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Fecal microbiota transplantation from patients with polycystic ovary syndrome induces metabolic disorders and ovarian dysfunction in germ-free mice

BMC Microbiology volume 24, Article number: 364 (2024) Cite this article

Abstract

Background

Dysbiosis of the microbiome is a key hallmark of polycystic ovary syndrome (PCOS). However, the interaction between the host and microbiome and its relevance to the pathogenesis of PCOS remain unclear.

Methods

To evaluate the role of the commensal gut microbiome in PCOS, we gavaged germ-free mice with the fecal microbiota from patients with PCOS or healthy individuals and evaluated the reproductive endocrine features of the recipient mice.

Results

Mice transplanted with fecal microbiota from PCOS patients and those transplanted from healthy controls presented different bacterial profiles and reproductive endocrine features. The fecal microbiota of the mice in the PCOS group was enriched in Phocaeicola, Mediterraneibacter, Oscillospiraceae, Lawsonibacter and Rikenellaceae. Fecal microbiota transplantation (FMT) from PCOS patients induced increased disruption of ovarian functions, lipo-metabolic disturbance, insulin resistance and an obese-like phenotype in recipient mice.

Conclusion

Our findings suggest that the microbiome may govern the set point of PCOS-bearing individuals and that gut ecosystem manipulation may be a useful marker and target for the management of PCOS.

Peer Review reports

Introduction

Polycystic ovary syndrome (PCOS) is one of the most common reproductive endocrine disorders, impacting approximately 5–20% of reproductive-aged women worldwide [1]. This condition is characterized by ovulatory dysfunction, androgen excess, and polycystic ovaries and is usually accompanied by metabolic disorders such as obesity, dyslipidemia, and insulin resistance [2]. Its comorbidities are complicated and include an increased risk for metabolic abnormalities, infertility, obstetrical complications, psychological impairments, and even endometrial cancer [3]. PCOS is considered a polygenic complex genetic trait [4] because familial aggregation of hyperandrogenemia has been observed in individuals with PCOS [5] and multiple susceptibility loci relevant to an increased risk of developing PCOS have been reported in genome-wide association studies [6, 7]. However, environmental factors also play important roles in the etiologies of PCOS [1, 4]. The gut microbiome is an important factor.

The gut microbiome is a complex symbiotic ecosystem that mediates the interaction of the host with the environment. Intestinal microbiome dysbiosis has been widely reported in a series of common chronic disorders and a range of metabolic disorders, including obesity, which suggests that the gut microbiota plays a vital role in human health [8, 9]. The dysbiosis of the gut microbiota (DOGMA) theory of PCOS suggests that gut microbiota dysbiosis activates the immune system, drives increased serum insulin levels, interferes with insulin receptor function, increases the level of androgens deprived from ovaries, and interferes with normal follicle development [10]. During the last decade the association between the gut microbiome and PCOS has been reported in some human and experimental studies [11,12,13,14,15], and a discomposed gut microbiome is considered a key hallmark of PCOS [16, 17]. Transplantation of Bacteroides vulgatus to antibiotic-pretreated mice leads to glucose intolerance, insulin resistance, and ovarian dysfunction [14]. Fecal microbiota transplantation (FMT) of dehydroepiandrosterone-induced PCOS rats to recipients triggers disturbances in gonadal steroid hormone imbalance and hepatic glucolipid metabolism [18].

FMT, the transfer of the gut microbiota from donors to donees for treatment purposes, has been used to evaluate the impact of the gut microbiome on the development of several diseases and has become a hotspot of biomedical research in recent years [19, 20]. Although simulating germ-free (GF) conditions with antibiotic-treated recipients is a common experimental practice, the incomplete elimination of microbes may result in confusing outcomes due to off-target effects of the antibiotic instead of the direct effects of the gut microbiota [21]. Observations of enteritis in investigations of gut microbiota-dependent questions may result in opposing results in antibiotic-treated recipients compared with GF recipients [22, 23]. GF animals, which typically have no microorganisms, effectively allow the transfer of the whole fecal microbiota or selective bacterial species and still seem to be the best and most irreplaceable experimental models for studying the interrelationships among bacterial strains and hosts [24]. The humanized gnotobiotic mouse model provides an innovative and powerful tool for exploring the gut physiology and pathology of the human microbial system.

