Butyrate-Producing Bacteria in Pregnancy Maintenance: Mitigating Dysbiosis-Induced Preterm Birth

Butyrate-Producing Bacteria in Pregnancy Maintenance: Mitigating Dysbiosis-Induced Preterm Birth | 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 Research Article Butyrate-Producing Bacteria in Pregnancy Maintenance: Mitigating Dysbiosis-Induced Preterm Birth Azusa Uchida, Kenji Imai, Rika Miki, Tomonari Hamaguchi, Hiroshi Nishiwaki, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5770845/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 May, 2025 Read the published version in Journal of Translational Medicine → Version 1 posted 5 You are reading this latest preprint version Abstract Background: Preterm birth (PTB) is a major contributor to neonatal morbidity, mortality, and long-term health complications. Despite advances in perinatal care, PTB rates remain high, and its multifactorial etiology is not fully understood. Increasing evidence suggests that maternal gut microbiota plays a critical role in pregnancy maintenance, potentially through modulation of immune responses. However, the underlying causal mechanisms remain unclear. We hypothesized that dysbiosis disrupts immune tolerance and promotes PTB, and that butyrate (short-chain fatty acid produced by specific gut bacteria) may counteract this effect by enhancing regulatory T cell (Treg)-mediated immune regulation. Methods: We established a dysbiosis-induced PTB mouse model using vancomycin treatment combined with subclinical immune activation via anti-CD3ε antibody. Pregnant mice were fed either a standard or butyrate-enriched diet. Outcomes included gestational length, PTB incidence, live pup rates, and Treg cell levels assessed by flow cytometry. Parallelly, 16S rRNA gene sequencing was performed on fecal samples from 32 pregnant women to compare gut microbial composition between spontaneous PTB and term birth groups. Multivariate logistic regression and correlation analyses were conducted to assess associations with gestational outcomes. Results: Vancomycin-induced dysbiosis in mice significantly reduced Treg cell populations and increased PTB rates (43.3% in dysbiosis vs. 0% in controls; p < 0.05), while butyrate supplementation reduced PTB incidence (p = 0.03), prolonged gestation (p = 0.01), and restored Treg counts (p < 0.001). In human samples, significant reductions in Lachnospiraceae and Ruminococcaceae, representative butyrate-producing bacteria, were seen in PTB cases. Their combined abundance was independently associated with sPTB risk (p = 0.019) and positively correlated with gestational age (r = 0.59, p < 0.001). Conclusions: Our findings demonstrate that maternal dysbiosis increases PTB risk via impaired immune tolerance, and that butyrate supplementation effectively reverses this effect in vivo. Human data support the translational relevance of butyrate-producing microbiota in pregnancy maintenance. These results highlight butyrate as a promising target for dietary interventions aimed at reducing PTB incidence by restoring immune homeostasis. Trial registration: Not applicable. Dysbiosis Preterm birth Short-chain fatty acid Inflammation Treg Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Background Preterm birth (PTB), defined as delivery before 37 weeks of gestation, remains one of the foremost causes of neonatal mortality and long-term health complications globally ( 1 , 2 ). Children born preterm are at heightened risk for a range of morbidities, including respiratory diseases, cognitive impairments, and learning disabilities, which often persist into adolescence and adulthood ( 3 ). Despite advancements in perinatal care, PTB rates have not declined, and the precise etiology of PTB remains be elusive. PTB is widely regarded as a multifactorial syndrome that may result from sterile inflammation, maternal genetics, environmental factors such as maternal diet, and immune responses ( 4 , 5 ). In recent years, attention has increasingly focused on the gut microbiota’s role in both health and disease. Dysbiosis, defined as an imbalance in the gut microbial community, has been implicated in conditions such as obesity, diabetes, and inflammatory bowel disease, all of which involve inflammatory mechanisms ( 6 , 7 ). Emerging data indicate that maternal dysbiosis is similarly associated with an elevated risk of adverse pregnancy outcomes; however, only limited research has examined the relationship between maternal gut microbiota and spontaneous PTB (sPTB), with these studies primarily highlighting correlations ( 8 – 10 ). To date, few studies have definitively established specific causal relationships between dysbiosis and sPTB, particularly regarding the mechanisms involved. Short-chain fatty acids (SCFAs), pivotal metabolites produced by gut bacteria through the fermentation of dietary fiber, have shown anti-inflammatory and immunomodulatory effects ( 11 ). SCFAs, particularly butyrate, play essential roles in intestinal health and modulating immune responses, notably by promoting immune tolerance via the expansion of regulatory T cells (Tregs) ( 6 , 12 ). Given that sPTB is often linked to inflammation and disruptions in immune tolerance, we hypothesize that SCFAs may act as important mediators in the interplay between maternal dysbiosis and the initiation of preterm labor. Therefore, this study aims to establish a mouse model of Dysbiosis-induced PTB to investigate the role of maternal gut microbiota on pregnancy outcomes. This model will allow us to explore how changes in the gut microbiota contribute to immune dysregulation and the pathogenesis of PTB. Additionally, using the mouse model, we seek to evaluate the potential role of butyrate, particularly its anti-inflammatory properties, in mitigating PTB risk. By advancing our understanding of these interactions, we hope to provide new insights into preventive and therapeutic strategies to improve maternal and neonatal health outcomes. 2. Methods 2.1 Experimental Design of Dysbiosis-Induced Preterm Birth Model Six-week-old C57BL/6J mice, purchased from Japan SLC, Shizuoka, Japan, were housed under specific pathogen-free (SPF) conditions in temperature- and humidity-controlled rooms (20–25°C, 40–70%) with a 12-hour light/dark cycle and ad libitum access to standard chow and water. To ensure baseline comparability across groups, all mice were housed, and mated under identical environmental and dietary conditions. To establish a mouse model of Dysbiosis-induced PTB, the pregnant mice were randomly assigned to three groups: control, vancomycin-treated, and polymyxin B-treated. The control group received regular drinking water (reverse osmosis [RO] water), while the antibiotic groups were administered Vancomycin (500 mg/L) (FUJIFILM Wako Pure Chemical Corporation) or polymyxin B (100 mg/L) (FUJIFILM Wako Pure Chemical Corporation), both dissolved in the same RO water. Antibiotic solutions were replaced every two or three days using autoclaved bottles, and fluid intake was routinely monitored to maintain consistent dosing. The antibiotics were provided via free drinking water for two weeks. Vancomycin, a glycopeptide antibiotic, has a broad spectrum of activity against Gram-positive bacteria and has been shown to fundamentally alter gut microbial diversity, although it does not completely eliminate all commensal species. In contrast, polymyxin B, a cyclic peptide antibiotic, has a narrower spectrum of activity, primarily targeting Gram-negative bacteria. This selective action makes polymyxin B less disruptive to the overall composition of the gut microbiota compared to vancomycin. Both antibiotics are commonly used in experimental models to induce dysbiosis and to investigate its effects on host immunity and disease development ( 13 ). After the two-week antibiotic treatment, mating was initiated to confirm pregnancy. Female mice spent the night before estrus with fertile males at a ratio of 2:1. Male mice were treated with the same antibiotics as the females they were paired with to maintain consistency in microbial exposure. The day sperm was detected in the vaginal smear was defined as gestational day 0 [embryonic day (E) 0]. Pregnant mice continued receiving the same antibiotic treatment until delivery. Then, pregnant mice received a subclinical dose (2 µg per mouse, intraperitoneally) of anti-CD3ε antibody (Nippon Becton Dickinson Company Ltd.) once at E16. The dose of anti-CD3ε antibody (2 µg/mouse) was selected as the highest subclinical dose that does not independently induce PTB, based on prior literature ( 14 ) and our own preliminary data, which demonstrated PTB rates of 100% with 10 µg, 80% with 5 µg, 10% with 2.5 µg, and 0% with 1 µg. The antibiotic treatment schedule was designed to span approximately four weeks prior to immune activation at embryonic day 16, in accordance with previous reports indicating that such a duration is sufficient to modulate Tregs and gut microbiota ( 13 , 15 ). PTB rates, gestational duration, and live pup rates were closely monitored across all three groups. PTB was defined as delivery occurring before E18.5. At E16, before the administration of the antibody, blood pressure (BP) was measured in a subset of mice using tail-cuff plethysmography (BP-2010, Softron, Japan). Mice were placed in a 38°C heater to preheat and calm them before BP measurement. The average of three measurements was recorded as the final BP value. After BP measurement, the following tissues were collected from the mice in each group: spleen, cecum feces, timely voided feces, and maternal serum. Additionally, separate individuals were sacrificed at 15 hours after anti-CD3ε antibody administration, and the following tissues were collected: uterus, maternal serum, fetus, and placenta. Then, the pups' weight and placental wet weight were measured and recorded. The number of animals used in each experimental group was as follows: control (n = 6–11), polymyxin B (n = 6–11), and vancomycin (n = 6–30), depending on the specific assay. Exact sample sizes for each analysis are provided in the corresponding figure legends. 