Mechanistic insights into endometriosis: roles of Streptococcus agalactiae and L-carnitine in lesion development and angiogenesis

This study was approved by the Medical Ethics Committee of the Fifth Affiliated Hospital of Sun Yat-sen University (approval number: 2019-K330-1) and informed consent was obtained from the individual. Between 2019 and 2021, cervical mucosal samples were collected from 70 healthy physical examinees and 72 patients diagnosed with EMs at the Fifth Affiliated Hospital of Sun Yat-sen University. Diagnosis of EMs was established using B-ultrasound, followed by laparoscopic intervention and histopathological confirmation. Cervical mucosal samples were collected as follows. First, the cervix was exposed using a vaginal speculum. Next, vaginal cleansing was performed using 0.9% saline. Then, cervical mucus was collected from the cervical canal lumen, spanning from the external os to 2 cm inward. All samples were collected between the 7th and 14th day of the menstrual cycle. The cervical mucus was collected as evenly as possible using a sterile cotton swab and placed into tubes containing 1 mL of sterile phosphate-buffered saline (PBS). The tubes were immediately immersed in liquid nitrogen and stored at − 80 °C until further use. Ectopic EMs tissues were either fixed in 4% normal buffered formaldehyde for histological analysis or frozen in liquid nitrogen for subsequent experiments. Patients (1) aged 18–50 years, with a regular menstrual cycle (21–35 days) and menstrual bleeding lasting 2–7 days; (2) with no history of undergoing invasive procedures or using antibiotics or anti-inflammatory drugs in the past 3 months; and (3) with no documented history of sexually transmitted infections (e.g., Chlamydia trachomatis and Neisseria gonorrhoeae ), reproductive tract malformations, adenomyosis, endocrine diseases, or tumors were examined. Demographic and clinical data, including age, BMI, relationship status, pregnancy status, and other clinical parameters, were obtained from the medical records. For 16S rRNA gene sequence analysis, 32 cervical mucus specimens from EMs patients (generation dataset: n = 22; validation dataset: n = 10) and 30 from healthy controls (generation dataset: n = 20; validation dataset: n = 10) were analyzed, as detailed in Table S2 . The V4 and V5 hypervariable regions of the bacterial 16S rRNA gene were sequenced using the Illumina MiSeq platform. Raw sequence data were demultiplexed, quality-filtered, and merged to generate clean reads. Operational taxonomic units (OTUs) were clustered at a 97% similarity threshold. Representative sequences were selected for taxonomic annotation to determine species identity and abundance. OTUs abundance, alpha diversity (Shannon and Chao1 indices), and taxonomic composition were assessed to characterize species richness, evenness, and group-specific OTUs. Beta diversity was analyzed using non-metric multidimensional scaling analysis (NMDS) to explore the differences in microbial community structure among different samples or groups. To further investigate microbial differences between the EMs and control groups, multiple statistical and machine learning methods were employed—including EdgR, DESeq2, zero-inflated negative binomial model (ZINB), negative binomial model (NEGBIN), compound Poisson lognormal model (CPLM), Random Forest, and linear discriminant analysis (LDA) effect size (LEfSe). Functional predictions were performed using PICRUST [ 63 ], Tax4Fun [ 64 ], and FAPROTAX [ 65 ]. Cervical mucus specimens were collected from patients with EMs (n = 12) and healthy controls (n = 12). Genomic DNA was extracted using the Bacteria DNA Kit (catalog number: DP118-02, TIANGEN BIOTECH, Beijing) according to the manufacturer’s protocols. Quantitative reverse transcription polymerase chain reaction (qRT–PCR) was performed on a real-time PCR system (ABI7500, Bio-Rad, USA) using ChamQ Universal SYBR qPCR Master Mix (catalog number: Q711-03, Vazyme, Nanjing, China). Each 20 μL reaction included 10 μL of 2 × SYBR Green PCR Master Mix, 1 μL of each primer, and 8 μL of template DNA, with technical duplicates for each sample. The thermal cycling conditions were as follows: initial denaturation at 95 °C for 20 s, followed by 40 cycles of 95 °C for 3 s and 60 °C for 30 s. The S. agalactiae reference strain (ATCC 13813, American Type Culture Collection) served as a positive control, whereas nuclease-free water was used as a negative control. Primers (forward: 5′-AGGAAACCTGCCATTTGCG-3′; Reverse: 5′-CAATCTATTTCTAGATCGTGG-3′) were synthesized by Guangzhou IGE Biotechnology Ltd. For standard curve generation, serial tenfold dilutions of S. agalactiae genomic DNA (10 7 to 10 1 copies/μL; BTN14-18,810) were amplified to create a six-point calibration curve. The following quantification formula was used: Cт = a[log(Q)] + b, where Cт is the quantification cycle value, a is the standard curve slope (− 2.364), b is the y-intercept (16.113), and Q is the copy number of S. agalactiae . Bacterial load in samples was calculated using the experimentally derived Cт values and the standard curve equation. All animal procedures were performed in accordance with the Guidelines for the Ethical Review of Laboratory Animal Welfare (GB/T35892-2018) and approved by the Experimental Animal Ethics Committee of the Fifth Affiliated Hospital of Sun Yat-sen University. A total of 93 8-week-old nulliparous female C57BL/6 mice were purchased from Beijing Charles River Experimental Animal Technical Co., Ltd. (Beijing, China). Of these, 31 were randomly selected as uterine fragment donors, whereas the remaining 62 served as recipients. All animals were housed under standardized conditions at a constant temperature of 26 °C, a 14/10-h light/dark cycle, and free access to food and water. We established a mouse model of EMs via the intraperitoneal (i.p.) injection of endometrial fragments [ 66 ]. After 1 week of acclimatization, each donor mouse received an intramuscular injection of 2 μg/mouse estradiol benzoate (Sigma) to stimulate endometrial proliferation. One week later, the mice were euthanized, and their uteri were harvested. The uterine tissues were placed in a Petri dish containing warm sterile saline and bisected longitudinally with scissors. Both uterine horns from each mouse were minced with scissors into fragments < 1 mm in diameter. These fragments were then injected intraperitoneally into the recipient mice. To minimize potential bias, endometrial tissue fragments from 31 donor mice were pooled, evenly divided into 62 sections, and intraperitoneally into individual recipient mice. This approach reduced inter-donor variability. EMs-like lesions typically form within 3 days following uterine tissue induction [ 66 ]; therefore, the control group received 0.1 mL of PBS. To investigate the role of S. agalactiae in lesion progression, 1.0 × 10 5 colony-forming units (CFU)/mL of S. agalactiae were intraperitoneally injected into the EMs group on days 4, 5, and 6. From days 9 to 15, the mice were treated daily with teicoplanin (Sanofi), a glycopeptide antibiotic commonly used against Gram-positive bacterial infections, particularly Staphylococcus and Streptococcus species [ 67 ]. To further investigate whether S. agalactiae promotes EMs development via L-carnitine, we established three additional experimental groups in addition to the control and S. agalactiae groups. The first E. coli group received 1.0 × 10 5  CFU/mL of Escherichia coli ( E. coli ) on days 4, 5, and 6 via i.p. injection; the second L-carnitine group received 50 mg/kg L-carnitine (TargetMol, catalog number: T2203) from days 4 to 15 via i.p. injection; and the third S. agalactiae  + Mildronate ( S. agalactiae -challenged mice) group received a daily i.p. injection of 50 mg/kg Mildronate (TargetMol, catalog number:T6586), an inhibitor of L-carnitine synthesis, from days 4 to 15. Three weeks after the i.p. injection, the recipient mice were euthanized. The peritoneal cavities were photographed, and ectopic lesions were carefully excised from the surrounding tissue. Based on surface vascularization and color, the ectopic lesions were categorized as either red or white. The ectopic lesions were subsequently fixed in 4% normal buffered formaldehyde for histological evaluation or stored in liquid nitrogen for subsequent experiments. The total injected volume is consistent with the guidelines outlined in the National Institutes of Health Guide for the Care and Use of Laboratory Animals, which recommends an intraperitoneal injection volume of 5–10 mL/kg body weight in mice, equivalent to 125–250 μL for a 25 g mouse. The hEM15A cell line, a transformed eutopic endometrium stromal cell line [ 68 ], was kindly provided by Professor Xiaohong Chang of the Peking University People’s Hospital. The cells were cultured at 37 °C in 5% CO 2 using minimum essential medium (Gibco, Thermo Fisher Scientific, Waltham, MA, USA) supplemented with 15% fetal bovine serum (FBS) (Gibco, Thermo Fisher Scientific, Waltham, MA, USA). Human umbilical vein endothelial cells (HUVECs) were purchased from ScienCell (catalog number: 8000) and cultured in Endothelial Cell Medium (ECM) (ScienCell, catalog number: 1001) supplemented with 10% of FBS. For the S. agalactiae metabolic supernatant