To investigate the role of the human gut microbiome in PCOS in greater detail, we gavaged GF mice with stool from human patients with PCOS and healthy controls to explore the effects of the human gut microbial consortia on the development of PCOS. We characterized the microbiota and reproductive endocrine features of the recipient mice and evaluated the correlations between them. We demonstrated that the PCOS microbiota could induce lipid metabolic disorders and ovarian dysfunction in GF mice.

Materials and methods

Animal model

Progress of the animal experiment

To explore the relevance of the microbiome to PCOS, stools from PCOS patients (n = 20) or healthy controls (n = 20) were transferred to GF mice to test the effects on the mouse gut microbiomes in terms of PCOS features (Fig. 1). All participants were parts of our previous project recruited from the Peking Union Medical College Hospital [25]. PCOS diagnosis was based on the 2003 Rotterdam criteria [26] with at least two of the following symptoms: (1) oligo-ovulation and/or anovulation; (2) androgen excess, either biochemical or clinical; and (3) polycystic ovaries on ultrasound assessment. Those with thyroid disease, hyperprolactinemia, androgen-producing tumors, Cushing syndrome, and nonclassical congenital adrenal hyperplasia were excluded. None of the individuals with PCOS had been treated with PCOS-related therapy. The average age of the POCS patients was 28.33 ± 4.81 years. Age-matched healthy controls with normal ovarian morphology and regular menstrual cycles without signs of clinical and/or biochemical hyperandrogenism were recruited from the general community. Those who were pregnant or breastfeeding within a year; who received an antibiotic, traditional Chinese medicine, or oral contraceptive within the past 3 months; who had abnormal thyroid or adrenal function; or who were underweight were excluded.

Fig. 1
figure 1

Illustration of the study workflow. Flowchart of the data collection and methods implemented in this work

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Female GF C3H mice (8 weeks of age, n = 6) were obtained from Shanghai SLAC Laboratory Animal Co. Ltd. and bred in a gnotobiotic environment. The mice were housed under controlled humidity (40–70%), temperature (20 °C to 23 °C) and lighting (12-hour light/12-hour dark cycle) conditions and were allowed free access to both food and water. The GF mice were randomly divided into 2 groups (n = 6 per group). The mice in the “Trans-PCOS” group were given 100 µl of the fecal microbiota suspension from the PCOS patients, and the mice in the “Trans-control” group were given the same volume of the fecal microbiota suspension obtained from the healthy controls. The GF mice received oral FMT inoculations once a day during the first week. All the GF mice were subsequently bred regularly for another three weeks until sacrifice. In the fourth week, vaginal smears were taken daily at the same time for 5 days. The estrous cycle was determined on a glass slide for microscopic analysis to determine the dominant cell type following methylene blue staining. The proestrus was distinguished by round, nucleated epithelial cells; the estrus was distinguished by cornified squamous epithelial cells; the metestrus was distinguished by epithelial cells and leukocytes; and the diestrus was distinguished by nucleated epithelial cells and a dominance of leukocytes.

All animals were treated in a humane manner in accordance with the ARRIVE guidelines, and anesthetics and sedatives were used to minimize animal distress and discomfort during the experimental process.

Microbiota suspension preparation

Fresh stool samples from 20 PCOS patients and 20 healthy controls were collected according to the Human Microbiome Project (HMP) collection protocol [27]. To prepare the microbiota suspensions, the stools from each group were transferred to an anaerobic hood where they were pooled per group, homogenized 1:4 in saline with 20% sterile glycerol and then filtered through a 70-µm strainer to remove large particles before being aliquoted and frozen at − 80 °C until FMT. Before inoculation, the fecal samples were diluted and resuspended in sterile deionized water to a working concentration of 0.04 g/100 µl. All procedures were performed under anaerobic conditions and followed standard protocols as described in previous studies [28, 29], despite toxicity studies on the fecal samples before FMT were not performed.

