Efficacy of sclareol based on 16S rDNA sequencing in modulating gut microbiota composition in estradiol-treated mice.

Dysmenorrhea is a common gynecologic complaint characterized by painful cramps that occur during menstruation and can cause pelvic pain [ 1 ]. Primary dysmenorrhea is linked to normal ovulatory cycles without pelvic pathology [ 2 ]. On the other hand, secondary dysmenorrhea is more prevalent and is often caused by conditions such as endometriosis, which is characterized by symptoms such as dysmenorrhea, menorrhagia, and a uniformly enlarged uterus. Furthermore, dysmenorrhea is considered a predictor of endometriosis [ 1 ]. The pathogenesis of dysmenorrhea is thought to be associated with increased endometrial prostaglandin production and release induced by estrogen upregulation, which leads to inflammation and proliferation [ 3 ]. Estrobolome dysfunctions and gut microbiota dysbiosis are considered to influence the onset of gynecological diseases and infertility [ 4 ]. Through metagenomic analysis, Sasaki et al. reported that the gut microbiota is linked to ovarian processes, suggesting a mutual connection between estrogen levels and the gut microbial community [ 5 ]. Research has also demonstrated that modifications in the estrobolome and variations in the composition of the gut microbiota can control conditions and illnesses influenced by estrogen through the estrogen-gut microbiome axis [ 6 ]. Essential oil of Salvia sclarea , which belongs to the Lamiaceae family, contains a diterpene alcohol called sclareol [ 7 ]. Previous studies have demonstrated that S. sclarea L. was historically used to treat various conditions, such as arthritis, oral inflammation, gastrointestinal ailments, and menstrual pain (dysmenorrhea) [ 8 ]. Sclareol, primarily extracted from S. sclarea L., has been found to possess antioxidant, anti-inflammatory [ 9 ], antidiabetic [ 10 ], and antitumor properties [ 11 ]. A study also revealed that sclareol effectively suppressed the protein expression of cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS) triggered by carrageenan and inhibited nitric oxide (NO), tumor necrosis factor-alpha (TNF-α), and malondialdehyde (MDA) production. Notably, sclareol exhibited anti-inflammatory properties comparable to those of indomethacin, a nonsteroidal anti-inflammatory drug (NSAID) prescribed for pain management [ 12 ]. Primary dysmenorrhea (PD), a common gynecological disorder, was modeled using the protocol of Yang et al. [ 13 ] Nonpregnant female mice received intraperitoneal pretreatment with estradiol benzoate for three consecutive days. Subsequently, oxytocin injection induced a stretching or writhing response, which was recorded over 30 min. This pain model demonstrated increased levels of prostaglandin F 2α (PGF 2α ) and E 2 (PGE 2 ), a thicker uterine myometrium with a reduced area, and a decreased uterine artery blood flow velocity, similar to the human pathology of PD. Histopathological and biochemical analyses confirmed abnormal uterine contraction and ischemia. This method has recently been endorsed by the Experimental Pharmacology Professional Committee of Chinese Medicine [ 14 ]. In our previous studies, we demonstrated that sclareol inhibits uterine contractions caused by oxytocin-induced uterine hypercontraction in a rat dysmenorrhea model. The results also showed that sclareol inhibited PGF 2α -, oxytocin-, acetylcholine-, carbachol-, KCl-, and Bay K8644-induced uterine contraction and had an analgesic effect on the writhing test. Sclareol affects Ca 2+ levels and regulates the oxytocin receptor (OTR), myosin light chain kinase (MLCK), extracellular signal-regulated kinase, p-p38, COX-2, and phospho-myosin light chain 20 (p-MLC20) protein expression [ 15 ]. Nonetheless, limited data exist regarding the significance of the gut microbiota in estradiol treatment in animal model studies. According to Chen et al. , the microbiome can transform endogenous or dietary estrogens, creating new ligands for estrogen receptors or less potent estrogens, which could have implications for human health conditions [ 16 ]. However, there have been no reports that have utilized advanced 16S rRNA sequencing technology to examine the composition and impact of the gut microbiota when exposed to sclareol in a mice model subjected to estradiol treatment. In this study, we discuss the use of 16S rRNA gene sequencing to elucidate the impact of sclareol on the gut microbiota and its interplay with estrogen in a mouse model, thus providing valuable directions for understanding this issue.