assay, hEM15A cells and HUVECs were co-cultured using conditioned media. S. agalactiae (ATCC 13813) and S. anginosus (ATCC 33397) were cultured at 1 × 10 5  CFU/mL in brain heart infusion (BHI) broth (Genbank Biosciences Inc., China), whereas E. coli (ATCC 11775) was cultured at the same concentration in the Luria–Bertani broth (Beyotime Biotechnology, Shanghai, China). S. agalactiae at 1 × 10 5  CFU/mL was heat-inactivated by incubation at 75 °C for 30 min and subsequently cultured in BHI broth. After 12 h of incubation, the supernatants of live S. agalactiae and E. coli and heat-inactivated S. agalactiae were filtered twice using a 0.22-μm membrane filter. The co-cultured medium was prepared by mixing the filtered bacterial supernatant with the cell culture medium at a 1:1 ratio. Subsequently, 5 × 10 5 of hEM15A cells or HUVECs were cultured for 24 h in the co-culture media. The cells were divided into six groups: control (PBS), incubated with cultural supernatant of E. coli , incubated with culture supernatant of heat-inactivated S. agalactiae , incubated with cultural supernatant of S. agalactiae , incubated with L-carnitine (TargetMol, T2203, 40 µM), and incubated with culture supernatant of S. agalactiae and Mildronate (TargetMol, T6586, 40 µM). The migration and invasion abilities of hEM15A cells and HUVECs were evaluated using a modified Boyden chamber migration assay [ 69 ]. Briefly, Transwell chambers (Corning, Corning, NY, USA) with 8-μm pore inserts were used. Next, cells were starved for 12 h in a serum-free medium. For both assays, the cells were harvested, resuspended in serum-free medium at a concentration of 1 × 10 5 cells/100 μL, and seeded into the upper chambers of Transwell inserts (Corning, USA) either pre-coated with 100 μL of 1:8-diluted Matrigel (BD Biosciences) (for invasion assays) or left uncoated (for migration assays). The medium supplemented with 10% FBS was added to the lower chamber as a chemoattractant. After 20 h of incubation, the Transwell inserts were collected and fixed in 75% ethanol. Cells that migrated to the bottom of the top chamber were stained with crystal violet and counted in five randomly selected fields per insert under a light microscope (200 × magnification). Cell counts were analyzed using the ImageJ software (National Institutes of Health). Data obtained from three separate chambers were expressed as the mean values. Cell proliferation was monitored in real time using the IncuCyte S3 platform (Sartorius, Göttingen, Germany). Overall, 5000 hEM15A cells in 100 μL/well were seeded into 96-well plates and treated with the co-culture media prepared by mixing filtered bacterial supernatant and cell culture medium in a 1:1 ratio. Subsequently, the cells were imaged using a phase-contrast channel on an IncuCyte S3 platform (Sartorius, Göttingen, Germany). Phase-contrast images were captured from five distinct regions within each well every 12 h using a 10 × objective. Cell confluence was quantified as a percentage using the IncuCyte S3 image analysis software, which detects cell edges automatically. For metabolite extraction, 1 mL of ice-cold methanol was added to 200 μL ectopic endometrial homogenate. The mixture was vortexed for 1 min, incubated at 4 °C for 10 min, and centrifuged at 16,000 g for 10 min at 4 °C. The supernatants were evaporated to dryness and reconstituted in 100 μL of 0.1% formic acid in 5% acetonitrile. A 5 μL aliquot of each sample was then injected and separated on an ACQUITY UPLC HSS T3 analytical column (2.1 × 150 mm, 1.8 μm, 100 Å, Waters) maintained at 35 °C using a Thermo Scientific Dionex UltiMate 3000 Rapid Separation LC system. The mobile phases consisted of 0.1% formic acid in water (a) and acetonitrile (b), with a flow rate of 0.3 mL/min. Data were acquired using an Orbitrap Fusion Lumos Tribrid mass spectrometer (Thermo Scientific, San Jose, CA, USA) equipped with a heated electrospray ionization source operating in positive ion mode with a spray voltage of + 3500 V. The ion transfer tube temperature, vaporized temperature, sheath gas flow, auxiliary gas flow, and sweep gas were maintained at 300 °C, 350 °C, 40 units, 15 arbitrary units, and 1 unit, respectively. Metabolite profiling was performed in a data-dependent acquisition mode over a mass range of 100–1000 m/z, with resolutions set at 60,000 for MS1 and 15,000 for MS/MS. The automatic gain control target and maximum injection time were configured at 5 × 10 4 and 50 ms, respectively. Finally, the metabolomic features were analyzed using Compound Discoverer (v3.1, Thermo Fisher Scientific). The carnitine standard curve was constructed according to the manufacturer’s protocol (BioVision, K642-100). Briefly, ectopic lesions were homogenized in 100 µL of assay buffer and centrifuged at 13,000 g for 10 min to remove insoluble materials. Subsequently, 50 µL of the supernatant was directly diluted with assay buffer. The sample wells were adjusted to a final volume of 50 µL/well in a 96-well plate. Thereafter, 40 µL of reaction buffer—containing L-carnitine converting enzyme, development mix, substrate mix, and a probe—was added to each well. The reaction mixture was incubated in the dark for 30 min at room temperature. Finally, the optical density (OD) was measured at 570 nm, and the L-carnitine content was calculated using the standard curve. Immunohistochemical (IHC) staining was performed to assess the protein expression of CD34 and anti- Streptococcus Group B in EMs tissue. Briefly, formalin-fixed, paraffin-embedded tissue Sects. (4 µm) were blocked with 10% goat serum for 30 min at room temperature. The sections were then incubated with the primary antibody against CD34 (Invitrogen, catalog number: MA1-10,202, 1:500) or anti- Streptococcus Group B (Abcam, catalog number: ab53584, 1:200) for 15 h at 4 °C. After rinsing with PBS, the sections were incubated for 30 min at 37 °C with a horseradish peroxidase polymer conjugate (ZSGB-BIO). Hemangioma tissues and tissues from S. agalactiae -challenged mouse model were employed as positive controls for CD34 and Streptococcus Group B. PBS was used as a negative control in place of the primary antibody. Quantitative immunohistology analysis was performed by two independent pathologists (J.H. and Z.L.) using the ImageJ software (National Institutes of Health). The protein expression scores were calculated based on OD, yielding continuous values ranging from 0 to 300. Any discrepancies between observers were resolved by consensus. The VEGF concentrations in cell supernatants and tissue samples were measured using enzyme-linked immunosorbent assay (ELISA). For the supernatants of hEM15A cells or HUVECs, the VEGF concentration was quantified using Quantikine ELISA Human VEGF Immunoassay (catalog number: DVE00, R&D Systems) following the manufacturer’s instructions. For the mouse samples, the VEGF concentrations in tissue homogenates (prepared in cold PBS using an electric homogenizer) or serum were determined using the Mouse VEGF Simplestep ELISA Kit (catalog number: ab209882, Abcam) according to the manufacturer’s instructions. Ectopic lesions were rapidly frozen in liquid nitrogen, homogenized, and lysed with the extraction buffer provided in the kit (100 mg of wet tissue in 500 µL of cell extraction buffer PTR). Following centrifugation, the supernatants were analyzed using the Mouse VEGF ELISA Kit (Abcam, ab209882). The total VEGF content was calculated based on the standard curve and normalized to the tissue wet weight (g) to derive the VEGF concentration (pg/g tissue). Briefly, HUVECs were serum-starved in ECM supplemented with 1% FBS for 6 h, harvested using trypsin, and resuspended in the same medium. Next, 2.5 × 10 4 of HUVECs were seeded into a 96-well plate pre-coated with growth factor-reduced Matrigel and incubated with E. coli , heat-inactivated S. agalactiae , S. agalactiae , L-carnitine, and S. agalactiae combined with the L-carnitine synthesis inhibitor Mildronate (4 h after S. agalactiae treatment). Tube formation was evaluated after 8 h, and images were captured from five random fields of view per well using the IncuCyte S3 system (ESSEN Bioscience, Sartorius, Germany). The total length of tubular segments was quantified using the Angiogenesis Analyzer plugin for Image J, available at the NIH website ( https://imagej.nih.gov/ij/macros/toolsets/Angiogenesis%20Analyzer.txt ). FISH was performed on formalin-fixed paraffin-embedded endometrial and EMs specimens. Tissue sections were hybridized with a 5′ FAM-labeled S. agalactiae -specific probe, Saga [ 70 ]. The probe sequence (5′-GTAAACACC AAACMTCAGCG -3′) was obtained from probeBase ( http://probebase.csb.univie.ac.at/ ). A scrambled probe (5′-CAATTGGGCCCGCTTTAAC CCAATCTC-3′) was used as the nonspecific negative control. Following deparaffinization and rehydration, sections were treated sequentially with 0.2 N HCl and proteinase K for 10 min each. After blocking with buffer at 55 °C for 2 h, the FAM-labeled probe (diluted 1:50 in 25% hybridization buffer and pre-heated at 88 °C for 3 min) was applied and hybridized overnight in a dark, humid chamber at 42 °C. Specimens were then washed in wash solution (20 mM Tris–HCl, pH: 7.2; 40 mM NaCl) and mounted with DAPI-antifade solution. Fluorescent images were acquired