2.2 Preparation of Butyrate-Enriched Feed Butyrate is highly volatile, and achieving sufficient concentrations in the colon requires specially formulated butyrate-enriched feed. To create this feed, we replaced 10% of the standard starch in AIN93G with either butyrate-enriched high amylose maize starch (B-HAMS) or untreated high amylose maize starch (HAMS). The original method for producing butyrate-enriched starch was reported in 2003 ( 16 ). In contrast to the original method, which involved dissolving starch in large quantities of DMSO to bind SCFAs, our approach avoids dissolving the starch. Instead, we immerse intact starch granules in water and bind butyrate to the surface of the granules through an acid-base reaction. This process was carried out by Sanwa Starch Co., Ltd., who kindly provided both the B-HAMS and HAMS used in this study. Starch naturally consists of amylose and amylopectin, forming clumps. While the original process dissolved these clumps to allow binding, we target the surface of the intact granules, significantly simplifying the procedure and eliminating the need for large quantities of DMSO. Both methods achieve a comparable degree of substitution for butyrate, over 0.20, making our modified approach an effective method for producing chemically modified starch for experimental use. Following the free oral intake of this formulation, a significant increase in butyrate concentration was observed in the cecum feces (Fig. 8 B). 2.3 Butyrate Supplementation in Dysbiosis-Induced Preterm Birth Model This experiment utilized the butyrate-enriched feed described above. All mice were specific pathogen-free (SPF) 6-week-old female C57BL/6J mice and were housed under standard laboratory conditions as described in Section 2.1 . They were maintained in a temperature- and humidity-controlled environment (20–25°C, 40–70%) with a 12-hour light/dark cycle, and had ad libitum access to water and their assigned diets. All mice were allowed free access to the feed throughout the study. As shown in Fig. 8 A, the Dysbiosis-induced PTB mouse model, established using vancomycin, was used to compare two different diets; mice were fed either AIN93G + 10% HAMS or AIN93G + 10% B-HAMS. Both HAMS and B-HAMS diets were irradiated with 30 kGy of γ-rays prior to use. Vancomycin was administered via drinking water, which was replaced every two or three days using autoclaved bottles, and fluid intake was routinely monitored to ensure consistent exposure. The dietary intervention began two weeks prior to mating and continued throughout pregnancy until delivery. PTB rates, gestational duration, and live pup rates were closely monitored across both dietary groups. Before administering the anti-CD3ε antibody on E16, spleens from all mice were collected and analyzed by flow cytometry. The number of animals used in each experimental group was as follows: AIN93G + 10% HAMS group (n = 4–16), and AIN93G + 10% HAMS group (n = 8–14), depending on the specific assay. Exact sample sizes for each analysis are provided in the corresponding figure legends. 2.4 Treg Cell Isolation and Administration in Dysbiosis-Induced Preterm Birth Model Treg Isolation: CD4 + CD25 + regulatory T cells were isolated using the Mouse CD4 + CD25 + Regulatory T Cell Isolation Kit (Miltenyi Biotec) through magnetic-activated cell sorting, following the manufacturer’s instructions. Briefly, spleens were harvested from 8–10 week-old C57BL/6J female mice, minced in cold phosphate-buffered saline (PBS), and filtered through a 40 µm cell strainer to generate a single-cell suspension. The cells were then centrifuged at 300×g for 5 minutes at 4°C, followed by two washes with PBS. The splenocytes were counted using an automatic cell counter. Treg Administration: The isolated Treg cells were resuspended in sterile PBS. Each mouse in the treatment group received an intraperitoneal injection of 2×10 5 Treg cells suspended in 200 µL of PBS. As shown in Supplemental Fig. 4A, two groups of Dysbiosis-induced PTB model mice, previously treated with vancomycin, received different treatments on E15. The control group was administered an intraperitoneal injection of sterile PBS, while the experimental group received Treg cells. On E16, both groups were administered an intraperitoneal injection of anti-CD3ε antibody (2 µg/body). Preterm birth rates, pregnancy duration, and live birth rates were closely monitored in both groups. 2.5 DNA Isolation and 16S ribosomal RNA Sequencing of Mouse Fecal Samples The details of these procedures have been previously described ( 17 , 18 ). Briefly, mouse fecal samples were freeze-dried and ground. DNA was isolated from 20 mg of freeze-dried fecal material using the QIAamp PowerFecal Pro DNA Kit (Qiagen), following the manufacturer’s protocol with minor modifications. To ensure efficient bacterial DNA extraction, we are optimized vortex methods. These samples were homogenized in Solution C1 with beads Lysing Matrix E (MP Biomedicals) using FastPrep-24 5G (MP Biomedicals) at 6.0 m/s for 60 seconds for three cycles instead of vortex mixing. The V3–V4 regions of the bacterial 16S rRNA gene were amplified using primers 341F (5’-CCTACGGGNGGCWGCAG-3’) and 805R (5’-GACTACHVGGGTATCTAATCC-3’). PCR product quality was verified by gel electrophoresis. A sequencing library was prepared, and samples were barcoded. Paired-end sequencing of 300-nucleotide fragments was performed using the MiSeq reagent kit V3 600 cycle on a MiSeq system (Illumina). Data analysis, including clustering into Operational Taxonomic Units (OTUs), was conducted using QIIME 2 with the SILVA database. Statistical comparisons and diversity analyses were performed to evaluate differences in microbial compositions between groups. 2.6 Quantification of Short-Chain Fatty Acids of Mouse Fecal Samples Cecum feces were collected from mice, immediately sealed, and stored at -80°C. The samples were then freeze-dried using a freeze dryer (FDU-2110) connected to a drying chamber (DRC-1100, EYELA, Tokyo, Japan). After drying, the fecal samples were transferred to a disposable grinding chamber (MT 40, IKA, Staufen, Germany), ground into a fine powder using the IKA Tube Mill control, and stored at -30°C until analysis. For SCFAs quantification, approximately 20 mg of freeze-dried fecal powder was mixed with 1000 µL of 5 mmol/L sodium hydroxide and homogenized. After centrifugation at 13,200 ×g for 20 minutes at 4°C, the supernatant (333 µL) was mixed with 200 µL of water, 50 µL of hexanoic-6,6,6-d3 acid solution (internal standard), 200 µL of 2-methyl-1-propanol, 133 µL of pyridine, and 67 µL of isobutyl chloroformate. The resulting solution was shaken for 1 minute, then 0.3 mL of hexane was added and the mixture was shaken vigorously for 10 minutes. After centrifugation, the upper organic phase was transferred to a GC glass vial for analysis. Quantitative analysis of acetate, propionate, butyrate, and valerate was performed using an Agilent 7890A GC coupled with an Agilent 5975 inert mass spectrometer (Agilent Technologies), following the procedure reported by Ueyama et al. ( 17 , 19 ). 2.7 Measurement of Cytokines of Mouse Serum Samples Maternal and fetal blood samples were collected from mice on E16. Maternal serum was separated by centrifugation and stored at -80°C until analysis. Due to the limited amount of fetal serum available, the serum from all fetuses in each litter was pooled and treated as a single sample. A bead-based multiplex assay (Bio-Rad Laboratories, Inc., Hercules, CA, USA) was employed to measure the levels of 23 cytokines, including interleukin (IL)-1α, IL-1β, IL-2, IL-3, IL-4, IL-5, IL-6, IL-9, IL-10, IL-12(p40), IL-12(p70), IL-13, IL-17; Eotaxin; granulocyte colony-stimulating factor (G-CSF); granulocyte-macrophage colony-stimulating factor (GM-CSF); interferon-gamma (IFN-γ); C-X-C motif chemokine ligand 1 (CXCL1); C-C motif chemokine ligand (CCL) 2, CCL3, CCL4, CCL5; and tumor necrosis factor-alpha (TNF-α). The assay was performed on the Luminex200 system (Luminex, Austin, TX, USA) according to previously described techniques( 20 , 21 ). 2.8 Flowcytometry of Mouse Spleen Samples Spleen tissues of mice were collected and minced before being processed with a gentle MACS Dissociator (Miltenyi Biotec, San Diego, CA). After digestion, the homogenized tissues were washed and filtered through a cell strainer (Fisher Scientific, Durham, NC, USA). Cell suspensions were then centrifuged at 300 ×g for 5 minutes at 4°C. Cell debris was removed using Debris Removal Solution (Miltenyi Biotec, Bergisch Gladbach, Germany). This method was modified from a previously reported technique ( 22 ). The following monoclonal antibodies were purchased from BioLegend (San Diego, CA): Brilliant Violet (BV) 785-conjugated anti-CD8 (53 − 6.7), Allophycocyanin (APC)- and Cy7-conjugated anti-mouse CD4 (GK1.5), Alexa Fluor (AF) 700-conjugated anti-CD3 (HIT3a), BV605-conjugated anti-CD25 (PC61), and Alexa Fluor 647 anti-mouse Foxp3 (MF-14). Cells were stained for surface markers (CD3, CD4, CD8, CD25) and with a fixable viability dye (LIVE/DEAD Fixable Blue Dead Cell Stain Kit; Thermo Fisher Scientific). Intracellular FOXP3 staining was performed using the BD Biosciences Intracellular Staining Kit (BD Biosciences). The fluorescence of the cells was measured using a FACS ARIA II instrument (BD Biosciences, San Jose, CA, USA), and data analysis was performed using FlowJo software (Tree Star, San Carlos, CA). 2.9 Quantitative Real-Time PCR of Mouse Uterine Samples Total RNA was extracted from mouse uterine tissues using the RNeasy Mini Kit (Qiagen). cDNA was synthesized using the ReverTra Ace® qPCR RT Master Mix (Toyobo, Osaka, Japan). Quantitative Real-Time PCR was conducted using Fast SYBR Green Reaction Mix (Applied Biosystems) on a QuantStudio® 3 Real-Time PCR System (Applied Biosystems). The expression levels of target genes were normalized to GAPDH. The primers used were as follows: GAPDH (Forward: 5′-TCAACAGCAACTCCCACTCTT-3′, Reverse: 5′-ACCCTGTTGCTGTAGCCGTAT-3′), TNFα (Forward: 5′-GTAGCCCACGTCGTAGCAAAC-3′, Reverse: 5′-CTGGCACCACTAGTTGGTTGTC-3′), and Cox2 (Forward: 5′-TGCCCAGCACTTCACCCATCA-3′, Reverse: 5′-AGTCCACTCCATGGCCCAGTCC-3′). Data were analyzed using the ΔΔCt method and are presented as fold changes relative to the controls. 