{"paper_id":"85e473cb-0921-4c0e-bce4-dfa6609e7101","body_text":"Pan et al. BMC Microbiology          (2024) 24:281  \nhttps://doi.org/10.1186/s12866-024-03339-9\nRESEARCH Open Access\n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which \npermits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the \noriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or \nother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line \nto the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory \nregulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this \nlicence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecom-\nmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.\nBMC Microbiology\nVaginal microbiome differences \nbetween patients with adenomyosis \nwith different menstrual cycles and healthy \ncontrols\nZangyu Pan1,3,4,5, Jun Dai6, Ping Zhang1,2,3,4,5, Qianhui Ren1,3,4,5, Xinyu Wang1,3,4,5, Shumin Yan1,3,4,5, Hao Sun1,3,4,5, \nXue Jiao1,3,4,5, Ming Yuan1,2,3,4,5* and Guoyun Wang1,3,4,5* \nAbstract \nBackground Adenomyosis is a commonly observed benign gynecological disease that affects the quality of life \nand social psychology of women of childbearing age. However, because of the unknown etiology and incidence \nof adenomyosis, its pathophysiological mechanism remains unclear; further, because no noninvasive, accurate, \nand individualized diagnostic methods are available, treatment and efficacy evaluations are limited. Notably, the inter-\naction between the changes in the microecological environment of the female reproductive tract and human immu-\nnity, endocrine, and other links leads to the occurrence and development of diseases. In addition, the vaginal micro-\nbiome differs in different menstrual cycles; therefore, assessing the differences between the microbiomes of patients \nwith adenomyosis and healthy individuals in different menstrual cycles will improve the understanding of the disease \nand provide references for the search for noninvasive diagnosis and individualized precision treatment of adenomyo-\nsis. This study aimed to explored the data of individuals in different menstrual cycles.\nResults Differences in the vaginal microbiome between patients with adenomyosis and healthy individuals were \nobserved. At phylum level, the relative abundance of Firmicutes in the adenomyosis group was higher than that in \nthe control group, which contributed the most to the species difference between the two groups. At the genus \nlevel, Lactobacillus was the most dominant in both groups, Alpha-diversity analysis showed significant differences \nin the adenomyosis and control group during luteal phase (Shannon index, p = 0.0087; Simpson index, p = 0.0056). \nBeta-diversity index was significantly different between the two groups (p = 0.018). However, based on Weighted \nUnifrac analysis, significant differences were only observed throughout the luteal phase (p = 0.0146). Within the adeno-\nmyosis group, differences between women with different menstrual cycles were also observed. Finally, 50 possible \nbiomarkers including were screened and predicted based on the random forest analyse.\nConclusions The vaginal microbiome of patients with adenomyosis and healthy individuals differed during men-\nstrual periods, especially during the luteal phase. These findings facilitate the search for specific biological markers \n*Correspondence:\nMing Yuan\nsddxqlyyym@163.com\nGuoyun Wang\nwangguoy@sdu.edu.cn\nFull list of author information is available at the end of the article\n\nPage 2 of 14Pan et al. BMC Microbiology          (2024) 24:281 \nwithin a limited range and provide a more accurate, objective, and individualized diagnostic and therapeutic evalua-\ntion method for patients with adenomyosis, compared to what is currently available.\nKeywords Adenomyosis, Vaginal microbiome, Menstrual cycles\nIntroduction\nAdenomyosis is a benign uterine myometrial lesion \ncommonly found in women of reproductive age and \nis characterized by compensatory hypertrophy in the \nperipheral myometrium, with endometrioid glands and \nstroma found in the myometrium [1]. Pathological diag -\nnosis after surgery is the gold standard for clinical diag -\nnosis; however, the exact incidence and pathogenesis of \nadenomyosis remain unknown [2]. Studies have shown \nthat a history of uterine surgery is a high risk factor for \nadenomyosis. For example, the incidence of adenomyo -\nsis in patients with the aforementioned surgical history is \n1.5 times higher than in patients with a different history \n[3, 4]. In the treatment of adenomyopathy, in addition \nto surgical treatment, conservative programs are used \nto regulate endocrine and immune system functions. \nDiagnostic methods include magnetic resonance imag -\ning (MRI), transvaginal ultrasonography, and CA125 test, \nhowever, no specific, individualized diagnostic method \nis available. Adenomyosis and other benign gynaecologi -\ncal diseases, such as uterine fibroids, endometriosis, and \nendometrial polyps, have a high comorbidity rate, and \nattributing specific symptoms to adenomyosis in clinical \ndiagnosis and treatment is difficult.