{"paper_id":"8b1302b2-7c26-4322-b721-97455909a5a8","body_text":"Infertility affects millions of couples worldwide and is considered a major public\nhealth concern. Its increasing prevalence is attributed to factors, such as delayed\nchildbearing, modern lifestyles, and adverse environmental conditions ( Vander Borght & Wyns, 2018 ). Assisted\nreproductive technology (ART), including  in vitro  fertilization\n(IVF) and intracytoplasmic sperm injection (ICSI), constitute a pivotal strategy in\nmanaging infertility. A central step in optimizing oocyte production and increasing\nthe success rates of these treatments is controlled ovarian hyperstimulation (COH)\n( Ovarian Stimulation TEGGO  et\nal. , 2020 ). Despite advances in reproductive medicine, the\nsuccess rates for a single IVF cycle remain modest, ranging from approximately 30%\nto 50% in women under 35 years of age, with a significant decline in older age\ngroups ( Gleicher  et al. ,\n2019 ;  Sunkara  et al. ,\n2023 ). Multiple IVF attempts are often required due to the inherent\nlimitations of this technique. Other factors, such as the woman’s age, embryo\nquality, and underlying reproductive health conditions, also contribute to this\nrequirement ( Sunkara  et al. ,\n2023 ). Thus, repeated IVF cycles are often necessary to maximize the\nchances of successful conception, particularly in patients facing\ninfertility-related challenges or poor prognosis.\nBriefly, COH protocols use urinary or recombinant follicle-stimulating hormone and\nluteinizing hormone to stimulate the development of multiple ovarian follicles,\naiming to maximize the number of mature eggs obtained for subsequent fertilization\nand embryo transfer ( Ovarian Stimulation TEGGO\n et al ., 2020 ). As a result, while undergoing\ntreatment with these drugs, serum estradiol concentrations reach supraphysiological\nlevels, increasing from 150 to 400 pg/mL in a physiological menstrual cycle to\nbetween 1,000 and 6,000 pg/mL in a COH cycle, depending on the number of follicles\nrecruited ( Siddhartha  et al. ,\n2016 ;  Helmer  et al. ,\n2022 ). The short- and long-term systemic effects of repeated COH are not\nyet fully elucidated, and they continue to evoke significant scientific interest.\nThis is particularly true regarding the interactions between sex hormones and\nemotional, immunological, and metabolic abnormalities ( Miyamoto  et al. , 2010 ;  Marschalek  et al. , 2019 ;  Hu  et al. , 2021 ;  Ma  et al. , 2021 ;  Guo  et al. , 2023 ;  Sampaio  et al. , 2024 ). Sex steroids,\nparticularly estrogen, play a crucial role in female reproductive physiology and\ninteract intimately with the gut microbiota. Growing evidence suggests that\nfluctuations in estrogen levels can alter the composition and functionality of the\ngut microbiota ( Yang  et al. ,\n2022 ;  Schieren  et al. ,\n2024 ). Additionally, the gut microbiota plays a crucial role in\nregulating systemic estrogen levels by mediating its metabolism through the\ngut-liver axis ( Baker  et al. ,\n2017 ;  Siddiqui  et al. ,\n2022 ). These bidirectional interactions not only affect hormonal balance\nbut may also influence susceptibility to immune disorders, metabolic alterations,\nand estrogen-dependent gynecological diseases, such as endometriosis, polycystic\novary syndrome, and endometrial cancer ( Siddiqui\n et al. , 2022 ).\nAnimal models can be used to investigate the impact of estrogens on the gut\nmicrobiota. Mice and rats are among the most frequently studied species, often\nsubjected to surgical induction (ovariectomized [OVX]) or chemical induction\n(treatment with 4-vinyl cyclohexene diepoxide [VCD]) of a hypoestrogenic state to\nevaluate the effects of hormonal supplementation on the gut microbiota ( Song  et al. , 2020 ). Transgenic\nmice are also employed to explore the specific role of estrogen receptors\n(ERα, ERβ) ( Cook  et\nal. , 2014 ). The zebrafish model ( Danio\nrerio ) offers an innovative alternative, with studies using estradiol\nexposure to investigate  in vivo  intestinal changes ( Cornuault  et al. , 2022 ).\nNon-human primates are typically reserved for more complex research due to their\nphysiological similarity to humans, despite ethical and financial limitations ( Yan  et al. , 2022 ). Rabbit\nmodels are utilized in studies examining the interaction between estrogens and\nintestinal fermentation ( Ericsson, 2019 ).\nThese models are evaluated using techniques, such as 16S rRNA sequencing and\nmetabolomics, to elucidate the estrogen’s effects on microbiota diversity and\ncomposition, particularly through bile acid and secondary metabolite modulation\n( Guo  et al. , 2023 ).