2.10 Human Gut Microbiota Analysis in Pregnant Women This prospective observational study was conducted at Nagoya University Hospital between January 2023 and October 2024 to investigate the relationship between sPTB and gut microbiota in pregnant women. Singleton pregnant women between 25 and 31 weeks of gestation were enrolled, as gestational ages earlier than 25 weeks are often associated with severe intrauterine infection ( 23 ), which we aimed to avoid in order to focus on other etiologies of sPTB. The inclusion criteria required a singleton pregnancy, absence of clinical signs of infection or active labor or rupture of membranes, no history of antibiotic use during pregnancy, and willingness to cooperate with the study protocol. Women were excluded if they developed hypertensive disorders of pregnancy (HDP), gestational diabetes mellitus, placenta previa, or uterine malformations, or if they had major congenital fetal anomalies, maternal malignancy, or iatrogenic PTB. Although no formal power calculation was performed, we aimed to collect samples with an expected ratio of approximately 1:2 between sPTB and term birth groups, based on feasibility and clinical incidence, to allow for meaningful group comparisons. As illustrated in Fig. 4 A, a total of 35 eligible pregnant women were initially recruited, including 10 healthy participants and 25 asymptomatic women with a shortened cervix. Following enrollment, pregnancy management was left to the discretion of each participant's attending physician. Two participants from the healthy group and one from the short cervix group were excluded due to the development of HDP, resulting in 32 women completing the study protocol. Fecal samples were collected using the Mykinso fecal collection kit® (Cykinso, Inc.), which contains guanidine thiocyanate solution. Participants collected the fecal samples themselves according to the manufacturer’s instructions. All samples were collected prior to any antibiotic administration and were promptly transported at room temperature to the Medical Laboratory of Cykinso, Inc. for processing. DNA extraction, 16S rRNA gene sequencing, and taxonomic classification (via the SILVA database) were performed using the QIIME2 pipeline. The sequencing and analysis were commercially conducted by Cykinso, Inc. All participants completed a lifestyle questionnaire covering smoking habits, alcohol consumption, exercise routines, constipation, sleep duration, and dietary patterns during pregnancy. The cohort consisted exclusively of Japanese women to minimize ethnic variability in gut microbiota. Maternal characteristics and dietary information are summarized in Table 1 . BMI and other lifestyle factors were comparable between groups. Table 1 Maternal characteristics and dietary life of the study groups Preterm birth (n = 10) Term birth (n = 22) p Maternal characteristics GA at birth (weeks) 32.7 (28.8 to 34.3) 38.7 (37.9 to 39.4) < 0.001 Maternal age (years) 28.0 (27.3 to 32.3) 33.5 (29.0 to 37.8) 0.025 Primiparity 5 (50.0) 12 (54.4) 1.000 Body mass index 21.6 (20.3 to 22.3) 22.0 (20.2 to 23.9) 0.741 Infertility treatment 2 (20.0) 4 (18.2) 1.000 Race/Nationality (Asian/Japanese) 10 (100.0) 22 (100.0) 1.000 Smoking during pregnancy 0 (0.0) 0 (0.0) 1.000 Alcohol consumption during pregnancy 0 (0.0) 0 (0.0) 1.000 Regular exercise during pregnancy 0 (0.0) 1 (4.2) 1.000 Sleeping hours (hours) 7.0 (6.3 to 8.0) 7.0 (6.0 to 7.8) 0.641 Constipation 6 (60.0) 15 (68.2) 0.703 GA at feces collection (weeks) 28.3 (27.6 to 29.7) 26.5 (25.7 to 30.5) 0.803 CL at feces collection (mm) 10.0 (6.0 to 10.8) 18.5 (15.3 to 33.8) 0.016 History of preterm birth 0 (0.0) 4 (18.2) 1.000 Maternal dietary life Root vegetable (times per week) 2.0 (2.0 to 5.0) 4.3 (2.0 to 5.0) 0.231 Fruit vegetable (times per week) 2.0 (2.0 to 5.0) 4.6 (2.0 to 6.5) 0.052 Leaf vegetable (times per week) 5.0 (5.0 to 5.0) 5.6 (5.0 to 7.0) 0.503 Fruits (times per week) 2.0 (2.0 to 5.0) 4.8 (2.0 to 6.5) 0.040 Meat (times per week) 5.0 (2.0 to 5.0) 5.5 (5.0 to 7.0) 0.026 Fish (times per week) 2.0 (2.0 to 2.0) 4.0 (2.0 to 5.0) 0.083 Egg (times per week) 3.5 (2.0 to 5.0) 5.0 (2.0 to 5.0) 0.467 Dairy product (times per week) 2.0 (0.0 to 2.0) 4.3 (2.0 to 5.0) 0.015 Fermented food (times per week) 3.5 (2.0 to 7.0) 3.4 (2.0 to 5.0) 0.567 Soy product (times per week) 2.0 (0.5 to 4.0) 3.8 (2.0 to 4.8) 0.269 Seaweed (times per week) 2.0 (0.5 to 2.0) 2.0 (2.0 to 2.0) 0.307 Mushroom (times per week) 2.0 (0.0 to 2.0) 2.8 (2.0 to 5.0) 0.057 Sugar-sweetened beverage (times per week) 5.0 (0.5 to 5.0) 4.1 (2.0 to 5.0) 0.919 Data are presented as medians (interquartile ranges) or n (%). GA: gestational age; CL: cervical length. 2.11 Statistics Continuous variables were evaluated using the nonparametric Mann–Whitney U test for two-group comparisons. For comparisons involving three groups, one-way ANOVA was applied, followed by Tukey’s post-hoc test to identify specific group differences. The chi-squared test or Fisher’s exact test was used to compare categorical variables. The Kaplan–Meier method was employed to evaluate the rate of continuing pregnancy in mice, with comparisons made using the log-rank test. The relationships between the combined Lachnospiraceae and Ruminococcaceae relative abundances and gestational age were analyzed using Spearman's rank correlation. The association between the combined Lachnospiraceae and Ruminococcaceae relative abundances and sPTB outcomes was evaluated using a receiver operating characteristic (ROC) curve. All the statistical analyses were performed using SPSS (version 29) (IBM SPSS Statistics for Windows, Armonk, NY, USA). The principal component analysis (PCA) was conducted using GraphPad Prism 9 (GraphPad Software, Inc., La Jolla, CA, USA). Statistical significance was set at p < 0.05. 2.12. Study approval The experiment using human fecal samples was approved by the Institutional Review Board and Ethics Committee of the Nagoya University Graduate School of Medicine (approval number 20210353), and written informed consent was obtained from all participants. All experimental studies involving animals were approved by the Nagoya University Graduate School of Medicine (approval number: M240021) and performed according to the guidelines and regulations therein described. 3. Results 3.1 Effects of Antibiotic Treatment on Maternal Gut Microbiota Composition To assess the effects of antibiotic treatment on maternal gut microbiota, we analyzed fecal samples from C57BL/6J pregnant mice treated with vancomycin or polymyxin B, along with control group, as shown in Fig. 1 A. Microbiota composition at the class level showed substantial shifts in the vancomycin group compared to the control, whereas polymyxin B-treated mice retained a profile similar to the control (Fig. 1 B). The PCA highlights a distinct divergence in the microbial community structure of the vancomycin group from the other two groups, demonstrating the extent of dysbiosis induced by vancomycin treatment. Despite similar fecal DNA concentrations across groups (Fig. 1 D), the vancomycin group exhibited a marked reduction in Operational Taxonomic Units (OTUs) at the genus level (Fig. 1 E) and a lower Shannon diversity index (Fig. 1 F), confirming the specific dysbiotic effect of vancomycin. Polymyxin B did not significantly alter OTUs richness or Shannon diversity compared to the control. Analysis at the family level revealed distinct shifts in bacterial taxa, with 11 taxa significantly reduced and 9 taxa increased in vancomycin-treated mice compared to both control and polymyxin B-treated groups (Fig. 2 ; only bacterial taxa with > 1% relative abundance are displayed). Furthermore, no significant changes were observed in gut microbiota composition before and after delivery across all groups, underscoring the stability of microbial communities throughout the perinatal period (Supplemental Fig. 1). 3.2 Vancomycin-Induced Dysbiosis Potentiates PTB Following Anti-CD3ε Administration Next, to examine the relationship between dysbiosis and sPTB and to investigate the underlying mechanisms, we developed the first in vivo model of Dysbiosis-induced PTB. In this model, immune activation was induced by anti-CD3ε antibody administration. The anti-CD3ε antibody, commonly administered at doses up to 10 µg per mouse to induce PTB ( 14 , 22 ), was used at a subclinical dose of 2 µg per mouse on E16 to assess whether vancomycin-induced dysbiosis potentiates the effects of anti-CD3ε (Fig. 3 A). The findings indicate that only the combination of vancomycin-induced dysbiosis with anti-CD3ε administration significantly increased PTB rates, with a notable percentage (43.3%) of pregnant mice delivering prematurely before E18.5 compared to the control and polymyxin B-treated groups (Fig. 3 C). Neither the control nor the polymyxin B groups exhibited any PTB occurrences, regardless of anti-CD3ε exposure. Furthermore, pregnant mice in the vancomycin group receiving anti-CD3ε exhibited significantly shorter gestational lengths and a marked decrease in fetal survival, with survival rates dropping to 50%. (Fig. 3 B, 3 D, 3 E). These results validate the efficacy of our model, establishing a Dysbiosis-induced PTB mouse model using vancomycin combined with a subclinical anti-CD3ε dose. Therefore, we refer to this as “Dysbiosis-induced PTB mouse model” henceforth. Although fetal weights were significantly lower in the polymyxin B and vancomycin groups compared to controls at E16 (Fig. 3 F, 3 G), no significant differences were observed across groups in placental weight (Fig. 3 H), pups count (Fig. 3 I), maternal blood pressure, or heart rate (Supplemental Fig. 2), 3.3 Diminished Abundance of Lachnospiraceae and Ruminococcaceae in Women with Spontaneous Preterm Birth We examined the association between maternal gut microbiota and sPTB by analyzing fecal samples from 35 pregnant women. A total of 32 participants were included in the analysis, comprising 10 women who experienced sPTB and 22 who delivered at term (Fig. 4 A). Table 1 summarizes participant characteristics, with a significant difference in average gestational age at delivery between groups (32.7 weeks for the sPTB group vs. 38.7 weeks for the term group). The timing of fecal sample collection was comparable across both groups (median of 28.3 weeks for the sPTB group vs. 26.5 weeks for the term group). Participants in the sPTB group were notably younger at delivery and presented with shorter cervical lengths. Other variables, such as gravidity, body mass index, lifestyle habits (smoking, alcohol, exercise, sleep duration), constipation, and previous sPTB history, showed no significant differences. Microbial profiling at the class level revealed Bacteroidia as the predominant class in both groups; however, Clostridia were significantly reduced in the sPTB group, and Gammaproteobacteria were elevated, albeit at low proportions (Fig. 4 B). At the family level, we only display the distribution of bacterial taxa with a relative abundance of at > 1% in either group (Fig. 4 C). Both Lachnospiraceae and Ruminococcaceae , which belong to the Clostridia , were significantly diminished in the sPTB group, while no families within Gammaproteobacteria exhibited notable differences between groups. Moreover, combined abundance of Lachnospiraceae and Ruminococcaceae was markedly reduced in the sPTB group (Fig. 5 B) and correlated positively with gestational age at delivery (r: 0.585, 95% CI: 0.290–0.778, p < 0.001) (Fig. 5 C). ROC analysis revealed that the combined abundance of these taxa serves as a predictive marker for sPTB, yielding an AUC of 0.823 (95% CI: 0.659–0.86, p = 0.004; Fig. 5 D). To further assess whether the reduced abundance of Lachnospiraceae and Ruminococcaceae was independently associated with sPTB, we performed multivariate logistic regression analysis including CL at fecal sampling, GA at fecal collection, maternal age, and the combined relative abundance of Lachnospiraceae and Ruminococcaceae as explanatory variables. The analysis demonstrated that the microbial abundance remained a significant independent predictor of sPTB ( p = 0.019), while CL showed a trend toward significance ( p = 0.077) (Supplemental Table 1). PCA illustrated a slight separation in microbial community composition between sPTB and term groups along PC2 (Fig. 5 A). A dietary survey of all participants (Table 1 below) showed that sPTB group consumed vegetables, fruits, dairy, and mushrooms less frequently, suggesting potential dietary influence on gut microbiome composition. 