Insights into the alteration of vaginal microbiota and metabolites in pregnant woman with preterm delivery

Insights into the alteration of vaginal microbiota and metabolites in pregnant woman with preterm delivery | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Insights into the alteration of vaginal microbiota and metabolites in pregnant woman with preterm delivery Jiaoning Fang, Mengjun Zhang, Meizhu Lin, Jun Zhang, Yijing Zheng, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6171036/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Preterm delivery is a major reason of perinatal morbidity and mortality. The disorder of vaginal microbiota and metabolites in pregnant women may be the most important risk factor for preterm delivery. This study aims to explore whether vaginal microbiota and metabolites alteration may elevate the risk of preterm delivery. In this study, 63 cases of pregnant women were enrolled, comprising 32 cases of women with term births and 31 cases of women with preterm births. Compared with the pre-cerclage in the term birth group (PrTG), the proportion of beneficial bacteria ( Lactobacillus , Prevotella , Trichococcus , Neisseria and Gemella ) in the pre-cerclage in the preterm birth group (PrPG) were significantly reduced ( p < 0.05), while the proportion of harmful bacteria ( Thauera , Ochrobactrum , Gardnerella , Massilia , Phyllobacteriaceae and Atopobium ) were significantly increased ( p < 0.05), which is strongly associated with the preterm birth. In addition, vaginal metabolomics-based LC-Orbitrap-MS/MS revealed that the contents of 2-Piperidone, Melphalan, N-acetylputrescine, Obatoclax, Eurostoside, Pregnanediol 3-O-glucuronide, O-Phospho-L-serine, 1-Kestose and N-arachidonylglycine were significantly decreased in the PrPG group compared with the PrTG group, while Acenocoumarol, Isopyrazam, Pentosidine, hexose, 7-Hydroxymitragynine, PE, Tamoxifen and 1-Deoxynojirimycin contents were significantly increased. These results elaborate that several candidate bacteria and metabolites could be applied as the prospective predictors for preterm birth, and approve the theoretical basis for the internation of preterm birth. Biological sciences/Microbiology Biological sciences/Microbiology/Clinical microbiology Health sciences/Biomarkers Health sciences/Biomarkers/Predictive markers Preterm birth vaginal microbiome vaginal metabolomics microbial-marker metabolic pathway Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Preterm delivery is described as birth prior to 37 weeks of gestation (or less than 259 days from the first day of a woman’s last menstrual period), including spontaneous and iatrogenic preterm births. Preterm delivery is the major cause of infant morbidity and mortality worldwide, and is related to long-time adverse outcomes in children 1 . High incidence of preterm delivery not only increases the annual societal economic burden, and seriously influences family happiness and social harmony. According to the Global Disease Burden Study from 2022, more than 15 million babies are born preterm every year, and the prevalence of preterm birth is globally increasing 2 . Among these, approximately 45% of premature infants are diagnosed with spontaneous preterm labor with intact membranes, and approximately 30% of premature infants are diagnosed with spontaneous preterm labor with ruptured membranes in the world 3 . According a previous report, the occurrence of preterm delivery is associated with a short cervix, extremes of maternal age ( 35 years) and body mass index (BMI 28), low socio-economic status, smoking, and genetic polymorphisms 4 . Among them, there is obviously difference in the incidence rate of preterm delivery among different regions. For example, it is reported that the rates of preterm delivery accounting for more than 80% of global cases in low-income and middle-income countries, such as Southern Asia and Sub-Saharan Africa 5 . At present, cervical cerclage and vaginal progesterone are widely used in the preventive treatment of preterm delivery and preterm premature rupture of membranes. Among these, cervical cerclage mechanically maintains a long and closed cervix, and is one of the universal methods applied to decrease preterm birth in pregnant women with older age or high risk 6 . However, there is an obvious difference in the intervention effect of cervical cerclage among the different people. As is well known, the vaginal microbiota is an essential regulator of reproductive tract pathophysiology, but the diversity of vaginal microbiome was obviously lower than that of other mucosal surfaces 7 . Some studies suggested that pregnant women with vaginal dysbiosis because of bacterial vaginosis, before 20 weeks of gestation, have a 5-time elevated risk of late miscarriage or preterm birth before 34 weeks of gestation and a 7-fold increased risk if bacterial vaginosis is detected before 16 weeks 8 . Lactobacillus species is one of the major bacterium in the vagina, and is generally regarded as a hallmark of health, especially during reproductive years. High proportion of Lactobacillus in the vagina is beneficial for suppressing the growth of harmful bacteria, and elevating the level of short-chain fatty acids that provides energy for the growth of vaginal epithelial cells 9 . Some studies also displayed that elevated the abundance of Lactobacillus effectively suppressed the product of pro-inflammatory cytokines, relieved oxidative stress, and regulated the composition of vaginal microbiota 10 . On the contrary, the reduction of Lactobacillus species and increases in microbial diversity elevates the risk of bacterial vaginosis, which may be associated with the high rate of preterm delivery 11 . The changes in vaginal microbiota induce the alteration in vaginal metabolite contents. Microbial metabolites are reported to exert prominence and various influences on body health and are detectable in a series of biological tissues, including the colon, liver, brain and vagina 12 – 13 . At present, nuclear magnetic resonance (NMR), liquid chromatography tandem mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) are extensively applied to detect the metabolites because of their relatively high sensitivity, which is beneficial for obtaining the different vaginal metabolites between the pregnant woman with preterm birth and term birth. Some studies mainly focus on explored the association between the vaginal microbiota and the occurrence of preterm delivery, which may not accurately predict premature birth. In the present study, the vagina microbiota and vaginal metabolites between pregnant women (years: 21–38 and BMI: 19.57–28.13) with preterm delivery and term birth from Fuzhou before cervical cerclage were measured by 16S rRNA gene sequencing and untargeted metabolomics-based liquid chromatography-Orbitrap-mass spectrometry/mass spectrometry (LC-Orbitrap-MS/MS), respectively. Then, the key microbial phylotypes and marked metabolites in the vagina of pregnant women with preterm delivery were screened using statistical analysis, which offers useful information to predict preterm delivery or the development of new therapeutics for pregnant women with preterm delivery. 2 Material and methods 2.1 Trial design and oversight The trial complies with all relevant ethical regulations, and the protocol was approved by the Ethics Committee of Fujian Maternity and Child Health Hospital (Approval No. 2021KLR601). It started in January 2021 and was conducted in Fujian Maternity and Child Health Hospital (Fuzhou, China) in accordance with the principles of the Declaration of Helsinki and Good Clinical Practice. This study has received written informed consent from participants for use of samples and data from the participant used in this study. 2.2 Patients A total of 132 participants (without antibiotic use, sexual activity, and tobacco use in the 12 weeks prior) offered written informed consent before enrollment in accordance with the approved institutional guidelines, but only 63 participants met the requirements of experiment (no including 46 cases of multiple pregnancies, 20 cases of lost contact, 2 cases of uterine malformations, and 1 case of severe fetal malformations). 2.3 Data collection The current and historical pregnancy outcomes of participants were collected and recorded, including maternal age, body mass index (BMI), abortion and fertility frequency). All participants were assigned to 2 groups, namely pre-cerclage in the preterm delivery group (PrPG) and pre-cerclage in the term birth groups (PrTG). 2.4 Sample collection The vagina was exposed using a single-use sterile endoscope, and then a cotton swab was gently rotated across the vaginal wall for 20 s. The sampling loop was taken out from the cannula to fully exposed in the uterine, hen rotate the handle of the sampler ten times to collect the endometrial sample, and hen rotate the handle of the sampler ten times to collect the endometrial sample. In the passage of the sampler through the vagina, the sampling loop remains retracted within the cannula to avoid contact with microorganisms from these two body parts, thus eliminating cross-contamination between intrauterine and vaginal samples. All samples were immediately frozen for 3–4 min using liquid nitrogen, placed in -80°C until further use. The specific surgical steps and preoperative management of cervical cerclage surgery were carried out according to our previous study 14 . 2.5 16S rRNA gene sequencing The sequencing analysis of vaginal microbiota was implemented by the MiSeq platform based on the methodology outlined in a previous report with mini modifications 15 . In brief, total bacterial DNA from CVF sample was extracted using a commercially available total DNA extraction kit (MoBio, Carlsbad, CA, USA), and then the V3-V4 regions of bacterial 16S rRNA genes were amplified by broad-range bacterial primers, namely 338F primers (5′-CCTAYGGGRBGCASCAG-3′) and 806R primers (5′-GGACTACHVGGGTWTCTAAT-3′). These products were purified using 2.0% agarose gel electrophoresis, target fragment was collected and recovered by Agencourt AMPure XP Kit (Hangzhou, China). The content of each sample was detected by a Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific, CA, USA). Sequencing libraries consisted of equal concentrations of each sample, and their quality was evaluated on the Qubit@ 2.0 Fluorometer (Thermo Scientific, CA, USA), and then was implemented on Illumina Miseq platform (San Diego, CA, USA) at Shanghai Biotree Biotech. Co., Ltd. The raw data were filtered, denoised, merged and chimera removed using Microbial Ecology software (v 2.0), and the high-quality sequences were collected, and then grouped into operational taxonomic units (OTUs) with similarities more than 97%. Taxonomy annotation process was carried out on the OTU sequences by the Mothur approach and the SSU rRNA database of SILVA138.1. Alpha and beta diversity of vaginal microbiota were analyzed by X shell (v 7.0). The overall differences of vaginal microbiota were assessed based on the principal coordinates analysis (PCoA) by R software (v 4.4.2), the key microbial phylotypes were screened using Microbial Ecology software. 2.6 Untargeted vagina metabolomics analysis Untargeted vagina metabolomics analysis was implemented by LC-Orbitrap-MS/MS based on a previous report with minor modifications 16 . Briefly, the vaginal contents were freeze-dried, weighted, and extracted using the organic solution (methanol: acetonitrile = 1:1). The mixture solution was sufficiently vibrated using a high-throughput oscillator, and placed at 0–4℃ environment. After 2 h of stillness, the supernatant of each sample was collected by centrifugation (14000 rpm, 10 min, 4℃), and then dried at 25℃ under a vacuum environment. The sediment of each sample was resuspended using the organic solution (methanol: acetonitrile = 1:1), the supernatant was assembled by centrifugation (14000 rpm, 15 min, 2℃), and then filtrated by 0.22-µm aqueous membrane. Quality control samples consist of an equal volume of each sample, so as to assess the stability of instruments during the experiment. The vagina metabolic profiling was analyzed using LC-Orbitrap-MS/MS with an ACQUITY UPLC BEH Amide (50 × 2.1 mm, 1.7 µm; Waters, Milford, USA). Among these, MS detection of metabolites was carried out on Orbitrap Exploris 120 (Thermo Fisher Scientific, USA) with an ESI ion source in positive and negative modes, the mobile phase A: 0.1% formic acid and 5 mM ammonium acetate, the mobile phase B: acetonitrile. The raw data were preliminarily treated using ProteoWizard software and R software (v 4.2.1), including peak alignment, peak identification, and deconvolution. Principal components analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least-squares discrimination analysis (OPLS-DA) of vaginal metabolomics were carried out by MetaboAnalyst (v 6.0), and vaginal metabolites of an outstanding difference between the PrPG and PrTG groups are screened by orthogonal partial least-squares discrimination analysis (OPLS-DA) and S-loading plot based on OPLS-DA. The proportion of obviously different vaginal metabolites (VIP > 1.0, and p < 0.05) between the PrPG and PrTG groups was analyzed by R software (v 4.4.2). 2.7 Statistical analysis All data of the present study were presented as the mean ± SD. The significant differences were assessed by one-way analysis of variance (ANOVA) according to Duncan’s multiple range test with GraphPad Prism (v 9.0). Different letters present statistically different between the groups. 