\nRepeated COH, which clinically refers to multiple cycles of COH used in ART, has not\nbeen directly associated with alterations in gut microbiota composition or diversity\nin humans. Despite the established significance of the gut microbiota-hormone axis,\nresearch on the impact of repeated COH protocols on the gut microbiota remains\nlimited. It is imperative to evaluate how these interventions may affect gut\nmicrobiota and potentially influence pregnancy outcomes. Additionally, estrogen and\nits fluctuations are vital for both overall and reproductive health, making it\nessential to understand their role in the onset and progression of diseases in both\nthe short- and long-term. The present study aimed to investigate whether COH induces\nalterations in the gut microbiota composition of female mice. Given the known\ninteractions between sex hormones and microbial communities, we hypothesized that\nsupraphysiological estrogen fluctuations resulting from repeated COH protocols could\nmodulate gut microbial diversity and structure. By employing a well-established\nanimal model subjected to weekly COH for ten consecutive weeks, this study seeks to\nprovide novel insights into the potential impact of sustained hormonal stimulation\non gut microbiota dynamics, contributing to a better understanding of the systemic\neffects of ART.\n\nFor this study, we used 8-week-old sexually mature female Swiss mice, weighing\nbetween 20 and 30 g. The animals were sourced from the Vivarium at the\nExperimental Biology Center, University of Fortaleza. A total of 20 animals were\nused, and they were randomly assigned to two experimental groups (n=10 per\ngroup)-control and treatment-using a computer-generated randomization sequence\nto ensure unbiased allocation. The animals were housed in individually\nventilated cages (Techniplast IVC), with autoclaved pine shavings used as\nbedding. The mice were maintained under pathogen-free conditions, with an\naverage ambient temperature of 26°C, 15-20 air changes per hour, and a 12-hour\nlight/ dark cycle. They were provided  ad libitum  access to\nfiltered water and commercial chow, both sterilized prior to use to maintain\nmicrobiological safety. All experimental procedures followed international\nguidelines for the care and use of laboratory animals and were approved by the\nAnimal Use Ethics Committee of the University of Fortaleza (CEUA protocol number\n3828200123). Furthermore, this study adhered to the principles outlined in the\nAnimal Research: Reporting of In Vivo Experiments (ARRIVE 2.0) guidelines, to\nensure transparency and reproducibility in animal research ( Percie du Sert  et al. ,\n2020 ).\nThe mice (n=10 per group) were randomly assigned to one of the two experimental\ngroups: placebo (control) or treatment. The treatment group underwent ovarian\nhyperstimulation following established protocols described in the literature\n( Van Blerkom & Davis, 2001 ;  Zhang  et al. , 2018 ).\nSpecifically, mice in the treatment group received an intraperitoneal (i.p.)\ninjection of 7.5 IU of human menopausal gonadotropin (HMG, Menopur™,\nFERRING Pharmaceuticals), a gonadotropin preparation containing both FSH and LH,\nto stimulate follicular development. After 48 hours, mice received a second i.p.\ninjection of 5 IU of human chorionic gonadotropin (hCG, Choriomon™, IBSA\nInstitut Biochimique), a hormone that mimics the LH surge and induces ovulation.\nIn contrast, mice in the control group were administered an equivalent volume of\nsaline solution (i.p.) at the same time points. Ovulation was induced ten times,\nwith intervals of one week between each induction ( Figure 1A ).\nFigure 1 Effects of Repeated Ovarian Hyperstimulation on Body Weight in Female\nMice. (A) Experimental design of repeated ovarian hyperstimulation\nin female mice. The treated group received 7.5 IU of human\nmenopausal gonadotropin (HMG) once per week (on Tuesdays) followed\nby 5.0 IU of human chorionic gonadotropin (hCG) 48 hours later (on\nThursdays) for 10 weeks. The placebo group received saline solution\nat equivalent time points. (B) Body weight progression of treated\nand control groups during the 10-week protocol. Data are presented\nas mean±SEM, with significant differences between groups\nmarked as follows: * p <0.05,\n** p <0.01, *** p <0.001, and ns\nfor not significant. Treated mice exhibited a significant increase\nin body weight compared to controls starting from the second\nweek.\nEffects of Repeated Ovarian Hyperstimulation on Body Weight in Female\nMice. (A) Experimental design of repeated ovarian hyperstimulation\nin female mice. The treated group received 7.5 IU of human\nmenopausal gonadotropin (HMG) once per week (on Tuesdays) followed\nby 5.0 IU of human chorionic gonadotropin (hCG) 48 hours later (on\nThursdays) for 10 weeks. The placebo group received saline solution\nat equivalent time points. (B) Body weight progression of treated\nand control groups during the 10-week protocol. Data are presented\nas mean±SEM, with significant differences between groups\nmarked as follows: * p <0.05,\n** p <0.01, *** p <0.001, and ns\nfor not significant. Treated mice exhibited a significant increase\nin body weight compared to controls starting from the second\nweek.\nMice were weighed one week prior to the beginning of the experimental protocol,\nweekly throughout the ten-week COH treatment, and again one week after the final\nadministration of either ovulation-inducing agents or saline placebo. Fecal\nsamples were collected from each animal one week after the conclusion of the\ntreatment phase and were immediately frozen at -80°C for subsequent analysis.