using a fluorescence microscope (Leica, DM6B). Differentially expressed mRNAs were identified, and Gene Set Enrichment Analysis (GSEA) was performed to compare endometrial tissue samples with a high abundance of S. agalactiae with those from uninfected controls. The abundance of S. agalactiae was determined by 16S rRNA sequencing, and tissue samples were collected from the Department of Pathology at the Fifth Affiliated Hospital, Sun Yat-sen University. Total RNA was extracted using RNeasy Micro Kit (Qiagen, catalog number: 74,004), and paired-end libraries were prepared using the TruSeq RNA Sample Preparation Kit (Illumina, USA) following the TruSeq RNA Sample Preparation Guide. Gene expression data were obtained using the Illumina NovaSeq 6000 platform. Quality control and preprocessing of the raw sequencing data were performed using FastQC and TrimGalore. Gene-level expression counts were calculated utilizing the STAR aligner. Differential gene expression analysis was conducted using the DESeq2 package in R. In this study, differentially expressed genes related to EMs were defined by an absolute log fold change greater than 1 (|log2FC|> 1) and a P value of < 0.05. Furthermore, GSEA was performed to identify the key pathways associated with S. agalactiae infection in EMs. Statistical analyses were performed using SPSS version 18.0, and results were expressed as the mean ± standard deviation. Student’s t-test was used for comparisons between two groups. For comparisons among multiple groups, the homogeneity of variance was first assessed; a one-way analysis of variance or non-parametric independent sample t-test (Mann–Whitney U test) was used when was confirmed. Pearson’s correlation analysis was performed to evaluate the correlation between protein expression levels. All tests were two-tailed, and a P value of < 0.05 was considered significant.

Cervical mucosal samples were obtained from 22 patients diagnosed with EMs and 20 matched healthy individuals serving as controls, to investigate the microbiota distribution within cervical mucus in the generation dataset. Statistical analysis of the samples at 97% sequence similarity produced a rarefaction curve and random abundance metrics, indicating that the sequencing depth and species detection rate were approaching saturation. Consequently, the samples were deemed suitable for subsequent bioinformatics analysis. Analysis indices, including Shannon’s diversity index across taxonomic levels (phylum ( P  = 0.202), class ( P  = 0.041), order ( P  = 0.023), family ( P  = 0.017), and genus ( P  = 0.016), revealed significantly higher bacterial diversity in the EMs group compared with the control group (Fig.  1 A). This conclusion was further corroborated by an NMDS of microorganism community composition based on the relative abundance of bacterial OTUs (Fig.  1 B). Additionally, taxonomy abundance analysis was performed to differentiate taxa at the family and genus levels, focusing on the top 21 strains based on relative abundance (Fig.  1 C). Multiple differential abundance analysis methods—EdgR, DESeq2, the zero-inflated negative binomial model (ZINB), the negative binomial model (NEGBIN), and the compound Poisson lognormal model (CPLM)—were employed to identify significant differences in microbial composition between the groups. The following 11 bacterial genera were consistently identified as differentially abundant, as illustrated in the Venn diagram (Fig.  1 D and E): Acidonorax , Atopobium , Prevotella , Streptococcus , Parvimonas , Dorea , Finegoldia , Pseudomonas , Megasphaera , Comamonas, and Alicycliphilus . S. agalactiae was subsequently identified as a potential keystone bacterium in the EMs group through phylogenetic tree analysis, linear discriminant analysis (LDA) effect size (LEfSe), and Random Forest classification (Fig.  1 F–H). A scatter plot showed that the positive detection rate of S. agalactiae in the generation dataset was 45.5% (Fig.  1 I). To further validate these findings, 16S rRNA gene sequencing was conducted on an independent validation cohort comprising patients with EMs (n = 10) and healthy controls (n = 10). Consistent with the findings from the initial dataset, the EMs group exhibited significant dysbacteriosis in cervical mucus, characterized by an abnormal increase in the relative abundance of Streptococcus , and a 60% positivity rate for S. agalactiae (Fig. S1 A–G). qRT-PCR further confirmed these results, with elevated levels of S. agalactiae detected in the cervical mucus of 11 patients with EMs (91.7%), whereas no positive detection was observed in the healthy control group (Fig.  1 J). Collectively, these findings suggest that S. agalactiae may play a critical role in the pathogenesis of cervical dysbacteriosis associated with EMs. Fig. 1 Differences in cervical mucus microbiota between the control and EMs groups. A Comparison of Shannon Diversity Index at phylum, class, order, family, and genus taxonomic levels between the control and EMs groups using box diagram. B Beta diversity analysis of nonmetric multidimensional scaling (NMDS) at various taxonomic levels including phylum, class, order, family, and genus. The corresponding NMDS stress values are shown on the graph. C The most abundant taxa at the family and genus levels in the control and EMs groups are presented. D Composite volcano plots illustrate the results of five statistical methods employed to screen for abnormal microbiota at the genus level. The abnormal genera are shown in red. E A Venn diagram was constructed using the discrepant bacterial genera, identifying 11 microorganisms shared between the EMs and control groups. F A phylogenetic tree is presented, with concentric circles representing taxonomic levels from phylum to genus or species. Red stacked bar charts indicate the abundance distribution at the genus level in the EMs group. A rectangular cladogram illustrates the bacterial composition detected in patients with EMs (red) and healthy controls (cyan). Streptococcus was the most differentially abundant genus in the EMs group compared with healthy controls. G Linear discriminant analysis effect size (LEfSe) was used to identify key microbial phylotypes associated with EMs. The Streptococcus agalactiae ( S. agalactiae ) species showed a significantly increased abundance in the EMs group, as indicated by the histogram of LDA scores. H A random forest model using 16S rRNA genus-level abundance was applied to classify EMs and control groups. The top 29 most discriminant genera were identified in the models used to classify patients with EMs and healthy controls. The lengths of the bars indicate the importance of each variable, whereas the colors represent EMs (red) or healthy controls (blue). I The abundance of S. agalactiae was compared between the control and EMs groups. J A scatter plot displays the copy number of S. agalactiae in the cervical mucus of patients with EMs compared with healthy controls, detected by qRT-PCR. Negative controls: ddH 2 O; positive controls: DNA from Streptococcus agalactiae (ATCC 13813). Data are expressed as the mean ± SEM. EMs, endometriosis Differences in cervical mucus microbiota between the control and EMs groups. A Comparison of Shannon Diversity Index at phylum, class, order, family, and genus taxonomic levels between the control and EMs groups using box diagram. B Beta diversity analysis of nonmetric multidimensional scaling (NMDS) at various taxonomic levels including phylum, class, order, family, and genus. The corresponding NMDS stress values are shown on the graph. C The most abundant taxa at the family and genus levels in the control and EMs groups are presented. D Composite volcano plots illustrate the results of five statistical methods employed to screen for abnormal microbiota at the genus level. The abnormal genera are shown in red. E A Venn diagram was constructed using the discrepant bacterial genera, identifying 11 microorganisms shared between the EMs and control groups. F A phylogenetic tree is presented, with concentric circles representing taxonomic levels from phylum to genus or species. Red stacked bar charts indicate the abundance distribution at the genus level in the EMs group. A rectangular cladogram illustrates the bacterial composition detected in patients with EMs (red) and healthy controls (cyan). Streptococcus was the most differentially abundant genus in the EMs group compared with healthy controls. G Linear discriminant analysis effect size (LEfSe) was used to identify key microbial phylotypes associated with EMs. The Streptococcus agalactiae ( S. agalactiae ) species showed a significantly increased abundance in the EMs group, as indicated by the histogram of LDA scores. H A random forest model using 16S rRNA genus-level abundance was applied to classify EMs and control groups. The top 29 most discriminant genera were identified in the models used to classify patients with EMs and healthy controls. The lengths of the bars indicate the importance of each variable, whereas the colors represent EMs (red) or healthy controls (blue). I The abundance of S. agalactiae was compared between the control and EMs groups. J A scatter plot displays the copy number of S. agalactiae in the cervical mucus of patients with EMs compared with