3.4 Reduction of SCFAs and Inflammatory Changes in the Dysbiosis-Induced Preterm Birth Model To clarify the role of specific gut bacteria in sPTB, we compared gut microbiota profiles in our Dysbiosis-induced PTB mouse model with those of fecal samples from pregnant women. Lachnospiraceae and Ruminococcaceae , representative SCFAs-producing bacteria ( 9 , 24 – 26 ) involved in fiber degradation, were significantly reduced in both PTB groups, highlighting their potential role in sPTB risk. Analysis of SCFAs levels in the Dysbiosis-induced PTB model showed significant reductions in acetate, propionate, butyrate, and valeric acid, with butyrate exhibiting the largest decline (Figs. 6 A and 6 B). Butyrate plays a critical role in immune modulation, particularly by promoting immune tolerance through Treg expansion ( 6 , 12 ), which informed our analysis of Treg (CD3 + CD4 + CD25 + FOXP3+) and CD8 T cell (CD3 + CD8+) populations via flow cytometry (Fig. 6 C). In the Dysbiosis-induced PTB model, Treg levels were reduced, and CD8 T cell proportions remained consistent across groups, indicating compromised immune tolerance (Figs. 6 D, 6 E, 6 F). Following anti-CD3ε treatment, elevated expression of inflammatory markers Cox2 and TNF-α was observed in uterine tissues (Figs. 6 G, 6 H), suggesting increased local inflammation. Systemic inflammation is linked to sPTB ( 27 ). Therefore, we measured systemic inflammatory markers in maternal serum. The Dysbiosis-induced PTB model displayed elevated levels of several cytokines, including IL-6, GM-CSF, IFN-γ, CCL3, and TNF-α in maternal serum (Fig. 7 ). Unexpectedly, no such increase in inflammatory mediators was observed in fetal serum (Supplemental Fig. 3). 3.5 Butyrate Supplementation Restores Immune Tolerance and Reduces Dysbiosis-Induced Preterm Birth In the Dysbiosis-induced PTB mouse model, we examined the preventive potential of butyrate supplementation by comparing a standard AIN93G diet with 10% HAMS to an AIN93G diet with 10% B-HAMS. Butyrate-enriched diets significantly reduced PTB rates (Fig. 8 C), extended gestation (Figs. 8 D and 8 E), and increased live pup births (Fig. 8 F). Although CD8 T cell proportions remained consistent across groups, butyrate supplementation significantly elevated Treg populations (Figs. 8 G, 8 H, 8 I), indicating restored immune tolerance. These findings suggest that enhanced butyrate levels in the colon may facilitate immune tolerance, effectively mitigating dysbiosis-induced inflammatory responses. Supporting this, Treg cell implantation in the Dysbiosis-induced PTB model yielded similar improvements, reducing PTB rates (Supplemental Fig. 4B), extending pregnancy (Supplemental Fig. 4C), and trending toward higher live pup birth rates (Supplemental Fig. 4D). These findings indicated butyrate’s potential role in promoting immune tolerance and reducing sPTB risk within the dysbiosis context. 4. Discussion This study indicates the potential influence of maternal gut microbiota in maintaining pregnancy and modulating the risk of sPTB. By establishing a Dysbiosis-induced PTB model in mice through vancomycin administration, we provide a foundational tool for investigating how specific bacterial taxa influence pregnancy outcomes. This model revealed reductions in Lachnospiraceae and Ruminococcaceae , consistent with fecal analyses from pregnant women, suggesting that these taxa may serve as cross-species biomarkers of healthy gestation. Although gut dysbiosis has previously been associated with pregnancy complications such as gestational diabetes mellitus and pre-eclampsia ( 28 – 30 ), direct evidence connecting dysbiosis to sPTB remains limited. The use of vancomycin, a non-absorbable antibiotic, was instrumental in selectively altering gut microbiota without systemic absorption, resulting in a dysbiotic state that enhanced both systemic and local inflammation—important contributors to sPTB. These microbial shifts mirrored those observed in human sPTB cases and underscore the value of this model for elucidating microbiota-related mechanisms in pregnancy. Our findings that Lachnospiraceae and Ruminococcaceae were reduced in the sPTB cohort aligns with previous findings ( 4 , 9 ), although some inconsistencies across studies, likely due to variations in methodologies and populations ( 8 ). Notably, Lachnospiraceae abundance declines physiologically near term, correlating with decreased butyrate levels ( 31 ). The accelerated reduction observed here may reflect early perturbations in this protective microbial community, potentially impairing immune regulation and increasing susceptibility to sPTB. While butyrate has been shown to exert a wide range of pharmacological activities—including anti-inflammatory, antioxidant, and metabolic effects—we focused our investigation on its immunomodulatory properties, particularly its role in promoting Tregs induction. Mechanistically, butyrate promotes Tregs differentiation through inhibition of histone deacetylases (HDACs) and activation of G protein-coupled receptors such as GPR43 and GPR41 ( 13 , 15 , 32 , 33 ). This immunoregulatory function is especially relevant during pregnancy, as Tregs play a pivotal role in maintaining maternal–fetal immune tolerance and preventing fetal rejection ( 34 ). Lachnospiraceae and Ruminococcaceae , recognized as representative butyrate-producing bacteria ( 9 , 24 – 26 ), may therefore contribute to pregnancy maintenance by supporting Tregs homeostasis. We hypothesize that a reduction in these taxa leads to decreased butyrate production, weakening immune tolerance and promoting a pro-inflammatory state that raises sPTB risk. This hypothesis is supported by our findings in the dysbiosis-induced PTB mouse model, in which reduced butyrate levels coincided with diminished Tregs populations. Furthermore, butyrate supplementation restored Tregs numbers and successfully prevented sPTB, while adoptive transfer of Tregs alone also rescued the PTB phenotype, underscoring the central role of this immune axis. In addition, we observed elevated levels of pro-inflammatory cytokines such as TNF-α and IL-6 in maternal serum, reflecting a systemic inflammatory state. This cytokine profile aligns with clinical observations in women experiencing preterm labor ( 35 – 37 ). Since maternal inflammation is known to disrupt immune tolerance at the maternal–fetal interface ( 5 ), it is plausible that elevated circulating cytokines contribute to a cascade of immune activation leading to premature labor. Collectively, these findings suggest that gut microbiota composition—through its impact on butyrate production, Tregs regulation, and systemic inflammation—can modulate immune homeostasis and significantly influence pregnancy outcomes. To achieve targeted delivery of butyrate to the colon, we employed a specialized feed formulation resistant to small-intestine digestion. This approach effectively promoted Tregs expansion, improved immune tolerance, reduced inflammatory responses, extended gestation, and increased live birth rates in our dysbiosis model. To our knowledge, this is the first study to demonstrate that targeted butyrate supplementation under dysbiosis conditions can mitigate inflammation-induced PTB by restoring Tregs population and immune balance during pregnancy. Our findings thereby underscore butyrate’s potential as a therapeutic agent for pregnancy maintenance, offering a multifaceted strategy to bolster immune tolerance and avert inflammation-related pregnancy complications. Additionally, our results build on prior research showing butyrate’s therapeutic potential in pregnancy complications, which reported benefits in preeclampsia models through sodium butyrate administration, such as reductions in blood pressure and inflammation markers, IL-1β and IL-6, alongside enhanced gut microbiota diversity and intestinal barrier function ( 30 , 38 ). Unlike these studies, however, our approach utilized a butyrate-enriched feed that ensures delivery to the colon, offering a novel method that may more effectively support immune balance and pregnancy maintenance. The dysbiosis model treated with anti-CD3ε antibody exhibited both systemic and local inflammation, as evidenced by elevated levels of TNF-α and COX2 in uterine tissues and increased inflammatory cytokines in maternal serum. The specific increases in TNF-α and COX2 underscore their roles in promoting uterine contractions and cervical ripening, both critical for initiating labor ( 39 ). Our findings imply that a reduction in butyrate-producing bacteria alone does not directly trigger PTB but instead primes the maternal immune system, rendering it more susceptible to inflammatory responses. This inflammation-prone state, coupled with even a subclinical inflammatory trigger like anti-CD3ε in this study, readily induced systemic and localized uterine inflammation, ultimately leading to PTB. This mechanism suggests that dysbiosis may contribute to sPTB by diminishing immune tolerance, thereby creating an environment conducive to inflammatory activation and increasing sPTB risk. The clinical implications of these findings are notable. Dietary habits are known to influence gut microbiota, and our cohort analysis revealed considerable differences in diet between sPTB and term groups. Previous research has reported links between dietary intake and sPTB risk. Ito et al. found