\nThe vagina is an important organ of the female lower \ngenital tract and is an important habitat for microor -\nganisms in the human body. Lactobacillus is the pre -\ndominant bacterial species and is affected by various \nexogenous and endogenous factors; furthermore, the \nspecies composition of the vaginal microbiome has a \nstrong dynamic change [5]. The vaginal microbiome is an \nimportant defence mechanism that regulates and main -\ntains reproductive function and relative homeostasis in \nhealthy environments. The stability of the microbiome \ncan prevent the proliferation of symbiotic microorgan -\nisms and the colonization of pathogens [6]. Microorgan -\nisms affect the balance of the microenvironment through \nnutritional competition, intraspecific and interspecific \nsignal transduction, metabolic pathways, and product \ninteractions. The mechanism of microenvironmental \nimbalance remains unclear; however, this imbalance can \ndisrupt normal homeostasis, resulting in certain patho -\nlogical signs. The female upper reproductive tract was \nonce considered a sterile environment; however, this \ntheory has been challenged. The presence of micro -\nbiota in the endometrial microbiota [7] was confirmed \nby the isolation of microbiota from female endometrial \naspirated fluid samples. Studies have shown that bacte -\nrial DNA can be detected in 95% of post-hysterectomy \nsamples [8]. Microbial switching occurs in the female \nreproductive tract, and the microbiota of the upper and \nlower reproductive tracts work synergistically to regulate \nthe uterine environment. With increasing age, synchro -\nnous changes in the microbiome of the uterus and vagina \nincreasingly converge, showing a mutually parallel rela -\ntionship. Animal studies have verified the damaging and \nprotective effects of vaginal bacteria on the endometrium \nusing microbiota transplantation techniques [9]. This \nalso indicates that lower reproductive tract bacteria affect \nor directly interfere with the regulation of some benign \nand malignant diseases, to some extent, through certain \nmechanisms.\nInitial research on vaginal microbes mainly relied on \nmicroscopy and microbial culture techniques; however, \nthe vast majority of microorganisms in the physiologi -\ncal or natural environment are difficult to obtain through \nculture. Using bioinformatics, high-throughput sequenc -\ning and analysis technology were performed to minimise \nthe dependence on bacterial culture technology used \nin the literature and enhance our understanding of the \nstructure and function of the microbial community, as \nwell as of the association between the bacterial commu -\nnity of this \"non-visual organ\" and benign and malignant \ndiseases of the female reproductive system.\nThe 16S-rRNA is a subunit of ribosomal RNA. With \nimprovements in sequencing technology, 16S-rDNA \namplicon sequencing has become an important method \nto evaluate the microenvironment, structure, and com -\nposition [10–13]. As research progresses, sequenc -\ning platforms are updated and iterated. Relying on the \nupgraded Illumina NovaSeq sequencing platform, we \ncompensated for the inefficiency of single-ended read -\ning and realized double-ended sequencing; that is, small \nfragment libraries were built according to the character -\nistics of the amplified regions.\nAccording to our review of the literature, no study has \ninvestigated the differences in the vaginal microbiome \nbetween adenomyosis patients with different menstrual \ncycles and healthy individuals. Therefore, this study \naimed to elucidate the differences in the vaginal micro -\nbiota between women with and without adenomyosis, \nwith different menstrual cycles. Our results provide a ref-\nerence for the subsequent screening of characteristic bio-\nlogical markers, disease diagnosis, non-invasive precision \n\nPage 3 of 14\nPan et al. BMC Microbiology          (2024) 24:281 \n \ntreatment, and efficacy prediction based on microbial \ndetection.