healthy controls, detected by qRT-PCR. Negative controls: ddH 2 O; positive controls: DNA from Streptococcus agalactiae (ATCC 13813). Data are expressed as the mean ± SEM. EMs, endometriosis As shown in Table S1 , a high abundance of S. agalactiae was strongly correlated with the number of ectopic lesions, whereas no significant associations were found with other clinical characteristics, such as age, body mass index (BMI), and rASRM classification. To evaluate the role of S. agalactiae in the development of EMs, a mouse model was established by the i.p. injection of minced mouse uterine tissue into the peritoneal cavity of a recipient mouse (Fig.  2 A). Figure  2 B presents the representative macroscopic images of the EMs-like lesions. Lesions in the S. agalactiae group were more widely distributed and exhibited greater vascularization compared with those in the control group (Fig.  2 B). Representative microscopic images of the hematoxylin and eosin (H&E)-stained lesions confirmed the presence of EMs-like lesions with glandular epithelial and stromal regions (Fig.  2 B). Subsequently, the number of histologically identified EMs lesions were counted, as lesion number reflects disease establishment. Consistent with expectations, mice exposed to S. agalactiae developed significantly more lesions than phosphate-buffered saline (PBS)-treated animals (Fig.  2 C). Another characteristic of EMs is the anatomical distribution of ectopic lesions. Consequently, the localization of lesions in the pelvic and abdominal cavities was investigated. In PBS-treated mice, lesions were predominantly confined to the abdominal wall. By contrast, lesions in the S. agalactiae -treated group were disseminated across multiple anatomical sites, including the postcaval vein cava, intestinal wall, mesentery, ovary, mesometrium, and paracystium, suggesting that the colonization potential of endometriotic lesions was enhanced by S. agalactiae (Fig.  2 D). To further evaluate whether limiting S. agalactiae colonization could attenuate disease progression, mice were injected with antibiotics targeting this bacterium. A marked reduction in both the total number and colonization of lesions was observed in the antibiotics-treated mice (Fig.  2 C and D). The presence of S. agalactiae in the endometriotic lesion was confirmed by fluorescence in situ hybridization using the Saga-specific probe (Fig.  2 E). To investigate the functional role of S. agalactiae , hEM15A cells were subjected to indirect co-culture with the bacterium. A significant enhancement in cellular migration, invasion, and proliferation was observed under these conditions (Fig.  2 F and G). Similar results were obtained using a clinical isolate of S. agalactiae derived from the cervical mucus of a patient with EMs, in both in vivo and in vitro experiments (Fig. S2A–E). Therefore, S. agalactiae may contribute to EMs progression by promoting lesion growth, and ectopic tissue colonization in murine models. Fig. 2 In vivo and in vitro analysis of the role of Streptococcus agalactiae in EMs formation. A A schematic diagram illustrating the experimental timeline for donor and recipient mice. Endometriotic lesions were induced at day 0. Recipient mice were intraperitoneally injected with Streptococcus agalactiae ( S. agalactiae ) or phosphate-buffered saline on days 4, 5, and 6. The S. agalactiae -treated mice received antibiotic treatment from days 9 to 15. On day 21, the mice were sacrificed, and endometriotic lesions were assessed. B Representative images showing the presence of single or multiple cystic lesions on the abdominal wall (upper panel). The middle image displays H&E staining of mouse lesions, confirming the histological diagnosis of EMs (scale bar: 100 µm). The lower panel presents photographs of excised lesions from mice. C Scatter plot showing the total number of lesions in the control, S. agalactiae -treated, and S. agalactiae plus antibiotic-treated groups (n = 6 mice per group). D Quantitative analysis of the adhesion area between the endometriotic cysts and adjacent organs, including the postcaval vein, intestinal wall, mesentery, ovary, mesometrium, and paracystium, across different groups (n = 6 mice per group). E Fluorescent in situ hybridization (FISH) detection of Streptococcus using Saga probes (green) in endometriotic lesions after antibiotic treatment. Nuclei are counterstained with DAPI (blue). Scale bar: 100 μm (mice). F Cell migration and Matrigel invasion assays were performed to assess the effect of S. agalactiae in hEM15A cell motility and invasiveness. Cells were fixed and stained, and quantification was conducted by counting cells in five random fields under a light microscope (× 200) (scale bar: 200 µm) (n = 3). Three independent biological replicates were used for the migration and invasion assays. G Real-time analysis of hEM15A cell proliferation, affected by S. agalactiae , performed using confluence (percentage) curves over a period of more than 90 h. Two independent biological replicates were included in the proliferation assay. Data are expressed as the mean ± SD; P values are determined using a one-way ANOVA for C and D and Student’s t-test for F and G ; * P  < 0.05, ** P  < 0.01. i.p., intraperitoneal; DAPI, 4′, 6-diamidino-2 phenylindole; ANOVA, analysis of variance; SD, standard deviation; EMs, endometriosis; H&E, hematoxylin & eosin In vivo and in vitro analysis of the role of Streptococcus agalactiae in EMs formation. A A schematic diagram illustrating the experimental timeline for donor and recipient mice. Endometriotic lesions were induced at day 0. Recipient mice were intraperitoneally injected with Streptococcus agalactiae ( S. agalactiae ) or phosphate-buffered saline on days 4, 5, and 6. The S. agalactiae -treated mice received antibiotic treatment from days 9 to 15. On day 21, the mice were sacrificed, and endometriotic lesions were assessed. B Representative images showing the presence of single or multiple cystic lesions on the abdominal wall (upper panel). The middle image displays H&E staining of mouse lesions, confirming the histological diagnosis of EMs (scale bar: 100 µm). The lower panel presents photographs of excised lesions from mice. C Scatter plot showing the total number of lesions in the control, S. agalactiae -treated, and S. agalactiae plus antibiotic-treated groups (n = 6 mice per group). D Quantitative analysis of the adhesion area between the endometriotic cysts and adjacent organs, including the postcaval vein, intestinal wall, mesentery, ovary, mesometrium, and paracystium, across different groups (n = 6 mice per group). E Fluorescent in situ hybridization (FISH) detection of Streptococcus using Saga probes (green) in endometriotic lesions after antibiotic treatment. Nuclei are counterstained with DAPI (blue). Scale bar: 100 μm (mice). F Cell migration and Matrigel invasion assays were performed to assess the effect of S. agalactiae in hEM15A cell motility and invasiveness. Cells were fixed and stained, and quantification was conducted by counting cells in five random fields under a light microscope (× 200) (scale bar: 200 µm) (n = 3). Three independent biological replicates were used for the migration and invasion assays. G Real-time analysis of hEM15A cell proliferation, affected by S. agalactiae , performed using confluence (percentage) curves over a period of more than 90 h. Two independent biological replicates were included in the proliferation assay. Data are expressed as the mean ± SD; P values are determined using a one-way ANOVA for C and D and Student’s t-test for F and G ; * P  < 0.05, ** P  < 0.01. i.p., intraperitoneal; DAPI, 4′, 6-diamidino-2 phenylindole; ANOVA, analysis of variance; SD, standard deviation; EMs, endometriosis; H&E, hematoxylin & eosin The observation that S. agalactiae enhanced hEM15A cell proliferation and migration in a non-contact co-culture system suggests that secreted metabolites may mediate its effects on endometrial cell biology. To investigate this possibility, untargeted metabolomic profiling was conducted using cervical mucosal samples (generation dataset: EMs: n = 30, controls: n = 30; validation dataset: EMs: n = 10, controls: n = 10) and serum samples (EMs: n = 30, controls: n = 30) to identify small-molecule metabolic signatures associated with S. agalactiae . Kyoto Encyclopedia of Genes and Genomes enrichment analysis of differential metabolites in the cervical mucus from both the generation and validation datasets, compared with healthy controls, revealed significant enrichment in fatty acid degradation pathways in EMs samples (Fig.  3 A). Furthermore, comparative metabolomic analysis across the generation, validation, and serum datasets consistently identified L-carnitine as a significantly upregulated metabolite in EMs samples relative to controls (Fig.  3 B). Elevated L-carnitine concentrations were confirmed in the cervical mucus from the generation (Fig.  3 C) and validation datasets (Fig. S3 A), as well as in the serum of patients with EMs (Fig. S3 B), compared with healthy controls. Using an hEM15A co-culture model, substantial increases in intracellular L-carnitine levels were observed following treatment with S. agalactiae and exogenous L-carnitine compared with controls and E. coli -treated, Streptococcus anginosus ( S. anginosus )-treated, and heat-inactivated S. agalactiae groups (Fig.  3 D). Conversely, co-treatment with S. agalactiae and Mildronate (3-(2,2,2-trimethylhydrazine propionate), a specific inhibitor of L-carnitine biosynthesis/transport, significantly reduced intracellular L-carnitine accumulation (Fig.  3 D). Metabolomic profiling of the culture supernatants further demonstrated decreased L-carnitine concentrations in culture supernatants of S. agalactiae -treated groups compared with the S. agalactiae  + Mildronate co-treated counterparts (Fig. S3 C). L-carnitine is a crucial amino acid-derived transporter that facilitates the translocation of acyl residues into the mitochondrial matrix, thereby contributing to fatty acid beta-oxidation, tricarboxylic acid (TCA) cycle activity, adenosine triphosphate production, and overall energy metabolism [ 33 ]. Based on these findings, a contribution of S. agalactiae to EMs pathogenesis through the modulation of L-carnitine levels in an allograft mouse model was proposed. Representative histological images of H&E-stained sections displayed EMs-like histological features characterized by glandular epithelial and stromal areas (Fig.  3 E). Significantly elevated L-carnitine levels were detected in S. agalactiae -treated ectopic lesions compared with those in the PBS- or E. coli -treated mice (Fig.  3 F). Treatment with L-carnitine alone, similar to S. agalactiae , increased the lesion number, weight, and anatomical colonization in the EMs mouse model compared with the control group (Fig.  3 G–I). Notably, co-administration of Mildronate effectively abolished both L-carnitine upregulation and the associated increase in lesion burden following S. agalactiae exposure (Fig.  3 E–I) [ 34 ]. Evaluation of a different Gram-negative bacterium, E. coli , in the same mouse model revealed no effect on the number or weight of endometriotic lesions (Fig.  3 E–I). Although cell migration and invasion abilities were enhanced by indirect co-culture with S. agalactiae or direct stimulation with L-carnitine, these effects were inhibited by Mildronate treatment, which was consistent with the in vivo results (Fig.  3 J). Collectively, these findings indicate that S. agalactiae promotes EMs growth and development through an L-carnitine-dependent mechanism. Fig. 3 L-carnitine elevation in the cervical mucus of patients with EMs and its regulation by Streptococcus agalactiae in experimental models. A Bubble diagram showing KEGG enrichment analysis comparing the EMs group and healthy controls (n = 30 per group, human specimens). Red bubbles indicate upregulated pathways, and green bubbles indicate downregulated pathways. B Venn diagram depicting the overlap of upregulated metabolites among the generation dataset, cervical mucous validation dataset, and serum dataset (human specimens). C Relative abundance of L-carnitine in the cervical mucus was analyzed in the generation dataset by untargeted metabolomics, comparing patients with EMs and healthy controls (n = 30 per group, human specimens). D Untargeted metabolomic profiling of intracellular L-carnitine levels was performed in hEM15A cells. The S. agalactiae and L-carnitine treatment groups showed significant elevation of L-carnitine levels, whereas the S. agalactiae  + Mildronate co-treatment group exhibited marked reduction (n = 3 biological replicates per group). E H&E staining of mouse lesions confirmed histological evidence of EMs in the different groups (scale bar: 100 µm, mice). F L-carnitine levels in mouse lesions were quantified using a colorimetric assay, revealing significantly elevated L-carnitine levels in lesions from Streptococcus agalactiae ( S. agalactiae )-treated mice (n = 6 for each group, mice). G , H Scatter plots depicting the number ( G ) and weight ( H ) of mouse lesions (n = 6 for the E. coli , n = 10 for the control and S. agalactiae -groups, and n = 9 for the L-carnitine and S. agalactiae  + Mildronate treatment groups). Each data point represents a single anatomically distinct ectopic lesion, with all lesions per mouse subjected to independent gravimetric analysis. I Analysis of the percentage of adhesion area points between the endometriotic cysts and other adjoining mice organs (mice). J Cell migration (left panel) and Matrigel invasion (right panel) assays were performed to assess the effects of S. agalactiae , L-carnitine, and S. agalactiae combined with L-carnitine synthesis inhibitor (Mildronate) on hEM15A cell migration and invasion. Cells were fixed and stained, and representative fields were photographed. Three independent biological replicates were generated for the migration and invasion assay. For quantification, the cells were counted in five random fields under a light microscope (× 200) (scale bar: 200 µm) (n = 3). Data are expressed as the mean ± SD; P values are determined using a one-way ANOVA, * P  < 0.05, ** P  < 0.01, *** P  < 0.001. SD, standard deviation L-carnitine elevation in the cervical mucus of patients with EMs and its regulation by Streptococcus agalactiae in experimental models. A Bubble diagram showing KEGG enrichment analysis comparing the EMs group and healthy controls (n = 30 per group, human specimens). Red bubbles indicate upregulated pathways, and green bubbles indicate downregulated pathways. B Venn diagram depicting the overlap of upregulated metabolites among the generation dataset, cervical mucous validation dataset, and serum dataset (human specimens). C Relative abundance of L-carnitine in the cervical mucus was analyzed in the generation dataset by untargeted metabolomics, comparing patients with EMs and healthy controls (n = 30 per group, human specimens). D Untargeted metabolomic profiling of intracellular L-carnitine levels was performed in hEM15A cells. The S. agalactiae and L-carnitine treatment groups showed significant elevation of L-carnitine levels, whereas the S. agalactiae  + Mildronate co-treatment group exhibited marked reduction (n = 3 biological replicates per group). E H&E staining of mouse lesions confirmed histological evidence of EMs in the different groups (scale bar: 100 µm, mice). F L-carnitine levels in mouse lesions were quantified using a colorimetric assay, revealing significantly elevated L-carnitine levels in lesions from Streptococcus agalactiae ( S. agalactiae )-treated mice (n = 6 for each group, mice). G , H Scatter plots depicting the number ( G ) and weight ( H ) of mouse lesions (n = 6 for the E. coli , n = 10 for the control and S. agalactiae -groups, and n = 9 for the L-carnitine and S. agalactiae  + Mildronate treatment groups). Each data point represents a single anatomically distinct ectopic lesion, with all lesions per mouse subjected to independent gravimetric analysis. I Analysis of the percentage of adhesion area points between the endometriotic cysts and other adjoining mice organs (mice). J Cell migration (left panel) and Matrigel invasion (right panel) assays were performed to assess the effects of S. agalactiae , L-carnitine, and S. agalactiae combined with L-carnitine synthesis inhibitor (Mildronate) on hEM15A cell migration and invasion. Cells were fixed and stained, and representative fields were photographed. Three independent biological replicates were generated for the migration and invasion assay. For quantification, the cells were counted in five random fields under a light microscope (× 200) (scale bar: 200 µm) (n = 3). Data are expressed as the mean ± SD; P values are determined using a one-way ANOVA, * P  < 0.05, ** P  < 0.01, *** P  < 0.001. SD, standard deviation To investigate the downstream signaling mechanisms involved in S. agalactiae - induced EMs, RNA sequencing (RNA-seq) was performed on ovarian endometriotic lesions obtained from patients with high S. agalactiae abundance, using uninfected normal endometrial tissue as the control. Volcano plots showed that 233 genes were upregulated, whereas 800 genes were downregulated in S. agalactiae -infected endometriotic lesions (Fig.  4 A). Subsequent enrichment analysis of biological processes identified twenty upregulated pathways with the highest enrichment observed in the positive regulation of VEGF production (Fig.  4 B and C). Given the critical role of angiogenesis in the survival and progression of ectopic endometrial implants, this process has been recognized as central to EMs pathogenesis [ 35 , 36 ]. Variability in vascularization among murine endometriotic implants was observed, reflected by lesion coloration. Highly vascularized lesions appeared red, whereas poorly vascularized lesions appeared white (Fig.  4 D). Consequently, lesions from two prior animal experiments were classified accordingly for further analysis. A significant increase in both red and white lesions was detected following inoculation with S. agalactiae (Fig.  4 E and F). Antibiotic treatment targeting S. agalactiae led to a reduction in lesion development (Fig.  4 E). Administration of L-carnitine yielded comparable increases in lesion number and weight to those observed in the S. agalactiae -treated group (Fig.  4 F). Notably, treatment with Mildronate (3-(2,2,2-trimethylhydrazinium) propionate), a specific inhibitor of L-carnitine biosynthesis and transport, significantly reduced the