3 Results 3.1 Clinical characteristics and pregnancy outcome of participants As indicated in Table 1 , a total of 63 participants were recruited, including 32 cases (50.79%) of pregnant women with term birth and 31 cases (49.21%) of pregnant women with preterm delivery. There was no remarkable difference in maternal age, BMI, gravida, parity and cervical length between the PrPG and PrTG groups ( p > 0.05), but the gestational age at delivery and birth weight in the PrTG group were higher than that in the PrPG group ( p < 0.05). Table 1 Descriptive statistics of study participants. Data are presented as mean ± SD Characteristic PrPG (n = 32) PrTG (n = 31) P Value Age (years) 31.19 ± 3.68 30.65 ± 3.58 0.56 BMI (m 2 /kg) 23.85 ± 3.54 22.79 ± 3.34 0.22 Gravida 3.0 ± 1.44 2.29 ± 1.01 0.057 Parity 0.56 ± 0.67 0.42 ± 0.56 0.36 Cervical length (cm) 1.34 ± 0.67 a 1.70 ± 1.17 a 0.24 Gestational age at delivery (weeks) 32.27 ± 3.29 b 38.16 ± 1.03 a 5.90×10 − 11 Birth weight (g) 2007.93 ± 559.58 b 3188.07 ± 435.89 a 3.50×10 − 12 3.2 Alteration of vaginal microbiota in pregnant women with preterm delivery The vaginal microbiome diversity and composition were detected by 16S rDNA sequencing. As indicated in Fig. 1 A, the observed (180.53 ± 75.71 vs 178.10 ± 100.50), Shannon (1.93 ± 1.19 vs 1.86 ± 1.87), Simpson (0.45 ± 0.26 vs 0.38 ± 0.27) and Chao1 (183.19 ± 76.81 vs 180.59 ± 101.12) indexes of vaginal microbiota in the PrPG group were slight high than that in the PrTG group ( p > 0.05). The venn diagram exhibited that the quantity of OUTs shared by the two groups was 839, among which, the number of unique OTUs in the PrPG and PrTG groups was 1470 and 1301 respectively (Figure S1 ). In addition, PCoA was used to explore the relationship between preterm delivery and vaginal microbiota composition (Fig. 1 B). The first principal components (PC1), second principal components (PC2) and third principal components (PC3) contributed 61%, 15% and 11% of the total variance in the PCA score plot, respectively. The sample from the PrTG group were major distributed in the negative of PC1, but the distribution of samples from the PrPG group is relatively dispersed, suggesting that the disorder of vaginal microbiota was presented in pregnant women with preterm delivery. 3.3 Screening for key microbial phylotypes The changes in microbiota composition in the vagina from pregnant women with preterm delivery and term birth were revealed at differing levels. Firmicutes, Actinobacteriota, Proteobacteria, Bacteroidota, Fusobacteriota, Verrucomicrobiota and Acidobacteriota were mainly microorganisms in the PrPG and PrTG groups at the phylum level (Fig. 2 A). Noticeably, compared with the PrTG group, the proportion of Firmicutes (from 83.68–57.42%), Verrucomicrobiota (from 0.39–0.20%) and Acidobacteriota (from 1.35–0.31%) in the PrPG group was obviously reduced ( p < 0.05), but the proportion of Actinobacteriota (from 6.23–27.92%), Proteobacteria (from 6.24–7.78%), Bacteroidota (from 1.69–3.70%) and Fusobacteriota (from 0.11–2.67%) were remarkably increased ( p < 0.05). At the genus level, the proportion of Prevotella 7 , Trichococcus , Actinomyces , Neisseria , Lactobacillus , Rothia , Gemella , Haemophilus and Porphyromonas were remarkably reduced in the PrPG group compared with that in the PrTG group ( p < 0.05), but the relative abundance of Pseudoxanthomonas , Thauera , Ochrobactrum , Olivibacter , Gardnerella , Massilia , Phyllobacteriaceae _ unclassified , Buchnera , Staphylococcus and Atopobium were significantly increased ( p < 0.05) (Fig. 2 B). These results suggested that vaginal microbiota played a vital role in altering the gestational age. 3.4 Alteration of vagina metabolic profiling in pregnant women with preterm delivery As everyone knows, the imbalance of gut microbiota causes the alteration in the gut metabolites that is strongly related to the host's health. Nevertheless, the association between the vaginal microbiota and its metabolites remains poorly understood. In the present study, the vaginal metabolites between the PrTG and PrBG groups were detected by untargeted metabolomics based on LC-Orbitrap-MS/MS. PCA was used to reveal the possible clustering between pregnant women with preterm delivery and term birth. The result of PCA analysis displayed that PC1 and PC2 contributed to 26.6% and 12.7% of the total variation in the positive ion modes, while PC1 and PC2 accounted for 36.0% and 8.4% of the total variation in the negative ion modes (Fig. 3 A). There was the obvious separation between the PrTG and PrBG groups, suggesting that alteration of vagina metabolic profiling may be one of the essential causes for preterm delivery. Subsequently, PLS-DA and OPLS-DA were applied to further revealed the alterations in vagina metabolites (Fig. 3 B and 3 C). PLS-DA and OPLS-DA scores plot exhibit that an obvious distinction was observed between the PrTG and PrBG groups for both the positive and negative ion modes. Furthermore, the S-plots of OPLS-DA displayed differences in the vaginal metabolites between the PrTG and PrBG groups (Fig. 3 D). 3.5 Screening for differential vagina metabolites According to the results of S-plots of OPLS-DA, a total of 42 differential metabolites between the PrTG and PrBG groups were screened (VIP value > 1 and p < 0.05) and identified in the positive ion modes (Fig. 4 ). Among them, the levels of 2,4,6-Trimethylpyridine, N1,N1-Diethyl-1,6-hexanediamine, Tebuconazole, 1-[4-(4-Quinazolinylamino)phenyl]ethenone, Trimethoprim, Merphalan, 3-Piperidinecarboxamide, Tetrahydrofurfuryl_acetate, 2-Piperidone and N-Acetylputrescine in the PrPG group were significantly reduced compared with that in the PrTG group, while the levels of Pimonidazole, 1,5-Naphthalenediamine, Racemoramide, Acenocoumarol, Caryoptosidic_acid, Finasteridecarboxylic acid, Iprovalicarb, Gly-Pro-Arg, Isopyrazam, Pravastatin lactone, Pentosidine, 1-Phenylicosane-1,3-dione, Epoxyfumitremorgin_C, 3-Isoxazolecarboxamide, Bipindogulomethyloside, Marimastat, Hexose, Arg-Asn, Buxifoliadine H, 7-Hydroxymitragynine, Estriol-17-glucuronide, PE(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/0:0), Isopetasoside, Rhodamine 6G cation, Desferrichrome, Tamoxifen, 1-Deoxymannojirimycin (hydrochloride), N-Acetyl-S-farnesyl-L-cysteine, N,N-Bis(2-hydroxyethyl)glycine, PI(20:4(5Z,8Z,11Z,14Z)/0:0), Genistein and Sarcosine ethyl ester were significantly increased. In the negative ion modes, a total of 43 differential metabolites between the PrTG and PrBG groups were screened (VIP value > 1 and p < 0.05) and identified (Fig. 5 ). Among these, the proportion of 2-Hydroxy-3-methylbutyric acid, 1,7-Bis(4-hydroxyphenyl)-4-hepten-3-one, Platyphylloside, Piroxicam, Oxypurinol, Xanthine, 1,6-Bis-O-(4-hydroxycinnamoyl)glucose, 6'-Sialyllactose, 9-Oxo-10(E),12(E)-octadecadienoic acid, Calceolarioside B and 9-HPODE in the PrPG group were significantly elevated compared with the PrBG group, but the proportion of Lys-Val, Obatoclax, L-Alanyl-gamma-D-glutamyl-L-lysine, Ser-Ile, L-Alanyl-L-leucine, Val-Ile, Leu-Val, Lys-Gln, Eurostoside, 2'-N-Acetylparomamine, Pregnanediol 3-O-glucuronide, Ritalinic_acid, 1-Hydroxy-2-naphthoic acid, Leucylphenylalanine, Phe-Leu, N-Acryloyl-DL-aspartic acid, O-Phospho-L-serine, 3-(2-Chlorophenyl)-1H-pyrazol-5-amine, 16-Glucuronide-estriol, 2,2',4,4'-Tetrahydroxybenzophenone, Fluvoxamine_acid, 1-Kestose, Indoxyl sulfate, 2-Propenoic acid, His-Val, 2,4-Dodecadienamide, N-Arachidonylglycine, (3.beta.)-Allopregnanolone sulfate, Phenylsulfate, 3-Phosphonopropanoic acid, 8-Acetyl-7-hydroxy-4-methylcoumarin and Mevalonic acid 5-pyrophosphate were significantly decreased. 3.6 Metabolic pathway analysis According to untargeted metabolomic profiling, significant differences in vaginal metabolites between PrTG and PrBG groups were imported into MetaboAnalyst 6.0, in order to obtain and analyze the specific pathways related to the metabolites based on the KEGG database. In the metabolic pathway, each circle expresses one metabolic pathway, and the color and size of the circles depend on the importance and p -values of the pathway. In the positive ion modes, galactose metabolism, arginine and proline metabolism, and drug metabolism-cytochrome P450 were significantly disturbed in pregnant women with preterm delivery (Fig. 6 A). In the negative ion modes, ascorbate and aldarate metabolism, terpenoid backbone biosynthesis, pentose and glucuronate interconversions, cysteine and methionine metabolism, glycine, serine and threonine metabolism, purine metabolism and steroid hormone biosynthesis were significantly disturbed in the pregnant women with preterm delivery (Fig. 6 B). The above results revealed that these metabolic pathways were associated with preterm delivery in pregnant women. 4 Discussion Preterm delivery has become an alarming public health concern because of the relatively higher mortality rate presented in preterm infants. According to a previous report, about three-quarters of cases are diagnosed as spontaneous preterm deliverys that contain previous spontaneous preterm delivery or preterm prelabour rupture of the membranes 17 . Although the great progress in cervical cerclage, significant differences in treatment effectiveness among different populations 18 . Recently, some reports confirmed that gut microbiota and its metabolites take a vital role in improving the host's health, such as hypoglycemic, hypolipidemic, antidiabetic and antidepressant effects. Therefore, we hypothesized that preterm delivery is related to the alteration in vaginal microbiota and its metabolites. In the present study, 16S rRNA gene sequencing and untargeted metabolomics were used to detect and identify the vaginal microbiota and its metabolites, in order to screen the key microbial phylotypes and differential vagina metabolites. Some investigations exhibited that the risk of preterm delivery is strongly related to the BMI of pregnant women, namely BMI more than 28.0 or less than 18.0 elevate the risk of preterm delivery 19 . A high BMI of pregnant women elevates the risk of gestational diabetes, hypertensive disorders and fetal malformations, which is one of the most important causes for medically indicated preterm delivery 20 . In addition, the advanced maternal age (more than 35 years) and maternal age (less than 25 years) further elevate the risk of preterm delivery 21 . Therefore, participants were collected by the BMI within the range of 18 to 28 and years within the range of 25 to 35 of pregnant women in this study, which is beneficial for eliminating the influence of BMI and year of pregnant woman on preterm delivery. In addition, the body weight of preterm delivery was significantly lower than that of term birth, because of the infant development is not yet complete, which is in agreement with the result of this study 22 . The vaginal microbiota accounts for about 9% of the total human microbiota, which take an essential role in improving the vagina's health. The vaginal microbiota is a dynamic ecosystem composed of various microorganisms with different quantities and ratios, which maintains the integrity of the vaginal barrier and prevents the growth of harmful bacteria 23 . Recently, it is reported that pregnancy outcomes are usually decided by the composition of vaginal microbiota 24 . In reproductive-age women, the proportion of Lactobacillus was obviously higher than that in others 25 . Feehily et al. found vaginal Lactobacillus is consisted of L. crispatus , L. delbruecki , L. gasseri , L. gasseriA , L. H fermentum , L. H gastricus , L. helveticus , L. jensenii , L. kefiranofaciens and L. taiwanensis 26 . Lactobacillus is regarded as a probiotic that is beneficial for the host's health when given in adequate amounts. Lactobacillus processes a series of physiological effects, such as suppressing oxidative stress and inflammatory responses 27 . Lactobacillus also prevents the growth of harmful bacteria in the vagina by elevating the levels of short-chain fatty acids. Therefore, the lower abundance of Lactobacillus may be related to preterm delivery. Prevotella could regulate mucin metabolism by stimulating their production and their degradation, which maintain the integrity of the vaginal barrier 28 . Trichococcus act as short-chain fatty acid-producing bacteria, which offer energy to the proliferation vaginal epithelial cells 29 . Gemella is an essential member of the human microbiome in healthy subjects, and rarely causes systemic illness 30 . In this study, the proportion of Prevotella , Trichococcus , Neisseria and Gemella was remarkably reduced in the PrPG group, while Thauera , Ochrobactrum , Gardnerella , Massilia , Phyllobacteriaceae and Atopobium were significantly increased. Among them, Thauera has the capacity to disintegrate androgen in aerobic and anaerobic conditions 31 . Ochrobactrum is a non-enteric and Gram-negative organism, and its abundance is strongly related to inflammatory responses 32 . Gardnerella is widely distributed in women of childbearing age, and the high proportion of Gardnerella causes a series of some diseases, which is extensively used to establish the bacterial vaginitis model 33 . Massilia belongs to the family Oxalobacteraceae, which is associated with bacteremia, CNS infections, wound infections, lymphadenitis, and osteomyelitis 34 . Phyllobacteriaceae is confirmed to destroy carbohydrate metabolism and/or fat metabolism, as well as stimulate inflammatory responses by producing lipopolysaccharide 35 . High abundance of Staphylococcus causes severe infectious diseases, such as impetigo, folliculitis, and cutaneous abscesses 36 . In addition, a previous study found that high abundance of Atopobium is positive associated with the incidence rate of infertility, endometritis, and pelvic inflammatory disease 37 . These results suggest that the alterations in vaginal microbiota is strongly related to the preterm delivery. Apart from vaginal microbiota, vaginal metabolites play the most important role in pregnant women with preterm delivery. 