\nMaterials and methods\nThe case group in this study comprised patients with aden-\nomyosis in the gynecological outpatient department of \nAffiliated Hospital of Shandong University from Novem -\nber 2021 to October 2022 were selected as the case group. \nThey were evaluated by professional gynecologists, and \nadenomyosis was confirmed by ultrasound or magnetic \nresonance imaging (MRI). The control group comprised \nhealthy individuals. The inclusion criteria were as follows: \n(1) 18–49 years old; (2) no unhealthy lifestyle; (3) Regular \nmenstrual cycle; (4) non-pregnant, non-puerperal, non-\nlactation, not during the menstrual phase of the estrogen \ncycle; (6) pre-menopause. The exclusion criteria were as \nfollows: (1) no medical history could be provided; (2) cer-\nvical intraepithelial lesions, cervical malignancies, vulva \nlesions and other HPV-related diseases; (3) virus or bacte-\nrial infection; (4) history and treatment of endocrine sys -\ntem diseases; (5) autoimmune diseases; (6) acute/chronic \ninflammation of the urogenital tract; (7) sexually transmit-\nted diseases and infectious diseases; (8) malignant tumors; \n(9) history of sexual life, vaginal bleeding, vaginal douch -\ning, vaginal medication, sitting bath, pelvic bath, trans -\nvaginal examination 48 h before sampling; (10) history of \nuse of antibiotics, antifungals, and hormonal treatments \nwithin 30  days before sampling; (11) intrauterine device \nimplantation; (12) recent history of pelvic and abdominal \nsurgery and intrauterine operation.\nSample collection\nThe individuals who fulfilled the inclusion criteria had a \nclinical sample collected on the day of the clinical visit \nbefore they received a transvaginal gynecologic exami -\nnation or gynecologic ultrasound. The posterior vaginal \nfornix was fully sampled using disposable sterile swabs. \nDuring the procedure, contact between the swab head \nand the speculum, vaginal wall, and other non-sampling \nsites was avoided. The swab head was cut off with sterile \nscissors and placed in a sterile centrifuge tube containing \nAmies culture medium (JINAN BABIO BIOTECHNOL -\nOGY CO,.LTD.), and stored at -80 ℃ in the laboratory.\nExtraction of genome DNA\nThe genomic DNA of the sample is extracted by cetyltri -\nmethylammonium bromide (CTAB) method. DNA con -\ncentration and purity was monitored on 1% agarose gels. \nAccording to the concentration, DNA was diluted to 1 ng/\nµL using sterile water. Using the diluted genomic DNA as a \ntemplate, the V3-V4 region of 16S-rDNA gene was ampli-\nfied. The primer sequence was as follows: ① F:CCT AYG \nGGRBGCASCAG; ②R:GGA CTA CNNGGG TAT CTAAT \n(Phusion® High-Fidelity PCR Master Mix with GC Buffer, \nNew England Biolabs,lnc.). Polymerase Chain Reaction \n(PCR) was performed using specific primers with Bar -\ncode and high-efficiency high-fidelity enzyme according \nto the selection of sequencing region to ensure amplifica-\ntion efficiency and accuracy. All PCR reactions were car -\nried out with 15µL of Phusion® High-Fidelity PCR Master \nMix (New England Biolabs); 2 µM of forward and reverse \nprimers, and about 10 ng template DNA. Thermal cycling \nconsisted of initial denaturation at 98℃ for 1 min, followed \nby 30 cycles of denaturation at 98℃ for 10 s, annealing at \n50℃ for 30 s, and elongation at 72℃  for 30 s. Finally 72℃ \nfor 5 min.\nLibrary construction and sequencing\nSequencing libraries were generated using TruSeq ® \nDNA PCR-Free Sample Preparation Kit (Illumina, USA) \nfollowing manufacturer’s recommendations and index \ncodes were added. The library quality was assessed on \nthe Qubit@2.0 Fluorometer (Thermo Scientific) and \nAgilent Bioanalyzer 2100 system. At last, the library was \nsequenced on an Illumina NovaSeq platform and 250 bp \npaired-end reads were generated.\nPaired‑end reads assembly and quality control\nPaired-end reads was assigned to samples based on their \nunique barcode and truncated by cutting off the barcode \nand primer sequence. Paired-end reads were merged using \nFLASH (V1.2.7, http:// ccb. jhu. edu/ softw  are/ FLASH/) \n[14], which was designed to merge paired-end reads when \nat least some of the reads overlap the read generated from \nthe opposite end of the same DNA fragment, and the splic-\ning sequences were called raw tags. Quality filtering on the \nraw tags were performed under specific filtering condi -\ntions to obtain the high-quality clean tags [15] according \nto the QIIME(V1.9.1, http:// qiime. org/ scrip ts/ split_ libra \nries_ fastq. html) [16] quality controlled process. The tags \nwere compared with the reference database (Silva data -\nbase, https:// www. arb- silva. de/) [17] to detect chimera \nsequences, and then the chimera sequences were removed \n[18]. Then the Effective Tags finally obtained.