\nGenomic DNA was extracted from approximately 100 mg of murine fecal material\nusing the QIAamp® PowerFecal® Pro DNA kit (Qiagen, CA, USA),\nfollowing the manufacturer’s instructions. The composition of the gut microbiota\nwas assessed using 16S rRNA gene sequencing on the Illumina MiSeq platform,\nemploying bioinformatics methodologies as previously described ( Nagpal  et al. , 2020a ; b ;  Mishra\n et al. , 2023 ). The V4 hypervariable region of\nthe bacterial 16S rDNA gene was amplified using universal primer pairs 515F\n(5’-GTGCCAGCMGCCGCGGTAA-3’) and 806R (5’-GGACTACHVGGGTWTCTAAT-3’) ( Caporaso  et al. , 2012 ).\nBarcoded amplicons were then purified with AMPure® magnetic beads\n(Agencourt, Beckman Coulter, CA, USA), quantified using the dsDNA HS assay kit\n(Life Technologies, Carlsbad, CA, USA) on a Qubit-3 fluorometer (Invitrogen,\nCarlsbad, CA, USA), and normalized for constructing an amplicon library ( Caporaso  et al. , 2012 ).\nEach amplicon was standardized to a final concentration of 8 pM before\nsequencing on the Illumina MiSeq system (MiSeq reagent kit v3).\nWe then used the QIIME2 (Quantitative Insights into Microbial Ecology) software\nto process the generated fastq files from the sequencing. The quality control,\nclustering, and demultiplexing of the sequences were performed using the unique\nbarcodes assigned to each sample. Subsequently, quality control evaluations were\nconducted using the DADA2 pipeline ( Nagpal\n et al. , 2019 ;  2020b ;  Mishra  et\nal. , 2024 ). High-quality sequences were obtained by\nremoving adapters and non-chimeric amplicons using the default parameters of\nDADA2. The filtered sequences were then used for the taxonomy classification\nusing the Greengenes-trained Naive Bayes classifier ( Bokulich  et al. , 2018 ).\nAlpha-diversity indices for community richness included the observed operational\ntaxonomic units (OTUs), Shannon index, Simpson index, and Faith’s phylogenetic\ndiversity index. Community dissimilarities (beta diversity) were quantitatively\nassessed using unweighted UniFrac, weighted UniFrac, Jaccard index, and\nBray-Curtis distances within QIIME2, and these were visualized with a principal\ncoordinate analysis (PCoA) plot. To obtain relative abundances, raw read counts\nwere divided by the total number of reads per sample. Subsequently, OTUs were\naggregated to taxonomic levels by summing their respective relative\nabundances.\nGraphPad Prism 10.2.0 software (GraphPad Software Inc., La Jolla, CA, USA) was\nused for statistical analysis. Body weight was described as the mean and\nstandard error of the mean (mean±SEM). For analysis of the alpha\ndiversity indices (Observed OTUs, Shannon, Simpson, and Faith’s Phylogenetic\nDiversity), comparisons between the control and COH-treated groups were\nconducted using a two-tailed unpaired Student’s  t -test,\nassuming equal variance unless otherwise specified. Statistical significance was\ndefined as  p <0.05. To identify differentially abundant taxa\nbetween groups, LEfSe (Linear Discriminant Analysis Effect Size) was applied,\nusing default parameters with an LDA score threshold>2.0 and a significance\nlevel of  p <0.01, as previously described by  Segata  et al . (2011) . In\naddition, a Random Forest classification algorithm was used to evaluate taxa\nimportance and identify microbial features that best distinguished between the\ntwo experimental groups. Beta diversity distances (Jaccard, Bray-Curtis,\nunweighted and weighted UniFrac) were statistically compared using PERMANOVA\n(permutational multivariate analysis of variance) within the QIIME2\nframework.\n\nBody weights were collected weekly from both experimental groups: control (n=10) and\ntreatment (n=10). Longitudinal analysis of weight change revealed that both groups\nexperienced progressive weight gain. However, the treated group showed significantly\ngreater weight gain starting from the fourth week of COH ( Figure 1B ).\nThe results of the alpha and beta diversity analyses are summarized in  Figure 2 . The boxplots compare the\nalpha-diversity metrics between the control (blue) and treatment (red) groups. The\nalpha diversity of the samples was assessed by counting the number of OTUs and\ncalculating the Shannon, Simpson, and Faith’s Phylogenetic Diversity (PD) indices.