This study was supported by grants MOST110-2314-B-038-158, 111-2320-B-218-001-MY3, and MOST110-2628-B-038-018 from the Ministry of Science and Technology, Taiwan , and grants NSTC111-2811-B-038-022 and 111-2628-B-038-019 from the National Science and Technology Council, Taiwan . This work was financially supported of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan, grant number DP2-TMU-114-O-13 .

Sclareol (CAS: 515-03-7, 98%), mannitol, potassium chloride, potassium phosphate, sodium bicarbonate, magnesium sulfate, glucose, calcium chloride, and estradiol were purchased from Sigma Chemical Company (St. Louis, MO, USA). Female imprinting control region (ICR) mice weighing 18–22 g were purchased commercially (BioLASCO Taiwan Co., Ltd., Taipei, Taiwan). All animals were housed in an environment with a temperature of 22 ± 2°C and relative humidity ranging from 60% to 80% and were subjected to a standard 12-hr light/dark cycle to acclimatize for two weeks before the experiments began. Water and food were provided ad libitum during the study. ICR mice were randomly assigned to three groups: the control (C) group, model control (MC) group administered 1 mg/kg of estradiol, and sclareol (SL) group administered 50 mg/kg of sclareol, which is the optimal dose based on previous literature [ 15 ]. Each group consisted of 3 mice (n=3). Subsequently, they received either dimethyl sulfoxide (DMSO) daily (C and MC groups) or sclareol daily (50 mg/kg; SL group) by gavage following a two-day initiation period for the experiment. To the best of our knowledge, two primary dysmenorrhea models have been established using estradiol benzoate and oxytocin. In this study, the MC and SL groups received intraperitoneal injections of 1 mg/kg estradiol for seven consecutive days. All experimental procedures received approval from the Institutional Animal Care and Use Committee (IACUC) of Taipei Medical University (Permit No. LAC-2019-0350, LAC-2019-0351, and LAC-2019-0645) in accordance with the guidelines outlined in the Guide for the Care and Utilization of Laboratory Animals provided by the National Institutes of Health. For animal sacrifice, a combination of Zoletil (20 mg/kg) and xylazine (5 mg/kg) was administered to induce unconsciousness while maintaining cardiac activity. Subsequently, blood collection and euthanasia were performed via cardiac puncture, following approval by the IACUC of Taipei Medical University. Preparation was carried out as previously described [ 17 , 18 ]. In brief, a Krebs solution (consisting of 113 mM NaCl, 4.8 mM KCl, 2.5 mM CaCl 2 , 18 mM NaHCO 3 , 1.2 mM KH 2 PO 4 , 1.2 mM MgSO 4 , 5.5 mM glucose, and 30 mM mannitol; adjusted to pH 7.4) was prepared to maintain uterine tissue under conditions of 95% O 2 and 5% CO 2 at 37°C. Every segment of uterine tissue was uniformly trimmed to the same length, and the attached fat and connective tissues around the ovary were meticulously removed before recording their weight. The commercially available QIAamp PowerFecal DNA Kit (Qiagen, Venlo, Netherlands) was used for DNA extraction, following the manufacturer’s protocol. This was combined with PCR amplification of the V3–V4 regions of the 16S rRNA gene from collected fecal samples using forward (5′-TCG TCG GCA GCG TCA GAT GTG TAT AAG AGA CAG CCT ACG GGN GGC WGC AG-3′) and reverse (5′-GTC TCG TGG GCT CGG AGA TGT GTA TAA GAG ACA GGA CTA CHV GGG TAT CTA ATC C-3′) primers, which were custom-designed based on the V3–V4 region sequence provided by Illumina. The PCR cycling parameters were as follows: initial denaturation at 95°C for 3 min, followed by 25 cycles of denaturation at 95°C for 30 sec, annealing at 55°C for 30 sec, and a final extension step at 72°C for 5 min. Next, the amplified products were analyzed using electrophoresis on a 2% agarose gel. Subsequently, an Illumina MiSeq system was used to sequence the 16S V3–V4 region. The pooled libraries underwent paired-end sequencing (2 × 300 bp) on the Illumina MiSeq platform. All processing steps were performed using the QIIME pipeline for analysis. The UCLUST algorithm was used to cluster sequences into operational taxonomic units (OTUs), which were