more difference of intestinal flora composition between control group and PCOS group, similar composition between PCOS group and quinoa group.

Fig. 6

Quinoa regulated the gut microbiota composition in PCOS rats. (A) Analyses of alpha-diversity (Chao1, Simpson and Shannon indices) in each group. (B) Principal coordinates analysis (PCoA) analysis. (C) Gut microbiota composition at the phylum level among groups. (D) The ratio of Firmicutes/Bacteroidetes (F/B) in three groups. (E) Gut microbiota composition at the genus level among groups. (F) The relative abundances of Firmicutes, Bacteroides, Proteobacteria and Tenericutes. (G) The relative abundances of Lactobacillu, Bacteroides, Oscillospira, Blautia, Prevotella and Streptococcus in three groups. (H) LEfSe analysis. (I) Heat map among groups. The red color indicates that the abundance of the genus higher than other, while the blue color showed that lower than other

Our study also explores the specific changes in fecal microbiota, the related level of microbial community was evaluated at the phylum and genus levels. In control rats, the gut flora was mainly composed of Firmicutes (72.67%), Bacteroides (24.94%), Proteobacteria (1.58%), and Tenericutes (1.48%) at phylum levels. However, the abundance of Firmicutes was increased and Bacteroides and Tenericutes were decreased in the model group, following the supplementation of quinoa reversed the variation (Fig. 6C). Also worthy of mention was the ratio of Firmicutes to Bacteroidetes (F/B ratio), and they together accounted for approximately 96% of gut microbiota, which increased in the PCOS group than the control group. Interestingly, the ratio level was upregulated with quinoa supplementation (Fig. 6D). These results suggested that quinoa could regulated the dysbacteriosis in PCOS-like rats. At the genus level, the abundance of Lactobacillu was downregulated in the PCOS group than the control group, while Prevotella was increased. Compared to the PCOS group, Lactobacillu abundance increased in the quinoa group, whereas the Prevotella decreased (Fig. 6E). As shown in Fig. 6F-G, we further analyze the abundance on the phylum and the genus. Compared to the PCOS group, quinoa supplementations increased of Lactobacillu, Bacteroides and Coprococcus, while decreased the level of Blautia.

LEfSe (LDA Effect Size) analysis was used to evaluate fecal microbiota with a statistically significant difference at the Phylum, Class, Order, Family, and Genus levels. In total, 28 biomarkers were observed from Phylum level to Species level among the control group, PCOS group, and quinoa group, respectively 5, 4, 4, 9 and 6 (Fig. 6H). The purpose of the heat map is to further compare the differences in species composition and to explore the distribution trend of species abundance among groups (Fig. 6I). The results showed that the genus of Alistipes, Desulfovibrio, Coprococcus, Pseudoxanthomonas, Bosea, Dehalobacterium, Bacillus, Helicobacter, Pseudobutyrivibrio were higher in control group than PCOS group and quinoa group. The genus of p − 75 − a5, Anaerostipes, Mycoplasma, Clostridium, Adlercreutzia, Porphyromonas, Ethanoligenens, Caloramator, rc4 − 4, SMB53 increased in PCOS group compared to control group and quinoa group. Interestingly, Lactobacillus, Paraprevotella, Roseburia, Bacteroides, Parabacteroides, Rothia, Anaerofustis, Lachnospira, Butyricicoccus, Bifidobacterium, Streptococcus, Faecalibacterium higher in the quinoa group than PCOS group. Collectively, our results suggested that quinoa supplementation could effectively regulate the intestinal microflora structure in PCOS-like rats.

Correlations of the key gut microbiota with hormone levels of PCOS rats

In order to investigate the relationships between gut microbiota and PCOS-related hormon, we used Spearman correlation analysis and generated a heatmap to visualize these associations. We selected the top 20 most abundant bacteria based on their relative abundance at both the phylum and genus levels for further analysis. At the phylum level (Fig. 7A), Tenericutes displayed a strong negative correlation with T levels (P < 0.05). Elusimicrobia showed a negative association with LH (P < 0.05). Conversely, Fusobacteria exhibited positive correlations with LH and FINS. At the genus level (Fig. 7B), Prevotella had negative correlations with FSH (P < 0.05), but positive correlations with LH (P < 0.05) and LH/FSH (P < 0.01). Unclassified_Prevotellaceae exhibited positive associations with LH/FSH (P < 0.05), while negative correlations with FSH (P < 0.01). Parabacteroides and unidentified_Coriobacteriaeae showed negative correlations with LH/FSH (P < 0.05), while positively correlated with FSH (P < 0.01, P < 0.05). Porphyromonas was significantly positively correlated with T, LH and LH/FSH (P < 0.01), while negatively correlated with E2 and FSH (P < 0.01, P < 0.05). FBG was significantly negatively correlated with unclassified_ Rikenellaceae (P < 0.05). Unclassified_Coriobacteriaceae and T, Bifidobacterium and E2 exhibited positive correlations (P < 0.05).

Fig. 7

Spearman’s relationships between the most important gut bacteria and hormone levels of PCOS rats. (A) Heatmap of the relationship between the key gut microbiota and hormone levels at the phylum level. (B) Heatmap of the relationship between the key gut microbiota and hormone levels at the genus level (|r| > 0.5, FDR adjusted *P < 0.05, **P < 0.01)