2-Piperidone could suppress the accumulation of reactive oxygen species and lipid peroxidation by regulating the activity of cytochrome P450 2E1 38 . Obatoclax is a synthetic derivative of bacterial prodiginines that promote the apoptosis of human colorectal carcinoma cells by suppressing Wnt/β-catenin signaling. Eurostoside act as a useful organic compound that is proven to inhibit the inflammatory responses by regulating the expression of iNOS and COX-2 39 . Pregnanediol 3-O-glucuronide is a natural metabolite, and its levels are negatively associated with the risk of preterm delivery 40 . O-Phospho-L-serine is an inhibitor of serine racemase that can elevate the regulatory cytokine (TGF-β) level and reduce the pro-inflammatory cytokines (TNF‐α and IL12p70) in bone‐marrow‐derived dendritic cells 41 . 1-Kestose is the smallest fructooligosaccharide component that regulates the vaginal microbiota composition, especially up-regulation of Bifidobacteria abundance 42 . Oral administration of 1-Kestose improves the symptoms of type 2 diabetes by elevating the short-chain fatty acids levels, such as acetate, butyrate and lactate 43 . N-arachidonylglycine acts as an amino acid derivative of arachidonic acid that attenuates CD4T cell responsiveness by reducing the levels of Th1 and Th17 cytokines, and regulating the GPR18 MTORC1 signaling 44 . In this study, the relative contents of 2-Piperidone, Melphalan, N-acetylputrescine, Obatoclax, Eurostoside, Pregnanediol 3-O-glucuronide, O-Phospho-L-serine, 1-Kestose and N-arachidonylglycine in the PrPG group were obviously lower than that in the PrTG group. Acenocoumarol is one of the important organic compounds of 4-hydroxycoumarins that is widely regarded as a potentially toxic compound 45 . Isopyrazam is one of the broad-spectrum succinate dehydrogenase inhibitor fungicides, which is confirmed to destroy the heart function by stimulating oxidative stress 46 . Pentosidine is one of the best-characterized advanced glycation end-products that play a pathologic role in some disorders related to aging, and promote the development of diabetes 47 . Long-term consumption of hexose causes the disorder of glucose metabolism, which elevated the risk of hyperglycemia and hyperlipidemia 48 . 7-Hydroxymitragynine is widely used as an anesthetic, but excessive use leads to a series of adverse reactions 49 . A previous study found that high-fat diet accelerates PE accumulation in the liver, which promotes liver function injury 50 . Tamoxifen is regarded as a path-breaking medication in tumor treatment, but is also reported to it give rise to thrombosis, epigastric discomfort and nausea 51 . 1-Deoxynojirimycin destroy the endoplasmic reticulum function, which mainly manifests as the unfolded or misfolded proteins accumulate 52 . In the present study, the Acenocoumarol, Isopyrazam, Pentosidine, hexose, 7-Hydroxymitragynine, PE, Tamoxifen and 1-Deoxynojirimycin concentrations in the PrPG group were higher than that in the PrTG group. Therefore, we preliminarily surmise that the alteration in vaginal metabolites may be is one of the important causes for preterm delivery. 5 Conclusion In the present study, we suggested that significantly difference in vaginal microbiota between the PrPG and PrTG groups, which is characterized by the reduction in Lactobacillus and the increase in Gardnerella . In addition, vaginal metabolomics analysis revealed the marked metabolites in pregnant women with preterm delivery, such as pregnanediol 3-O-glucuronide. These results offer useful information to elevate the accuracy of the model of premature birth prediction by combining vaginal microbiomics and metabolomics. Declarations Conflict of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This study was supported by Fujian Provincial Technology Innovation Project (No.2021Y9175 and 2020Y9148), Natural Science Foundation of Fujian Province (No.2022J011034 and 2021J01406). Author Contribution J. F. Conceptualization, Methodology, Investigation, Data curation, Writing-original draft; M. Z. Writing-original draft, Formal analysis, Software, Validation; M. L. Writing-original draft, Data curation. J. Z. Writing-original draft, Visualization; Y. Z. Writing-review & editing, Resources; L. W. Writing-review & editing, Methodology; Y. L. Writing-original draft, Project administration, Funding acquisition; M. P. Writing-review & editing, Supervision, Project administration, Funding acquisition. Data Availability The 16S rRNA sequencing data were deposited in the National Center for Biotechnology Information (No. PRJNA1122359) and the untargeted vagina metabolomics data were deposited in the MetaboLights database (No. MTBLS11563). Ethics statement The human participants in this study were permitted by the Ethics Committee of Fujian Maternity and Child Health Hospital (No. 2021KLR601). The participants offered their written informed consent to participate in the present study. References Xie, Y., et al., Interrupted-time-series analysis of the immediate impact of COVID-19 mitigation measures on preterm birth in China. Nature Commun. 13 (1), 5190. https://doi.org/10.1038/s41467-022-32814-y (2022). Adane, H. A.， Iles, R.， Boyle, J. A.， Gelaw, A. & Collie, A., Effects of psychosocial work factors on preterm birth: systematic review and meta-analysis. Public Health. 228 , 65-72. https://doi.org/10.1016/j.puhe.2023.12.002 (2024). Beernink, R. H. J.， Schuitemaker, J. H. N.， Zwertbroek, E. F.， Scherjon, S. A. & Cremers, T. I. F. H., Early pregnancy biomarker discovery study for spontaneous preterm birth. Placenta. 139 , 112-119. https://doi.org/10.1016/j.placenta.2023.06.011 (2023). Wei, J.， Zhang, L.， Xu, H. & Luo, Q., Preterm birth, a consequence of immune deviation mediated hyperinflammation. Heliyon. 10 (7), e28483. https://doi.org/10.1016/j.heliyon.2024.e28483 (2024). Ohuma, E. O., et al., National, regional, and global estimates of preterm birth in 2020, with trends from 2010: a systematic analysis. The Lancet. 402 (10409), 1261-1271. https://doi.org/10.1016/S0140-6736(23)00878-4 (2023). Seyama, R.， Makino, S.， Takeda, J.， Takeda, S. & Itakura, A., The retrospective study for effectiveness of cervical cerclage in preventing recurrent preterm birth. Taiwan. J. Obstet. Gyne. 61 (1), 63-69. https://doi.org/10.1016/j.tjog.2021.11.012 (2022). Golob, J. L., et al., Microbiome preterm birth dream challenge: crowdsourcing machine learning approaches to advance preterm birth research. medRxiv : the preprint server for health sciences. https://doi.org/10.1101/2023.03.07.23286920 (2023). Peelen, M. J., et al., The influence of the vaginal microbiota on preterm birth: A systematic review and recommendations for a minimum dataset for future research. Placenta. 79 , 30-39. https://doi.org/10.1016/j.placenta.2019.03.011 (2019). Hong, X., et al., Genome-wide association study identifies a novel maternal gene × stress interaction associated with spontaneous preterm birth. Pediatr. Res. 89 (6), 1549-1556. https://doi.org/10.1038/s41390-020-1093-1 (2021). Chee, W. J. Y.， Chew, S. Y. & Than, L. T. L., Vaginal microbiota and the potential of Lactobacillus derivatives in maintaining vaginal health. Microb. Cell Fact. 19 (1), 203. https://doi.org/10.1186/s12934-020-01464-4 (2020). Fettweis, J. M., et al., The vaginal microbiome and preterm birth. Nat. Med. 25 (6), 1012-1021. https://doi.org/10.1038/s41591-019-0450-2 (2019). Guo, W., et al., Improvement of inflammatory bowel disease by lactic acid bacteria-derived metabolites: a review. Crit. Rev. Food. Sci. Nutr. 65 (7), 1-18. https://doi.org/10.1080/10408398.2023.2291188 (2025). Guo, W., et al., Intestinal microbiomics and metabolomics insights into the hepatoprotective effects of Lactobacillus paracasei CCFM1222 against the acute liver injury in mice. Probiotics Antimicro. 15 (5), 1063-1077. https://doi.org/10.1007/s12602-022-09986-6 (2023). Fang, J.， Chen, L.， Chen, Z.， Jiang, X. & Pan, M., Association of the vaginal microbiota with pregnancy outcomes in Chinese women after cervical cerclage. Reprod. Biomed. Online. 41 (4), 698-706. https://doi.org/10.1016/j.rbmo.2020.06.016 (2020). Guo, W., et al., Protective effects of microbiome-derived inosine on lipopolysaccharide-induced acute liver damage and inflammation in mice via mediating the TLR4/NF-κB pathway. J. Agric. Food Chem. 69 (27), 7619-7628. https://doi.org/10.1021/acs.jafc.1c01781 (2021). Guo, W., et al., Intestinal microbiomics and hepatic metabolomics insights into the potential mechanisms of probiotic Bifidobacterium pseudolongum CCFM1253 preventing acute liver injury in mice. J. Sci. Food Agric. 103 (12), 5958-5969. https://doi.org/10.1002/jsfa.12665 (2023). Humberg, A., et al., Preterm birth and sustained inflammation: consequences for the neonate. Sem. Immunopathol. 42 (4), 451-468. https://doi.org/10.1007/s00281-020-00803-2 (2020). Park, S., et al., Predicting preterm birth through vaginal microbiota, cervical length, and WBC using a machine learning model. Front. Microbiol. 13 , 912853. https://doi.org/10.3389/fmicb.2022.912853 (2022). Cnattingius, S., et al., Maternal obesity and risk of preterm delivery. JAMA. 309 (22), 2362-70. https://doi.org/10.1001/jama.2013.6295 (2013). Robinson, D. T., et al., Dietary fat and fatty acid intake in nulliparous women: associations with preterm birth and distinctions by maternal BMI. Curr. Develop. Nutr. 5 (6), nzab074. https://doi.org/10.1093/cdn/nzab074 (2021). Wu, S. T., et al., Maternal risk factors for preterm birth in Taiwan, a nationwide population-based cohort study. Pediatr. Neonatol. 65 (1), 38-47. https://doi.org/10.1016/j.pedneo.2023.03.014 (2024). Kozuki, N., et al., Short maternal stature increases risk of small-for-gestational-age and preterm births in low- and middle-income countries: Individual participant data meta-analysis and population attributable fraction1, 2, 3. J. Nutr. 145 (11), 2542-2550. https://doi.org/10.3945/jn.115.216374 (2015). Shen, J., et al., Effects of low dose estrogen therapy on the vaginal microbiomes of women with atrophic vaginitis. Sci. Rep. 6 , 24380. https://doi.org/10.1038/srep24380 (2016). Salinas, A. M., et al., Vaginal microbiota evaluation and prevalence of key pathogens in ecuadorian women: an epidemiologic analysis. Sci. Rep. 10 (1), 18358. https://doi.org/10.1038/s41598-020-74655-z (2020). Hayashida, S., et al., How were Lactobacillus species selected as single dominant species in the human vaginal microbiota? Coevolution of humans and Lactobacillus . Med. Hypotheses. 163 , 110858. https://doi.org/10.1016/j.mehy.2022.110858 (2022). Feehily, C., et al., Shotgun sequencing of the vaginal microbiome reveals both a species and functional potential signature of preterm birth. NPJ Biofilms Microbi. 6 (1), 50. https://doi.org/10.1038/s41522-020-00162-8 (2020). Qin, J., et al., Effects of carnosine combined with Lactobacillus on the antioxidant capacity of liver and kidney in normal or stressed mice. Food Biosci. 59 , 103904. https://doi.org/10.1016/j.fbio.2024.103904 (2024). Tett, A.， Pasolli, E.， Masetti, G.， Ercolini, D. & Segata, N., Prevotella diversity, niches and interactions with the human host. Nat. Rev. Microbiol. 19 (9), 585-599. https://doi.org/10.1038/s41579-021-00559-y (2021). Xiang, J. Y., et al., Litchi chinensis seed prevents obesity and modulates the gut microbiota and mycobiota compositions in high-fat diet-induced obese zebrafish. Food Funct. 13 (5), 2832-2845. https://doi.org/10.1039/d1fo03991a (2022). Zaidi, S. J.， Husayni, T. & Collins, M. A., Gemella bergeri infective endocarditis: a case report and brief review of literature. Cardiol. Young. 28 (5), 762-764. https://doi.org/10.1017/s1047951118000070 (2018). Hsiao, T. H., et al., Circulating androgen regulation by androgen-catabolizing gut bacteria in male mouse gut. Gut Microbes. 15 (1), 2183685. https://doi.org/10.1080/19490976.2023.2183685 (2023). Gigi, R.， Flusser, G.， Kadar, A.， Salai, M. & Elias, S., Ochrobactrum anthropi-caused osteomyelitis in the foot mimicking a bone tumor: case report and review of the literature. The Journal of foot and ankle surgery : official publication of the American College of Foot and Ankle Surgeons. 56 (4), 851-853. https://doi.org/10.1053/j.jfas.2017.02.008 (2017). Jothi, R., et al., Untargeted metabolomics uncovers prime pathways linked to antibacterial action of citral against bacterial vaginosis-causing Gardnerella vaginalis: An in vitro and in vivo study. Heliyon. 10 (6), e27983. https://doi.org/10.1016/j.heliyon.2024.e27983 (2024). Ali, G. A.， Ibrahim, E. B.， Doiphode, S. H. & Goravey, W., Massilia timonae bacteremia: An unusual pathogen of septic abortion. IDCases. 29 , e01592. https://doi.org/10.1016/j.idcr.2022.e01592 (2022). Chen, P.， Chen, P.， Guo, Y.， Fang, C. & Li, T., Interaction between chronic endometritis caused endometrial microbiota disorder and endometrial immune environment change in recurrent implantation failure. Front. Immunol. 12 , 748447. https://doi.org/10.3389/fimmu.2021.748447 (2021). Tabiś, A., et al., Analysis of enterotoxigenic effect of Staphylococcus aureus and Staphylococcus epidermidis enterotoxins C and L on mice. Microbiol. Res. 258 , 126979. https://doi.org/10.1016/j.micres.2022.126979 (2022). Ravel, J.， Moreno, I. & Simón, C., Bacterial vaginosis and its association with infertility, endometritis, and pelvic inflammatory disease. Am. J. Obstet. Gynecol. 