\nResults\nThe study enrolled 43 patients with adenomyosis and \n40 healthy people. There were no significant differences \nin demographic background between the two groups of \nparticipants (Table 1).\nThe vaginal samples were collected from all partici -\npants; however, 7 samples in total were excluded from \nthe control group due to poor DNA quality after library \nquality check. Therefore, 83 samples were used in the \nsubsequent analysis. (Fig. 1).\n\nPage 4 of 14Pan et al. BMC Microbiology          (2024) 24:281 \nNext, the vaginal microbiota was analyzed using \n16  s rDNA sequencing techniques. The Raw PE data \nsequenced by Illumina Novaseq were splicing and quality \ncontrol to obtain Clean Tags, and then chimeric filtering \nwas performed to obtain Effective Tags for subsequent \nanalysis (S1 Table).\nSpecies relative abundances\nAt phylum level, the relative abundance of Firmicutes  \nin adenomyosis group was higher than that in control \ngroup (80.70% and 69.72% in adenomyosis and con -\ntrol groups). At the genus level, the Lactobacillus rela -\ntive abundance in both adenomyosis group and control \ngroup was the highest (72.10% and 66.08%). But the rel -\native abundance of Gardnerella and Atopobium in the \nadenomyosis group was lower than that in the control \ngroup (9.67% and 1.04% in adenomyosis and 14.95% \nand 4.69% in control groups); At the Species level, the \nLactobacillus_iners abundance in the adenomyosis \ngroup was higher than that in the control group(43.74% \nand 32.14%), and showed a diversity of Lactobacillus, \nincluding Lactobacillus_delbrueckii and Lactobacillus_\njensenii (Fig. 2 ).\nDifferent menstrual cycles\nThe top 35 species with the average abundance of all sam-\nples of the same level and different groups are selected for \nclustering, and the heatmap is drawn by heatmap pack -\nage of R software, which is convenient to find the number \nor content of species in the sample (Fig. 3).\nSample complexity analysis\nIn order to study the influence of menstrual cycle on vag-\ninal microecology, we named all the samples in the luteal \nTable 1 Demographic data of the subjects\nAdenomyosis (N = 40) Control (N = 40) P‑value\nAge, years (mean ± SD) 39.81 ± 5.62 38.38 ± 5.51 0.243\nBMI, kg/m2, (mean ± SD) 23.73 ± 2.81 22.32 ± 3.88 0.060\nGestation 2.19 ± 1.20 1.77 ± 1.07 0.055\nDelivery 1.07 ± 0.67 1.00 ± 0.56 0.687\nMenstrual cycle, days 26.88 ± 3.67 27.97 ± 2.15 0.066\nMenstrual period, days 5.81 ± 1.33 5.47 ± 1.32 0.153\nFig. 1 Study process\n\nPage 5 of 14\nPan et al. BMC Microbiology          (2024) 24:281 \n \nphase of the adenomyosis group as group C and the fol -\nlicular phase as group D. All the samples in the luteal \nphase of the control group were named group E and \ngroup F. in the follicular phase.\nAlpha-diversity analysis showed significant dif -\nferences in the adenomyosis and control group dur -\ning luteal phase (Shannon index, p  = 0.0087; Simpson \nindex, p = 0.0056), but we didn’t find the statistically \ndifference in ACE and chao 1 index (Fig.  4). It was \nverified that the amount of sequencing data was pro -\ngressive and reasonable, and more data would only \nproduce a few new species, thus suggesting a uniform \ndistribution of species (Fig. 5 ).\nComparative analysis of multiple copies\nThe species distributions in the adenomyosis group and \nthe control group were not completely separated, but \nwere similar (Fig. 6).\nWe analyzed the Beta-diversity index by using the \nt-test and found that the species Beta-diversity index \nwas significantly different between the adenomyosis \ngroup and the control group (p = 0.018). However, based \non Weighted Unifrac analysis, significant differences \nbetween the disease group and the control group were \nonly observed throughout the luteal phase (p = 0.0146) \n(Fig. 7 A, B, C, D).\nR value was between (-1, 1), and R value was greater \nthan 0, indicating that the difference between groups was \ngreater than the difference within groups, which was sig -\nnificant (P < 0.05). The reasonableness of the grouping in \nthis study was proved. (Table 2).