formation of red lesions without affecting white lesions. This finding suggests the potential role of L-carnitine in mediating S. agalactiae -induced angiogenesis (Fig.  4 F). Given that the primary distinction between red and white lesions lies in vascular density, further investigation of blood vessel distribution was conducted. IHC analysis revealed significantly increased CD34 expression from mice treated with S. agalactiae or L-carnitine compared with those treated with PBS or E. coli (Fig.  4 G–I). Importantly, vascularization induced by S. agalactiae was attenuated by Mildronate treatment (Fig.  4 G–I). To evaluate the relationship between S. agalactiae infection and vascularization in human lesions, IHC co-localization of S. agalactiae and CD34 + endothelial cells was performed on ovarian ectopic lesions from patients with EMs exhibiting high S. agalactiae abundance, as confirmed by 16S rRNA sequencing (Fig.  4 J and K). A positive S. agalactiae staining was detected in 75% (6/8) of surgical ectopic lesions obtained from patients with EMs. However, IHC analysis revealed a significant correlation between S. agalactiae abundance and CD34 + vascular density in ectopic lesions ( P  = 0.031, R = 0.754) (Fig.  4 L). A significant correlation was also observed between CD34 + vascular density and S. agalactiae abundance in cervical mucosal sample, as determined by 16S rRNA sequencing (Fig. S4 A). Fig. 4 Increased CD34 + vasculature in the Streptococcus agalactiae -treated mouse ectopic lesions and Streptococcus agalactiae -positive human ectopic lesions. A Volcano plots of RNA sequencing dataset showing differentially expressed genes (DEGs) in the ovarian endometrial cysts from S. agalactiae -colonized individuals (n = 14), identified via 16S rRNA gene sequencing, compared with normal endometrial tissue (n = 10, P  < 0.05, fold-change [FC] ≥ 2 or ≤ 0.5, human specimens). B Mountain plot depicting the functional terms identified by gene set enrichment analysis, with adjusted P  < 0.05 (human specimens). C Gene set enrichment analysis showing significant enrichment of the positive regulation of VEGF production biological process (human specimens). D Representative macroscopic images of EMs lesions in S. agalactiae -infected mice showing red and white lesions (mice). E Bar graph showing the number of red and white lesions in control, S. agalactiae , and S. agalactiae combined with antibiotics group (n = 6 for each group, mice). F Bar graph showing the number (left panel) and weight (right panel) of red and white lesions (n = 6 for E. coli ; n = 10 for the control, S. agalactiae , L-carnitine, and S. agalactiae  + Mildronate groups; mice). G Representative CD34 immunohistochemical results of mice ectopic lesions in mice. Black arrows indicate CD34 new blood vessels in the mouse ectopic lesion (upper panel). Scale bar:100 µm (n = 6 for the Escherichia coli and n = 10 for the other four groups, mice). H Hemangioma tissue was used as a positive or negative control for CD34 staining. Scale bar: 100 µm (n = 6 for the Escherichia coli and n = 10 for the other four groups, human specimens). I Scatter plot showing the quantitative analysis of CD34-positive new blood vessels in each group. Data are expressed as the mean ± SD; P values are determined using the Mann–Whitney U test; ** P  < 0.01 (mice). J Representative S. agalactiae and CD34 immunohistochemical staining of the ectopic lesions from patients with EMs (n = 8 for each group, human specimens). K The S. agalactiae + mice model tissue was used as a positive or negative control for Streptococcus IHC staining (mice). L Left panel: scatter plot showing the quantitative analysis of CD34-positive new blood vessels based on IHC staining in ectopic lesions from patients with EMs compared with adjacent normal tissue (n = 8 per group). P values were determined using Student’s t-test. *** P  < 0.001. Right panel: CD34 expression in ectopic lesions correlates with the abundance of S. agalactiae in the ectopic lesions (n = 8). Scale bar: 100 µm (human specimens). P values are determined using a one-way ANOVA in A . Pearson’s correlation was used to assess the correlation between CD34 expression and S. agalactiae abundance in J . ANOVA, analysis of variance; SD, standard deviation; IHC, immunohistochemistry Increased CD34 + vasculature in the Streptococcus agalactiae -treated mouse ectopic lesions and Streptococcus agalactiae -positive human ectopic lesions. A Volcano plots of RNA sequencing dataset showing differentially expressed genes (DEGs) in the ovarian endometrial cysts from S. agalactiae -colonized individuals (n = 14), identified via 16S rRNA gene sequencing, compared with normal endometrial tissue (n = 10, P  < 0.05, fold-change [FC] ≥ 2 or ≤ 0.5, human specimens). B Mountain plot depicting the functional terms identified by gene set enrichment analysis, with adjusted P  < 0.05 (human specimens). C Gene set enrichment analysis showing significant enrichment of the positive regulation of VEGF production biological process (human specimens). D Representative macroscopic images of EMs lesions in S. agalactiae -infected mice showing red and white lesions (mice). E Bar graph showing the number of red and white lesions in control, S. agalactiae , and S. agalactiae combined with antibiotics group (n = 6 for each group, mice). F Bar graph showing the number (left panel) and weight (right panel) of red and white lesions (n = 6 for E. coli ; n = 10 for the control, S. agalactiae , L-carnitine, and S. agalactiae  + Mildronate groups; mice). G Representative CD34 immunohistochemical results of mice ectopic lesions in mice. Black arrows indicate CD34 new blood vessels in the mouse ectopic lesion (upper panel). Scale bar:100 µm (n = 6 for the Escherichia coli and n = 10 for the other four groups, mice). H Hemangioma tissue was used as a positive or negative control for CD34 staining. Scale bar: 100 µm (n = 6 for the Escherichia coli and n = 10 for the other four groups, human specimens). I Scatter plot showing the quantitative analysis of CD34-positive new blood vessels in each group. Data are expressed as the mean ± SD; P values are determined using the Mann–Whitney U test; ** P  < 0.01 (mice). J Representative S. agalactiae and CD34 immunohistochemical staining of the ectopic lesions from patients with EMs (n = 8 for each group, human specimens). K The S. agalactiae + mice model tissue was used as a positive or negative control for Streptococcus IHC staining (mice). L Left panel: scatter plot showing the quantitative analysis of CD34-positive new blood vessels based on IHC staining in ectopic lesions from patients with EMs compared with adjacent normal tissue (n = 8 per group). P values were determined using Student’s t-test. *** P  < 0.001. Right panel: CD34 expression in ectopic lesions correlates with the abundance of S. agalactiae in the ectopic lesions (n = 8). Scale bar: 100 µm (human specimens). P values are determined using a one-way ANOVA in A . Pearson’s correlation was used to assess the correlation between CD34 expression and S. agalactiae abundance in J . ANOVA, analysis of variance; SD, standard deviation; IHC, immunohistochemistry ELISA was initially used in determining VEGF expression, a potent angiogenic factor, in the tissues of the EMs mouse model to elucidate the potential mechanism by which S. agalactiae promotes EMs angiogenesis (Fig.  5 A). A marked increase in VEGF expression was observed in ectopic lesions following treatment with S. agalactiae or L-carnitine (Fig.  5 A). However, VEGF expression was significantly reduced in S. agalactiae -treated tissues upon administration of Mildronate (Fig.  5 A). To identify the source of VEGF in ectopic lesions, attention was focused on endometrial and endothelial cells. Co-culture of human umbilical vein endothelial cells (HUVECs) with S. agalactiae or L-carnitine supernatant did not alter the VEGF levels in the medium (Fig.  5 B). However, a significant elevation in VEGF levels was detected in the supernatant of hEM15A cells co-cultured with S. agalactiae or L-carnitine (Fig.  5 C). This VEGF upregulation was diminished when L-carnitine production was inhibited by Mildronate, consistent with in vivo observations (Fig.  5 C). These findings suggest that S. agalactiae promotes endometrial cell angiogenesis in ectopic lesions by stimulating the release of VEGF from the endometrial stromal cells. To further validate the S. agalactiae ’s pro-angiogenesis function, HUVEC tube formation assays were conducted in vitro , and the tubule length was quantified. No significant difference was observed in tubule formation between the control and E. coli- or heat-inactivated S. agalactiae groups (Fig.  5 D). Furthermore, the conditioned media derived from hEM15A cells co-cultured with S. agalactiae or L-carnitine significantly enhanced vascular tubule formation (Fig.  5 D). This effect was attenuated by Mildronate, which inhibited the S. agalactiae -induced enhancement (Fig.  5 D). In addition to stimulating VEGF-mediated angiogenesis, S. agalactiae and L-carnitine promoted the migration and invasion of HUVECs (Fig.  5 E and F). These effects were reversed by Mildronate treatment (Fig.  5 E and F). Notably, no impact on HUVEC proliferation was observed following