224 (3), 251-257. https://doi.org/10.1016/j.ajog.2020.10.019 (2021). Cheng, J., et al., Identification of 2-piperidone as a biomarker of CYP2E1 activity through metabolomic phenotyping. Toxicological sciences : an official journal of the Society of Toxicology. 135 (1), 37-47. https://doi.org/10.1093/toxsci/kft143 (2013). Le, D. D., et al., Iridoid derivatives from Vitex rotundifolia L. f. with their anti-inflammatory activity. Phytochemistry. 210 , 113649. https://doi.org/10.1016/j.phytochem.2023.113649 (2023). Zhang, Y., et al., Chemical fingerprint analysis and ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry-based metabolomics study of the protective effect of buxue yimu granule in medical-induced incomplete abortion rats. Front. Pharmacol. 11 , 578217. https://doi.org/10.3389/fphar.2020.578217 (2020). Fathallah, A. M.， Ramakrishnan, R. & Balu‐Iyer, S. V., O‐Phospho‐l‐Serine mediates Hyporesponsiveness toward FVIII in hemophilia a‐murine model by inducing tolerogenic properties in dendritic cells. J. Pharm. Sci. 103 (11), 3457-3463. https://doi.org/https://doi.org/10.1002/jps.24173 (2014). Tochio, T.， Kadota, Y.， Tanaka, T. & Koga, Y., 1-Kestose, the Smallest fructooligosaccharide component, which efficiently stimulates faecalibacterium prausnitzii as well as Bifidobacteria in humans. Foods (Basel, Switzerland). 7 (9). https://doi.org/10.3390/foods7090140 (2018). Watanabe, A., et al., 1-Kestose supplementation mitigates the progressive deterioration of glucose metabolism in type 2 diabetes OLETF rats. Sci. Rep. 10 (1), 15674. https://doi.org/10.1038/s41598-020-72773-2 (2020). Meadows, A. M., et al., N-arachidonylglycine is a caloric state-dependent circulating metabolite which regulates human CD4+T cell responsiveness. iScience. 26 (5), 106578. https://doi.org/10.1016/j.isci.2023.106578 (2023). Valdivielso, M.， Longo, I.， Lecona, M. & Lázaro, P., Cutaneous necrosis induced by acenocoumarol. J. Eur. Acad. Dermatol. Venereology : JEADV. 18 (2), 211-215. https://doi.org/10.1111/j.1468-3083.2004.00735.x (2004). Yan, Y., et al., Acute exposure of Isopyrazam damages the developed cardiovascular system of zebrafish (Danio rerio). J. Environ. Sci. Heal. Part. B. 58 (4), 367-377. https://doi.org/10.1080/03601234.2023.2197655 (2023). Li, H. & Yu, S. J., Review of pentosidine and pyrraline in food and chemical models: formation, potential risks and determination. J. Sci. Food Agric. 98 (9), 3225-3233. https://doi.org/10.1002/jsfa.8853 (2018). Ai, Y.-l., et al., Mannose antagonizes GSDME-mediated pyroptosis through AMPK activated by metabolite GlcNAc-6P. Cell Res. 33 (12), 904-922. https://doi.org/10.1038/s41422-023-00848-6 (2023). Vento, A. E., et al., Case report: Treatment of kratom use disorder with a classical tricyclic antidepressant. Front. psychiatry. 12 , 640218. https://doi.org/10.3389/fpsyt.2021.640218 (2021). Guo, W. L., et al., Ganoderic acid A from Ganoderma lucidum ameliorates lipid metabolism and alters gut microbiota composition in hyperlipidemic mice fed a high-fat diet. Food Funct. 11 (8), 6818-6833. https://doi.org/10.1039/d0fo00436g (2020). Wibowo, E.， Pollock, P. A.， Hollis, N. & Wassersug, R. J., Tamoxifen in men: a review of adverse events. Andrology. 4 (5), 776-788. https://doi.org/10.1111/andr.12197 (2016). Lu, Y.， Xu, Y. Y.， Fan, K. Y. & Shen, Z. H., 1-Deoxymannojirimycin, the alpha1,2-mannosidase inhibitor, induced cellular endoplasmic reticulum stress in human hepatocarcinoma cell 7721. Biochem Biophys Res. Commun. 344 (1), 221-225. https://doi.org/10.1016/j.bbrc.2006.03.111 (2006). Additional Declarations No competing interests reported. Supplementary Files Supplementarydata.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 22 Aug, 2025 Reviews received at journal 21 Aug, 2025 Reviewers agreed at journal 30 Jul, 2025 Editor assigned by journal 29 Jul, 2025 Editor invited by journal 02 Jul, 2025 Reviews received at journal 13 May, 2025 Reviewers agreed at journal 08 Apr, 2025 Reviewers agreed at journal 04 Apr, 2025 Reviewers invited by journal 03 Apr, 2025 Submission checks completed at journal 02 Apr, 2025 First submitted to journal 27 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6171036","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":440136843,"identity":"94fa976e-b5f9-47a0-8b3a-6ab680854143","order_by":0,"name":"Jiaoning Fang","email":"","orcid":"","institution":"Fujian Maternity and Child Health Hospital, Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiaoning","middleName":"","lastName":"Fang","suffix":""},{"id":440136844,"identity":"c51776f8-c6ed-4ac0-8f4b-d2ef6250ebb7","order_by":1,"name":"Mengjun Zhang","email":"","orcid":"","institution":"Fujian Maternity and Child Health Hospital, Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mengjun","middleName":"","lastName":"Zhang","suffix":""},{"id":440136845,"identity":"0403c80c-7b32-457c-ba02-ef0bf95c3308","order_by":2,"name":"Meizhu Lin","email":"","orcid":"","institution":"Fujian Maternity and Child Health Hospital, Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Meizhu","middleName":"","lastName":"Lin","suffix":""},{"id":440136846,"identity":"2b91b968-9782-4420-bcea-12feb979be82","order_by":3,"name":"Jun Zhang","email":"","orcid":"","institution":"Fujian Maternity and Child Health Hospital, Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Zhang","suffix":""},{"id":440136847,"identity":"9c62e757-ee6d-4838-94fb-371a3e75356f","order_by":4,"name":"Yijing Zheng","email":"","orcid":"","institution":"Fujian Maternity and Child Health Hospital, Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yijing","middleName":"","lastName":"Zheng","suffix":""},{"id":440136848,"identity":"1873afbd-33af-437f-a759-76e047448dd4","order_by":5,"name":"Lihua Wang","email":"","orcid":"","institution":"Fujian Maternity and Child Health Hospital, Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lihua","middleName":"","lastName":"Wang","suffix":""},{"id":440136849,"identity":"c8a8f4e1-cc77-4f0b-81f8-8d2c0f4941ac","order_by":6,"name":"Yan Lin","email":"","orcid":"","institution":"Fujian Maternity and Child Health Hospital, Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Lin","suffix":""},{"id":440136850,"identity":"ff19f2eb-86df-4aa5-82ff-28e34e1290b5","order_by":7,"name":"Mian Pan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIie3PrQ7CMBDA8VNgLhTZJQReoapkyRJe5QjJ1CBIBAIMCD40j8EjdDRMjWBHMjHMMBgMAUOYBrJNIvqXl/vlWgCT6V8jAGSti5/QyClPGtbE64kkdMsfcoQiaZ1nu+LVdvWw3ybjGIVS7ogqCth8QbnEXg7ciIIULX8aRIQx8PCwzSVCeTKiisaahszyFATvF5DjNSMvjRCAHJLQJUiUXenONNZDkEBUgtib7Ep3rdHaYI+TcrHwL23mydPjrjuMV/3b8+U02XxV8LDPAeau/yQmk8lk+uoNMVhQR9oYr2gAAAAASUVORK5CYII=","orcid":"","institution":"Fujian Maternity and Child Health Hospital, Fujian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Mian","middleName":"","lastName":"Pan","suffix":""}],"badges":[],"createdAt":"2025-03-06 13:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6171036/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6171036/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82130726,"identity":"2ac469db-9f2b-4ea6-b365-70c545e6e4cd","added_by":"auto","created_at":"2025-05-07 05:29:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":256465,"visible":true,"origin":"","legend":"\u003cp\u003eThe diversity of between pregnant women with preterm delivery and term birth. (A) Observed, Shannon, Simpson and Chao1 indexes, and (B) PCoA analysis based on Bray-Curtis distance.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6171036/v1/f70ba6c3e62f2c77c32ad698.png"},{"id":82131599,"identity":"db6cb8aa-f2e8-474f-92da-50fdffce4181","added_by":"auto","created_at":"2025-05-07 05:37:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":184547,"visible":true,"origin":"","legend":"\u003cp\u003eThe relative abundance of vaginal microbiota between pregnant women with preterm delivery and term birth before cervical cerclage. (A) phylum and (B) genus.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6171036/v1/4ba81552cf7ab591ddfd116c.png"},{"id":82130711,"identity":"1f864df7-9f59-4cdd-b66f-90592da63b9d","added_by":"auto","created_at":"2025-05-07 05:29:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":489814,"visible":true,"origin":"","legend":"\u003cp\u003eThe alteration in vaginal metabolites in pregnant women with preterm delivery in the positive and negative ion modes. (A) PCA, (B)PLS-DA, (C) OPLS-DA, and (D) S-plots of OPLS-DA.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6171036/v1/9b3aee9f6fa3429cf0f9a650.png"},{"id":82130713,"identity":"63ee27b1-7a05-41c2-88cf-cdcffceb72c8","added_by":"auto","created_at":"2025-05-07 05:29:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":921650,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap exhibited the differential metabolites in the positive ion modes between the PrPG and PrTG groups.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6171036/v1/f44fc2439f8398d91e91ffc1.png"},{"id":82131603,"identity":"cbc8a6f2-bcd7-422f-95d7-237c1992303e","added_by":"auto","created_at":"2025-05-07 05:37:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":906628,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap exhibited the differential metabolites in the negative ion modes between the PrPG and PrTG groups.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6171036/v1/20b13df0491d5219a83f5922.png"},{"id":82130715,"identity":"5a4bb77a-37fb-4784-9712-8341843e945a","added_by":"auto","created_at":"2025-05-07 05:29:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":483711,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of key metabolic pathways. (A) Pathway analysis in the positive ion modes and (B) in the negative ion modes.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6171036/v1/639273541ec8d36f054b4506.png"},{"id":82134324,"identity":"8c92596c-2c5a-48c3-8bcc-b8e5a49c7145","added_by":"auto","created_at":"2025-05-07 06:02:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4052113,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6171036/v1/5528a48b-4f30-4b88-b24c-16731ad9756a.pdf"},{"id":82130740,"identity":"49848434-85b1-436d-b68a-a46d2c769c5d","added_by":"auto","created_at":"2025-05-07 05:29:40","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":170819,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata.docx","url":"https://assets-eu.researchsquare.com/files/rs-6171036/v1/d9dde6d2329a54f06da3a5c1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Insights into the alteration of vaginal microbiota and metabolites in pregnant woman with preterm delivery","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003ePreterm delivery is described as birth prior to 37 weeks of gestation (or less than 259 days from the first day of a woman\u0026rsquo;s last menstrual period), including spontaneous and iatrogenic preterm births. Preterm delivery is the major cause of infant morbidity and mortality worldwide, and is related to long-time adverse outcomes in children\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. High incidence of preterm delivery not only increases the annual societal economic burden, and seriously influences family happiness and social harmony. According to the Global Disease Burden Study from 2022, more than 15\u0026nbsp;million babies are born preterm every year, and the prevalence of preterm birth is globally increasing\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Among these, approximately 45% of premature infants are diagnosed with spontaneous preterm labor with intact membranes, and approximately 30% of premature infants are diagnosed with spontaneous preterm labor with ruptured membranes in the world\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. According a previous report, the occurrence of preterm delivery is associated with a short cervix, extremes of maternal age (\u0026lt;\u0026thinsp;25 years and \u0026gt;\u0026thinsp;35 years) and body mass index (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18 and BMI\u0026thinsp;\u0026gt;\u0026thinsp;28), low socio-economic status, smoking, and genetic polymorphisms\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Among them, there is obviously difference in the incidence rate of preterm delivery among different regions. For example, it is reported that the rates of preterm delivery accounting for more than 80% of global cases in low-income and middle-income countries, such as Southern Asia and Sub-Saharan Africa\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. At present, cervical cerclage and vaginal progesterone are widely used in the preventive treatment of preterm delivery and preterm premature rupture of membranes. Among these, cervical cerclage mechanically maintains a long and closed cervix, and is one of the universal methods applied to decrease preterm birth in pregnant women with older age or high risk\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. However, there is an obvious difference in the intervention effect of cervical cerclage among the different people.