Laboratory examination

At the end of the experiment, before blood collection, the mice were anesthetized via an intraperitoneal injection of 0.25% avertin to minimize animal distress and discomfort. Blood samples were collected from the orbital sinuses of the mice under anesthesia and then allowed to clot at room temperature for one and a half hours, followed by 15 min of centrifugation at 3,500 rpm to obtain the serum, which was stored at − 80 °C for hormone and biochemical tests. The levels of testosterone (ab116597; Abcam), insulin (90080; Crystal Chem), and TNF-α (ab1793; Abcam) were analyzed with enzyme-linked immunosorbent assay kits for mice.

The levels of fasting glucose, total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were determined with an autoanalyzer (Toshiba, TBA-2000FR).

Morphology and immunohistochemistry

At the end of the experiment, the mice were anesthetized via an intraperitoneal injection of 1% pentobarbital sodium solution before humane euthanasia to minimize distress to the mice. Adipose tissues (inguinal subcutaneous fat and parametrial fat) were collected and weighed. Ovaries and colon tissues were collected, fixed in 4% paraformaldehyde, dipped in 70% ethanol, dehydrated, and embedded in paraffin for further staining with hematoxylin and eosin. The ovaries were serially sectioned to a thickness of 5 μm and stained with hematoxylin and eosin. All the slides were placed under a light microscope (Axio Imager. D2; Zeiss) for histomorphological examination and the number of corpora lutea were counted.

Immunohistochemistry was performed on the colons with an occludin antibody (DF7504; Affinity Bioscience) for intestinal barrier assessment. The slides were incubated with the secondary antibody (PV-6001; Zhongshan) for 10 min after being washed with PBS. After 5 min of incubation with 3,3’-diaminobenzidine to change color, the slides were dehydrated and then sealed before observation. The average optical density was calculated with Image-Pro Plus 6.0 (Media Cybernetics, Inc., Rockville, MD, USA).

16 S amplicon sequencing and analysis

DNA extraction and sequencing

DNA was extracted using a TIANamp stool DNA kit (DP328, TIANGEN BIOTECH). The concentrations of DNA were then measured with a NanoDrop 1000 (Thermo Fisher Scientific). After assessment of DNA quality and quantification with a Qubit fluorometer (Thermo Fisher Scientific), the V3-V4 region of the 16S rRNA gene was amplified using primers 341F (5’- CCTACGGRRBGCASCAGKVRVGAAT-3’) and 806R (5’-GGACTACNVGGGTWTCTAATCC-3’). After the size and specificity of the PCR products were checked and the products were purified with agarose gel electrophoresis, the amplicons were sequenced using Illumina MiSeq (Illumina, San Diego, CA, USA).

16 S amplicon analysis

Paired-end reads of each sample were split from the raw data generated by the sequencer through the barcode sequence. These paired-end reads were processed using QIIME2 (version 2023.07), and a series of plugins were embedded in QIIME2 [30]. The Cutadapt plugin was used to trim the PCR primers from the paired-end reads [31]. Both the profiles of amplicon sequence variants (ASVs) and representative sequences were obtained via the DADA2 plugin, which performed the steps of quality filtering, denoising, tag merging, chimera removal and dereplication [32]. The taxonomic classification of ASVs was assigned via the greengenes2 plugin in QIIME2 and the GreenGenes2 database [33].

The within-sample (α) diversity indices and distance matrices of β diversity were calculated using the diversity plugin in QIIME2, and PCoA was performed and displayed on the basis of the calculated unweighted UniFrac distances with the stats package in R software (version 4.2.2). The Wilcoxon rank sum test was used to evaluate between-group differences in α diversity indices. Permutational multivariate analysis of variance (PERMANOVA) was applied with the pairwiseAdonis package (version 0.4.1) in R to compare microbial composition differences between the two groups. Linear discriminant analysis effect size (LEfSe) was performed to distinguish significantly different microorganisms between the two groups, with a cut-off of |LDA score| > 3. Correlations between genera and clinical indices were calculated with Spearman’s correlation coefficients with the stats package in R. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2, version 2.5.2) [34] was used to predict functional abundances on the basis of 16 S rRNA gene sequencing data. Differential abundance analysis of KEGG pathways was performed using the Maaslin2 method [35] in the R package ggpicrust2 (version 1.7.2) [36]. A P value < 0.05 was considered statistically significant.