\nOverall, the findings suggest that the repeated COH protocol did not significantly\naffect the richness or PD of the microbial community in the studied groups ( Figures 2A-D ). There was no significant\ndifference in species richness, as measured by observed OTUs, between the control\ngroup (430.5±93.8) and the treatment group (418.7±45.3)\n(mean±SD;  p =0.7294, η 2 =0.0068) ( Figure 2A ). The Shannon diversity index, which\naccounts for both species richness and evenness, did not differ significantly\nbetween the treatment group (6.27±0.67) and the control group\n(6.02±0.98) (mean±SD;  p =0.5252,\nη 2 =0.0227), indicating similarly high levels of microbial\ndiversity in both groups ( Figure 2B ). The\nSimpson diversity index, which reflects species dominance within the microbial\ncommunity, did not differ significantly between the treatment group\n(0.97±0.01) and the control group (0.96±0.02) (mean±SD;\n p =0.3260, η 2 =0.0536). The high Simpson values\nobserved in both groups indicate a community structure characterized by strong\ndominance of a few taxa, consistent with low overall evenness ( Figure 2C ). Faith’s PD did not differ significantly between the\ntreatment group (18.19±1.29) and the control group (17.41±1.73)\n(mean±SD;  p =0.2702, η 2 =0.0670) ( Figure 2D ), indicating comparable levels of\nevolutionary diversity across the microbial communities in both groups.\nFigure 2 Impact of Repeated Controlled Ovarian Hyperstimulation (COH) on Alpha and\nBeta Diversity of the Gut Microbiota in Female Mice. Female Swiss mice\n(n=10 per group) were assigned to a treatment group receiving repeated\nCOH for 10 weeks or a placebo-treated control group. (A-D) Alpha\ndiversity was assessed using four metrics: (A) Observed OTUs (Control:\n430.5±29.7; Treatment: 418.7±15.6;\n p =0.7294), (B) Shannon diversity index (Control:\n6.02±0.98; Treatment: 6.27±0.67;\n p =0.5252), (C) Simpson diversity index (Control:\n0.96±0.02; Treatment: 0.97±0.01;\n p =0.3260), and (D) Faith’s Phylogenetic Diversity\n(Control: 17.41±1.73; Treatment: 18.19±1.29;\n p =0.2702). Data are presented as\nmean±standard deviation (SD). No significant differences were\nobserved between groups for any of the alpha diversity metrics (unpaired\ntwo-tailed t-test). (E) Beta diversity was evaluated using weighted\nUniFrac distances, and visualized via principal coordinates analysis\n(PCoA). Each dot represents a fecal sample; ellipses denote 95%\nconfidence intervals for each group. No clear clustering by group was\nobserved (PERMANOVA;  p >0.05).\nImpact of Repeated Controlled Ovarian Hyperstimulation (COH) on Alpha and\nBeta Diversity of the Gut Microbiota in Female Mice. Female Swiss mice\n(n=10 per group) were assigned to a treatment group receiving repeated\nCOH for 10 weeks or a placebo-treated control group. (A-D) Alpha\ndiversity was assessed using four metrics: (A) Observed OTUs (Control:\n430.5±29.7; Treatment: 418.7±15.6;\n p =0.7294), (B) Shannon diversity index (Control:\n6.02±0.98; Treatment: 6.27±0.67;\n p =0.5252), (C) Simpson diversity index (Control:\n0.96±0.02; Treatment: 0.97±0.01;\n p =0.3260), and (D) Faith’s Phylogenetic Diversity\n(Control: 17.41±1.73; Treatment: 18.19±1.29;\n p =0.2702). Data are presented as\nmean±standard deviation (SD). No significant differences were\nobserved between groups for any of the alpha diversity metrics (unpaired\ntwo-tailed t-test). (E) Beta diversity was evaluated using weighted\nUniFrac distances, and visualized via principal coordinates analysis\n(PCoA). Each dot represents a fecal sample; ellipses denote 95%\nconfidence intervals for each group. No clear clustering by group was\nobserved (PERMANOVA;  p >0.05).\nBeta diversity analysis was conducted using the weighted UniFrac index, which\naccounts for the phylogenetic distance between microbial communities, weighted by\nthe relative abundance of species. A PCoA plot revealed overlapping ellipses for the\ncontrol and treatment groups, indicating no substantial differences in the overall\ncomposition of microbial communities. The first principal axis (PC1) accounted for\n80.8% of the total variation, while the second principal axis (PC2) contributed\n9.4%. Beta diversity analysis did not reveal distinct clusters between groups,\nindicating no major changes in the microbial pattern due to treatment ( Figure 2E ). However, this may be attributed to\nthe smaller sample size.\nThe gut microbiota’s taxonomic composition was evaluated across five levels: phylum,\nclass, order, family, and genus. Phylum-level analysis revealed that the treatment\ngroup had a higher relative abundance of  Bacteroidetes  (23.1% in\nthe treated group  vs . 19.2% in the control group) and a lower\npercentage of  Firmicutes  (74.9% in the treated group\n vs . 78.9% in the control group) as compared to the control\ngroup ( Figure 3A ). The  Firmicutes:\nBacteroidetes  (F: B) ratio was reduced by the COH protocol, decreasing\nto 3.24 in the treatment group compared to 4.11 in the control group.\n Proteobacteria, Actinobacteria , and\n Deferribacteres  constituted a minor fraction of the total\ncommunity and maintained consistent proportions across both groups ( Figure 3A ). The gut microbiota composition at the\nclass level was primarily dominated by  Clostridia, Bacilli,  and\n Bacteroidia.  We observed a reduction in\n Clostridia  and  Bacilli , while\n Bacteroidia  was elevated in the treatment group compared to the\ncontrol group. Minor classes, including  Coriobacteriia, Deferribacteres,\nEpsilonproteobacteria, Deltaproteobacteria, Betaproteobacteria,\nErysipelotrichi,  and  Mollicutes , showed no significant\ndifferences between the groups, indicating that the COH protocol did not notably\nalter class-level microbial distributions ( Figure\n3B ).