then aligned against the SILVA database (version 132) with a sequence similarity threshold of greater than 97%. A rarefaction depth of 30,000 reads was applied. The data are expressed as the mean ± standard deviation (mean ± SD). One-way analysis of variance (ANOVA) followed by Dunnett’s test was used for analysis, and p<0.05 was considered to indicate a statistically significant difference. The intestinal microbiota data were analyzed with independent Student’s t tests (unpaired Student’s tests) or Kruskal‒Wallis tests performed using RStudio software V.3.6.1 and GraphPad Prism 7. The values represented by the color scale bars are Z-scores ranging from −1 to 1. These values indicate how many standard deviations each data point is from the mean, with negative values representing below-average abundance and positive values indicating above-average abundance. A linear discriminant analysis (LDA) effect size (LEfSe) algorithm was used for detecting biomarkers (http://huttenhower.sph.harvard.edu/lefse/). Welch’s t-test was used to identify significantly abundant bacterial genera between two groups.

Body, uterine, and ovary weights were measured for all animals in the different groups. The results regarding the body and uterine weights indicated that there were no significant differences among the groups throughout this experiment. However, the ovarian weights of the mice in the 50 mg/kg SL group and the MC group were notably greater than those of the mice in the C group ( Table 1 Table 1. The effects of sclareol administration on body weight, uterine weight, and ovary weight of the estradiol exposure in the mice (n=3) Groups n Body weight (g) Uterine weight (g) Ovary weight (g) C 3 28.0 ± 1.4 0.14 ± 0.07 0.06 ± 0.03 MC 3 27.89 ± 1.8 0.18 ± 0.02 0.03 ± 0.005*** SL 3 29.4 ± 1.0 0.22 ± 0.06 0.04 ± 0*** Statistical analysis was performed using one-way ANOVA with Dunnett’s multiple comparison test. *p<0.05; **p<0.01; ***p<0.001 for comparison with the control group. C: control; MC: model control; SL: 50 mg/kg sclareol. ). Statistical analysis was performed using one-way ANOVA with Dunnett’s multiple comparison test. *p<0.05; **p<0.01; ***p<0.001 for comparison with the control group. C: control; MC: model control; SL: 50 mg/kg sclareol. Rarefaction analysis revealed a sequencing depth of 27,730 reads. The rarefaction curves, depicting richness across all nine samples, exhibited a horizontal plateau at the maximum depth ( Fig. 1A Fig. 1. Rarefaction curves (A) and rank abundance curves (B) of each sample. Venn diagram (C) showing the overlap of the current fecal microbiome of rat. The total number in each group and the overlapping areas are listed. C: control; SL: 50 mg/kg sclareol; MC: model control. ), suggesting that the sampling procedure was adequate. The rank abundance curve also tested the adequacy of intestinal flora sequencing based on the number of OTUs ( Fig. 1B ). To display the overlapping regions to reflect the similarity in OTU composition, a Venn diagram ( Fig. 1C ) was generated to illustrate the differences in OTUs among the three groups. There were 292, 283, and 289 OTUs in the C, MC, and SL groups, respectively. The numbers of OTUs in common between the groups were as follows: 267 between the C and SL groups, 271 between the C and MC groups, and 266 between the SL and MC groups. The alpha diversity of the gut microbiota in the rats was assessed by the number of species (or OTUs) observed and the Chao1, Shannon, and Simpson diversity indices ( Fig. 2A–2D Fig. 2. The diversity and richness of the gut microbiota in three groups. The Observed operational taxonomic unit (OTU) (A) Chao 1 (B) Shannon index (C) Simpson (D) of the gut microbiome of rats. Values are expressed as median (n=3). Whiskers show the minimum and maximum values. C: control; SL: 50 mg/kg sclareol; MC: model control. ). The results of the alpha diversity analysis indicated that there were no significant differences among the three groups (Kruskal–Wallis test, p=0.39, 0.83, 0.56, and 0.58, respectively). Rarefaction curves (A) and rank abundance curves (B) of each sample. Venn diagram (C) showing the overlap of the current fecal