Statistical analysis

GraphPad Prism (version 9.0) was used for statistical analysis. The Kolmogorov–Smirnov normality test was used to determine the sample distribution. For normally distributed data, the evaluation of statistical significance between two groups was conducted using two-tailed Student’s t tests. For nonparametric distributions, the evaluation of statistical significance between two groups was conducted using a two-tailed Mann–Whitney U test. The data are shown as mean ± s.e.m., and P < 0.05 was considered statistically significant.

Results

Fecal bacteria derived from PCOS patients induce gut microbiota dysbiosis in recipient mice

We collected fecal samples from the mice at two time points, Day 14 and Day 28 after the start of FMT, and 16 S rRNA sequencing was carried out to characterize the microbial community structure. As demonstrated in Fig. 2A, the transplanted bacteria successfully colonized the GF mice during week 2. Over time (from Day 14 to Day 28), we detected a decreased abundance of the genera Faecalibacterium and Parabacteroides in both groups of mice subjected to FMT from PCOS patients and healthy controls. Moreover, a decrease in the abundance of the genera Parabacteroides and Phocaeicola and an increase in the abundance of the genus Phascolarctobacterium were detected in mice transplanted with healthy controls. When comparing FMT from healthy controls and FMT from PCOS patients on Day 28, the relative abundances of Parabacteroides and Bacteroides were greater in FMT from healthy controls, whereas the genera Prevotella, Megamonas and Phocaeicola were present in higher relative abundances in FMT from PCOS patients. For alpha diversity, no significant differences among the four compartments in terms of Faith pd or observed ASVs were found (Fig. 2B). We observed a clear difference in microbial structure between mice transplanted from healthy controls and mice transplanted from PCOS patients. Specifically, PCoA revealed a significant separation between the mice transplanted from healthy controls and the mice transplanted from PCOS patients on both Days 14 and 28 (PERMANOVA P < 0.05). Samples from the mice transplanted from healthy controls at Days 14 and 28 showed no differences, whereas a distinct deviation was observed between the samples from the mice transplanted from PCOS patients at Days 14 and 28 (Fig. 2C). Moreover, LEfSe analysis was conducted to evaluate the differences in microbes between groups. As shown in Fig. 2D and E, mice transplanted from healthy controls and mice transplanted from women with PCOS presented different bacterial profiles, driven by various taxa. On Day 14, the family Ruminococcaceae, order Oscillospiraceae and genera Faecalibacterium and Akkermansia were the top-ranking features evaluated using the LDA score in the LEfSe analysis; these features were elevated in the mice that received FMT from the PCOS patients. However, the microbial communities changed significantly over time. On Day 28, mice subjected to FMT from PCOS patients exhibited enrichment of Phocaeicola, Mediterraneibacter, Oscillospiraceae, Lawsonibacter and Rikenellaceae.

Fig. 2
figure 2

FMT alleviates fecal microbiota dysbiosis in GF mice. (A) Average relative abundance of prevalent microbiota at the genus level in the four compartments studied. D28-P (fecal samples of mice transplanted from PCOS patients on Day 28); D28-H ( fecal samples of mice transplanted from healthy controls on Day 28); D14-P ( fecal samples of mice transplanted from PCOS patients on Day 14); D14-H ( fecal samples of mice transplanted from healthy controls on Day 14). (B) Comparison of the alpha diversity of the gut and saliva microbiotas using the Faith pd and observed features. (C) PCoA of beta diversity among samples of the four compartments analyzed (*P < 0.05; **P < 0.01). (D) Results of LEfSe analysis showing bacterial taxa whose abundance in the gut significantly differed between D14-P and D14-H (upper), D28-H and D28-P (lower). (E) Cladogram of LEfSe analysis showing the contribution of taxa to group separation, D14-P (left), and D28-P (right). n = 6 mice per group

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Fecal bacteria derived from PCOS patients initiate metabolic disorders in mice

Metabolic disorders were more likely to be present in patients with PCOS. Individuals with PCOS had significantly greater body mass indices (P = 0.005), body fat percentages (P < 0.001), serum testosterone levels (P < 0.001), fasting insulin levels (P = 0.006), HOMA-IR values (P = 0.017) and triglyceride levels (P = 0.031) than did healthy controls (Table 1). Compared with healthy control mice, mice transplanted with fecal bacteria from PCOS patients displayed increased food and water intake, faster weight gain and greater fat accumulation not only subcutaneously but also in the parauterine zone (Fig. 3A-E). The group treated with the fecal microbiota from PCOS patients exhibited insulin resistance as revealed by the fasting insulin level and HOMA-IR (Fig. 3F-G), a tendency to lipid metabolic disturbance (Fig. 3H-J).