\u003c/p\u003e \u003cp\u003eAs is well known, the vaginal microbiota is an essential regulator of reproductive tract pathophysiology, but the diversity of vaginal microbiome was obviously lower than that of other mucosal surfaces\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Some studies suggested that pregnant women with vaginal dysbiosis because of bacterial vaginosis, before 20 weeks of gestation, have a 5-time elevated risk of late miscarriage or preterm birth before 34 weeks of gestation and a 7-fold increased risk if bacterial vaginosis is detected before 16 weeks\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eLactobacillus\u003c/em\u003e species is one of the major bacterium in the vagina, and is generally regarded as a hallmark of health, especially during reproductive years. High proportion of \u003cem\u003eLactobacillus\u003c/em\u003e in the vagina is beneficial for suppressing the growth of harmful bacteria, and elevating the level of short-chain fatty acids that provides energy for the growth of vaginal epithelial cells\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Some studies also displayed that elevated the abundance of \u003cem\u003eLactobacillus\u003c/em\u003e effectively suppressed the product of pro-inflammatory cytokines, relieved oxidative stress, and regulated the composition of vaginal microbiota\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. On the contrary, the reduction of \u003cem\u003eLactobacillus\u003c/em\u003e species and increases in microbial diversity elevates the risk of bacterial vaginosis, which may be associated with the high rate of preterm delivery\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The changes in vaginal microbiota induce the alteration in vaginal metabolite contents. Microbial metabolites are reported to exert prominence and various influences on body health and are detectable in a series of biological tissues, including the colon, liver, brain and vagina\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. At present, nuclear magnetic resonance (NMR), liquid chromatography tandem mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) are extensively applied to detect the metabolites because of their relatively high sensitivity, which is beneficial for obtaining the different vaginal metabolites between the pregnant woman with preterm birth and term birth.\u003c/p\u003e \u003cp\u003eSome studies mainly focus on explored the association between the vaginal microbiota and the occurrence of preterm delivery, which may not accurately predict premature birth. In the present study, the vagina microbiota and vaginal metabolites between pregnant women (years: 21\u0026ndash;38 and BMI: 19.57\u0026ndash;28.13) with preterm delivery and term birth from Fuzhou before cervical cerclage were measured by 16S rRNA gene sequencing and untargeted metabolomics-based liquid chromatography-Orbitrap-mass spectrometry/mass spectrometry (LC-Orbitrap-MS/MS), respectively. Then, the key microbial phylotypes and marked metabolites in the vagina of pregnant women with preterm delivery were screened using statistical analysis, which offers useful information to predict preterm delivery or the development of new therapeutics for pregnant women with preterm delivery.\u003c/p\u003e"},{"header":"2 Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Trial design and oversight\u003c/h2\u003e \u003cp\u003eThe trial complies with all relevant ethical regulations, and the protocol was approved by the Ethics Committee of Fujian Maternity and Child Health Hospital (Approval No. 2021KLR601). It started in January 2021 and was conducted in Fujian Maternity and Child Health Hospital (Fuzhou, China) in accordance with the principles of the Declaration of Helsinki and Good Clinical Practice. This study has received written informed consent from participants for use of samples and data from the participant used in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Patients\u003c/h2\u003e \u003cp\u003e A total of 132 participants (without antibiotic use, sexual activity, and tobacco use in the 12 weeks prior) offered written informed consent before enrollment in accordance with the approved institutional guidelines, but only 63 participants met the requirements of experiment (no including 46 cases of multiple pregnancies, 20 cases of lost contact, 2 cases of uterine malformations, and 1 case of severe fetal malformations).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data collection\u003c/h2\u003e \u003cp\u003eThe current and historical pregnancy outcomes of participants were collected and recorded, including maternal age, body mass index (BMI), abortion and fertility frequency). All participants were assigned to 2 groups, namely pre-cerclage in the preterm delivery group (PrPG) and pre-cerclage in the term birth groups (PrTG).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Sample collection\u003c/h2\u003e \u003cp\u003eThe vagina was exposed using a single-use sterile endoscope, and then a cotton swab was gently rotated across the vaginal wall for 20 s. The sampling loop was taken out from the cannula to fully exposed in the uterine, hen rotate the handle of the sampler ten times to collect the endometrial sample, and hen rotate the handle of the sampler ten times to collect the endometrial sample. In the passage of the sampler through the vagina, the sampling loop remains retracted within the cannula to avoid contact with microorganisms from these two body parts, thus eliminating cross-contamination between intrauterine and vaginal samples. All samples were immediately frozen for 3\u0026ndash;4 min using liquid nitrogen, placed in -80\u0026deg;C until further use. The specific surgical steps and preoperative management of cervical cerclage surgery were carried out according to our previous study\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 16S rRNA gene sequencing\u003c/h2\u003e \u003cp\u003eThe sequencing analysis of vaginal microbiota was implemented by the MiSeq platform based on the methodology outlined in a previous report with mini modifications \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In brief, total bacterial DNA from CVF sample was extracted using a commercially available total DNA extraction kit (MoBio, Carlsbad, CA, USA), and then the V3-V4 regions of bacterial 16S rRNA genes were amplified by broad-range bacterial primers, namely 338F primers (5\u0026prime;-CCTAYGGGRBGCASCAG-3\u0026prime;) and 806R primers (5\u0026prime;-GGACTACHVGGGTWTCTAAT-3\u0026prime;). These products were purified using 2.0% agarose gel electrophoresis, target fragment was collected and recovered by Agencourt AMPure XP Kit (Hangzhou, China). The content of each sample was detected by a Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific, CA, USA). Sequencing libraries consisted of equal concentrations of each sample, and their quality was evaluated on the Qubit@ 2.0 Fluorometer (Thermo Scientific, CA, USA), and then was implemented on Illumina Miseq platform (San Diego, CA, USA) at Shanghai Biotree Biotech. Co., Ltd.\u003c/p\u003e \u003cp\u003eThe raw data were filtered, denoised, merged and chimera removed using Microbial Ecology software (v 2.0), and the high-quality sequences were collected, and then grouped into operational taxonomic units (OTUs) with similarities more than 97%. Taxonomy annotation process was carried out on the OTU sequences by the Mothur approach and the SSU rRNA database of SILVA138.1. Alpha and beta diversity of vaginal microbiota were analyzed by X shell (v 7.0). The overall differences of vaginal microbiota were assessed based on the principal coordinates analysis (PCoA) by R software (v 4.4.2), the key microbial phylotypes were screened using Microbial Ecology software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Untargeted vagina metabolomics analysis\u003c/h2\u003e \u003cp\u003eUntargeted vagina metabolomics analysis was implemented by LC-Orbitrap-MS/MS based on a previous report with minor modifications\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Briefly, the vaginal contents were freeze-dried, weighted, and extracted using the organic solution (methanol: acetonitrile\u0026thinsp;=\u0026thinsp;1:1). The mixture solution was sufficiently vibrated using a high-throughput oscillator, and placed at 0\u0026ndash;4℃ environment. After 2 h of stillness, the supernatant of each sample was collected by centrifugation (14000 rpm, 10 min, 4℃), and then dried at 25℃ under a vacuum environment. The sediment of each sample was resuspended using the organic solution (methanol: acetonitrile\u0026thinsp;=\u0026thinsp;1:1), the supernatant was assembled by centrifugation (14000 rpm, 15 min, 2℃), and then filtrated by 0.22-\u0026micro;m aqueous membrane. Quality control samples consist of an equal volume of each sample, so as to assess the stability of instruments during the experiment.\u003c/p\u003e \u003cp\u003eThe vagina metabolic profiling was analyzed using LC-Orbitrap-MS/MS with an ACQUITY UPLC BEH Amide (50 \u0026times; 2.1 mm, 1.7 \u0026micro;m; Waters, Milford, USA). Among these, MS detection of metabolites was carried out on Orbitrap Exploris 120 (Thermo Fisher Scientific, USA) with an ESI ion source in positive and negative modes, the mobile phase A: 0.1% formic acid and 5 mM ammonium acetate, the mobile phase B: acetonitrile. The raw data were preliminarily treated using ProteoWizard software and R software (v 4.2.1), including peak alignment, peak identification, and deconvolution. Principal components analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least-squares discrimination analysis (OPLS-DA) of vaginal metabolomics were carried out by MetaboAnalyst (v 6.0), and vaginal metabolites of an outstanding difference between the PrPG and PrTG groups are screened by orthogonal partial least-squares discrimination analysis (OPLS-DA) and S-loading plot based on OPLS-DA. The proportion of obviously different vaginal metabolites (VIP\u0026thinsp;\u0026gt;\u0026thinsp;1.0, and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between the PrPG and PrTG groups was analyzed by R software (v 4.4.2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll data of the present study were presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. The significant differences were assessed by one-way analysis of variance (ANOVA) according to Duncan\u0026rsquo;s multiple range test with GraphPad Prism (v 9.0). Different letters present statistically different between the groups.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Clinical characteristics and pregnancy outcome of participants\u003c/h2\u003e \u003cp\u003eAs indicated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, a total of 63 participants were recruited, including 32 cases (50.79%) of pregnant women with term birth and 31 cases (49.21%) of pregnant women with preterm delivery. There was no remarkable difference in maternal age, BMI, gravida, parity and cervical length between the PrPG and PrTG groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), but the gestational age at delivery and birth weight in the PrTG group were higher than that in the PrPG group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of study participants. Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrPG (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrTG (n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.19\u0026thinsp;\u0026plusmn;\u0026thinsp;3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.65\u0026thinsp;\u0026plusmn;\u0026thinsp;3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (m\u003csup\u003e2\u003c/sup\u003e/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.85\u0026thinsp;\u0026plusmn;\u0026thinsp;3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.79\u0026thinsp;\u0026plusmn;\u0026thinsp;3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGravida\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCervical length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational age at\u003c/p\u003e \u003cp\u003edelivery (weeks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.27\u0026thinsp;\u0026plusmn;\u0026thinsp;3.29\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.90\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth weight (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2007.93\u0026thinsp;\u0026plusmn;\u0026thinsp;559.58\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3188.07\u0026thinsp;\u0026plusmn;\u0026thinsp;435.89\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.50\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;12\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Alteration of vaginal microbiota in pregnant women with preterm delivery\u003c/h2\u003e \u003cp\u003eThe vaginal microbiome diversity and composition were detected by 16S rDNA sequencing. As indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, the observed (180.53\u0026thinsp;\u0026plusmn;\u0026thinsp;75.71 vs 178.10\u0026thinsp;\u0026plusmn;\u0026thinsp;100.50), Shannon (1.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19 vs 1.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87), Simpson (0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26 vs 0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27) and Chao1 (183.19\u0026thinsp;\u0026plusmn;\u0026thinsp;76.81 vs 180.59\u0026thinsp;\u0026plusmn;\u0026thinsp;101.12) indexes of vaginal microbiota in the PrPG group were slight high than that in the PrTG group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The venn diagram exhibited that the quantity of OUTs shared by the two groups was 839, among which, the number of unique OTUs in the PrPG and PrTG groups was 1470 and 1301 respectively (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In addition, PCoA was used to explore the relationship between preterm delivery and vaginal microbiota composition (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The first principal components (PC1), second principal components (PC2) and third principal components (PC3) contributed 61%, 15% and 11% of the total variance in the PCA score plot, respectively. The sample from the PrTG group were major distributed in the negative of PC1, but the distribution of samples from the PrPG group is relatively dispersed, suggesting that the disorder of vaginal microbiota was presented in pregnant women with preterm delivery.