microbiome of rat. The total number in each group and the overlapping areas are listed. C: control; SL: 50 mg/kg sclareol; MC: model control. The diversity and richness of the gut microbiota in three groups. The Observed operational taxonomic unit (OTU) (A) Chao 1 (B) Shannon index (C) Simpson (D) of the gut microbiome of rats. Values are expressed as median (n=3). Whiskers show the minimum and maximum values. C: control; SL: 50 mg/kg sclareol; MC: model control. Βdiversity (bacterial community) was calculated using nonmetric multidimensional scaling (NMDS), weighted/unweighted UniFrac, and Bray‒Curtis distances between all samples. NMDS ( Fig. 3A Fig. 3. Beta diversity visualized using (A) Non-metric Multidimensional Scaling (NMDS) by the Bray–Curtis metric (B) Principal coordinate analysis (PCoA) plot of weighted Unifrac distance (C) PCoA plot of unweighted Unifrac distance (D) Principal Component Analysis (PCA) plot of gut microbiomes. C: control; SL: 50 mg/kg sclareol; MC: model control. ) was performed based on evolutionary relationships or quantitative distance matrices and showed no significant differences among the three groups. Each data point corresponds to an individual sample, with the color used to denote the categorization of samples in the graph. Weighted UniFrac analysis and unweighted UniFrac matrix data were visualized in principal coordinate analysis (PCoA) plots to show similarities or differences ( Fig. 3B and 3C ). There was no significant impact of sample group on the weighted UniFrac distance matrix or unweighted UniFrac distance matrix in this study. Principal component analysis (PCA) was conducted to provide further information on the variances and distances among the samples. The proportions of PC1 to PC2 were 24.7% and 17%, respectively, while those of PC1 to PC3 were 24.7% and 14.2%, respectively ( Fig. 3D ). The results showed that most of the samples (C, MC, and SC groups) were largely indistinguishable due to the MC group variability of small samples. Beta diversity visualized using (A) Non-metric Multidimensional Scaling (NMDS) by the Bray–Curtis metric (B) Principal coordinate analysis (PCoA) plot of weighted Unifrac distance (C) PCoA plot of unweighted Unifrac distance (D) Principal Component Analysis (PCA) plot of gut microbiomes. C: control; SL: 50 mg/kg sclareol; MC: model control. The top 10 phyla according to the microbial diversity analysis indicated the relative abundances of the cecal bacteria in the three groups. The dominant bacteria at the phylum level (>90%) were Bacteroidetes, Firmicutes, and Actinobacteria. In the fecal samples, the SL group showed increased levels of Actinobacteria and Firmicutes, while the abundance of Bacteroidetes was notably lower than that observed in the control group. Compared with those in the control group, there were decreases in the abundances of Actinobacteria and Bacteroidetes and an increase in the abundance of Firmicutes in the MC group ( Fig. 4A Fig. 4. Histogram of relative abundance at phylum and genus levels. The x-axis represents each sample and the y-axis represents relative abundance presented as a percentage. (A) Relative abundance of the top 10 phyla. (B) Relative abundance of the top 10 genera. The heatmap shows the hierarchical clustering of groups based on the relative abundance of the top ranked 9 phyla (C) and 35 genera (D) of fecal microbiota in different groups. (E) Comparison of genus level between MC and SL groups of microbiota. The p-values were calculated based on two-sided Welch’s t-test. C: control; SL: 50 mg/kg sclareol; MC: model control. ). The heatmap from our cluster analysis was used to analyze the top 10 phyla. The abundances of Actinobacteria, Verrucomicrobia, and Cyanobacteria in the SL group were significantly greater than those in the C group. In the MC group, the prevalences of Deferribacteres, Patescibacteria, and Proteobacteria increased, while that of Bacteroidetes decreased compared with those in the C group ( Fig. 4C ). Histogram of relative abundance at phylum and genus levels. The x-axis represents each sample and the y-axis represents relative abundance presented as a percentage. (A) Relative