Table 1 Clinical characteristics of participants
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Fig. 3
figure 3

Fecal bacteria derived from PCOS patients initiate the progression of PCOS in mice. (A) Weight change after FMT. (B) Food intake. (C) Water intake. (D) Subcutaneous fat/body weight. (E) Parametrial fat/body weight. (F) Fasting insulin level. (G) HOMA-IR value. (H) Total cholesterol level. (I) Triglyceride level. (J) High-density lipoprotein cholesterol level. n = 6 mice per group. P values were determined using two-tailed Student’s t tests. Data are presented as mean ± s.e.m. (*P < 0.05; **P < 0.01)

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Fecal bacteria derived from PCOS patients affect ovarian and intestinal barrier function in mice

Testosterone levels were elevated in recipient mice from patients with PCOS (Fig. 4A), with a disturbed estrous cycle (Fig. 4B-C). Ovaries from recipient mice from healthy controls presented normal numbers of corpora lutea and follicles at different stages. In contrast, ovaries from recipient mice transplanted from patients with PCOS presented fewer corpora lutea, but no obvious cyst-like follicles were observed (Fig. 4D-E). Notably, gut microbial dysbiosis is closely related to disruption of the gut barrier [37]. After oral gavage of microbiota from patients with PCOS, gut barrier damage to the colon tissue of the mice was observed (Fig. 4F-G). However, TNF-α levels in the serum were not significantly different between the groups (Fig. 4H). Thus, transplantation from PCOS donors into GF mice can transfer PCOS-relevant phenotypes, suggesting that the gut microbiome might be a potential factor in the development of PCOS.

Fig. 4
figure 4

Fecal bacteria derived from PCOS patients alter ovarian and intestinal barrier function in mice. (A) Testosterone levels. (B) Representative estrous cycles. (C) Quantitative analysis of estrous cycles. (D) Hematoxylin and eosin staining of representative ovaries. Scale bar: 200 μm. (E) Quantitative analysis of corpora lutea per ovary in mice. (F) Hematoxylin and eosin staining of representative colons. Scale bar: 200 μm. (G) Average optical density of Occludin in colon tissue. (H) TNF-a levels in the serum of the mice. P, proestrus; E, estrus; M/D, metestrus/diestrus. n = 6 mice per group. P values were determined using two-tailed Student’s t tests. Data are presented as mean ± s.e.m. (*P < 0.05; **P < 0.01)

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Correlation analysis of gut microbiota dysbiosis and PCOS features

We next explored whether specific bacteria were negatively or positively correlated with PCOS-related clinical factors such as TC, LDL-C, TG, and testosterone levels, the subcutaneous fat weight/body weight ratio and the HOMA-IR of colonized GF mice. Spearman correlation analysis demonstrated that 27 genera were correlated with these clinical factors (Supplementary Material 1 and 2). The testosterone level was positively related to the relative abundances of Catenibacillus and Massiliimalia but negatively correlated with Clostridium, Parabacteroides, Negativibacillus and Fusobacterium. The HOMA-IR was positively related to Scatocola, Clostridium, Catenibacillus and Mediterraneibacter but negatively related to Hungatella, Klebsiella and Clostridium. The TG level was positively related to Bilophila, Phocaeicola, Butyricimonas, Alistipes, Extibacter, Dysosmobacter and Scatocola. The TC level was positively associated with Parasutterella but negatively associated with UBA3402, Bacteroides and Desulfovibrio (Fig. 5). The associations of specific bacterial taxa were highly correlated with PCOS-related consequences, supporting the hypothesis that specific bacterial taxa may contribute to specific symptoms of PCOS.