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Screening for key microbial phylotypes\u003c/h2\u003e \u003cp\u003eThe changes in microbiota composition in the vagina from pregnant women with preterm delivery and term birth were revealed at differing levels. Firmicutes, Actinobacteriota, Proteobacteria, Bacteroidota, Fusobacteriota, Verrucomicrobiota and Acidobacteriota were mainly microorganisms in the PrPG and PrTG groups at the phylum level (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Noticeably, compared with the PrTG group, the proportion of Firmicutes (from 83.68\u0026ndash;57.42%), Verrucomicrobiota (from 0.39\u0026ndash;0.20%) and Acidobacteriota (from 1.35\u0026ndash;0.31%) in the PrPG group was obviously reduced (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but the proportion of Actinobacteriota (from 6.23\u0026ndash;27.92%), Proteobacteria (from 6.24\u0026ndash;7.78%), Bacteroidota (from 1.69\u0026ndash;3.70%) and Fusobacteriota (from 0.11\u0026ndash;2.67%) were remarkably increased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). At the genus level, the proportion of \u003cem\u003ePrevotella 7\u003c/em\u003e, \u003cem\u003eTrichococcus\u003c/em\u003e, \u003cem\u003eActinomyces\u003c/em\u003e, \u003cem\u003eNeisseria\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eRothia\u003c/em\u003e, \u003cem\u003eGemella\u003c/em\u003e, \u003cem\u003eHaemophilus\u003c/em\u003e and \u003cem\u003ePorphyromonas\u003c/em\u003e were remarkably reduced in the PrPG group compared with that in the PrTG group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but the relative abundance of \u003cem\u003ePseudoxanthomonas\u003c/em\u003e, \u003cem\u003eThauera\u003c/em\u003e, \u003cem\u003eOchrobactrum\u003c/em\u003e, \u003cem\u003eOlivibacter\u003c/em\u003e, \u003cem\u003eGardnerella\u003c/em\u003e, \u003cem\u003eMassilia\u003c/em\u003e, \u003cem\u003ePhyllobacteriaceae\u003c/em\u003e_\u003cem\u003eunclassified\u003c/em\u003e, \u003cem\u003eBuchnera\u003c/em\u003e, \u003cem\u003eStaphylococcus\u003c/em\u003e and \u003cem\u003eAtopobium\u003c/em\u003e were significantly increased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). These results suggested that vaginal microbiota played a vital role in altering the gestational age.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Alteration of vagina metabolic profiling in pregnant women with preterm delivery\u003c/h2\u003e \u003cp\u003eAs everyone knows, the imbalance of gut microbiota causes the alteration in the gut metabolites that is strongly related to the host's health. Nevertheless, the association between the vaginal microbiota and its metabolites remains poorly understood. In the present study, the vaginal metabolites between the PrTG and PrBG groups were detected by untargeted metabolomics based on LC-Orbitrap-MS/MS. PCA was used to reveal the possible clustering between pregnant women with preterm delivery and term birth. The result of PCA analysis displayed that PC1 and PC2 contributed to 26.6% and 12.7% of the total variation in the positive ion modes, while PC1 and PC2 accounted for 36.0% and 8.4% of the total variation in the negative ion modes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). There was the obvious separation between the PrTG and PrBG groups, suggesting that alteration of vagina metabolic profiling may be one of the essential causes for preterm delivery. Subsequently, PLS-DA and OPLS-DA were applied to further revealed the alterations in vagina metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). PLS-DA and OPLS-DA scores plot exhibit that an obvious distinction was observed between the PrTG and PrBG groups for both the positive and negative ion modes. Furthermore, the S-plots of OPLS-DA displayed differences in the vaginal metabolites between the PrTG and PrBG groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Screening for differential vagina metabolites\u003c/h2\u003e \u003cp\u003eAccording to the results of S-plots of OPLS-DA, a total of 42 differential metabolites between the PrTG and PrBG groups were screened (VIP value\u0026thinsp;\u0026gt;\u0026thinsp;1 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and identified in the positive ion modes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Among them, the levels of 2,4,6-Trimethylpyridine, N1,N1-Diethyl-1,6-hexanediamine, Tebuconazole, 1-[4-(4-Quinazolinylamino)phenyl]ethenone, Trimethoprim, Merphalan, 3-Piperidinecarboxamide, Tetrahydrofurfuryl_acetate, 2-Piperidone and N-Acetylputrescine in the PrPG group were significantly reduced compared with that in the PrTG group, while the levels of Pimonidazole, 1,5-Naphthalenediamine, Racemoramide, Acenocoumarol, Caryoptosidic_acid, Finasteridecarboxylic acid, Iprovalicarb, Gly-Pro-Arg, Isopyrazam, Pravastatin lactone, Pentosidine, 1-Phenylicosane-1,3-dione, Epoxyfumitremorgin_C, 3-Isoxazolecarboxamide, Bipindogulomethyloside, Marimastat, Hexose, Arg-Asn, Buxifoliadine H, 7-Hydroxymitragynine, Estriol-17-glucuronide, PE(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/0:0), Isopetasoside, Rhodamine 6G cation, Desferrichrome, Tamoxifen, 1-Deoxymannojirimycin (hydrochloride), N-Acetyl-S-farnesyl-L-cysteine, N,N-Bis(2-hydroxyethyl)glycine, PI(20:4(5Z,8Z,11Z,14Z)/0:0), Genistein and Sarcosine ethyl ester were significantly increased.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the negative ion modes, a total of 43 differential metabolites between the PrTG and PrBG groups were screened (VIP value\u0026thinsp;\u0026gt;\u0026thinsp;1 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Among these, the proportion of 2-Hydroxy-3-methylbutyric acid, 1,7-Bis(4-hydroxyphenyl)-4-hepten-3-one, Platyphylloside, Piroxicam, Oxypurinol, Xanthine, 1,6-Bis-O-(4-hydroxycinnamoyl)glucose, 6'-Sialyllactose, 9-Oxo-10(E),12(E)-octadecadienoic acid, Calceolarioside B and 9-HPODE in the PrPG group were significantly elevated compared with the PrBG group, but the proportion of Lys-Val, Obatoclax, L-Alanyl-gamma-D-glutamyl-L-lysine, Ser-Ile, L-Alanyl-L-leucine, Val-Ile, Leu-Val, Lys-Gln, Eurostoside, 2'-N-Acetylparomamine, Pregnanediol 3-O-glucuronide, Ritalinic_acid, 1-Hydroxy-2-naphthoic acid, Leucylphenylalanine, Phe-Leu, N-Acryloyl-DL-aspartic acid, O-Phospho-L-serine, 3-(2-Chlorophenyl)-1H-pyrazol-5-amine, 16-Glucuronide-estriol, 2,2',4,4'-Tetrahydroxybenzophenone, Fluvoxamine_acid, 1-Kestose, Indoxyl sulfate, 2-Propenoic acid, His-Val, 2,4-Dodecadienamide, N-Arachidonylglycine, (3.beta.)-Allopregnanolone sulfate, Phenylsulfate, 3-Phosphonopropanoic acid, 8-Acetyl-7-hydroxy-4-methylcoumarin and Mevalonic acid 5-pyrophosphate were significantly decreased.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Metabolic pathway analysis\u003c/h2\u003e \u003cp\u003eAccording to untargeted metabolomic profiling, significant differences in vaginal metabolites between PrTG and PrBG groups were imported into MetaboAnalyst 6.0, in order to obtain and analyze the specific pathways related to the metabolites based on the KEGG database. In the metabolic pathway, each circle expresses one metabolic pathway, and the color and size of the circles depend on the importance and \u003cem\u003ep\u003c/em\u003e-values of the pathway. In the positive ion modes, galactose metabolism, arginine and proline metabolism, and drug metabolism-cytochrome P450 were significantly disturbed in pregnant women with preterm delivery (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). In the negative ion modes, ascorbate and aldarate metabolism, terpenoid backbone biosynthesis, pentose and glucuronate interconversions, cysteine and methionine metabolism, glycine, serine and threonine metabolism, purine metabolism and steroid hormone biosynthesis were significantly disturbed in the pregnant women with preterm delivery (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The above results revealed that these metabolic pathways were associated with preterm delivery in pregnant women.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003ePreterm delivery has become an alarming public health concern because of the relatively higher mortality rate presented in preterm infants. According to a previous report, about three-quarters of cases are diagnosed as spontaneous preterm deliverys that contain previous spontaneous preterm delivery or preterm prelabour rupture of the membranes\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Although the great progress in cervical cerclage, significant differences in treatment effectiveness among different populations\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Recently, some reports confirmed that gut microbiota and its metabolites take a vital role in improving the host's health, such as hypoglycemic, hypolipidemic, antidiabetic and antidepressant effects. Therefore, we hypothesized that preterm delivery is related to the alteration in vaginal microbiota and its metabolites. In the present study, 16S rRNA gene sequencing and untargeted metabolomics were used to detect and identify the vaginal microbiota and its metabolites, in order to screen the key microbial phylotypes and differential vagina metabolites.\u003c/p\u003e \u003cp\u003eSome investigations exhibited that the risk of preterm delivery is strongly related to the BMI of pregnant women, namely BMI more than 28.0 or less than 18.0 elevate the risk of preterm delivery\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. A high BMI of pregnant women elevates the risk of gestational diabetes, hypertensive disorders and fetal malformations, which is one of the most important causes for medically indicated preterm delivery\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. In addition, the advanced maternal age (more than 35 years) and maternal age (less than 25 years) further elevate the risk of preterm delivery\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Therefore, participants were collected by the BMI within the range of 18 to 28 and years within the range of 25 to 35 of pregnant women in this study, which is beneficial for eliminating the influence of BMI and year of pregnant woman on preterm delivery. In addition, the body weight of preterm delivery was significantly lower than that of term birth, because of the infant development is not yet complete, which is in agreement with the result of this study\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe vaginal microbiota accounts for about 9% of the total human microbiota, which take an essential role in improving the vagina's health. The vaginal microbiota is a dynamic ecosystem composed of various microorganisms with different quantities and ratios, which maintains the integrity of the vaginal barrier and prevents the growth of harmful bacteria\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Recently, it is reported that pregnancy outcomes are usually decided by the composition of vaginal microbiota\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. In reproductive-age women, the proportion of \u003cem\u003eLactobacillus\u003c/em\u003e was obviously higher than that in others\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Feehily et al. found vaginal \u003cem\u003eLactobacillus\u003c/em\u003e is consisted of \u003cem\u003eL. crispatus\u003c/em\u003e, \u003cem\u003eL. delbruecki\u003c/em\u003e, \u003cem\u003eL. gasseri\u003c/em\u003e, \u003cem\u003eL. gasseriA\u003c/em\u003e, \u003cem\u003eL. H fermentum\u003c/em\u003e, \u003cem\u003eL. H gastricus\u003c/em\u003e, \u003cem\u003eL. helveticus\u003c/em\u003e, \u003cem\u003eL. jensenii\u003c/em\u003e, \u003cem\u003eL. kefiranofaciens\u003c/em\u003e and \u003cem\u003eL. taiwanensis\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eLactobacillus\u003c/em\u003e is regarded as a probiotic that is beneficial for the host's health when given in adequate amounts. \u003cem\u003eLactobacillus\u003c/em\u003e processes a series of physiological effects, such as suppressing oxidative stress and inflammatory responses\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eLactobacillus\u003c/em\u003e also prevents the growth of harmful bacteria in the vagina by elevating the levels of short-chain fatty acids. Therefore, the lower abundance of \u003cem\u003eLactobacillus\u003c/em\u003e may be related to preterm delivery.