\nAt the phylum level, there were no significant spe -\ncies differences between the adenomyosis group and the \ncontrol group. At the class level the significant differ -\nences was in Coriobacteriia and Gammaproteobacteria \n(p < 0.01). At the class level the significant differences \nFig. 2 Taxonomy bar charts of vaginal microbiame at (A) phylum, (B) class, (C) order, (D) family, (D) genus and (E) species level\n\nPage 6 of 14Pan et al. BMC Microbiology          (2024) 24:281 \nwas in Lactobacillales, Coriobacteriales (p < 0.01),and in \nPseudomonadales (p < 0.05). At the class level the signifi -\ncant differences was in Beijerinckiaceae and Listeriaceae \n(p < 0.05). At the genus level, that were in Listeria, Ral-\nstonia, Acinetobacter, and Haemophilus (p < 0.01), and \nAlloscardovia,Ureaolasma (p < 0.05). Finally, at the spe -\ncies level,there was significant difference in Alloscardo -\nvia_omnicolens and Lactobacillus_delbrueckii (p < 0.01) \n(Fig. 8).\nAt the phylum level, Firmicutes showed the highest \nspecies abundance in both the adenomyosis group and \nthe control group, and at the same time, contributed the \nmost to the species difference between the two groups \n(Fig. 9).\nRandom forest is a classical machine learning model \nbased on classification tree algorithm to screen fea -\ntures (biomarkers) that play an important role in classi -\nfication or grouping. A default tenfold cross-validation \nwas performed for each model, and Receiver Operating \nCharacteristic Curve (ROC) curves were drawn to select \npotential Biomaker 50 as shown in Fig. 10.\nDiscussion\nSpecies diversity was analyzed using alpha diversity \nindices (Shannon index, chao1 index, ACE, and Simp -\nson indices), and the number of microbial species and \nproportion of each species in a single sample were cal -\nculated. Results showed that species diversity of the two \ngroups did not show significant differences, similar to the \nresults of Chen et al. [19]. Although the species compo -\nsition of the two groups was similar, species abundance \nsignificantly differed. At the phylum level, the relative \nabundance of Firmicutes was higher in the adenomyo -\nsis group than in the control group. At the genus level, \nexcept for the absolute species dominance of Lactoba -\ncillus in both groups, the relative abundance of Gard -\nnerella in the adenomyosis group was significantly lower \nthan that in the control group, which differed from the \nresults of Kunaseth [20]. Other groups of vaginal bacilli \nwere also detected, second only to Lactobacillus in over -\nall abundance.\nLactobacillus vegetation in the female reproductive \ntract is critical for the maintenance of genital health. \nFig. 3 Heatmap of species abundance clustering during different menstrual cycles. The top 35 species with the average abundance of all samples \nof the same level and different groups are selected for clustering at (A) phylum, (B) class, (C) order, (D) family, (D) genus and (E) species level.The \nheatmap is drawn by heatmap package of R software, which is convenient to find the number or content of species in the sample\n\nPage 7 of 14\nPan et al. BMC Microbiology          (2024) 24:281 \n \nHowever, the exact pathogenesis of Gardnerella vagi -\nnalis remains unclear [21]. Lactobacillus and Gard -\nnerella interact in the female reproductive tract; when \nthe abundance of Lactobacillus decreases to a certain \nextent, the growth of Gardnerella can decrease or stop \n[22], and the imbalance of the two bacteria can change \nthe acid–base environment of the vagina and produce \nmucosal adsorption and biofilm, promoting chronic, \npersistent infection and inflammation [23, 24]. A data \nanalysis using the dominance network analysis frame -\nwork found that Lactobacillus is not the dominant spe -\ncies in some healthy African women, and very few \nbacteria have a cooperative and mutually beneficial \nrelationship with Gardnerella and Lactobacillus iners \n[25], contrary to previous views [26]. L. iners cooperate \nwith Gardnerella but are inhibited by other species [27]. \nA high abundance of Gardnerella genomospecies indi -\ncate the presence of gene variants coding for virulence \nfactors, such as cholesterol-dependent pore-forming \ncytotoxin vaginolysin and neuraminidase sialidase [28]. \nIn this study, the abundance of L. iners in the adeno -\nmyosis group was found to be significantly higher than \nthat in the control group, which was verified using the \nMetaStat method. Microbiomes from women diagnosed \nwith Amsel-bacterial vaginosis (BV) were enriched for \nhost immune response evasion and colonization func -\ntions by L. iners, and its role in the vaginal microbiome \nhas been widely debated. A study has identified a specific \nset of L. iners genes associated with positive Amsel-BV \ndiagnoses, and their data suggested that certain L. Iners  \nstrains may adhere to epithelial cells, contributing to the \nappearance of clue cells and becoming more difficult \nto displace in the vaginal environment [27]. In conclu -\nsion, the variation in L. iners and Gardnerella abundance \nmay be a potential cause of adenomyosis, and maintain -\ning the balance of Lactobacillus and Gardnerella in the \nFig. 4 Alpha-diversity analysis. A shannon index, (B)Simpson index, (C) ACE index, (D) chao1 index. Alpha-diversity analysis indices for different \nsamples at 97% consistency thresholds\n\nPage 8 of 14Pan et al. BMC Microbiology          (2024) 24:281 \nbody may be a self-mechanism to maintain the stability \nof vaginal microecology.