A total of 48 premenopausal women aged 17–45 years were enrolled from individuals who visited the department of endocrinology or gynecology at Shanghai General Hospital (Shanghai, China) between January 2014 and July 2014. Diagnosis criteria for PCOS established by Chinese Medical Association was based on Rotterdam criteria and the currently available evidences in Han Chinese. In our study, to avoid the confusion due to the different diagnostic criteria, each participant had met the three features based on Rotterdam criteria and the Chinese criteria as follows: oligo- or anovulation, clinical and/or biochemical signs of hyperandrogenism, and polycystic ovaries (Rotterdam Eshre/Asrm-Sponsored Pcos Consensus Workshop Group, 2004). Women in control group had no history of diagnosed PCOS and did not meet any of the Rotterdam Criteria. Individuals with one of the followings were excluded from the study: pregnancy, androgen-secreting tumors, adrenal disorders, thyroid dysfunction, Cushing’s syndrome, smoking, hypertension, and lipid-dysregulation. Included participants had also no administration of hormonal medication, insulin sensitizer or antibiotics within the preceding 3 months. After submitting written informed consent, all participants with PCOS (n = 33) or without PCOS (n = 15) were then stratified according to BMI into the following four subgroups: obese PCOS patients (PO group, n = 21), non-obese PCOS patients (PN group, n = 12), obese controls (CO group, n = 6) and non-obese controls (CN group, n = 9). Obesity was defined as BMI ≥ 25 kg/m² and the non-obesity otherwise according to the World Health Organization (World Health Organization, 2000). The study protocol was approved by the Human Research Ethics Committee of Shanghai General Hospital (No. 2014KY091) ahead of procedure of enrollment. The trial was registered in Chinese Clinical Trial Registry with the registration number ChiCTR-TRC-14005075.

All subjects were examined in the morning after overnight fasting (≥8 h). 4-(2-aminoethyl)-benzenesulfonyl fluoride was immediately added to the blood samples for enzyme-linked immunosorbent assay (ELISA) of ghrelin at a final concentration of 1 mg/mL. After clotting at room temperature for 30 min, the blood samples were centrifuged at 3,000 ×g for 15 min at 4°C, and were transferred into separate tubes and acidified by adding 5 N HCl to a final concentration of 0.05 N. The processed serum samples were stored at -20°C until analysis. For PYY, blood samples were immediately added DPP-IV inhibitor and aprotinin to a final concentration 500 KIU/mL. Fecal samples were collected on the same day. Feces was divided into aliquots and was frozen on dry ice immediately upon collection and stored at -80°C until analysis.

Waist circumference was measured midway between the lower costal margin and the iliac crest, and hip circumference at the level of maximum extension of the buttocks. WHR was calculated as waist circumference divided by hip circumference. BMI was calculated as body weight in kilograms divided by body height squared in meters.

Biochemical indexes were measured on automatic biochemical analyzer (Beckman AU5800 Clinical Chemistry System, Beckman Coulter, Inc., South Kraemer Boulevard, Brea, CA, USA). HbA1c level was measured by high-performance liquid chromatography (Bio-Rad Variant II Turbo, Bio-Rad Laboratories, Co., Ltd, Germany). FINS, FSH, LH, and testosterone were tested using an automated immunoassay system (TOSOHAIA-1800ST, TOSOH Corporation, Tokyo, Japan). The HOMA-IR and beta cell function (HOMA-beta) were calculated using the following formula:

Plasma serotonin, ghrelin, and PYY levels were determined with the commercially available ELISA kit according to manufacturer’s instructions. The kit for serotonin was purchased from EUROIMMUNUS, Inc. (EA602/96, Mountain Lakes, NJ, USA), and kits for ghrelin and PYY were bought from EMD Millipore Corporation (EZGRT-89K, EZHPYYT66K, Saint Charles, MO, USA, respectively). The intra-assay and inter-assay coefficients of variation were <5 and <10, respectively.

The investigation of mental psychological health was conducted with SDS and SAS.

DNA extraction from each frozen fecal sample was conducted as previously described (Godon et al., 1997). All samples were sequenced on the Illumina Miseq platform (Illumina, Inc., USA) with Miseq reagent kit v3 (600-cycle) (MS-102-3033, Illumina, USA). Sequencing library of the V3–V4 regions of the 16S rRNA gene was constructed based on the manufacturer’s instruction (Part # 15044223 Rev. B, Illumina, USA), with some modifications as previously published (Zhang et al., 2016).

The clinical data of all the 48 participants was summarized in Table 1. After adjusted P value for age, the differences between groups reflected in the following aspects: anthropometric parameters, sex hormones, metabolic parameters, mediators of the brain–gut axis and psychological scales. Compared to women without PCOS, women with PCOS showed significantly higher levels of testosterone and hirsutism score. PO group also had increased levels of metabolic parameters and SDS value than PN group, indicating that excess weight aggravated the metabolic abnormalities in women with PCOS. The level of serotonin was significantly higher in CN group than the other three groups. The level of ghrelin was lower in women with PCOS compared with women without PCOS, and it was also lower in obese women with PCOS compared with non-obese women with PCOS. PYY showed a significant decrease in PO group compared with CN and CO groups, while PN group was also decreased compared to CN and CO and it was higher than the PO group. Both PCOS patients and obese individuals possessed abnormal levels of the mediators of the brain–gut axis.