abundance of the top 10 phyla. (B) Relative abundance of the top 10 genera. The heatmap shows the hierarchical clustering of groups based on the relative abundance of the top ranked 9 phyla (C) and 35 genera (D) of fecal microbiota in different groups. (E) Comparison of genus level between MC and SL groups of microbiota. The p-values were calculated based on two-sided Welch’s t-test. C: control; SL: 50 mg/kg sclareol; MC: model control. At the genus level, the top 10 genera were Lactobacillus , Enterorhabdus , Lachnospiraceae_NK4A136_group , Ruminococcaceae_UCG_014 , Lachnoclostridium , and Ruminiclostridium . In the SL group, the abundances of Lactobacillus , Candidatus_Arthromitus, Ruminococcaceae_UCG_014 , and Enterorhabdus were increased compared with those in the C group. Conversely, the abundances of Candidatus Arthromitus , Enterorhabdus , and Ruminococcaceae UCG_014 were lower in the MC group than in the C group. Moreover, Lachnospiraceae_NK4A136_group , Lachnoclostridium , and Ruminiclostridium_9 increased in abundance in the MC group ( Fig. 4B ). At the genus level, the heatmap from our cluster analysis shows the abundance of the top 35 species of OTUs. In the SL group, the abundances of Enterorhabdus , Ruminococcaceae_UCG_013 , Ruminococcaceae_UCG_014 , A2 , Akkermansia , Candidatus_Arthromitus , Bacteroides , and Ruminococcus_1 increased compared with those in the C group, whereas those of Muribaculum , Marvinbryantia , Parabacteroides , Erysipelatoclostridium , and Blautia decreased. In the MC group, the abundances of Roseburia, Tyzzerella_3 , Ruminiclostridium_5 , Ruminiclostridium , Lachnospiraceae_NK4A136_group , Lachnoclostridium , Anaerotruncus, Acetatifactor , Candidatus_Saccharimonas , and Butyricicoccus increased compared with those in the C group. As shown in Fig. 4D , the abundances of the dominant microbiota exhibited alterations in line with the relative enrichment. We identified genera that drove the mean differences between the MC and SL groups, including Candidatus _ Arthromitus , Ruminococcaceae_UCG_014 , Enterorhabdus , A2 , and Ruminococcaceae_UCG_01 3, which were significantly enriched in the SL group compared with the MC group, and Lachnospiraceae_NK4A136_group , Lactobacillus , Ruminiclostridium , Lachnoclostridium , Roseburia , Ruminiclostridium_9 , and Anaerotruncus , which were significantly enriched in the MC group compared with the SL group ( Fig. 4E ). Comparative analyses between the groups revealed significant differences in the abundances of genera. A significant difference in genera was observed between the SL and MC groups. According to the 95% confidence intervals, there was an increase in the abundance of the genus Streptococcus in the SL group compared with that in the MC group. The abundance of Anaerotruncus was also lower in the SL and C groups than in the MC group ( Fig. 5A and 5B Fig. 5. Taxa that showed a significant difference between the groups at genus level. Confidence intervals and mean abundance values of the relationships that showed significant differences by the t-test (p<0.05) are presented (n=3). C, control; SL: 50 mg/kg sclareol, MC: model control. ). In the SL group, Clostridium_sensu_stricto_1 increased and Anaerotruncus , ASF356 , and Acetatifactor decreased compared with the control group ( Fig. 5C ). Taxa that showed a significant difference between the groups at genus level. Confidence intervals and mean abundance values of the relationships that showed significant differences by the t-test (p2) and plotted a taxonomic cladogram from our LEfSe analysis with LEfSE software, which revealed enrichment in Firmicute taxa in the SL group, including g__Ruminococcus_1 , g__Defluviitaleaceae_UCG_011 , f__Defluviitaleaceae (all from the order Clostridiales ) and g__Streptococcus (family Streptococcaceae ; Fig. 6A and 6B Fig. 6. (A) Cladogram showing the most differentially abundant taxa identified by linear discriminant analysis (LDA) effect size (LEfSe). Red indicates clades enriched in the C group, green indicates clades enriched in the MC group, whereas blue indicates clades enriched in the SL group. (B) Comparison of gut microbiota