\nHowever, little is known about how the genital micro -\nbiota affects host immune function and regulates disease \nsusceptibility. Lactobacillus imbalance and high ecologi -\ncal diversity may be closely related to the concentration \nof pro-inflammatory cytokines in genital organs [29]. \nPatients with adenomyosis show leukocyte infiltration in \nthe endometrial functional layer, and the number of mac-\nrophages and natural killer (NK) cells increased [30, 31]. \nTranscriptional analysis showed that antigen-presenting \ncells sense gram-negative bacterial products in  situ via \nToll-like receptor 4 (TLR-4) signalling, promoting geni -\ntal organ inflammation by activating the nuclear factor \nkappa-B (NF-κB) signalling pathway and recruiting lym -\nphocytes through chemokine production [29]. Immune \ndysregulation is present in the ectopic endometrium of \npatients with adenomyopathy and manifests as elevated \nT Cell Immunoglobulin Domain and Mucin Domain-3/\nGalectin-9 (Tim-3/Gal-9) expression and differential \nRNA methylation [32, 33]. Therefore, we speculated that \nvaginal microecological changes affect the important role \nof Tim-3/Gal-9 in immunosuppression through some \nmechanism, causing the persistence of infection, affect -\ning the growth environment of the endometrial tissue, \nand causing adenomyosis. In addition, the expression \nof Type I interferon (IFN-I) inducers is increased in the \nectopic endometrium in adenomyosis. The increased lev-\nels of IFN-Is and expression of IFN-stimulating genes and \npro-inflammatory cytokines in tissues may be related to \nhost immunity under the influence of certain microor -\nganisms [34]. Recent literature has suggested that micro -\nbiota-induced interferon activation does not require \ndirect host-bacterial interaction but the remote trans -\nport of bacterial DNA into host cells via bacteria-derived \nmembrane vesicles [35]. In contrast with our finding \nthat the beta diversity index was significantly higher \nin the adenomyosis group than in the control group, \nthe increased bacterial diversity in the vagina prob -\nably explains the activation of the host’s innate immune \nresponse in the ectopic endometrium in adenomyosis [5, \n20]. Endometriosis and adenomyosis are closely related \ndisorders. Their pathophysiology and clinical symptoms \nsuch as chronic pain are extremely similar [36]. There is \na correlation in the microbial composition of both intes -\ntinal and cervicovaginal microbial niches, and over 50% \noverlap in species abundance and cell density [37]. Cen -\ntral sensitisation is known to be significantly involved in \nendometriosis-associated chronic pelvic pain [38]. Dysbi-\nosis may potentially lead to incorrect immune responses, \ntriggering the development of inflammatory pain [39], \nsuch as that seen in endometriosis and adenomyosis. All \nthe patients with adenomyosis included in the study have \nobvious dysmenorrhea. However, further studies are may \nelucidate the association between microbial changes and \nchronic pain.\nThe microbiota of the female reproductive system is \ninfluenced by changes in age and system physiology, and \nFig. 5 Rarefaction curve and Rank Abundance curve. In the (A) Rarefaction curve, horizontal coordinate is the number of sequencing strips \nrandomly selected from a sample, and the vertical coordinate is the number of Operational Taxonomic Units (OTUs) that can be constructed \nbased on the number of sequencing strips, which is used to reflect the sequencing coverage, and different samples are represented by different \ncolored curves; in the (B) Rank Abundance curve, the horizontal coordinate is the serial number sorted by the abundance of OTUs, and the vertical \ncoordinate is the relative abundance of the corresponding OTUs, and different samples are represented by different colored fold lines\n\nPage 9 of 14\nPan et al. BMC Microbiology          (2024) 24:281 \n \nthe menstrual cycle is a major disruptor of the vaginal \nmicrobiome. Different microbiota characteristics are \nobserved in women at different