Fig. 5
figure 5

Heatmap of Spearman’s correlations between key bacteria of mice on Day 28 (genus level) and clinical factors (+, P < 0.01; *, P < 0.05); TC, total cholesterol; LDL-C; low-density lipoprotein cholesterol; TG, triglycerides; SF/Wt, subcutaneous fat weight/body weight; T, testosterone; HOMAIR, insulin resistance index; n = 6 mice per group

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Conclusion

This study helps elucidate the role of gut microbiota dysbiosis in obesity, metabolic disorders, and ovarian dysfunction in PCOS patients. FMT of PCOS fecal samples from GF mice can result in significant consequences of PCOS, such as an obese phenotype, lipo-metabolic disorders, insulin resistance and disrupted ovarian functions. In brief, disturbed microbial communities interfere with host glycemia metabolism, adipocyte function, and insulin resistance, resulting in pancreatic and ovarian dysfunction in PCOS patients. These PCOS features can be transmitted by the PCOS microbiota, and these findings suggest that modulation of the gut microbiota could be highly important for the long-term management of PCOS (Fig. 6).

Fig. 6
figure 6

Schema summarizing key findings. A deteriorated gut microbial pattern in PCOS patients was associated with the PCOS phenotype. PCOS features can be transmitted by the PCOS microbiota. Animal experiments revealed that the gut microflora plays an important role in the regulation of glucose and lipid metabolism, ovarian function and gut barrier damage in individuals with PCOS. Created with Biorender.com

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Discussion

The high prevalence and increased risk of metabolic complications in reproductive-aged women make PCOS a serious health concern worldwide, and the management of PCOS remains challenging and unsatisfactory, probably due to the lack of clarity concerning its etiology. The gut microbiome has emerged as an important regulator of the physics and pathogenesis of PCOS. The gut microbiome has been reported to be correlated with the PCOS phenotype [14, 18, 38,39,40], although no consensus on PCOS-related microbial features has been reached. A study by Qi et al. provided strong evidence of an active role of the gut microbiome in PCOS pathogenesis [14]. However, evidence from Han et al. showed that androgens led to PCOS phenotypes independent of the gut microbiome [18]. Furthermore, the depletion of the microbiome by antibiotic treatment may contribute to alterations in metabolic homeostasis in mice [41], which implies that the microbiota-independent and direct effects of antibiotic treatment cannot be ignored.

One particular strength of our present study was that we used a GF model for microbial transplantation whose blank microbial background and immunological naivety prevent competition between the resident and newly transplanted microbes, offering an invaluable chance to explore the correlations as well as causal effects of the gut microbiome with the host. Through the manipulation of FMT to GF mice an obese-like phenotype, lipid metabolism disorder, insulin resistance and ovarian dysfunction were successfully induced. We report here that the colonization of mice with gut microbiota from patients with PCOS induces the pathophysiology of PCOS features.

Although GF animals have advantages in in vivo experiments on gut microbiota interactions [42], what is actually transferred is heterogenous [43, 44]. Investigations with gnotobiotic approaches have shown that the gut microbial community in recipient animals substantially differs from that in human donors [45,46,47].

The loss of microbial species may be attributed to cage and/or isolator effects, sample processing and host incompatibility (strong genetic effects), resulting, in mice, in some taxa failing to engraft while others thrive [43, 44, 48,49,50,51]. Despite the similar general anatomy of mice and humans, the structure of the gastrointestinal tract remains considerably distinct. The proportionally larger colon, taller intestinal villi and cecum surface area in mice allow a subsequently larger microbial flora than in humans [52,53,54]. Moreover, the effects of FMT are likely to occur via a pleiotropic mechanism, one feature of which is the alteration of microbe-associated metabolites [55]. These substances may already be present in the transplant or newly produced by colonization [44]. In addition, diet, as an important regulator of the gut microbiome, may alter the balance of gut microbial communities [56], and may exert a stronger influence on PCOS pathology than the gut microbiota [16].