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePrevotella\u003c/em\u003e could regulate mucin metabolism by stimulating their production and their degradation, which maintain the integrity of the vaginal barrier\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eTrichococcus\u003c/em\u003e act as short-chain fatty acid-producing bacteria, which offer energy to the proliferation vaginal epithelial cells\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eGemella\u003c/em\u003e is an essential member of the human microbiome in healthy subjects, and rarely causes systemic illness\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In this study, the proportion of \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eTrichococcus\u003c/em\u003e, \u003cem\u003eNeisseria\u003c/em\u003e and \u003cem\u003eGemella\u003c/em\u003e was remarkably reduced in the PrPG group, while \u003cem\u003eThauera\u003c/em\u003e, \u003cem\u003eOchrobactrum\u003c/em\u003e, \u003cem\u003eGardnerella\u003c/em\u003e, \u003cem\u003eMassilia\u003c/em\u003e, \u003cem\u003ePhyllobacteriaceae\u003c/em\u003e and \u003cem\u003eAtopobium\u003c/em\u003e were significantly increased. Among them, \u003cem\u003eThauera\u003c/em\u003e has the capacity to disintegrate androgen in aerobic and anaerobic conditions\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eOchrobactrum\u003c/em\u003e is a non-enteric and Gram-negative organism, and its abundance is strongly related to inflammatory responses\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eGardnerella\u003c/em\u003e is widely distributed in women of childbearing age, and the high proportion of \u003cem\u003eGardnerella\u003c/em\u003e causes a series of some diseases, which is extensively used to establish the bacterial vaginitis model\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eMassilia\u003c/em\u003e belongs to the family Oxalobacteraceae, which is associated with bacteremia, CNS infections, wound infections, lymphadenitis, and osteomyelitis\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003ePhyllobacteriaceae\u003c/em\u003e is confirmed to destroy carbohydrate metabolism and/or fat metabolism, as well as stimulate inflammatory responses by producing lipopolysaccharide\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. High abundance of \u003cem\u003eStaphylococcus\u003c/em\u003e causes severe infectious diseases, such as impetigo, folliculitis, and cutaneous abscesses\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. In addition, a previous study found that high abundance of \u003cem\u003eAtopobium\u003c/em\u003e is positive associated with the incidence rate of infertility, endometritis, and pelvic inflammatory disease\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. These results suggest that the alterations in vaginal microbiota is strongly related to the preterm delivery.\u003c/p\u003e \u003cp\u003eApart from vaginal microbiota, vaginal metabolites play the most important role in pregnant women with preterm delivery. 2-Piperidone could suppress the accumulation of reactive oxygen species and lipid peroxidation by regulating the activity of cytochrome P450 2E1\u003csup\u003e38\u003c/sup\u003e. Obatoclax is a synthetic derivative of bacterial prodiginines that promote the apoptosis of human colorectal carcinoma cells by suppressing Wnt/β-catenin signaling. Eurostoside act as a useful organic compound that is proven to inhibit the inflammatory responses by regulating the expression of iNOS and COX-2\u003csup\u003e39\u003c/sup\u003e. Pregnanediol 3-O-glucuronide is a natural metabolite, and its levels are negatively associated with the risk of preterm delivery\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. O-Phospho-L-serine is an inhibitor of serine racemase that can elevate the regulatory cytokine (TGF-β) level and reduce the pro-inflammatory cytokines (TNF‐α and IL12p70) in bone‐marrow‐derived dendritic cells\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. 1-Kestose is the smallest fructooligosaccharide component that regulates the vaginal microbiota composition, especially up-regulation of \u003cem\u003eBifidobacteria\u003c/em\u003e abundance\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Oral administration of 1-Kestose improves the symptoms of type 2 diabetes by elevating the short-chain fatty acids levels, such as acetate, butyrate and lactate\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. N-arachidonylglycine acts as an amino acid derivative of arachidonic acid that attenuates CD4T cell responsiveness by reducing the levels of Th1 and Th17 cytokines, and regulating the GPR18 MTORC1 signaling\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. In this study, the relative contents of 2-Piperidone, Melphalan, N-acetylputrescine, Obatoclax, Eurostoside, Pregnanediol 3-O-glucuronide, O-Phospho-L-serine, 1-Kestose and N-arachidonylglycine in the PrPG group were obviously lower than that in the PrTG group. Acenocoumarol is one of the important organic compounds of 4-hydroxycoumarins that is widely regarded as a potentially toxic compound\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Isopyrazam is one of the broad-spectrum succinate dehydrogenase inhibitor fungicides, which is confirmed to destroy the heart function by stimulating oxidative stress\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Pentosidine is one of the best-characterized advanced glycation end-products that play a pathologic role in some disorders related to aging, and promote the development of diabetes\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Long-term consumption of hexose causes the disorder of glucose metabolism, which elevated the risk of hyperglycemia and hyperlipidemia\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. 7-Hydroxymitragynine is widely used as an anesthetic, but excessive use leads to a series of adverse reactions\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. A previous study found that high-fat diet accelerates PE accumulation in the liver, which promotes liver function injury\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Tamoxifen is regarded as a path-breaking medication in tumor treatment, but is also reported to it give rise to thrombosis, epigastric discomfort and nausea\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. 1-Deoxynojirimycin destroy the endoplasmic reticulum function, which mainly manifests as the unfolded or misfolded proteins accumulate\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. In the present study, the Acenocoumarol, Isopyrazam, Pentosidine, hexose, 7-Hydroxymitragynine, PE, Tamoxifen and 1-Deoxynojirimycin concentrations in the PrPG group were higher than that in the PrTG group. Therefore, we preliminarily surmise that the alteration in vaginal metabolites may be is one of the important causes for preterm delivery.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn the present study, we suggested that significantly difference in vaginal microbiota between the PrPG and PrTG groups, which is characterized by the reduction in \u003cem\u003eLactobacillus\u003c/em\u003e and the increase in \u003cem\u003eGardnerella\u003c/em\u003e. In addition, vaginal metabolomics analysis revealed the marked metabolites in pregnant women with preterm delivery, such as pregnanediol 3-O-glucuronide. These results offer useful information to elevate the accuracy of the model of premature birth prediction by combining vaginal microbiomics and metabolomics.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by Fujian Provincial Technology Innovation Project (No.2021Y9175 and 2020Y9148), Natural Science Foundation of Fujian Province (No.2022J011034 and 2021J01406).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ. F. Conceptualization, Methodology, Investigation, Data curation, Writing-original draft; M. Z. Writing-original draft, Formal analysis, Software, Validation; M. L. Writing-original draft, Data curation. J. Z. Writing-original draft, Visualization; Y. Z. Writing-review \u0026amp; editing, Resources; L. W. Writing-review \u0026amp; editing, Methodology; Y. L. Writing-original draft, Project administration, Funding acquisition; M. P. Writing-review \u0026amp; editing, Supervision, Project administration, Funding acquisition.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe 16S rRNA sequencing data were deposited in the National Center for Biotechnology Information (No. PRJNA1122359) and the untargeted vagina metabolomics data were deposited in the MetaboLights database (No. MTBLS11563).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe human participants in this study were permitted by the Ethics Committee of Fujian Maternity and Child Health Hospital (No. 2021KLR601). The participants offered their written informed consent to participate in the present study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eXie, Y., et al., Interrupted-time-series analysis of the immediate impact of COVID-19 mitigation measures on preterm birth in China. \u003cem\u003eNature Commun. 13\u003c/em\u003e (1), 5190. https://doi.org/10.1038/s41467-022-32814-y (2022).\u003c/li\u003e\n\u003cli\u003eAdane, H. A.， Iles, R.， Boyle, J. A.， Gelaw, A. \u0026amp; Collie, A., Effects of psychosocial work factors on preterm birth: systematic review and meta-analysis. \u003cem\u003ePublic Health. 228\u003c/em\u003e, 65-72. https://doi.org/10.1016/j.puhe.2023.12.002 (2024).\u003c/li\u003e\n\u003cli\u003eBeernink, R. H. J.， Schuitemaker, J. H. N.， Zwertbroek, E. F.， Scherjon, S. A. \u0026amp; Cremers, T. I. F. H., Early pregnancy biomarker discovery study for spontaneous preterm birth. \u003cem\u003ePlacenta. 139\u003c/em\u003e, 112-119. https://doi.org/10.1016/j.placenta.2023.06.011 (2023).\u003c/li\u003e\n\u003cli\u003eWei, J.， Zhang, L.， Xu, H. \u0026amp; Luo, Q., Preterm birth, a consequence of immune deviation mediated hyperinflammation. \u003cem\u003eHeliyon. 10\u003c/em\u003e (7), e28483. https://doi.org/10.1016/j.heliyon.2024.e28483 (2024).\u003c/li\u003e\n\u003cli\u003eOhuma, E. O., et al., National, regional, and global estimates of preterm birth in 2020, with trends from 2010: a systematic analysis. \u003cem\u003eThe Lancet. 402\u003c/em\u003e (10409), 1261-1271. https://doi.org/10.1016/S0140-6736(23)00878-4 (2023).\u003c/li\u003e\n\u003cli\u003eSeyama, R.， Makino, S.， Takeda, J.， Takeda, S. \u0026amp; Itakura, A., The retrospective study for effectiveness of cervical cerclage in preventing recurrent preterm birth. \u003cem\u003eTaiwan. J. Obstet. Gyne. 61\u003c/em\u003e (1), 63-69. https://doi.org/10.1016/j.tjog.2021.11.012 (2022).\u003c/li\u003e\n\u003cli\u003eGolob, J. L., et al., Microbiome preterm birth dream challenge: crowdsourcing machine learning approaches to advance preterm birth research. \u003cem\u003emedRxiv : the preprint server for health sciences.\u003c/em\u003e https://doi.org/10.1101/2023.03.07.23286920 (2023).\u003c/li\u003e\n\u003cli\u003ePeelen, M. J., et al., The influence of the vaginal microbiota on preterm birth: A systematic review and recommendations for a minimum dataset for future research. \u003cem\u003ePlacenta. 79\u003c/em\u003e, 30-39. https://doi.org/10.1016/j.placenta.2019.03.011 (2019).\u003c/li\u003e\n\u003cli\u003eHong, X., et al., Genome-wide association study identifies a novel maternal gene\u0026thinsp;\u0026times;\u0026thinsp;stress interaction associated with spontaneous preterm birth. \u003cem\u003ePediatr. Res. 89\u003c/em\u003e (6), 1549-1556. https://doi.org/10.1038/s41390-020-1093-1 (2021).\u003c/li\u003e\n\u003cli\u003eChee, W. J. Y.， Chew, S. Y. \u0026amp; Than, L. T. L., Vaginal microbiota and the potential of Lactobacillus derivatives in maintaining vaginal health. \u003cem\u003eMicrob. Cell Fact. 19\u003c/em\u003e (1), 203. https://doi.org/10.1186/s12934-020-01464-4 (2020).\u003c/li\u003e\n\u003cli\u003eFettweis, J. M., et al., The vaginal microbiome and preterm birth. \u003cem\u003eNat. Med. 25\u003c/em\u003e (6), 1012-1021. https://doi.org/10.1038/s41591-019-0450-2 (2019).\u003c/li\u003e\n\u003cli\u003eGuo, W., et al., Improvement of inflammatory bowel disease by lactic acid bacteria-derived metabolites: a review. \u003cem\u003eCrit. Rev. Food. Sci. Nutr.\u003c/em\u003e \u003cem\u003e65\u003c/em\u003e(7), 1-18. https://doi.org/10.1080/10408398.2023.2291188 (2025).\u003c/li\u003e\n\u003cli\u003eGuo, W., et al., Intestinal microbiomics and metabolomics insights into the hepatoprotective effects of \u003cem\u003eLactobacillus paracasei \u003c/em\u003eCCFM1222 against the acute liver injury in mice. \u003cem\u003eProbiotics Antimicro. 15\u003c/em\u003e (5), 1063-1077. https://doi.org/10.1007/s12602-022-09986-6 (2023).\u003c/li\u003e\n\u003cli\u003eFang, J.， Chen, L.， Chen, Z.， Jiang, X. \u0026amp; Pan, M., Association of the vaginal microbiota with pregnancy outcomes in Chinese women after cervical cerclage. \u003cem\u003eReprod. Biomed. Online. 41\u003c/em\u003e (4), 698-706. https://doi.org/10.1016/j.rbmo.2020.06.016 (2020).\u003c/li\u003e\n\u003cli\u003eGuo, W., et al., Protective effects of microbiome-derived inosine on lipopolysaccharide-induced acute liver damage and inflammation in mice via mediating the TLR4/NF-\u0026kappa;B pathway. \u003cem\u003eJ. Agric. Food Chem. 69\u003c/em\u003e (27), 7619-7628. https://doi.org/10.1021/acs.jafc.1c01781 (2021).\u003c/li\u003e\n\u003cli\u003eGuo, W., et al., Intestinal microbiomics and hepatic metabolomics insights into the potential mechanisms of probiotic \u003cem\u003eBifidobacterium pseudolongum\u003c/em\u003e CCFM1253 preventing acute liver injury in mice. \u003cem\u003eJ. Sci. Food Agric. 103\u003c/em\u003e (12), 5958-5969. https://doi.org/10.1002/jsfa.12665 (2023).\u003c/li\u003e\n\u003cli\u003eHumberg, A., et al., Preterm birth and sustained inflammation: consequences for the neonate. \u003cem\u003eSem. Immunopathol. 42\u003c/em\u003e (4), 451-468. https://doi.org/10.1007/s00281-020-00803-2 (2020).\u003c/li\u003e\n\u003cli\u003ePark, S., et al., Predicting preterm birth through vaginal microbiota, cervical length, and WBC using a machine learning model. \u003cem\u003eFront. Microbiol. 13\u003c/em\u003e, 912853. https://doi.org/10.3389/fmicb.2022.912853 (2022).\u003c/li\u003e\n\u003cli\u003eCnattingius, S., et al., Maternal obesity and risk of preterm delivery. \u003cem\u003eJAMA. 309\u003c/em\u003e (22), 2362-70. https://doi.org/10.1001/jama.2013.6295 (2013).\u003c/li\u003e\n\u003cli\u003eRobinson, D. T., et al., Dietary fat and fatty acid intake in nulliparous women: associations with preterm birth and distinctions by maternal BMI. \u003cem\u003eCurr. Develop. Nutr. 5\u003c/em\u003e (6), nzab074. https://doi.org/10.1093/cdn/nzab074 (2021).\u003c/li\u003e\n\u003cli\u003eWu, S. T., et al., Maternal risk factors for preterm birth in Taiwan, a nationwide population-based cohort study. \u003cem\u003ePediatr. Neonatol. 