using LDA. Differential taxa between three groups (C, MC and SL) gut microbiota based on LDA. Taxa with LDA score >2 at the family and genus level are defined as statistical differences. C: control; SL: 50 mg/kg sclareol; MC: model control. ). The MC treatment had a powerful effect on the abundance of g_Parvibacter , the family Eggerthellaceae , and the phylum Actinobacteria. In the control group, g__Muribaculum , which belongs to the phylum Bacteroidetes, showed a particular effect. (A) Cladogram showing the most differentially abundant taxa identified by linear discriminant analysis (LDA) effect size (LEfSe). Red indicates clades enriched in the C group, green indicates clades enriched in the MC group, whereas blue indicates clades enriched in the SL group. (B) Comparison of gut microbiota using LDA. Differential taxa between three groups (C, MC and SL) gut microbiota based on LDA. Taxa with LDA score >2 at the family and genus level are defined as statistical differences. C: control; SL: 50 mg/kg sclareol; MC: model control.

Many studies have suggested that changes in the gut microbiome play a role in the development of various diseases, including inflammatory bowel conditions [ 19 ], cardiovascular diseases [ 20 ], autoimmune diseases [ 21 ], and neuropsychiatric disorders [ 22 ]. The normal intestinal microbiota not only modulates host metabolism and gene expression but also interacts with specific disease pathways directly [ 23 ]. Previous research has demonstrated that microbial community profiles differ between the endocervix and endometrium, as well as between menorrhagia and dysmenorrhea. Pyrosequencing revealed that the microbial communities of the endocervix and endometrium exhibit limited overlap, with each location harboring its own unique and separate microbial population. Moreover, the endometrium was found to contain Lactobacillus spp. as the most abundant operational taxonomic units [ 24 ]. Estrogens are thought to increase the abundance of Lactobacillus species in the human vaginal microbiome by stimulating the production of glycogen in vaginal epithelial cells. This supports the growth of Lactobacillus . When Lactobacillus dominates the healthy adult vaginal microbiome, it helps protect against bacterial vaginosis by producing lactic acid, creating an environment that is hostile to pathogenic bacteria. This also helps maintain the normal immune response of the vaginal epithelium by regulating low pH levels [ 25 ]. In this study, 16S NGS was used to analyze the microbiota in an estradiol-induced uterine hypercontraction dysmenorrhea model treated with sclareol, confirming the presence of bacteria in all analyzed samples. The results indicated that treatment with sclareol significantly altered the bacterial composition in the gut. Catechin has been shown to modulate estrogen levels in individuals experiencing menorrhagia and dysmenorrhea by inhibiting β-glucuronidase and aromatase activities [ 26 ]. Enteric bacteria, such as those from the genera Bifidobacterium , Clostridium , and Lactobacillus , produce β-glucuronidases and β-glucuronides, which are crucial in regulating the fate of estrogens in the gut through deconjugation and conjugation processes [ 25 ]. Estrogen levels play a crucial role in hormonal equilibrium, and a bidirectional relationship exists between the gut microbiota and dysbiosis. An imbalance in the gut microbiota can result in pathologies related to estrogen, either due to a deficiency or an increase in free estrogen [ 6 ]. In a previous study by Sasaki et al. [ 5 ], a decrease in the abundances of Clostridiales , Ruminococcus , and Lachnospiraceae at the genus level (butyrate-producing bacteria) was detected in an irregular menstrual cycle group compared with a normal menstrual cycle group (control group). The results of the present study, which show an increase in the abundance of Ruminococcus-1 in the SL group, align with these findings. However, the increase in the abundances of Lachnospiraceae_NK4A136_group and Lachnospiraceae_UCG_006 in the MC group is inconsistent with these findings. Another study revealed an increase in potentially pathogenic species, including