Based on the present results, we can find only some microbial groups that are closely associated with PCOS. Although these data suggest that metabolic homeostasis and ovarian function may be regulated through gut dysbiosis, the molecular mechanism of specific bacterial species and their metabolites has not yet been confirmed. Studies have reported that both serum and gut metabolite disorders exist in the PCOS population [14, 15, 18, 38] and that some metabolites may alleviate the PCOS phenotype [14]. Thus, certain metabolites need to be further identified and validated. Several studies have reported that individuals with PCOS have gut microbiota communities different from those of healthy controls [13, 14, 57, 58]; however, we did not perform 16 S rRNA analysis on patient samples. Thus, the similarities and differences in microbiota profiles before and after transplantation could not be compared; however, we did find that FMT from PCOS patients induced increased disruption of ovarian functions, lipo-metabolic disturbance, insulin resistance and an obese-like phenotype in recipient mice. Metagenomic analysis has advantages in understanding the functional potential of microbial communities compared with 16 S rRNA analysis; however, we performed only 16 S rRNA analysis in this study, which is also a limitation of our study. Finally, as a key factor involved in the progression of diseases related to gut dysbiosis, immunohistochemistry, one of the most commonly used methods for evaluating gut barrier damage, was performed in this study, and still, future studies should explore this topic in greater depth.

Data availability

The datasets generated and/or analysed during the current study are available in the Genome Sequence Archive repository (GSA: CRA015332) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa.

Abbreviations

PCOS:

Polycystic ovary syndrome

FMT:

Fecal microbiota transplantation

GF:

Germ free

FIN:

Fasting insulin

HOMA-IR:

Insulin resistance index

TC:

Total cholesterol

TG:

Triglycerides

HDL-C:

High-density lipoprotein cholesterol

LDL-C:

Low-density lipoprotein cholesterol

ASVs:

Amplicon sequence variants

PCoA:

Principal coordinate analysis

AUC:

Area under the curve

LEfSe:

Linear discriminant analysis effect size

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Acknowledgements

We thank the Shanghai SLAC Laboratory Animal Co. Ltd. and Beijing ClouDNA Technology Co., Ltd for the help of the study. We also appreciate the Biomedical Engineering Facility of National Infrastructures for Translational Medicine, Institute of Clinical Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College for their valuable guidance and support in our experiment.

Funding

This work was supported by (1) the National Natural Science Foundation of China (81871141 and 82201781), (2) the National High-Level Hospital Clinical Research Funding (2022-PUMCH-B-123), (3) the National Key Research and Development Program (2018YFC1004801), and (4) the CAMS Innovation Fund for Medical Sciences (CIFMS) (2020-I2M-CT-B-040).

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Author notes

  1. Feiling Huang and Yuzhoujia Deng contributed equally to this work.

Authors and Affiliations

  1. Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Obstetric & Gynecologic Diseases, Beijing, 100730, China

    Feiling Huang, Ruiyi Tang & Rong Chen

  2. Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China

    Yuzhoujia Deng

  3. Beijing ClouDNA Technology Co., Ltd, Beijing, China

    Miao Zhou

  4. Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute, MOE Key Laboratory of Major Diseases in Children; Rare Disease Center, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China

    Peng Zhang

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  1. Feiling Huang
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Contributions

R.C. initiated the project and designed the research and R.T., and P.Z. supervised the studies and P.Z. designed the analysis and F.H., and Y.D. wrote and R.C, and P.Z. revised the manuscript and Y.D. conducted participants enrollment and part of the experiments and F.H., Y.D. and M.Z. analyzed the data. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Peng Zhang or Rong Chen.

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Ethics approval and consent to participate

All participants were recruited from the Peking Union Medical College Hospital and written informed consent was acquired from all patients and healthy controls. The animal experimental procedures were approved by the Welfare and Ethical Committee of Peking Union Medical College Hospital (XHDW-2022-152).

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Not applicable.

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The authors declare no competing interests.

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Huang, F., Deng, Y., Zhou, M. et al. Fecal microbiota transplantation from patients with polycystic ovary syndrome induces metabolic disorders and ovarian dysfunction in germ-free mice. BMC Microbiol 24, 364 (2024). https://doi.org/10.1186/s12866-024-03513-z

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BMC Microbiology

ISSN: 1471-2180

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