\nFigure 3 Taxonomic Composition of Gut Microbiota at Multiple Levels Following\nRepeated Controlled Ovarian Hyperstimulation (COH) in Female Mice. This\nfigure presents the relative abundance (%) of bacterial taxa at the\nphylum (A), class (B), order (C), family (D), and genus (E) levels in\ncontrol mice (n=10) and mice subjected to 10 weeks of COH (n=10). At the\nphylum level (A),  Firmicutes  and\n Bacteroidetes  dominated both groups, with a modest\nreduction in the Firmicutes:Bacteroidetes ratio in the treatment group.\nAt the class level (B),  Clostridia  and\n Bacteroidia  remained predominant, while minor\nincreases in  Erysipelotrichi  and\n Coriobacteriia  were observed after COH. At the\norder level (C),  Bacteroidales  showed a slight increase\nin treated mice, whereas  Clostridiales  were more\nabundant in controls. At the family level (D),  Lactobacillaceae,\nLachnospiraceae , and  Ruminococcaceae  were\nthe major families across groups, with  Rikenellaceae \nshowing a small decrease in the COH group. At the genus level (E),\n Lactobacillus  was the dominant genus in both\ngroups, followed by  Bacteroides  and\n Parabacteroides , which were slightly more abundant\nin treated animals, while genera such as\n Rikenellaceae_RC9  and  Clostridium \nwere relatively enriched in controls. These patterns indicate largely\nconserved community structure, with subtle COH-associated shifts in\nspecific taxa.\nTaxonomic Composition of Gut Microbiota at Multiple Levels Following\nRepeated Controlled Ovarian Hyperstimulation (COH) in Female Mice. This\nfigure presents the relative abundance (%) of bacterial taxa at the\nphylum (A), class (B), order (C), family (D), and genus (E) levels in\ncontrol mice (n=10) and mice subjected to 10 weeks of COH (n=10). At the\nphylum level (A),  Firmicutes  and\n Bacteroidetes  dominated both groups, with a modest\nreduction in the Firmicutes:Bacteroidetes ratio in the treatment group.\nAt the class level (B),  Clostridia  and\n Bacteroidia  remained predominant, while minor\nincreases in  Erysipelotrichi  and\n Coriobacteriia  were observed after COH. At the\norder level (C),  Bacteroidales  showed a slight increase\nin treated mice, whereas  Clostridiales  were more\nabundant in controls. At the family level (D),  Lactobacillaceae,\nLachnospiraceae , and  Ruminococcaceae  were\nthe major families across groups, with  Rikenellaceae \nshowing a small decrease in the COH group. At the genus level (E),\n Lactobacillus  was the dominant genus in both\ngroups, followed by  Bacteroides  and\n Parabacteroides , which were slightly more abundant\nin treated animals, while genera such as\n Rikenellaceae_RC9  and  Clostridium \nwere relatively enriched in controls. These patterns indicate largely\nconserved community structure, with subtle COH-associated shifts in\nspecific taxa.\nAt the order level, the control group exhibited an increase in abundance of\n Clostridiales  and  Lactobacillales , while the\ntreatment group exhibited a higher abundance of  Bacteroidales .\nOther orders, including  Coriobacteriales, Deferribacterales,\nBurkholderiales, Campylobacterales, Turicibacterales,\nDesulfovibrionales , and  Erysipelotrichales , showed\ncomparable relative abundances, indicating that the treatment protocol did not\nsubstantially affect these microbial taxa ( Figure\n3C ). At the family level, the  Lactobacillaceae,\nRuminococcaceae,  and  Rikenellaceae  showed increased\nabundance in the control group, while the treatment group exhibited higher abundance\nfor  Lachnospiraceae, Muribaculaceae,  and\n Bacteroidaceae  ( Figure\n3D ). At the genus level,  Lactobacillus  and\n Oscillospira  were more abundant in the control group, while\n Bacteroides, Ruminococcus, Turicibacter , and\n Parabacteroides  were more abundant in the treatment group\n( Figure 3E ).\nTwo taxa were statistically significant:  Mogibacteriaceae \n( p =0.049) and  Clostridiaceae \n( p =0.038) at the family level. At the genus level, four genera\nwere significant:  Parabacteroides  ( p =0.034),\n Anaeroplasma  ( p =0.041),  Candidatus\nArthromitus  ( p =0.029), and\n Clostridium  ( p =0.031). Furthermore, the random\nforest analysis indicated that  Bacteroidales  was the most abundant\nin the treatment group, serving as a discriminating biomarker between the two\ngroups. We also observed that one bacterial family,\n Erysipelotrichaceae , and three genera,  Parabacteroides,\nOdoribacter , and  Rikenellaceae , could serve as\nbiomarkers.  Erysipelotrichaceae  and\n Parabacteroides  were more abundant in the treatment group,\nwhile  Odoribacter  and  Rikenellaceae  were more\nabundant in the control group ( Figure 4 ).