65\u003c/em\u003e (1), 38-47. https://doi.org/10.1016/j.pedneo.2023.03.014 (2024).\u003c/li\u003e\n\u003cli\u003eKozuki, N., et al., Short maternal stature increases risk of small-for-gestational-age and preterm births in low- and middle-income countries: Individual participant data meta-analysis and population attributable fraction1, 2, 3. \u003cem\u003eJ. Nutr. 145\u003c/em\u003e (11), 2542-2550. https://doi.org/10.3945/jn.115.216374 (2015).\u003c/li\u003e\n\u003cli\u003eShen, J., et al., Effects of low dose estrogen therapy on the vaginal microbiomes of women with atrophic vaginitis. \u003cem\u003eSci. Rep. 6\u003c/em\u003e, 24380. https://doi.org/10.1038/srep24380 (2016).\u003c/li\u003e\n\u003cli\u003eSalinas, A. M., et al., Vaginal microbiota evaluation and prevalence of key pathogens in ecuadorian women: an epidemiologic analysis. \u003cem\u003eSci. Rep. 10\u003c/em\u003e (1), 18358. https://doi.org/10.1038/s41598-020-74655-z (2020).\u003c/li\u003e\n\u003cli\u003eHayashida, S., et al., How were \u003cem\u003eLactobacillus\u003c/em\u003e species selected as single dominant species in the human vaginal microbiota? Coevolution of humans and \u003cem\u003eLactobacillus\u003c/em\u003e. \u003cem\u003eMed. Hypotheses. 163\u003c/em\u003e, 110858. https://doi.org/10.1016/j.mehy.2022.110858 (2022).\u003c/li\u003e\n\u003cli\u003eFeehily, C., et al., Shotgun sequencing of the vaginal microbiome reveals both a species and functional potential signature of preterm birth. \u003cem\u003eNPJ Biofilms Microbi. 6\u003c/em\u003e (1), 50. https://doi.org/10.1038/s41522-020-00162-8 (2020).\u003c/li\u003e\n\u003cli\u003eQin, J., et al., Effects of carnosine combined with \u003cem\u003eLactobacillus\u003c/em\u003e on the antioxidant capacity of liver and kidney in normal or stressed mice. \u003cem\u003eFood Biosci. 59\u003c/em\u003e, 103904. https://doi.org/10.1016/j.fbio.2024.103904 (2024).\u003c/li\u003e\n\u003cli\u003eTett, A.， Pasolli, E.， Masetti, G.， Ercolini, D. \u0026amp; Segata, N., Prevotella diversity, niches and interactions with the human host. \u003cem\u003eNat. Rev. Microbiol. 19\u003c/em\u003e (9), 585-599. https://doi.org/10.1038/s41579-021-00559-y (2021).\u003c/li\u003e\n\u003cli\u003eXiang, J. Y., et al., Litchi chinensis seed prevents obesity and modulates the gut microbiota and mycobiota compositions in high-fat diet-induced obese zebrafish. \u003cem\u003eFood Funct. 13\u003c/em\u003e (5), 2832-2845. https://doi.org/10.1039/d1fo03991a (2022).\u003c/li\u003e\n\u003cli\u003eZaidi, S. J.， Husayni, T. \u0026amp; Collins, M. A., Gemella bergeri infective endocarditis: a case report and brief review of literature. \u003cem\u003eCardiol. Young. 28\u003c/em\u003e (5), 762-764. https://doi.org/10.1017/s1047951118000070 (2018).\u003c/li\u003e\n\u003cli\u003eHsiao, T. H., et al., Circulating androgen regulation by androgen-catabolizing gut bacteria in male mouse gut. \u003cem\u003eGut Microbes. 15\u003c/em\u003e (1), 2183685. https://doi.org/10.1080/19490976.2023.2183685 (2023).\u003c/li\u003e\n\u003cli\u003eGigi, R.， Flusser, G.， Kadar, A.， Salai, M. \u0026amp; Elias, S., Ochrobactrum anthropi-caused osteomyelitis in the foot mimicking a bone tumor: case report and review of the literature. \u003cem\u003eThe Journal of foot and ankle surgery : official publication of the American College of Foot and Ankle Surgeons. 56\u003c/em\u003e (4), 851-853. https://doi.org/10.1053/j.jfas.2017.02.008 (2017).\u003c/li\u003e\n\u003cli\u003eJothi, R., et al., Untargeted metabolomics uncovers prime pathways linked to antibacterial action of citral against bacterial vaginosis-causing Gardnerella vaginalis: An in vitro and in vivo study. \u003cem\u003eHeliyon. 10\u003c/em\u003e (6), e27983. https://doi.org/10.1016/j.heliyon.2024.e27983 (2024).\u003c/li\u003e\n\u003cli\u003eAli, G. A.， Ibrahim, E. B.， Doiphode, S. H. \u0026amp; Goravey, W., Massilia timonae bacteremia: An unusual pathogen of septic abortion. \u003cem\u003eIDCases. 29\u003c/em\u003e, e01592. https://doi.org/10.1016/j.idcr.2022.e01592 (2022).\u003c/li\u003e\n\u003cli\u003eChen, P.， Chen, P.， Guo, Y.， Fang, C. \u0026amp; Li, T., Interaction between chronic endometritis caused endometrial microbiota disorder and endometrial immune environment change in recurrent implantation failure. \u003cem\u003eFront. Immunol. 12\u003c/em\u003e, 748447. https://doi.org/10.3389/fimmu.2021.748447 (2021).\u003c/li\u003e\n\u003cli\u003eTabiś, A., et al., Analysis of enterotoxigenic effect of \u003cem\u003eStaphylococcus aureus\u003c/em\u003e and \u003cem\u003eStaphylococcus epidermidis \u003c/em\u003eenterotoxins C and L on mice. \u003cem\u003eMicrobiol. Res. 258\u003c/em\u003e, 126979. https://doi.org/10.1016/j.micres.2022.126979 (2022).\u003c/li\u003e\n\u003cli\u003eRavel, J.， Moreno, I. \u0026amp; Sim\u0026oacute;n, C., Bacterial vaginosis and its association with infertility, endometritis, and pelvic inflammatory disease. \u003cem\u003eAm. J. Obstet. Gynecol. 224\u003c/em\u003e (3), 251-257. https://doi.org/10.1016/j.ajog.2020.10.019 (2021).\u003c/li\u003e\n\u003cli\u003eCheng, J., et al., Identification of 2-piperidone as a biomarker of CYP2E1 activity through metabolomic phenotyping. \u003cem\u003eToxicological sciences : an official journal of the Society of Toxicology. 135\u003c/em\u003e (1), 37-47. https://doi.org/10.1093/toxsci/kft143 (2013).\u003c/li\u003e\n\u003cli\u003eLe, D. D., et al., Iridoid derivatives from Vitex rotundifolia L. f. with their anti-inflammatory activity. \u003cem\u003ePhytochemistry. 210\u003c/em\u003e, 113649. https://doi.org/10.1016/j.phytochem.2023.113649 (2023).\u003c/li\u003e\n\u003cli\u003eZhang, Y., et al., Chemical fingerprint analysis and ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry-based metabolomics study of the protective effect of buxue yimu granule in medical-induced incomplete abortion rats. \u003cem\u003eFront. Pharmacol. 11\u003c/em\u003e, 578217. https://doi.org/10.3389/fphar.2020.578217 (2020).\u003c/li\u003e\n\u003cli\u003eFathallah, A. M.， Ramakrishnan, R. \u0026amp; Balu‐Iyer, S. V., O‐Phospho‐l‐Serine mediates Hyporesponsiveness toward FVIII in hemophilia a‐murine model by inducing tolerogenic properties in dendritic cells. \u003cem\u003eJ. Pharm. Sci. 103\u003c/em\u003e (11), 3457-3463. https://doi.org/https://doi.org/10.1002/jps.24173 (2014).\u003c/li\u003e\n\u003cli\u003eTochio, T.， Kadota, Y.， Tanaka, T. \u0026amp; Koga, Y., 1-Kestose, the Smallest fructooligosaccharide component, which efficiently stimulates faecalibacterium prausnitzii as well as \u003cem\u003eBifidobacteria\u003c/em\u003e in humans. \u003cem\u003eFoods (Basel, Switzerland). 7\u003c/em\u003e (9). https://doi.org/10.3390/foods7090140 (2018).\u003c/li\u003e\n\u003cli\u003eWatanabe, A., et al., 1-Kestose supplementation mitigates the progressive deterioration of glucose metabolism in type 2 diabetes OLETF rats. \u003cem\u003eSci. Rep. 10\u003c/em\u003e (1), 15674. https://doi.org/10.1038/s41598-020-72773-2 (2020).\u003c/li\u003e\n\u003cli\u003eMeadows, A. M., et al., N-arachidonylglycine is a caloric state-dependent circulating metabolite which regulates human CD4+T cell responsiveness. \u003cem\u003eiScience. 26\u003c/em\u003e (5), 106578. https://doi.org/10.1016/j.isci.2023.106578 (2023).\u003c/li\u003e\n\u003cli\u003eValdivielso, M.， Longo, I.， Lecona, M. \u0026amp; L\u0026aacute;zaro, P., Cutaneous necrosis induced by acenocoumarol. \u003cem\u003eJ. Eur. Acad. Dermatol. Venereology : JEADV. 18\u003c/em\u003e (2), 211-215. https://doi.org/10.1111/j.1468-3083.2004.00735.x (2004).\u003c/li\u003e\n\u003cli\u003eYan, Y., et al., Acute exposure of Isopyrazam damages the developed cardiovascular system of zebrafish (Danio rerio). \u003cem\u003eJ. Environ. Sci. Heal. Part. B. 58\u003c/em\u003e (4), 367-377. https://doi.org/10.1080/03601234.2023.2197655 (2023).\u003c/li\u003e\n\u003cli\u003eLi, H. \u0026amp; Yu, S. J., Review of pentosidine and pyrraline in food and chemical models: formation, potential risks and determination. \u003cem\u003eJ. Sci. Food Agric. 98\u003c/em\u003e (9), 3225-3233. https://doi.org/10.1002/jsfa.8853 (2018).\u003c/li\u003e\n\u003cli\u003eAi, Y.-l., et al., Mannose antagonizes GSDME-mediated pyroptosis through AMPK activated by metabolite GlcNAc-6P. \u003cem\u003eCell Res. 33\u003c/em\u003e (12), 904-922. https://doi.org/10.1038/s41422-023-00848-6 (2023).\u003c/li\u003e\n\u003cli\u003eVento, A. E., et al., Case report: Treatment of kratom use disorder with a classical tricyclic antidepressant. \u003cem\u003eFront. psychiatry. 12\u003c/em\u003e, 640218. https://doi.org/10.3389/fpsyt.2021.640218 (2021).\u003c/li\u003e\n\u003cli\u003eGuo, W. L., et al., Ganoderic acid A from Ganoderma lucidum ameliorates lipid metabolism and alters gut microbiota composition in hyperlipidemic mice fed a high-fat diet. \u003cem\u003eFood Funct. 11\u003c/em\u003e (8), 6818-6833. https://doi.org/10.1039/d0fo00436g (2020).\u003c/li\u003e\n\u003cli\u003eWibowo, E.， Pollock, P. A.， Hollis, N. \u0026amp; Wassersug, R. J., Tamoxifen in men: a review of adverse events. \u003cem\u003eAndrology. 4\u003c/em\u003e (5), 776-788. https://doi.org/10.1111/andr.12197 (2016).\u003c/li\u003e\n\u003cli\u003eLu, Y.， Xu, Y. Y.， Fan, K. Y. \u0026amp; Shen, Z. H., 1-Deoxymannojirimycin, the alpha1,2-mannosidase inhibitor, induced cellular endoplasmic reticulum stress in human hepatocarcinoma cell 7721. \u003cem\u003eBiochem Biophys Res. Commun. 344\u003c/em\u003e (1), 221-225. https://doi.org/10.1016/j.bbrc.2006.03.111 (2006).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Preterm birth, vaginal microbiome, vaginal metabolomics, microbial-marker, metabolic pathway","lastPublishedDoi":"10.21203/rs.3.rs-6171036/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6171036/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePreterm delivery is a major reason of perinatal morbidity and mortality. The disorder of vaginal microbiota and metabolites in pregnant women may be the most important risk factor for preterm delivery. This study aims to explore whether vaginal microbiota and metabolites alteration may elevate the risk of preterm delivery. In this study, 63 cases of pregnant women were enrolled, comprising 32 cases of women with term births and 31 cases of women with preterm births. Compared with the pre-cerclage in the term birth group (PrTG), the proportion of beneficial bacteria (\u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eTrichococcus\u003c/em\u003e, \u003cem\u003eNeisseria\u003c/em\u003e and \u003cem\u003eGemella\u003c/em\u003e) in the pre-cerclage in the preterm birth group (PrPG) were significantly reduced (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while the proportion of harmful bacteria (\u003cem\u003eThauera\u003c/em\u003e, \u003cem\u003eOchrobactrum\u003c/em\u003e, \u003cem\u003eGardnerella\u003c/em\u003e, \u003cem\u003eMassilia\u003c/em\u003e, \u003cem\u003ePhyllobacteriaceae\u003c/em\u003e and \u003cem\u003eAtopobium\u003c/em\u003e) were significantly increased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which is strongly associated with the preterm birth. In addition, vaginal metabolomics-based LC-Orbitrap-MS/MS revealed that the contents of 2-Piperidone, Melphalan, N-acetylputrescine, Obatoclax, Eurostoside, Pregnanediol 3-O-glucuronide, O-Phospho-L-serine, 1-Kestose and N-arachidonylglycine were significantly decreased in the PrPG group compared with the PrTG group, while Acenocoumarol, Isopyrazam, Pentosidine, hexose, 7-Hydroxymitragynine, PE, Tamoxifen and 1-Deoxynojirimycin contents were significantly increased. These results elaborate that several candidate bacteria and metabolites could be applied as the prospective predictors for preterm birth, and approve the theoretical basis for the internation of preterm birth.\u003c/p\u003e","manuscriptTitle":"Insights into the alteration of vaginal microbiota and metabolites in pregnant woman with preterm delivery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 05:29:33","doi":"10.21203/rs.3.rs-6171036/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-22T21:28:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-21T19:45:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"108373900441001176560199054139980303473","date":"2025-07-30T08:25:25+00:00","index":"hide","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-29T18:51:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-02T21:56:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-13T15:23:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25468835303883651494095917189775434557","date":"2025-04-08T13:46:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161730027994691608849692862814191585466","date":"2025-04-04T13:40:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-03T05:04:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-02T08:25:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-27T13:27:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eaaebd0f-dc35-43c3-8dcb-32fe01d65627","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":46856946,"name":"Biological sciences/Microbiology"},{"id":46856947,"name":"Biological sciences/Microbiology/Clinical microbiology"},{"id":46856948,"name":"Health sciences/Biomarkers"},{"id":46856949,"name":"Health sciences/Biomarkers/Predictive markers"}],"tags":[],"updatedAt":"2025-08-22T21:38:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 05:29:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6171036","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6171036","identity":"rs-6171036","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}