that higher consumption of fermented foods, including yogurt and fermented soybeans, before pregnancy was associated with a lower risk of early sPTB ( 40 ). In our study, sPTB cases showed lower intake of vegetables, fruits, and dairy products—all of which are known to promote beneficial gut bacteria, including Lachnospiraceae and Ruminococcaceae ( 41 – 44 ). These findings highlight the value of dietary interventions that foster a microbiota composition conducive to pregnancy maintenance. Given diet’s dynamic influence on gut health, dietary modifications before conception or early in pregnancy could help support a balanced microbiome, potentially lowering sPTB risk. This study has several limitations. First, our use of 16S rRNA sequencing limits species-level resolution, constraining our ability to analyze specific microbial contributors to sPTB. More advanced metagenomic or metatranscriptomic analyses could provide deeper insights into species-specific microbial interactions and metabolic pathways relevant to pregnancy. Although direct measurement of SCFAs in human fecal samples could further strengthen the link between microbial composition and immune tolerance in pregnancy, such analysis was not performed in the present study due to technical and logistical constraints. Accurate SCFA quantification requires immediate freezing of stool samples following defecation, as emphasized by Ueyama et al., who demonstrated that this step is critical for preserving SCFA stability ( 17 ). While feasible under controlled laboratory conditions, such handling precision is difficult to achieve in clinical settings. Given these considerations, we selected SCFA-producing bacterial taxa as a practical and biologically relevant surrogate marker. Future studies incorporating direct SCFA measurements under optimized clinical protocols will be important to further validate these associations. Additionally, our mouse model does not replicate the dynamic shifts in gut microbiota observed in humans throughout pregnancy, particularly the substantial changes from the first to third trimesters ( 28 ). While our cohort consisted exclusively of Japanese women, thereby reducing inter-individual variation due to ethnic or dietary differences, this homogeneity may limit the generalizability of our findings to more diverse populations. It is well established that gut microbiota composition varies across ethnic groups, largely due to differences in dietary patterns, lifestyle, and environmental exposures ( 45 – 47 ). Therefore, future studies involving multi-ethnic cohorts are warranted to validate the broader applicability of our findings and to elucidate potential ethnic-specific microbial signatures related to pregnancy maintenance and PTB risk. Lastly, the differences in gut microbiota composition and dietary response between mice and humans warrant caution when applying these findings to human populations. 5. Conclusions Our study advances the understanding of dysbiosis’s importance on immune tolerance and inflammation during pregnancy, identifying butyrate as a crucial modulator of these processes. Future research should further investigate the therapeutic potential of SCFAs or dietary strategies supporting pregnancy outcomes through gut microbial balance. By establishing a clear link between microbiota composition and sPTB risk, our findings indicate the significance of gut health in maternal-fetal medicine and paves the way for innovative preventive strategies targeting the gut microbiome. Abbreviations AIN93G (American Institute of Nutrition 1993 Growth Diet), ANOVA (Analysis of Variance), AUC (Area Under the Curve), B-HAMS (Butyrate-Enriched High Amylose Maize Starch), BP (Blood Pressure), CCL (C-C Motif Chemokine Ligand), CD (Cluster of Differentiation), CXCL1 (C-X-C Motif Chemokine Ligand 1), DMSO (Dimethyl Sulfoxide), DNA (Deoxyribonucleic Acid), E (Embryonic Day), GC (Gas Chromatography), GM-CSF (Granulocyte-Macrophage Colony-Stimulating Factor), GPR (G Protein-Coupled Receptor), HAMS (High Amylose Maize Starch), HDAC (Histone Deacetylase), IFN-γ (Interferon-Gamma), IL (Interleukin), KSPD (Kyoto Scale of Psychological Development), OTU (Operational Taxonomic Unit), PCA (Principal Component Analysis), PBS (Phosphate-Buffered Saline), PTB (Preterm Birth), qPCR (Quantitative Polymerase Chain Reaction), rRNA (Ribosomal RNA), ROC (Receiver Operating Characteristic), SCFAs (Short-Chain Fatty Acids), sPTB (Spontaneous Preterm Birth), SPSS (Statistical Package for the Social Sciences), Tregs (Regulatory T Cells), TNF-α (Tumor Necrosis Factor-Alpha). Declarations Acknowledgments We would like to express our sincere gratitude to Junichi Takahara from the Research and Development Department at Sanwa Starch Co., Ltd., for his invaluable support and expertise in providing the B-HAMS and untreated HAMS used in our study. We are also deeply grateful to Dr. Chieko Aoki for her valuable insights and guidance throughout the research process. Additionally, we would like to thank Cykinso, Inc. for 16S rRNA gene sequencing, and Editage for the English language editing. Authors' contributions Azusa Uchida: Sample Collection, Methodology, Investigation, Data curation, Writing-original draft. Kenji Imai: Conceptualization, Methodology, Investigation, Data curation, Funding acquisition, Writing – original draft, Writing – review & editing. Rika Miki, and Tomonari Hamaguchi: Methodology, Investigation, Data curation. Hiroshi Nishiwaki, Mikako Ito, Jun Ueyama, and Satomi Hattori: Methodology, Data curation. Sho Tano, Kazuya Fuma, Seiko Matsuo, Takafumi Ushida, Kinji Ohno, and Hiroaki Kajiyama: Methodology, Investigation, Data curation. Tomomi Kotani: Conceptualization, Funding acquisition, Writing – review & editing. Funding This work was supported in part by JSPS KAKENHI Grant No.21K16812, the Hori Sciences And Arts Foundation, and the Aichi Health Promotion Foundation. Availability of data and materials The BioSample metadata for bacterial 16S rRNA gene information is available in the DDBJ BioSample database under accession numbers DRR623374-DRR623456. For other data, restrictions imposed by the Ethics Committee of Nagoya University prevent the authors from making the minimal dataset publicly available. Researchers who wish to access the data must meet the criteria for accessing confidential data as set by Nagoya University. For inquiries, please contact [email protected] . Ethics approval and consent to participate The experiment using human fecal samples was approved by the Institutional Review Board and Ethics Committee of the Nagoya University Graduate School of Medicine (approval number 20210353), and written informed consent was obtained from all participants. All experimental studies involving animals were approved by the Nagoya University Graduate School of Medicine (approval number: M240021) and performed according to the guidelines and regulations therein described. Consent for publication All authors acknowledge and consent to the paper’s content and are included as co-authors. Competing interests The authors declared no competing interests. References Otsuka N, Imai K, Tano S, Matsuo S, Ushida T, Nomoto M, et al. Possible Efficacy of Vaginal Progesterone on Asymptomatic Women with a Short Cervix after 24 Weeks of Gestation: A Historical Cohort Study in Japan. JMA J. 2024;7(4):582-9. 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Stability of Gut Microbiota Composition Across the Perinatal Period in Mice The percentage of taxonomic distribution at the genus level in fecal samples collected from control (n = 8), polymyxin B (n = 7), and vancomycin (n = 13) groups at three time points: before antibiotic treatment, before anti-CD3ε administration on E16, and shortly after delivery. Although the vancomycin group displayed marked differences in microbial distribution compared to the control and polymyxin B groups, within-group comparisons showed stable microbiota composition across these time points, underscoring the persistence of microbial communities throughout the perinatal period. renamed74dc0.tif Supplemental figure 2. Maternal blood pressure and heart rate measurements in the dysbiosis-induced preterm birth mouse model Systolic blood pressure (SBP), diastolic blood pressure (DBP), mean blood pressure (BP), and Maternal heart rate (HR) measurements in pregnant mice treated with control, polymyxin B, or vancomycin, showing no significant differences among groups. For blood pressure measurements: control (n = 6), polymyxin B (n = 6), vancomycin (n = 7). BP, blood pressure; SBP, systolic blood pressure; DBP, diastolic blood pressure; HR, heart rate. renamed327b2.tif Supplemental figure 3. Fetal Cytokine Levels in Each Group. Cytokine analysis in fetal serum collected on E16 after anti-CD3ε antibody administration. No notable elevation in inflammatory markers, suggesting limited transference of inflammation to the fetal compartment. Sample sizes: control (n = 8), polymyxin B (n = 9), vancomycin (n = 10). Statistical significance: *p < 0.05. renamedacb21.tif Supplemental figure 4. Effect of Treg Cell Administration on Dysbiosis-Induced Preterm Birth Model (vancomycin group). (A) Experimental setup; Dysbiosis-PTB model mice were administered Tregs or PBS control on E15, followed by anti-CD3ε on E16. (B) PTB rate was significantly reduced in Treg-treated group compared to the control (PBS-treated group). (C) Pregnancy duration was extended in Treg-treated group. (D) Live pup birth rate was higher in Treg-treated group. Sample sizes: vancomycin + PBS (n = 12), vancomycin + Treg (n = 14). Statistical significance: *p < 0.05 Cite Share Download PDF Status: Published Journal Publication published 12 May, 2025 Read the published version in Journal of Translational Medicine → Version 1 posted Editorial decision: Accept 24 Apr, 2025 Reviewers agreed at journal 20 Apr, 2025 Reviewers invited by journal 20 Apr, 2025 Editor assigned by journal 18 Apr, 2025 First submitted to journal 17 Apr, 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-5770845","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":445381957,"identity":"1b4240d0-ad03-488a-b39a-508b31f0cc4f","order_by":0,"name":"Azusa Uchida","email":"","orcid":"","institution":"Nagoya University: Nagoya Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Azusa","middleName":"","lastName":"Uchida","suffix":""},{"id":445381958,"identity":"b7377df4-08be-4ec6-85ee-f0469bb28b11","order_by":1,"name":"Kenji 