S. agalactiae exposure (Fig. S5 A). Collectively, these results show that S. agalactiae promotes VEGF expression via L-carnitine and significantly enhances migration, invasion, and tubule formation. Fig. 5 Promotion of VEGF secretion by EMs cells and enhancement of HUVEC migration and invasion by Streptococcus agalactiae through L-carnitine. A VEGF levels in the ectopic lesion tissues of mice treated with PBS (control group), Escherichia coli ( E. coli ), Streptococcus agalactiae (S. agalactiae) , L-carnitine, and S. agalactiae combined with Mildronate were measured using ELISA (n = 6 for E. coli group, n = 10 for other groups, mice). B , C VEGF levels in the supernatant of HUVECs ( B ) and hEM15A cells ( C ) were quantified following indirect co-culture with medium control, E. coli , heat-inactivated S. agalactiae , S. agalactiae , L-carnitine, and S. agalactiae combined with Mildronate using ELISA. (n = 3 for each group). Two independent biological replicates were performed. D Representative images of HUVEC tube formation using conditioned media are shown (left panel, scale bar: 200 μm). Quantification analysis of the total segment length of HUVECs in the different groups is shown (right panel) (n = 3 for each group). Two biological replicates were performed. E and F Cell migration and Matrigel invasion assays were employed to assess the effect of S. agalactiae , L-carnitine, and S. agalactiae combined with L-carnitine synthesis inhibitors (Mildronate) in HUVEC migration ( E ) and invasion ( F ). Cells were fixed and stained, and representative fields were photographed. For quantification, the cells were counted in five random fields under a light microscope (× 200) (scale bar: 200 µm) (n = 3). Two biological replicates were performed. Data are expressed as the mean ± SD; P values are determined by one-way ANOVA, * P  < 0.05, ** P  < 0.01, *** P  < 0.001. G Working model of S. agalactiae promotes EMs by enhancing ectopic lesion motility and angiogenesis through L-carnitine. S. agalactiae , a highly abundant bacterium in the cervical mucus of patients with EMs, promoted EMs progression and upregulated L-carnitine metabolism. S. agalactiae stimulated the secretion of VEGF in endometrial cells via L-carnitine, leading to the significant enhancement of EMs angiogenesis. ANOVA, analysis of variance; SD, standard deviation; EMs, endometriosis; HUVECs; human umbilical vein endothelial cells; ELISA, enzyme-linked immunosorbent assay; VEGF, vascular endothelial growth factor; PBS, phosphate-buffered saline Promotion of VEGF secretion by EMs cells and enhancement of HUVEC migration and invasion by Streptococcus agalactiae through L-carnitine. A VEGF levels in the ectopic lesion tissues of mice treated with PBS (control group), Escherichia coli ( E. coli ), Streptococcus agalactiae (S. agalactiae) , L-carnitine, and S. agalactiae combined with Mildronate were measured using ELISA (n = 6 for E. coli group, n = 10 for other groups, mice). B , C VEGF levels in the supernatant of HUVECs ( B ) and hEM15A cells ( C ) were quantified following indirect co-culture with medium control, E. coli , heat-inactivated S. agalactiae , S. agalactiae , L-carnitine, and S. agalactiae combined with Mildronate using ELISA. (n = 3 for each group). Two independent biological replicates were performed. D Representative images of HUVEC tube formation using conditioned media are shown (left panel, scale bar: 200 μm). Quantification analysis of the total segment length of HUVECs in the different groups is shown (right panel) (n = 3 for each group). Two biological replicates were performed. E and F Cell migration and Matrigel invasion assays were employed to assess the effect of S. agalactiae , L-carnitine, and S. agalactiae combined with L-carnitine synthesis inhibitors (Mildronate) in HUVEC migration ( E ) and invasion ( F ). Cells were fixed and stained, and representative fields were photographed. For quantification, the cells were counted in five random fields under a light microscope (× 200) (scale bar: 200 µm) (n = 3). Two biological replicates were performed. Data are expressed as the mean ± SD; P values are determined by one-way ANOVA, * P  < 0.05, ** P  < 0.01, *** P  < 0.001. G Working model of S. agalactiae promotes EMs by enhancing ectopic lesion motility and angiogenesis through L-carnitine. S. agalactiae , a highly abundant bacterium in the cervical mucus of patients with EMs, promoted EMs progression and upregulated L-carnitine metabolism. S. agalactiae stimulated the secretion of VEGF in endometrial cells via L-carnitine, leading to the significant enhancement of EMs angiogenesis. ANOVA, analysis of variance; SD, standard deviation; EMs, endometriosis; HUVECs; human umbilical vein endothelial cells; ELISA, enzyme-linked immunosorbent assay; VEGF, vascular endothelial growth factor; PBS, phosphate-buffered saline

Characterizing the microbiome of the female reproductive tract is essential for elucidating novel molecular mechanisms that regulate the progression of EMs. Although numerous studies have reported the presence and composition of the reproductive tract microbiota in patients with EMs, the role of these microbial communities in disease pathogenesis remains poorly understood. In this study, S. agalactiae was identified as a highly abundant bacterium in the cervical mucus of patients with EMs. Moreover, its potential to promote disease progression was demonstrated in a mouse model. However, the precise role of S. agalactiae in the initiation of EMs in humans requires further investigation. S. agalactiae upregulates genes involved in fatty acid degradation during EMs progression. Notably, supplementation with L-carnitine alone accelerated disease development. This finding suggests that S. agalactiae may serve as an independent risk factor for EMs. Mechanistically, S. agalactiae was found to stimulate VEGF secretion from endometrial cells via L-carnitine, thereby enhancing angiogenesis, a key event in EMs pathogenesis (Fig.  5 G). Moreover, the administration of antibiotics to inhibit the growth of S. agalactiae , combined with the use of Mildronate to suppress L-carnitine synthesis, effectively mitigated the formation of endometrial microenvironments induced by S. agalactiae . These findings highlight the critical roles of cervical mucus colonization, S. agalactiae , and L-carnitine in promoting angiogenesis during the development of EMs. Among various Streptococcus species, S. agalactiae is a beta-hemolytic, catalase-negative, facultative anaerobe that is well recognized for causing invasive diseases in neonates [ 37 , 38 ]. Recent studies have reported a higher abundance of the Streptococcus genus in the cervical mucus of patients with EMs [ 22 – 25 , 28 – 31 ]. However, the specific Streptococcus strains contributing to EMs progression remain unclear. In the present study, a significantly higher abundance of S. agalactiae in the cervical mucus of patients with EMs was identified through 16S rRNA sequencing, consistent with findings from a previous study [ 30 ]. Although an association between the microbiome and EMs has been established, whether microbial alterations are a consequence of EMs or a contributing factor remains unclear. Evidence from experimental animal models supports the bidirectional relationship between EMs and microbial dysbiosis [ 39 – 41 ]. In one study, a reduction in the size of surgically induced EMs lesions in mice was observed following antibiotic treatment. Subsequent fecal microbiota transplantation from endometriotic mice led to lesion regrowth and increased inflammatory responses [ 40 ]. Similarly, changes in microbial diversity and abundance were reported in the vaginal and intestinal microbiota following the induction of EMs in non-human primates ( Papio anubis ) [ 39 ]. Furthermore, another study demonstrated that Fusobacterium may ascend from the vaginal tract to infect the uterus, recruiting macrophages and promoting M2 polarization. These macrophages, in turn, secreted TGF-β, which induced a fibroblast-to-myofibroblast transition in endometrial stromal cells, contributing to EMs pathogenesis [ 42 ]. Consistent with this mechanism, our study revealed that S. agalactiae , but not E. coli , promoted EMs progression in vivo. Nevertheless, prospective human studies are warranted to confirm the causal relationship between S. agalactiae colonization and EMs pathophysiology. In addition to cervical mucus, S. agalactiae has also been detected in peritoneal endometrial lesions, where its presence has been associated with the development of EMs [ 43 ]. In our study, S. agalactiae was similarly identified in human ectopic lesions of ovarian EMs (Fig.  4 B). These findings suggest that S. agalactiae infection originating from the outside environment and menstrual flow dispersion throughout the reproductive tract contributes significantly to EMs formation. A non-pathogenic E. coli K-12 strain was selected as a Gram-negative control due to its well-characterized lipopolysaccharide (LPS)-induced inflammatory properties and regulatory role in EMs pathogenesis [ 27 , 44 ]. The use of E. coli served to demonstrate that only specific microbial species trigger this effect, ruling out a general inflammatory response. In the 16S rRNA gene sequencing