Gardnerella , Streptococcus , Escherichia , Shigella , and Ureaplasma , within the cervical microbiota of women diagnosed with stage 3/4 endometriosis [ 4 ]. At the phylum level, Bacteroidetes, Firmicutes, and Actinobacteria were the dominant bacteria. Compared with the abundances in the SL group, the abundance of Firmicutes was greater in the MC group, while the abundances of Bacteroidetes and Actinobacteria were significantly lower. Previous research has indicated that the human endometrial microbiome exhibits considerable diversity and is characterized by a high abundance of the Firmicutes phylum, followed by Bacteroidetes, Proteobacteria, and Actinobacteria [ 24 , 27 ]. However, other studies have documented a relatively high abundance of Bacteroidetes or Proteobacteria within the uterine environment, with a low abundance or absence of Lactobacillu s [ 28 ]. Elevated serum estrogen concentrations and heightened localized estrogen synthesis are linked to increased body weight, potentially promoting the development of breast cancer in postmenopausal females [ 29 ]. The Firmicutes/Bacteroidetes (F/B) ratio is a subject of controversy in obesity, with some studies reporting a higher F/B ratio in obese individuals than in lean individuals [ 30 ]. Additionally, β-glucuronidase plays a key role in the deconjugation and reabsorption of estrogens, and dietary fiber can reduce intestinal β-glucuronidase activity [ 31 ]. Remarkably, the Clostridium leptum cluster and Clostridium coccoides cluster, which are classified within the Firmicutes phylum, are the predominant groups among β-glucuronidase-producing bacteria. Most reports have shown that Streptococcus and Roseburia are negatively correlated with the presence of β-glucuronidase and/or β-glucosidase enzymes [ 32 ], which is consistent with our findings. At the genus level, sclareol increased the relative abundances of Ruminococcus_1, Ruminococcaceae_UCG_013 , Ruminococcaceae_UCG_014 (all belonging to Ruminococcus ), and Streptococcus in mice after estradiol administration. Conversely, it decreased the abundances of Anaerotruncus , Ruminiclostridium_5 , and Ruminiclostridium in the dysmenorrhea mouse model induced by estradiol (MC group). Notably, the abundance of Anaerotruncus increased significantly in the MC group, suggesting that estradiol exposure may alter the gut microbiota composition. Ruminococcus-1 , a member of the Ruminococcaceae family, is an anaerobic gram-positive genus known for its capacity to metabolize carbohydrates and generate short-chain fatty acids (SCFAs) in postmenopausal women [ 33 ]. In the rumen, the genera Prevotella , Ruminococcus , and Butyrivibrio are predominant and are associated with functions such as the degradation of fibrous plants and proteins, lipid biohydrogenation, and the production of microbial inhibitors in healthy cows [ 34 ]. Ruminococcus-1 also appears to be related to thiamine synthesis [ 35 ]. SCFAs, such as acetic acid, propionic acid, butyric acid, lactic acid, isobutyric acid, isovaleric acid, and isohexanoic acid, have been shown to exhibit histone deacetylase inhibitory activity, downregulate estrogen receptor α (ERα), and act as selective estrogen receptor downregulators (SERDs) in breast cancer [ 36 ]. Furthermore, there is a positive correlation between the length of exposure to estrogens, progestins, and androgens and the risk of developing breast, ovarian, and prostate cancers [ 37 ]. At the phylum level, treatment with sclareol can regulate the abundances of Actinobacteria, Verrucomicrobia, and Cyanobacteria. Sclareol affects estrogen levels by regulating the abundances of Ruminococcus_1 , Ruminococcaceae_UCG-013 , and Ruminococcus_UCG-014 , which belong to the Ruminococcaceae family and are known to produce SCFAs. The Ruminococcaceae family predominantly resides in the cecum and colon and has been linked to the breakdown of various polysaccharides and dietary fibers, leading to the production of SCFAs [ 38 , 39 ]. A previous study revealed that the abundance of Ruminococcus was significantly greater in fucoidan-treated samples than in samples from plants exposed to opportunistic pathogenic