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Byoin","correspondingAuthor":false,"prefix":"","firstName":"Rika","middleName":"","lastName":"Miki","suffix":""},{"id":445381960,"identity":"cc2dab5d-8c3c-40e0-adc1-975959960884","order_by":3,"name":"Tomonari Hamaguchi","email":"","orcid":"","institution":"Nagoya University: Nagoya Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Tomonari","middleName":"","lastName":"Hamaguchi","suffix":""},{"id":445381961,"identity":"846be1ad-071c-4b38-982c-072cec5147c0","order_by":4,"name":"Hiroshi Nishiwaki","email":"","orcid":"","institution":"Nagoya University: Nagoya Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Hiroshi","middleName":"","lastName":"Nishiwaki","suffix":""},{"id":445381962,"identity":"b76c2f31-2067-471c-988b-46add013d7f7","order_by":5,"name":"Mikako Ito","email":"","orcid":"","institution":"Nagoya University: Nagoya Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Mikako","middleName":"","lastName":"Ito","suffix":""},{"id":445381963,"identity":"48c94051-f920-4ee9-a488-0ca2ae744613","order_by":6,"name":"Jun Ueyama","email":"","orcid":"","institution":"Nagoya University: Nagoya Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Ueyama","suffix":""},{"id":445381964,"identity":"1f78556a-aa4e-4d64-bbd7-fe16150233f4","order_by":7,"name":"Satomi Hattori","email":"","orcid":"","institution":"Nagoya University: Nagoya Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Satomi","middleName":"","lastName":"Hattori","suffix":""},{"id":445381965,"identity":"eebd9dd1-afe8-4891-80c4-7dc36a162974","order_by":8,"name":"Sho Tano","email":"","orcid":"","institution":"Nagoya University: Nagoya Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Sho","middleName":"","lastName":"Tano","suffix":""},{"id":445381966,"identity":"5d28c9b0-94d3-4293-83b1-d7398bfc987b","order_by":9,"name":"Kazuya Fuma","email":"","orcid":"","institution":"Nagoya University: Nagoya Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Kazuya","middleName":"","lastName":"Fuma","suffix":""},{"id":445381967,"identity":"afb834c9-8fec-4c50-bc11-7d2f21e499d5","order_by":10,"name":"Seiko Matsuo","email":"","orcid":"","institution":"Nagoya University: Nagoya Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Seiko","middleName":"","lastName":"Matsuo","suffix":""},{"id":445381968,"identity":"c3bb2e3a-cb4e-424c-9281-8f8b34b7952b","order_by":11,"name":"Takafumi Ushida","email":"","orcid":"","institution":"Nagoya University: Nagoya Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Takafumi","middleName":"","lastName":"Ushida","suffix":""},{"id":445381969,"identity":"d0b6738a-5601-499e-a08d-bea99eb9dfd0","order_by":12,"name":"Kinji Ohno","email":"","orcid":"","institution":"Nagoya University: Nagoya Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Kinji","middleName":"","lastName":"Ohno","suffix":""},{"id":445381970,"identity":"efb9a310-4061-41c2-8d3e-15ed9c532604","order_by":13,"name":"Hiroaki Kajiyama","email":"","orcid":"","institution":"Nagoya University: Nagoya Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Hiroaki","middleName":"","lastName":"Kajiyama","suffix":""},{"id":445381971,"identity":"77e03892-763f-43bf-8c05-5e794e12af2b","order_by":14,"name":"Tomomi Kotani","email":"","orcid":"","institution":"Nagoya University: Nagoya Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Tomomi","middleName":"","lastName":"Kotani","suffix":""}],"badges":[],"createdAt":"2025-01-06 05:38:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5770845/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5770845/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12967-025-06534-y","type":"published","date":"2025-05-12T15:57:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81541523,"identity":"4a2d511c-1b7e-440c-b9f5-5dd75243a906","added_by":"auto","created_at":"2025-04-28 11:15:13","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1716748,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVancomycin-Induced Dysbiosis in Maternal Gut Microbiota Compared to Polymyxin B and Control Treatments.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Schematic of the experimental design showing administration of vancomycin (n = 13), polymyxin B (n = 7), or control (n = 8) treatments in C57BL/6 pregnant mice. (B) Microbiota composition at the class level across the three groups (control, polymyxin B, and vancomycin). (C) Principal Component Analysis depicting distinct clustering of the microbial community in vancomycin-treated mice. (D) Fecal DNA concentrations, (E) Operational Taxonomic Units (OTUs) at the genus level, and (F) Shannon diversity index, for each group. Statistical significance: ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001. E; embryo.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5770845/v1/76c7b79775b82e26eb52da87.jpg"},{"id":81541527,"identity":"6e5f0bd0-ce44-4e98-afaa-3e8ceaf1d82a","added_by":"auto","created_at":"2025-04-28 11:15:13","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":959852,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVancomycin-Induced Dysbiosis in Maternal Gut Microbiota at Family-level analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Family-level analysis illustrating taxa with a relative abundance \u0026gt; 1% in at least one sample. In the vancomycin-treated group, many bacterial taxa were undetectable, reflecting a marked depletion of microbial diversity at the family level.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5770845/v1/0378a04935e9e0eb29d9327a.jpg"},{"id":81542914,"identity":"de17c9b0-ffe1-4994-b4de-125f7c9e069e","added_by":"auto","created_at":"2025-04-28 11:23:13","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1891858,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVancomycin-Induced Dysbiosis Potentiates Preterm Birth Following Anti-CD3ε Administration.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Schematic of the experimental setup illustrating antibiotic treatment and subsequent anti-CD3ε administration. (B) Kaplan-Meier survival curve showing gestational duration in control, polymyxin B, and vancomycin-treated groups. (C) Preterm birth rates, with significant increases observed in the vancomycin-induced dysbiosis group following anti-CD3ε administration. (D) Gestational length among groups, highlighting the significant reduction in the vancomycin-induced dysbiosis group. (E) Pups survival rates, significantly decreased in the vancomycin-treated group after anti-CD3ε administration. (F, G) Pups weights significantly reduced in the polymyxin B and the vancomycin-induced dysbiosis group compared to the control. (H, I) Placental weight and pups count in each litter indicating no significant differences. Sample sizes: control (n = 11), polymyxin B (n = 11), vancomycin (n = 30). For pups and placental weights, and pups count: control group (65 pups from 8 litters), polymyxin B group (58 pups from 8 litters), vancomycin group (70 pups from 9 litters). Statistical significance: *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001. E; embryo.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5770845/v1/254b9434c34bb85e1dd8e7fd.jpg"},{"id":81541529,"identity":"67dc546f-8460-483a-92e3-afcdfd72d332","added_by":"auto","created_at":"2025-04-28 11:15:13","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1904389,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGut Microbiota Composition in Pregnant Women with Preterm Birth Compared to Term Delivery.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Flow diagram of participant recruitment and final analysis grouping, including 10 women with spontaneous PTB and 22 with term delivery. (B) Microbial profiling at the class level, \u003cem\u003eClostridia\u003c/em\u003e were significantly reduced in the PTB group. (C) Family-level distribution of bacterial taxa with a relative abundance \u0026gt;1% in at least one group, highlighting significant reductions in \u003cem\u003eLachnospiraceae\u003c/em\u003eand \u003cem\u003eRuminococcaceae\u003c/em\u003e in the spontaneous PTB group.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5770845/v1/ee017f83114a0f2a338fa064.jpg"},{"id":81541537,"identity":"a5427cb7-5288-4323-bd79-dc65866a0645","added_by":"auto","created_at":"2025-04-28 11:15:14","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":682401,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredictive potential of gut microbiota composition for spontaneous preterm birth.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Principal Component Analysis depicting slight separation between spontaneous PTB and term groups. (B) Combined relative abundance of \u003cem\u003eLachnospiraceae\u003c/em\u003eand \u003cem\u003eRuminococcaceae\u003c/em\u003e, markedly decreased in the spontaneous PTB group. (C) Positive correlation between the combined abundance of\u003cem\u003e Lachnospiraceae\u003c/em\u003e and \u003cem\u003eRuminococcaceae\u003c/em\u003eand gestational age at delivery (r: 0.585, p \u0026lt; 0.001). (D) Receiver Operating Characteristic curve demonstrating the predictive potential of combined \u003cem\u003eLachnospiraceae\u003c/em\u003eand \u003cem\u003eRuminococcaceae\u003c/em\u003e abundance for spontaneous PTB, with an AUC of 0.823. Statistical significance: **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5770845/v1/00b5f98d47e73ff51d260a6f.jpg"},{"id":81541534,"identity":"93d1301d-e4b5-4878-8fbd-2c52ecef2306","added_by":"auto","created_at":"2025-04-28 11:15:13","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1632266,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReduction of Short-Chain Fatty Acids and Immune Modulation in the Dysbiosis-Preterm Birth Model (vancomycin group).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Proportions of four short-chain fatty acids (acetate, propionate, butyrate, and valeric acid) in each group, showing a marked reduction in butyrate levels within the Dysbiosis-induced PTB model relative to other groups. (B) Quantitative comparison of short-chain fatty acid levels across groups, highlighting the marked decrease of acetate, propionate, butyrate, and valeric acid in the Dysbiosis-PTB model. (C) Flow cytometry analysis of Treg (CD3+CD4+CD25+FOXP3+) and CD8+ (CD3+CD8+) cell populations. (D-F) Reduction in Treg cells in the Dysbiosis-induced PTB model, suggesting decreased immune tolerance. (G,H) Elevated expression of inflammatory markers Cox2 and TNF-α in uterine tissues of the Dysbiosis-induced PTB model, indicating heightened local inflammation. Sample sizes: short-chain fatty acids measurements with control and vancomycin groups (n = 8) and polymyxin B group (n = 6); flow cytometry (n = 6 per group); and quantitative real-time PCR (n = 10 per group). Statistical significance: *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5770845/v1/ae108663cb370b9d0491ce62.jpg"},{"id":81542916,"identity":"84a33b9d-6321-4f2e-b265-cfb26637add9","added_by":"auto","created_at":"2025-04-28 11:23:13","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1600819,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eElevated Maternal Cytokines in the Dysbiosis-Preterm Birth Model (vancomycin group).