analysis, E. coli showed the highest overall abundance, with no difference found between the EMs and healthy control groups. Although E. coli did not significantly promote EMs progression in our study, a previous study reported elevated levels of E. coli in the menstrual blood of female patients with EMs, suggesting that LPS in menstrual and peritoneal fluids may stimulate lesion growth [ 27 , 45 ]. This discrepancy may arise from limitations in the EMs animal model, which does not recapitulate the pathogenic mechanisms and histological characteristics observed in humans due to the absence of spontaneous menstruation [ 46 , 47 ]. A recent study identified Acomys cahirinus as a menstruating rodent, offering a promising non-primate model for future studies into microbial contributions to EMs progression [ 48 ]. L-carnitine is a key energy-producing molecule, facilitating the transport of long-chain fatty acids into the mitochondrial matrix for β-oxidation [ 49 , 50 ]. Previous studies have demonstrated that L-carnitine administration alters the cellular and growth factor profiles in the uterus and peritoneum of female mice, resulting in phenotypic changes that resemble clinical EMs. Additionally, L-carnitine disrupts early embryo development, contributing to infertility [ 51 , 52 ]. To the best of our knowledge, this study is the first to explore the mechanism by which S. agalactiae promotes EMs through the modulation of L-carnitine levels. As both bacterial and host cells synthesize L-carnitine [ 48 ], whether S. agalactiae contributes to elevated L-carnitine levels through direct production or by altering host metabolic pathways remains unclear and warrants further investigation. Previous studies found a metabolite produced by intestinal bacteria, N,N,N-trimethyl-5-aminovaleric acid, that binds and inhibits γ-butyrobetaine hydroxylase, reducing endogenous carnitine synthesis [ 53 ]. However, an increase in L-carnitine levels was observed in the present study, suggesting the presence of an alternative metabolic pathway that regulates L-carnitine production. Angiogenesis is governed by endothelial cell proliferation and migration [ 54 ]. Endothelial cell behavior is regulated primarily through two metabolic pathways: glycolysis and fatty acid oxidation (FAO) [ 55 , 56 ]. FAO promotes endothelial proliferation by facilitating DNA synthesis, with carnitine palmitoyltransferase 1α (CPT1α) serving as a key metabolic regulator of vascular sprouting [ 55 , 56 ]. Activation of CPT1α by L-carnitine facilitates the conversion of L-carnitine and acyl-CoA into acylcarnitine, thereby modulating fatty acid metabolism [ 55 , 56 ]. The upregulation of CPT1α by L-carnitine has been shown to ameliorate hyperoxia-induced pulmonary endothelial dysfunction [ 57 , 58 ]. Additionally, L-carnitine improves functional outcomes via non-angiogenic mechanisms in rat models of peripheral artery disease [ 59 ]. Propionyl-L-carnitine (PLC) enhances VEGFA expression, promotes macrophage polarization from the M1 to M2 phenotype, and facilitates microvascular formation [ 60 ]. Conversely, L-carnitine suppresses angiogenesis in prostate cancer models by selectively inhibiting the VEGFR2 and CXCR4 signaling pathways [ 61 ]. Notably, increased VEGF expression was observed following exposure to S. agalactiae via L-carnitine, which remarkably enhanced HUVEC migration and invasion. In addition to inducing VEGF release from endometrial stromal cells, L-carnitine may further promote angiogenesis by participating in fatty acid oxidation, potentially influencing deoxynucleoside triphosphate synthesis in endothelial cells [ 62 ]. Interestingly, no effect on HUVEC proliferation was observed in response to S. agalactiae and L-carnitine, suggesting that further investigation is warranted to elucidate the mechanisms by which S. agalactiae angiogenesis in EMs through L-carnitine. In conclusion, our study found that S. agalactiae enhanced the formation of EMs lesions by enhancing EMs cell migration, invasion, and angiogenesis via L-carnitine. These findings imply that S. agalactiae and L-carnitine may represent therapeutic targets for EMs. However, further research is required to elucidate the precise role of S. agalactiae in the initiation and progression of EMs before clinical applications can be considered.

Endometriosis (EMs) is an estrogen-dependent, chronic inflammatory gynecological disease in women of reproductive age. It is characterized by the presence of tissue resembling the endometrial glands and stroma outside the uterus, predominantly in the pelvic region including the ovaries, ligaments, peritoneal surfaces, bowel, and bladder [ 1 , 2 ]. Globally, EMs affects ˃30% of infertile women and approximately 10% of all women of reproductive age [ 3 , 4 ]. However, the actual prevalence is likely underestimated, as the condition is frequently undiagnosed due to its heterogeneous clinical manifestations. Current therapeutic strategies for EMs include surgical resection of endometriotic lesions, nonsteroidal anti-inflammatory drug use, and hormone-based drug therapy [ 1 , 5 ]. However, these treatments often cause adverse reactions and have a high recurrence rate, reaching up to 50% within 5 years of follow-up [ 6 , 7 ]. Several theories have been proposed to explain the pathogenesis of EMs, including retrograde menstruation, lymphatic dissemination, coelomic metaplasia, Mullerian remnants, and stem cell recruitment [ 8 ]. Among these, retrograde menstruation is the most widely accepted hypothesis. However, as retrograde menstruation occurs in the majority of reproductive-aged women [ 8 ], but only 10%–15% develop EMs, additional factors must contribute to the implantation and survival of ectopic endometrial cells. The microbiota, comprising bacteria, fungi, viruses, and other microorganisms residing within the host, is essential for the regulation of physiological processes in the gut, respiratory tract, and other mucosal microenvironments [ 9 , 10 ]. Its critical role in immunomodulation and the pathogenesis of various inflammatory disorders has been well established [ 11 , 12 ]. Furthermore, the gut microbiome protects the gastrointestinal epithelial lining and maintains immunological homeostasis by preventing bacterial translocation, which can induce low-grade systemic inflammation [ 11 , 13 – 15 ]. However, limited knowledge exists regarding the presence, composition, and functional role of the microbiota in the female reproductive system, especially in relation to EMs and other gynecological disorders. Emerging studies suggest that microbial colonization of the reproductive tract may contribute to the pathogenesis and progression of EMs [ 16 – 22 ]. At the genus level, increased abundances of Alloprevotella , Enterococcus , Streptococcus , Pseudomonas, and Ruminococcaceae family have been reported in the reproductive tracts of patients with EMs compared with healthy controls [ 23 – 27 ]. By contrast, reduced levels of Atopobium , Barnesiella , Prevotella , Gemella , Lactobacillus, and Sneathia have been observed in the EMs cohorts compared with the control cohorts [ 24 , 25 ]. Chen et al. demonstrated that microbial communities vary across different anatomical sites of the reproductive tract, including the cervical canal, uterus, fallopian tubes, and peritoneal fluid [ 16 ]. Significantly distinct microbial profiles were detected in the cervical mucus of patients with EMs compared with controls. These differences appeared to increase progressively from the uterus toward the fallopian tubes along the vaginal tract [ 16 , 21 ]. Therefore, the cervical microbiome may serve as a more accessible and informative biosignature for the detection of EMs, particularly in light of the challenges associated with sampling from the upper reproductive tract. Notably, an increased abundance of Streptococcus genera in the cervical microbiome has been consistently reported in patients with EMs compared with controls [ 22 , 23 , 25 , 26 , 28 – 32 ]. Although a strong correlation between the microbiome and EMs has been established, most studies have focused on bacterial composition and abundance, with little emphasis on the associations between disease development and pathophysiological characteristics. In this study, increased cervical colonization by Streptococcus agalactiae ( S. agalactiae ) was found to enhance endometrial cell motility, invasion, and proliferation, as well as angiogenesis, by increasing L-carnitine levels. The presence of S. agalactiae increased human umbilical vein endothelial cell (HUVEC) mobility and invasion and induced vascular endothelial growth factor (VEGF) secretion in hEM15A cells through L-carnitine. This, in turn, facilitated the formation of vascular endothelial cell tubes. Active angiogenesis was thereby promoted, which is likely to support the extrauterine survival of ectopic endometrial lesions. These findings are expected to provide novel insights into the pathophysiology of EMs and may contribute to the identification of potential therapeutic options.

Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 20 KB) Supplementary file2 (DOCX 21 KB) Supplementary file3 (DOCX 117 KB) Supplementary file1 (DOCX 20 KB) Supplementary file2 (DOCX 21 KB) Supplementary file3 (DOCX 117 KB)