\nFigure 4 Random Forest Analysis Identifies Key Microbial Biomarkers Associated\nwith Controlled Ovarian Hyperstimulation (COH). Random forest\nclassification was performed to identify bacterial taxa that most\naccurately distinguish fecal microbiota profiles of COH-treated mice\n(n=10) from control mice (n=10). The x-axis shows the Mean Decrease\nAccuracy, reflecting the contribution of each taxon to model\nperformance. The y-axis lists the most discriminative taxa, with\n Bacteroidales, Erysipelotrichaceae , and\n Parabacteroides  emerging as top predictors. The\nheatmap on the right illustrates the relative abundance of each taxon\nacross groups, with the color scale ranging from red (high abundance) to\nblue (low abundance). Notably,  Bacteroidales  was more\nabundant in the COH group and ranked as the most informative biomarker,\nsuggesting its relevance in microbial shifts associated with ovarian\nhyperstimulation.\nRandom Forest Analysis Identifies Key Microbial Biomarkers Associated\nwith Controlled Ovarian Hyperstimulation (COH). Random forest\nclassification was performed to identify bacterial taxa that most\naccurately distinguish fecal microbiota profiles of COH-treated mice\n(n=10) from control mice (n=10). The x-axis shows the Mean Decrease\nAccuracy, reflecting the contribution of each taxon to model\nperformance. The y-axis lists the most discriminative taxa, with\n Bacteroidales, Erysipelotrichaceae , and\n Parabacteroides  emerging as top predictors. The\nheatmap on the right illustrates the relative abundance of each taxon\nacross groups, with the color scale ranging from red (high abundance) to\nblue (low abundance). Notably,  Bacteroidales  was more\nabundant in the COH group and ranked as the most informative biomarker,\nsuggesting its relevance in microbial shifts associated with ovarian\nhyperstimulation.\n\nThis study investigated whether repeated COH, a condition that raises endogenous\nestrogen concentrations to supraphysiological levels, can induce changes in the gut\nmicrobiota of female mice. The biological plausibility for the link between hormonal\nfluctuations (estrogen and progesterone) and microbial changes is supported by data\nshowing that estrogen promotes proliferation of the vaginal epithelium and increased\nglycogen, favoring the growth of  Lactobacillus , while progesterone\nmodulates glycogen release and vaginal pH. Furthermore, hormonal variations\nthroughout the menstrual cycle and in states such as hyperestrogenism induced by\ncontrolled ovarian stimulation alter the diversity and composition of the vaginal\nmicrobiota, with  Lactobacillus  predominating during periods of\nhigher estrogen levels ( Collins  et\nal ., 2022 ).\nThe results of this study revealed that prolonged COH treatment in female mice does\nnot significantly affect the diversity (alpha or beta) of the gut microbiota, as\nassessed by the metrics presented. The results also suggested that the 10-week COH\nprotocol does not induce notable changes in the taxonomic structure of the gut\nmicrobiota in female mice. The stability observed at several taxonomic levels\nreinforces the notion that the COH protocol has a limited impact on the microbial\ncommunity structure. However, the study identified biomarkers of gut microbiota,\nincluding one order, one family, and three genera. The most distinguishing factor\nbetween the groups was the abundance of bacteria from the order\n Bacteroidales  in the group subjected to repeated COH.\nFurthermore, the presence of one family ( Rikenellaceae ) and two\ngenera ( Parabacterioides  and  Odoribacter ) within\nthe order  Bacteriodales  was identified as potential biomarkers of\ngut microbiota. The only biomarker not belonging to the order\n Bacteroidales  was the family\n Erysipelotrichaceae .\nAlterations in the gut microbiota were observed at different phases of the menstrual\ncycle and during the menopausal transition in healthy women. These findings suggest\nthat fluctuations in physiological levels of endogenous estrogen significantly\ninfluence the composition of the gut microbiota ( Krog  et al. , 2022 ;  Schieren  et al. , 2024 ). Higher estrogen levels were\nassociated with a higher abundance of  Bacteroidetes , a lower\nabundance of  Firmicutes  and the  Ruminococcaceae \nfamily, as well as greater diversity ( Zhao\n et al. , 2019 ;  Krog\n et al. , 2022 ;  Schieren  et al. , 2024 ). Studies in animal models have\ndemonstrated that estrogen supplementation can significantly influence the diversity\nand composition of the gut microbiota ( Kaliannan\n et al. , 2018 ;  Acharya\n et al. , 2019 ;  Song\n et al. , 2020 ). However, studies are limited to\nestrogen supplementation via oral gavage, i.p. injection, or subcutaneous implants,\nwith no studies evaluating supraphysiological fluctuations of endogenous\nestrogen.\nSong  et al . (2020)  observed\nthat 7β-estradiol (E2, 10 mg/kg, i.p. injection) increased alpha diversity,\nas measured by the number of OTUs observed, in male mice. Interestingly, alpha\ndiversity, measured by the Shannon index, increased in female mice after OVX, a\ncondition that induces low levels of endogenous estrogens. The authors also observed\nthat estrogen levels influenced the composition of the gut microbiota across all\ntaxonomic levels. At the phylum level, the abundance ratio of\n Cyanobacteria  significantly decreased in male mice receiving\nE2, whereas the abundance ratio of  Verrucomicrobia  significantly\nincreased in the absence of estrogen (OVX). E2 supplementation or ovariectomy did\nnot affect the F: B ratio. In male mice, E2 supplementation increased the commensal\nbacteria/ opportunistic pathogens ratio (control male=1.5  vs . E2\nmale=5) ( Song  et al. , 2020 ).