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCytokine analysis in maternal serum collected on embryonic day 16, both before and after anti-CD3ε antibody administration. Maternal serum from the Dysbiosis-induced PTB model displayed significantly elevated levels of inflammatory cytokines, including IL-6, GM-CSF, IFN-γ, CCL3, and TNF-α, compared to the control, indicating a systemic inflammatory response associated with Dysbiosis-induced PTB. Sample sizes: control (n = 6), vancomycin (n = 7-9). Statistical significance: *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01. C; control group, P; polymyxin B group, V; vancomycin group.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5770845/v1/b2a1d3738b8427fae5a29fee.jpg"},{"id":81541535,"identity":"1be1b793-96d6-4de3-8b43-8cc0124162fa","added_by":"auto","created_at":"2025-04-28 11:15:13","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":995216,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eButyrate Supplementation Reduces Dysbiosis-Induced Preterm Birth and Restores Immune Tolerance in Mice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Schematic diagram of the experimental design, showing the use of the Dysbiosis-induced PTB model in mice with dietary interventions: AIN93G with 10% HAMS or 10% B-HAMS. (B) Butyrate concentration levels in the cecal feces of mice, significantly elevated in the B-HAMS group, compared to the control (HAMS) group. (C) Rate of PTB across groups, showing a notable reduction in the B-HAMS group. (D,E) Gestational duration, which is extended in the B-HAMS group, indicating protective effects of butyrate against PTB. (F) Rate of live pup births, showing an increase in the B-HAMS group compared to the control. (G-I) Flow cytometry results of Treg (CD3+CD4+CD25+FOXP3+) and CD8 T cell (CD3+ CD8+) populations, demonstrating a significant increase in Treg cells in the B-HAMS group, supporting restored immune tolerance. Sample sizes: for PTB rate, gestational duration, and live pup birth, HAMS (n = 16) and B-HAMS (n = 14); for SCFAs concentrations, both groups (n = 6); for Treg and CD8 analysis by flow cytometry, HAMS (n = 4) and B-HAMS (n = 8). Statistical significance: *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001. B-HAMS; butyrate-enriched high amylose maize starch, HAMS; high amylose maize starch, V.; vancomycin.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5770845/v1/f12d8f84f0510dad25871d96.jpg"},{"id":83068009,"identity":"ceb8719e-6e01-4190-be81-295c31ecffca","added_by":"auto","created_at":"2025-05-19 16:09:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13002593,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5770845/v1/7f3d2ebb-b450-4e7b-b16d-4651a8594597.pdf"},{"id":81541524,"identity":"85eb6d3c-2210-4380-9903-d7a8cd4e6c32","added_by":"auto","created_at":"2025-04-28 11:15:13","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":91824,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Figure 1. Stability of Gut Microbiota Composition Across the Perinatal Period in Mice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe percentage of taxonomic distribution at the genus level in fecal samples collected from control (n = 8), polymyxin B (n = 7), and vancomycin (n = 13) groups at three time points: before antibiotic treatment, before anti-CD3ε administration on E16, and shortly after delivery. Although the vancomycin group displayed marked differences in microbial distribution compared to the control and polymyxin B groups, within-group comparisons showed stable microbiota composition across these time points, underscoring the persistence of microbial communities throughout the perinatal period.\u003c/p\u003e","description":"","filename":"renamedbcc31.tif","url":"https://assets-eu.researchsquare.com/files/rs-5770845/v1/489a1594271d0fb4855567d6.tif"},{"id":81541526,"identity":"dad2acb7-762d-4f6b-afa2-dbb52fda6b1c","added_by":"auto","created_at":"2025-04-28 11:15:13","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":90816,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental figure 2. Maternal blood pressure and heart rate measurements in the dysbiosis-induced preterm birth mouse model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSystolic blood pressure (SBP), diastolic blood pressure (DBP), mean blood pressure (BP), and Maternal heart rate (HR) measurements in pregnant mice treated with control, polymyxin B, or vancomycin, showing no significant differences among groups. For blood pressure measurements: control (n = 6), polymyxin B (n = 6), vancomycin (n = 7). BP, blood pressure; SBP, systolic blood pressure; DBP, diastolic blood pressure; HR, heart rate.\u003c/p\u003e","description":"","filename":"renamed74dc0.tif","url":"https://assets-eu.researchsquare.com/files/rs-5770845/v1/31a07d25f040f2226da02c81.tif"},{"id":81541541,"identity":"d21d730c-26ea-4463-9283-b8c5723981e5","added_by":"auto","created_at":"2025-04-28 11:15:14","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2600672,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental figure 3. Fetal Cytokine Levels in Each Group.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCytokine analysis in fetal serum collected on E16 after anti-CD3ε antibody administration. No notable elevation in inflammatory markers, suggesting limited transference of inflammation to the fetal compartment. Sample sizes: control (n = 8), polymyxin B (n = 9), vancomycin (n = 10). Statistical significance: *p \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"renamed327b2.tif","url":"https://assets-eu.researchsquare.com/files/rs-5770845/v1/f41a92711f4870d5cc611d0a.tif"},{"id":81541531,"identity":"dbefce01-8c75-4ec9-8ca4-5a193a4c74ea","added_by":"auto","created_at":"2025-04-28 11:15:13","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":549916,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental figure 4. Effect of Treg Cell Administration on Dysbiosis-Induced Preterm Birth Model (vancomycin group).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Experimental setup; Dysbiosis-PTB model mice were administered Tregs or PBS control on E15, followed by anti-CD3ε on E16. (B) PTB rate was significantly reduced in Treg-treated group compared to the control (PBS-treated group). (C) Pregnancy duration was extended in Treg-treated group. (D) Live pup birth rate was higher in Treg-treated group. Sample sizes: vancomycin + PBS (n = 12), vancomycin + Treg (n = 14). Statistical significance: *p \u0026lt; 0.05\u003c/p\u003e","description":"","filename":"renamedacb21.tif","url":"https://assets-eu.researchsquare.com/files/rs-5770845/v1/c4795e06d3934b7d44777483.tif"}],"financialInterests":"","formattedTitle":"Butyrate-Producing Bacteria in Pregnancy Maintenance: Mitigating Dysbiosis-Induced Preterm Birth","fulltext":[{"header":"1. Background","content":"\u003cp\u003ePreterm birth (PTB), defined as delivery before 37 weeks of gestation, remains one of the foremost causes of neonatal mortality and long-term health complications globally (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Children born preterm are at heightened risk for a range of morbidities, including respiratory diseases, cognitive impairments, and learning disabilities, which often persist into adolescence and adulthood (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Despite advancements in perinatal care, PTB rates have not declined, and the precise etiology of PTB remains be elusive. PTB is widely regarded as a multifactorial syndrome that may result from sterile inflammation, maternal genetics, environmental factors such as maternal diet, and immune responses (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent years, attention has increasingly focused on the gut microbiota\u0026rsquo;s role in both health and disease. Dysbiosis, defined as an imbalance in the gut microbial community, has been implicated in conditions such as obesity, diabetes, and inflammatory bowel disease, all of which involve inflammatory mechanisms (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Emerging data indicate that maternal dysbiosis is similarly associated with an elevated risk of adverse pregnancy outcomes; however, only limited research has examined the relationship between maternal gut microbiota and spontaneous PTB (sPTB), with these studies primarily highlighting correlations (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). To date, few studies have definitively established specific causal relationships between dysbiosis and sPTB, particularly regarding the mechanisms involved.\u003c/p\u003e \u003cp\u003eShort-chain fatty acids (SCFAs), pivotal metabolites produced by gut bacteria through the fermentation of dietary fiber, have shown anti-inflammatory and immunomodulatory effects (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). SCFAs, particularly butyrate, play essential roles in intestinal health and modulating immune responses, notably by promoting immune tolerance via the expansion of regulatory T cells (Tregs) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Given that sPTB is often linked to inflammation and disruptions in immune tolerance, we hypothesize that SCFAs may act as important mediators in the interplay between maternal dysbiosis and the initiation of preterm labor.\u003c/p\u003e \u003cp\u003eTherefore, this study aims to establish a mouse model of Dysbiosis-induced PTB to investigate the role of maternal gut microbiota on pregnancy outcomes. This model will allow us to explore how changes in the gut microbiota contribute to immune dysregulation and the pathogenesis of PTB. Additionally, using the mouse model, we seek to evaluate the potential role of butyrate, particularly its anti-inflammatory properties, in mitigating PTB risk. By advancing our understanding of these interactions, we hope to provide new insights into preventive and therapeutic strategies to improve maternal and neonatal health outcomes.