physiological stages [40]. \nIn healthy women of reproductive age, the vaginal micro-\nbiome composition changes dramatically before and after \nmenstruation [41]. Menstrual blood flowing through the \nvagina leaves sufficient iron necessary for pathogens, and \nthe iron necessary for pathogen metabolism [42], which \nis reduced by the iron-binding affinity of lactoferrin, is \nreplenished. Additionally, studies measuring oestradiol \nlevels and vaginal microbiome composition in women \nwho use oral contraceptives to inhibit ovulation have \nshown that the high diversity observed during menstrua -\ntion is mainly driven by oestradiol withdrawal before \nmenstruation rather than by the dynamic drive of pro -\ngesterone. Lactobacillus abundance increases during the \nfollicular and luteal phases, gradually normalising the \nvaginal microecology [41, 43]. Under the influence of \nFig. 6 AWeighted Unifrac based distance from Principal Co-ordinates Analysis (PCoA) analysis. Horizontal coordinates indicate one principal \ncomponent, vertical coordinates indicate another principal component, and percentages indicate the contribution of the principal component \nto the sample variance; each point in the graph indicates a sample, and samples from the same group are indicated using the same color (B) \nUnweighted Unifrac based distance from PCoA analysis. C Euclidean based distances from Principal Component Analysis (PCA) analysis. The \nhorizontal coordinate indicates the first principal component, and the percentage indicates the contribution value of the first principal component \nto the sample difference; the vertical coordinate indicates the second principal component, and the percentage indicates the contribution value \nof the second principal component to the sample difference; each point in the graph indicates a sample, and samples in the same group are \nindicated using the same color; in PCA graphs with clustering circles, the clustering circle is added with the grouping information (clustering circles \nneed more than 3 samples in the group)\n\nPage 10 of 14Pan et al. BMC Microbiology          (2024) 24:281 \nthis periodicity, combined with our test results, differ -\nent types of dominant bacterial profiles were observed \nin patients with adenomyosis in both luteal and follicular \nstages, which provided a reference for the detection of \nbiomarkers in patients with specific menstrual cycles or \nto evaluate their efficacy.\nIn summary, in this study, an increase in microbial \nrichness was associated with adenomyosis, and the \nmicrobiome characteristics of patients with and with -\nout adenomyosis differed according to the menstrual \ncycle. This study has three notable limitations: 1) the \nfinal sample size was limited because of coronavirus \ndisease 2019 (COVID-19), 2) large sample of clini -\ncal data for verification was not available, and 3) the \ndifferent methods used in each study may have led \nto different conclusions. Furthermore, adenomyosis \ndiagnosis remains unconfirmed without histological \nFig. 7 A Weighted Unifrac based distance from Beta-diversity analysis. B Unweighted Unifrac based distance from Beta-diversity analysis. The box \nplots of Beta-diversity between-group difference analysis can visualize the median, dispersion, maximum, minimum, and outliers of within-group \nsample similarity. At the same time, the T-test test was used to analyze whether the Beta diversity differences of species between groups were \nsignificant or not. C Weighted unifrac ased distance from Beta-diversity analysis during different menstrual cycles. D Unweighted unifrac ased \ndistance from Beta-diversity analysis during different menstrual cycles\nTable 2 Anosim analysis based on the Bray–Curtis distance. \nAnosim analysis is a non-parametric test used to test whether \nthe difference between groups is significantly greater than \nthe difference within groups, so as to determine whether the \ngrouping is meaningful. We conducted the significance test of \nthe difference between groups based on the rank of the Bray–\nCurtis distance value\nGroup R value P value\nB-A 0.03067 0.044\n\nPage 11 of 14\nPan et al. BMC Microbiology          (2024) 24:281 \n \nFig. 8 MetaStat analysis at (A) phylum, (B) class, (C) order, (D) family, (D) genus and (E) species level. For the species with significant differences \nbetween study groups, MetaStat method was used to screen the species with significant differences based on the species abundance tables \nof different levels\nFig. 9 Simper analysis. It is a breakdown of the Bray–Curtis difference index that quantifies how