\n Acharya  et al . (2019) \nalso reported that E2 supplementation (via subcutaneous implants) in female OVX mice\nalters the gut microbial composition, altering alpha diversity (observed number of\nOTUs) and beta diversity (Bray-Curtis dissimilarity). This supplementation reduced\nthe relative abundance of  Firmicutes  and\n Actinobacteria  while increasing the presence of\n Bacteroidetes  ( Acharya\n et al. , 2019 ).\nStudies have shown that menopause is associated with significant changes in the\ncomposition of the gut microbiota, probably influenced by estrogen levels ( Baker  et al. , 2017 ;  Yang  et al. , 2022 ;  Huang  et al. , 2024 ).  Yang  et al . (2022)  observed a\nhigher relative abundance of the genera  Odoribacter  and\n Bilophila  in postmenopausal women. In contrast,  Zhao  et al.  (2019)  identified\na depletion of  Firmicutes  and  Roseburia  spp.,\nalong with an overrepresentation of  Bacteroidetes  and\n Tolumonas  in the same population ( Zhao  et al. , 2019 ;  Yang  et al. , 2022 ).  Santos-Marcos  et al.  (2018)  corroborated these\nfindings, reporting a higher F: B ratio and increased abundance of\n Lachnospira  and  Roseburia  in premenopausal\nwomen, highlighting the influence of hormonal status on the microbiota ( Santos-Marcos  et al. , 2018 ).\nIn animal models,  Dai  et al .\n(2023)  identified microbial and metabolic changes during the menopausal\ntransition, associated with neuroendocrine aging. However, there is a gap in\nunderstanding microbiota changes at earlier stages, such as premenarche and\nmenarche.\nThe interaction between estrogen levels and the gut microbiota is complex, involving\nboth the modulation of microbial composition by estrogens and the influence of the\nmicrobiota on estrogen metabolism through the estrobolome ( Baker  et al. , 2017 ;  Siddiqui  et al. , 2022 ;  Huang  et al. , 2024 ). Studies suggest that\nestrogen may indirectly modulate the microbiota through interactions with the immune\nsystem and bile, both of which play a role in selecting microorganisms in the\nintestinal tract ( Salliss  et al. ,\n2021 ;  Huang  et al. ,\n2024 ). Many bacterial genera and species in the intestine contain genes\nencoding β-glucuronidase and β-galactosidase ( Markowitz  et al. , 2012 ). Our results observed\nthat  Parabacteroides  and  Odoribacter , genera\nexpressing β-galactosidase and indirectly involved in the estrobolome, were\nmarkers of gut microbiota composition.\nThe changes in the gut microbiota observed in our study differed from those reported\nin other studies, especially because they did not affect alpha or beta diversity.\nHowever, most studies published previously have examined the effect of E2\nsupplementation on the gut microbiota composition in animal models of menopause,\ncomparing a low estrogen condition with sex hormone replacement. Our study compared\nanimals with normal estrogen levels to a group experiencing supraphysiological\nestrogen fluctuations for ten consecutive weeks. This study is pioneering in\nexploring the effects of repeated COH on gut microbiota composition, providing\nimportant insights into the interplay between reproductive interventions and gut\nhealth. A key strength of this study is the identification of a potential link\nbetween supraphysiological estrogen levels and changes in the gut microbiota, a\nnovel finding that underscores the systemic effects of hormonal manipulation. By\nhighlighting estrogen’s role in modulating gut microbial composition, the study\nopens new avenues for understanding how reproductive technologies, such as ovarian\nhyperstimulation, may have broader implications for women’s health. These\nimplications are particularly relevant in terms of metabolism, immunity, and overall\nwell-being.\nChanges in the composition of the intestinal microbiota observed in our study may\nhave significant implications for reproductive health through immune and metabolic\npathways.  Bacteroidales  are known to enhance the production of\nshort-chain fatty acids (SCFAs), which play a crucial role in maintaining intestinal\nintegrity and modulating systemic inflammation. Their ability to influence cytokine\nproduction and reduce lipopolysaccharide levels may contribute to a lower systemic\ninflammatory state, which is particularly relevant in reproductive disorders where\nchronic inflammation can impair implantation and pregnancy maintenance ( Fabersani  et al. , 2021 ).\n Erysipelotrichaceae , associated with reduced intestinal\ninflammation, may further support reproductive health by promoting a balanced immune\nenvironment, potentially decreasing the risk of immune-mediated pregnancy\ncomplications such as implantation failure or recurrent pregnancy loss ( Zhuang  et al ., 2022 ).