In order to evaluate the association between the mediators of the brain–gut axis and the various metabolic characteristics of PCOS, we calculated the partial correlation between serotonin, PYY, ghrelin and the clinical parameters of all subjects after adjusting for age and BMI (Table 2). Plasma serotonin level was significantly negatively correlated with waist circumference, hirsutism score, testosterone and SAS value, whereas, significantly positively correlated with neutrocyte count, HOMA-beta and TCH. Plasma PYY level was significantly negatively correlated with waist circumference, WHR, hirsutism score, testosterone, PPG, ALT and AST, whereas, significantly positively correlated with neutrocyte count and HOMA-beta. Plasma ghrelin level was significantly negatively correlated with waist circumference, WHR, hirsutism score, testosterone, PPG, P2hINS, ALT, and AST. To explore whether plasma brain–gut peptide/indole levels were independently associated with the clinical parameters, multiple stepwise regression analysis involving all the parameters with significant correlations with the mediators of the brain–gut axis was performed (with sle = 0.15, and sls = 0.15). Adjusting for age and BMI, it revealed that the testosterone level (β, -2.5296; P < 0.001), neutrocyte count (β, 0.38753; P < 0.01) and waist circumference (β, -0.29091; P < 0.05) were independently associated with plasma serotonin levels; the waist circumference (β, -0.27497; P < 0.05), the PCOS group (β, -9.447; P < 0.05) and neutrocyte count (β, 0.41872; P < 0.01) were independently associated with plasma PYY levels; while the testosterone level (β, -1.3728; P < 0.05), the PCOS group (β, -12.6121; P < 0.001), BMI (β, -0.889; P < 0.01), and P2hINS (β, -0.3382; P < 0.01) were independently associated with plasma ghrelin levels in all participants. Thus, the levels of the brain–gut peptides and the brain–gut indole were correlated with the PCOS related phenotypes.

We performed gene sequencing on the V3–V4 regions of the 16S rRNA to evaluate the dysbiosis of gut microbiota in PCOS. In total, 852,886 usable raw reads (264,105 unique sequences) were obtained from all 48 samples. 770,892 high-quality reads (14,480 ± 4,325 reads per sample) were clustered into 567 OTUs at 97% similarity level. According to observed OTUs and the Chao1 index, CN group owned the highest richness, followed by CO and PN group, and PO group showed the lowest richness of gut microbiota (Figures 1A,B), while no difference was found for the Shannon index and the Simpson’s diversity index. CAP and MANOVA based on the Bray–Curtis distance revealed a separation between CN group and the other three groups (Figures 1C,D). Notably, the gut microbiota composition of CO group was more similar with that of PN and PO groups, rather than CN group. CAP and MANOVA based on the UniFrac distance also showed similar trends (Supplementary Figure S1). Thus, the overall composition of the gut microbiota was disrupted in PCOS.

We then used RDA to identify the gut bacteria responding to PCOS and obesity. RDA confirmed that both PCOS and obesity were significantly correlated to the dysbiosis of gut microbiota (Monte Carlo Permutation Procedure, P = 0.0002 and 0.0490, respectively). We identified 28 key OTUs that responded to the differentiation between the four groups according to RDA (Figure 2). OTU4 belonging to genus Bacteroides was enriched in PO group than CN and CO groups (P < 0.01 and P < 0.05, respectively), with PN group falling in between them. The average relative abundances of 21 OTUs were higher in CN group than PO group (P < 0.05), which mostly belonged to the genera Akkermansia, Bacteroides, Clostridium IV, Lactobacillus, Oscillibacter, or unclassified genera from family Ruminococcaceae, while in CO and PN groups, the abundances of these OTUs were lower than CN group but were higher than PO group. Five OTUs were enriched in CO group, which belonged to the genera Collinsella, Paraprevotella, Slackia, and unclassified genera from class Clostridia and kingdom Bacteria. OTU7 belonging to Bacteroides showed decreased abundance in CO group, compared with the other three groups (Figure 3).

As the bacteria act as interdependent functional groups (guilds) in the gut ecosystem (Zhang C. et al., 2015), we constructed a co-abundance network at OTU level. There were 225 OTUs shared by at least 20% of the samples were clustered into 23 CAGs based on SparCC correlation coefficients (Figure 4A; Supplementary Table S1). The results showed bacteria species in the same genus could have different response to the environment perturbations. For example, OTU5, 86, 506, 393, 491, and 336 belonging to genus Blautia were divided into CAG1 (both OTU5 and 506), 3, 15, 22, and 23, respectively. According to the Kruskal–Wallis test, 14 CAGs showed no significant difference between the four groups. CAG1, 4, and 7 were enriched in PO group rather than CN group. CAG 1 contained 14 OTUs, which belonged to genera Bacteroides, Escherichia/Shigella, Streptococcus, Blautia, and Parabacteroides. CAG4 contained 5 OTUs, which mainly belonged to Clostridium XlVa, Alistipes and Weissella. CAG7 contained OTUs belonging to Granulicatella, Streptococcus, Peptostreptococcus, and Rothia. While, CAG10, 11, 12, and 13 decreased in PO group rather than CN group. The dominant OTUs in CAG10-13 mostly belonged to Akkermansia, Alistipes, Coprococcus, and Ruminococcus. In CO and PN groups, the relative abundances of all these above CAGs fell in between those in PO and CN groups. In addition, CAG17 and 18 showed increased trends in CO group compared with the other groups (Figure 4B).

To explore the relationship between gut microbiota and host phenotype, Spearman correlation between 27 clinical parameters and 9 CAGs that showed significant difference among the four groups (Figure 5). The results showed significant correlation between gut microbiota and PCOS related symptoms, including obesity, inflammation, hyperinsulinmia, hyperandrogenism, abnormal ghrelin level, and psychological state. CAG1, 4 and 7, which were enriched in PO group, had a positive correlation with the disease phenotypes; while CAG10–13 and 18, which were enriched in CN group, had an opposite trend.