much each species contributes to the difference \nbetween two groups. The results show the top 10 species with the highest contribution to the difference between the two groups and their \nabundance\n\nPage 12 of 14Pan et al. BMC Microbiology          (2024) 24:281 \nFig. 10 A MeanDecreaseAccuracy based analysis and MeanDecreaseGin based analysis. B proportion of false positive (Specificity), ordinate: \nproportion of true Sensitivity; (C) ROC curve of the test pair, abscess: proportion of false positive (Specificity), ordinate: proportion of true \nSensitivity (specificity) Mean Decrease Accuracy measures the extent to which the prediction accuracy of random forest is reduced when the value \nof a variable is changed to a random number. The greater the value, the greater the importance of the variable. MeanDecreaseGini compared \nthe importance of the variables by calculating the effect of each variable on the heterogeneity of the observed values at each node \nof the classification tree using the Gini index\n\nPage 13 of 14\nPan et al. BMC Microbiology          (2024) 24:281 \n \nassessment. This may have led to misclassification in \nboth cases (false positives) and controls (false nega -\ntives). In future research, we plan to develop stand -\nardized analysis software and large databases to \ncontinue our investigation of the mechanisms behind \nthis association.\nAbbreviations\nCA125  Carbohydrate Antigen 125\nPCR  Polymerase Chain Reaction\nCTAB  Cetyltrimethylammonium Bromide.\nOTUs  Operational Taxonomic Units\nPCoA  Principal Co-ordinates Analysis\nMRI  Magnetic resonance imaging\nBV  Bacterial Vaginosis\nNK cells  Natural killer\nTLR-4  Toll-like receptor 4\nNF-κB  Nuclear Factor kappa-B\nTim-3  T Cell Immunoglobulin Domain and Mucin Domain-3\nGAL-9  Galectin-9\nIFN-I  Type I interferon\nCOVID-19  Coronavirus disease 2019\nSupplementary Information\nThe online version contains supplementary material available at https:// doi. \norg/ 10. 1186/ s12866- 024- 03339-9.\nSupplementary Material 1.\nAcknowledgements\nWe would like to thank all of the volunteers who participated in this study.\nAuthors’ contributions\nAll authors read and approved the final manuscript. GYW and MY designed \nthe experiments. ZYP conceived and carried out sample collection, analysis, \nand interpretation. JD, PZ, QHR, XYW, HS, SMY and XJ carried out sample col-\nlection. ZYP drafted the manuscript and prepared the figures and tables. All \nauthors finalized the final manuscript.\nFunding\nThis study was supported by the National Key R&D Program of China \n[2023YFC2705400], the Major Basic Research of Natural Science Foundation \nof Shandong [ZR2021ZD34], and the National Natural Science Foundation of \nChina [grant numbers 82071621 and 81901458].\nAvailability of data and materials\nThe datasets generated and analyzed during the current study are available in \nthe [China National Center of Bioinformation (CNCB)]database. The number of \nthis project is CRA012802. [https:// ngdc. cncb. ac. cn/ gsub/ submit/ gsa/ list].\nDeclarations\nEthics approval and consent to participate\nThis study was reviewed and approved by the Medical Ethics Committee of \nQilu Hospital of Shandong University (ethics approval No.: KYLL-20211–092-1). \nThe research has been performed in accordance with the Declaration of Hel-\nsinki. The patients were informed about the sample collection and had signed \ninformed consent forms.\nConsent for publication\nNot applicable.\nCompeting interests\nThe authors declare no competing interests.\nAuthor details\n1 Department of Obstetrics and Gynecology, Shandong Provincial Hospital, \nJinan 250000, Shandong, China. 2 Medical Integration and Practice Center, \nCheeloo College of Medicine, Shandong University, Jinan 250000, Shandong, \nChina. 3 JiNan Key Laboratory of Diagnosis and Treatment of Major Gynae-\ncological Disease, Jinan 250000, Shandong Province, China. 4 Gynecology \nLaboratory, Shandong Provincial Hospital, Jinan 250000, Shandong Province, \nChina. 5 Gynecology Laboratory, Medical Science and Technology Innovation \nCenter, Shandong First Medical University & Shandong Academy of Medical \nSciences, Jinan 250000, Shandong Province, China. 6 Qilu Hospital of Shandong \nUniversity, Jinan 250000, Shandong Province, China. \nReceived: 1 September 2023   Accepted: 16 May 2024\nReferences\n 1. 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MSphere. 2020;5:1–14.\nPublisher’s Note\nSpringer Nature remains neutral with regard to jurisdictional claims in pub-\nlished maps and institutional affiliations.","source_license":"CC0","license_restricted":false}