\nSimilarly,  Parabacteroides  have been linked to anti-inflammatory\nand metabolic benefits, improving glucose homeostasis and reducing obesity-related\ninflammation-factors that are critical in conditions such as polycystic ovary\nsyndrome and infertility associated with metabolic dysfunction ( Cui  et al ., 2022 ).\nThe decrease of  Rikenellaceae  and  Odoribacter  in\nthe gut microbiota may also have significant immune and metabolic implications.\nThese bacterial genera are important producers of SCFAs, particularly butyrate,\nwhich serves as an energy source for colonocytes and has anti-inflammatory\nproperties that regulate both intestinal and systemic immune responses ( Zarrinpar  et al ., 2018 ;  Amabebe  et al ., 2020 ). A\nreduction in  Rikenellaceae  may lead to a decrease in butyrate\nproduction, compromising intestinal barrier integrity, increasing permeability and\npromoting bacterial translocation and endotoxemia, which are associated with chronic\nlow-grade inflammation, insulin resistance and obesity ( Zarrinpar  et al ., 2018 ;  Amabebe  et al ., 2020 ). Similarly, reduction of\n Odoribacter  further decreases SCFA production and disrupts\nmetabolic and immune homeostasis ( Zarrinpar\n et al ., 2018 ;  Amabebe\n et al ., 2020 ). Overall, the loss of these bacterial\ntaxa may contribute to gut inflammation, metabolic dysregulation and impaired immune\nfunction, highlighting their critical role in maintaining gut health.\nThe pattern of changes in gut microbiota composition in our study may promote\nbeneficial or detrimental effects on gut health and may have implications for\nreproductive health. Further research is needed to clarify the long-term\nconsequences, considering that host-microbiota interactions are influenced by\nmultiple factors, such as diet, hormonal fluctuations, and individual metabolic\nprofiles.\nDespite these strengths, the study has limitations that affect the interpretation of\nits results. Most importantly, the absence of estrogen monitoring throughout the\nprotocol prevents a detailed understanding of the relationship between hormonal\nfluctuations and microbial changes. In addition, the study does not identify the\nspecific timing of microbiota changes during the 10-week ovarian stimulation\nprotocol. Additionally, the study is limited by its single timepoint analysis of\nfecal samples and absence of functional data (e.g., metagenomics or host immune\nmarkers). This leaves a gap in our understanding of when and how these changes\noccur. These weaknesses limit the ability to establish causality and the precise\nmechanisms underlying the observed microbiota shifts. They highlight the need for\nfuture studies with more comprehensive temporal and hormonal monitoring to better\nelucidate these interactions.\nFuture research should aim to elucidate the long-term effects of repeated COH on gut\nmicrobiota composition and its potential systemic and reproductive implications. A\nkey area of investigation is determining whether the observed microbial shifts\npersist beyond the treatment period or if the microbiota gradually returns to its\nbaseline state. Additionally, exploring the role of probiotics supplementation prior\nto COH presents an exciting avenue for mitigating potential microbial disturbances.\nProbiotics have been shown to influence gut microbiota composition, immune\nregulation, and metabolic homeostasis, all of which are relevant to reproductive\nhealth. Future studies should evaluate whether probiotic interventions can prevent\nor attenuate COH-induced microbial alterations and, in turn, improve metabolic and\nreproductive outcomes. These investigations would not only enhance our understanding\nof the gut-reproductive axis but also contribute to optimizing strategies in ART to\npromote better clinical outcomes.\n\nThis study investigated the impact of repeated COH on the gut microbiota in female\nmice. The results demonstrated that COH did not significantly alter microbial\ndiversity (alpha and beta diversity); however, it induced notable changes in the\ntaxonomic composition of the gut microbiota. Specifically, COH was associated with a\nreduced F: B ratio and an increased abundance of microbial taxa. Biomarker analyses\nfurther identified potential discriminatory taxa, including  Bacteroidales,\nErysipelotrichaceae, Parabacteroides, Odoribacter , and\n Rikenellaceae , as markers of microbial shifts in response to\nCOH. These findings highlight the potential systemic implications of repeated COH\nexposure on gut microbial composition, which may contribute to broader physiological\nchanges associated with hormonal modulation. The study emphasizes the need for\nfurther research to investigate the long-term effects of COH on gut health, immune\nregulation, and metabolic outcomes, particularly in the context of ART. By\nelucidating these interactions, future studies could help develop strategies to\nmitigate the unintended consequences of COH on overall health.","source_license":"CC-BY-4.0","license_restricted":false}