bacteria, indicating that Ruminococcus plays a role in the degradation and fermentation of fucoidan [ 40 ]. Additionally, the size of the Ruminococcaceae population has been inversely correlated with several diseases, including alcoholic cirrhosis, hepatic encephalopathy, and increased intestinal permeability [ 41 , 42 ]. Studies have also shown a low diversity of bacteria, including Lactobacillus and Ruminococcaceae_UCG-014 , in obese individuals [ 43 ]. In antibiotic-free patients treated with sclareol, a high abundance of Ruminococcaceae_UCG_13 is positively correlated with the overall response rate, progression-free survival, and survival [ 44 ]. However, the specific effects of sclareol on gynecological diseases caused by Ruminococcaceae_UCG-013 and Ruminococcaceae_UCG-014 have not been investigated and require further study. In our study, the abundances of Ruminococcaceae_UCG_013 , Ruminococcaceae_UCG_014 , and Ruminococcus_1 decreased in the MC group. Streptococcus, a member of the Streptococcaceae family, is a gram-positive bacterium that can metabolize carbohydrates and generate lactic acid through a fermentative metabolic process [ 45 ]. While some members of the Streptococcus genus are pathogenic to humans and other mammals [ 46 ], our findings align with prior research demonstrating an inverse relationship between the abundances of the bacterial families Lactobacillaceae , Ruminococcaceae , and Streptococcaceae in fecal samples and β-glucuronidase activity [ 47 ]. Although β-glucuronidase has been identified in Streptococcus equi subsp., there is no direct association between any species within the Streptococcaceae family and estrogen metabolism. Anaerotruncus , a member of the Ruminococcaceae family, has been associated with prenatal stress and age-related macular degeneration [ 48 , 49 ]. Additionally, Anaerotruncu s has been found to be harmful to intestinal health and to be associated with obesity [ 50 ]. It has also been demonstrated that the abundance of Anaerotruncus was significantly lower in ovariectomized rats treated with curcumin [ 51 ]. Another study revealed that a high-fat diet enriched the population of Anaerotruncus , along with other bacteria, indicating a negative correlation between pyridoxine and the promotion of sexual development related to precocious puberty [ 52 ]. Our findings align with these results. Based on the above analysis, our results suggest that sclareol can modulate intestinal flora composition, particularly in relation to estradiol. Ruminococcus_1 , Ruminococcaceae_UCG-013 , and Ruminococcus_UCG-014 are SCFA-producing bacteria associated with histone deacetylase inhibitory activity and the downregulation of ERα. The Ruminococcaceae family has been negatively correlated with intestinal permeability. Sclareol can increase the abundance of these bacteria, potentially leading to the regulation of sex hormone levels and SCFA production and a reduction in intestinal permeability. Conversely, Streptococcus has been demonstrated to exhibit adverse associations with the presence of β-glucuronidase and/or β-glucosidase enzymes, potentially impacting estrogen metabolism. The ability of sclareol to increase the abundance of Streptococcus may be related to its effect on sex hormones. Furthermore, sclareol can reduce the abundance of Anaerotruncus , suggesting an interaction between hormones and the gut microbiota. However, this study has several limitations, including a small sample size, the inability to establish causality, and an incomplete understanding of the exact interactions between gut microbiota and estradiol.

The authors declare no conflicts of interest.

Yun-Ju Huang conducted the research, wrote the main manuscript, prepared the figures, and performed the statistical analysis. Jennifer Wong and Yi-Fen Chiang performed the behavioral experiments. Ko-Chieh Huang and Hsin-Yuan Chen provided technical support and revised the manuscript. Tsung-Sheng Cheng, Mohamed Ali, and Tzong-Ming Shieh were responsible for analyzing the results. Tzong-Ming Shieh and Shih-Min Hsia designed the study and revised the manuscript. Shih-Min Hsia mentored and supervised the study.