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Experimental Design of Dysbiosis-Induced Preterm Birth Model\u003c/h2\u003e \u003cp\u003eSix-week-old C57BL/6J mice, purchased from Japan SLC, Shizuoka, Japan, were housed under specific pathogen-free (SPF) conditions in temperature- and humidity-controlled rooms (20\u0026ndash;25\u0026deg;C, 40\u0026ndash;70%) with a 12-hour light/dark cycle and ad libitum access to standard chow and water. To ensure baseline comparability across groups, all mice were housed, and mated under identical environmental and dietary conditions. To establish a mouse model of Dysbiosis-induced PTB, the pregnant mice were randomly assigned to three groups: control, vancomycin-treated, and polymyxin B-treated. The control group received regular drinking water (reverse osmosis [RO] water), while the antibiotic groups were administered Vancomycin (500 mg/L) (FUJIFILM Wako Pure Chemical Corporation) or polymyxin B (100 mg/L) (FUJIFILM Wako Pure Chemical Corporation), both dissolved in the same RO water. Antibiotic solutions were replaced every two or three days using autoclaved bottles, and fluid intake was routinely monitored to maintain consistent dosing. The antibiotics were provided via free drinking water for two weeks. Vancomycin, a glycopeptide antibiotic, has a broad spectrum of activity against Gram-positive bacteria and has been shown to fundamentally alter gut microbial diversity, although it does not completely eliminate all commensal species. In contrast, polymyxin B, a cyclic peptide antibiotic, has a narrower spectrum of activity, primarily targeting Gram-negative bacteria. This selective action makes polymyxin B less disruptive to the overall composition of the gut microbiota compared to vancomycin. Both antibiotics are commonly used in experimental models to induce dysbiosis and to investigate its effects on host immunity and disease development (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). After the two-week antibiotic treatment, mating was initiated to confirm pregnancy. Female mice spent the night before estrus with fertile males at a ratio of 2:1. Male mice were treated with the same antibiotics as the females they were paired with to maintain consistency in microbial exposure. The day sperm was detected in the vaginal smear was defined as gestational day 0 [embryonic day (E) 0]. Pregnant mice continued receiving the same antibiotic treatment until delivery. Then, pregnant mice received a subclinical dose (2 \u0026micro;g per mouse, intraperitoneally) of anti-CD3ε antibody (Nippon Becton Dickinson Company Ltd.) once at E16. The dose of anti-CD3ε antibody (2 \u0026micro;g/mouse) was selected as the highest subclinical dose that does not independently induce PTB, based on prior literature (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) and our own preliminary data, which demonstrated PTB rates of 100% with 10 \u0026micro;g, 80% with 5 \u0026micro;g, 10% with 2.5 \u0026micro;g, and 0% with 1 \u0026micro;g. The antibiotic treatment schedule was designed to span approximately four weeks prior to immune activation at embryonic day 16, in accordance with previous reports indicating that such a duration is sufficient to modulate Tregs and gut microbiota (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). PTB rates, gestational duration, and live pup rates were closely monitored across all three groups. PTB was defined as delivery occurring before E18.5. At E16, before the administration of the antibody, blood pressure (BP) was measured in a subset of mice using tail-cuff plethysmography (BP-2010, Softron, Japan). Mice were placed in a 38\u0026deg;C heater to preheat and calm them before BP measurement. The average of three measurements was recorded as the final BP value. After BP measurement, the following tissues were collected from the mice in each group: spleen, cecum feces, timely voided feces, and maternal serum. Additionally, separate individuals were sacrificed at 15 hours after anti-CD3ε antibody administration, and the following tissues were collected: uterus, maternal serum, fetus, and placenta. Then, the pups' weight and placental wet weight were measured and recorded. The number of animals used in each experimental group was as follows: control (n\u0026thinsp;=\u0026thinsp;6\u0026ndash;11), polymyxin B (n\u0026thinsp;=\u0026thinsp;6\u0026ndash;11), and vancomycin (n\u0026thinsp;=\u0026thinsp;6\u0026ndash;30), depending on the specific assay. Exact sample sizes for each analysis are provided in the corresponding figure legends.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Preparation of Butyrate-Enriched Feed\u003c/h2\u003e \u003cp\u003eButyrate is highly volatile, and achieving sufficient concentrations in the colon requires specially formulated butyrate-enriched feed. To create this feed, we replaced 10% of the standard starch in AIN93G with either butyrate-enriched high amylose maize starch (B-HAMS) or untreated high amylose maize starch (HAMS). The original method for producing butyrate-enriched starch was reported in 2003 (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In contrast to the original method, which involved dissolving starch in large quantities of DMSO to bind SCFAs, our approach avoids dissolving the starch. Instead, we immerse intact starch granules in water and bind butyrate to the surface of the granules through an acid-base reaction. This process was carried out by Sanwa Starch Co., Ltd., who kindly provided both the B-HAMS and HAMS used in this study. Starch naturally consists of amylose and amylopectin, forming clumps. While the original process dissolved these clumps to allow binding, we target the surface of the intact granules, significantly simplifying the procedure and eliminating the need for large quantities of DMSO. Both methods achieve a comparable degree of substitution for butyrate, over 0.20, making our modified approach an effective method for producing chemically modified starch for experimental use. Following the free oral intake of this formulation, a significant increase in butyrate concentration was observed in the cecum feces (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e8\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Butyrate Supplementation in Dysbiosis-Induced Preterm Birth Model\u003c/h2\u003e \u003cp\u003eThis experiment utilized the butyrate-enriched feed described above. All mice were specific pathogen-free (SPF) 6-week-old female C57BL/6J mice and were housed under standard laboratory conditions as described in Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e. They were maintained in a temperature- and humidity-controlled environment (20\u0026ndash;25\u0026deg;C, 40\u0026ndash;70%) with a 12-hour light/dark cycle, and had ad libitum access to water and their assigned diets. All mice were allowed free access to the feed throughout the study. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, the Dysbiosis-induced PTB mouse model, established using vancomycin, was used to compare two different diets; mice were fed either AIN93G\u0026thinsp;+\u0026thinsp;10% HAMS or AIN93G\u0026thinsp;+\u0026thinsp;10% B-HAMS. Both HAMS and B-HAMS diets were irradiated with 30 kGy of γ-rays prior to use. Vancomycin was administered via drinking water, which was replaced every two or three days using autoclaved bottles, and fluid intake was routinely monitored to ensure consistent exposure. The dietary intervention began two weeks prior to mating and continued throughout pregnancy until delivery. PTB rates, gestational duration, and live pup rates were closely monitored across both dietary groups. Before administering the anti-CD3ε antibody on E16, spleens from all mice were collected and analyzed by flow cytometry. The number of animals used in each experimental group was as follows: AIN93G\u0026thinsp;+\u0026thinsp;10% HAMS group (n\u0026thinsp;=\u0026thinsp;4\u0026ndash;16), and AIN93G\u0026thinsp;+\u0026thinsp;10% HAMS group (n\u0026thinsp;=\u0026thinsp;8\u0026ndash;14), depending on the specific assay. Exact sample sizes for each analysis are provided in the corresponding figure legends.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Treg Cell Isolation and Administration in Dysbiosis-Induced Preterm Birth Model\u003c/h2\u003e \u003cp\u003eTreg Isolation: CD4\u0026thinsp;+\u0026thinsp;CD25\u0026thinsp;+\u0026thinsp;regulatory T cells were isolated using the Mouse CD4\u0026thinsp;+\u0026thinsp;CD25\u0026thinsp;+\u0026thinsp;Regulatory T Cell Isolation Kit (Miltenyi Biotec) through magnetic-activated cell sorting, following the manufacturer\u0026rsquo;s instructions. Briefly, spleens were harvested from 8\u0026ndash;10 week-old C57BL/6J female mice, minced in cold phosphate-buffered saline (PBS), and filtered through a 40 \u0026micro;m cell strainer to generate a single-cell suspension. The cells were then centrifuged at 300\u0026times;g for 5 minutes at 4\u0026deg;C, followed by two washes with PBS. The splenocytes were counted using an automatic cell counter.\u003c/p\u003e \u003cp\u003eTreg Administration: The isolated Treg cells were resuspended in sterile PBS. Each mouse in the treatment group received an intraperitoneal injection of 2\u0026times;10\u003csup\u003e5\u003c/sup\u003e Treg cells suspended in 200 \u0026micro;L of PBS. As shown in Supplemental Fig.\u0026nbsp;4A, two groups of Dysbiosis-induced PTB model mice, previously treated with vancomycin, received different treatments on E15. The control group was administered an intraperitoneal injection of sterile PBS, while the experimental group received Treg cells. On E16, both groups were administered an intraperitoneal injection of anti-CD3ε antibody (2 \u0026micro;g/body). Preterm birth rates, pregnancy duration, and live birth rates were closely monitored in both groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 DNA Isolation and 16S ribosomal RNA Sequencing of Mouse Fecal Samples\u003c/h2\u003e \u003cp\u003eThe details of these procedures have been previously described (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Briefly, mouse fecal samples were freeze-dried and ground. DNA was isolated from 20 mg of freeze-dried fecal material using the QIAamp PowerFecal Pro DNA Kit (Qiagen), following the manufacturer\u0026rsquo;s protocol with minor modifications. To ensure efficient bacterial DNA extraction, we are optimized vortex methods. These samples were homogenized in Solution C1 with beads Lysing Matrix E (MP Biomedicals) using FastPrep-24 5G (MP Biomedicals) at 6.0 m/s for 60 seconds for three cycles instead of vortex mixing. The V3\u0026ndash;V4 regions of the bacterial 16S rRNA gene were amplified using primers 341F (5\u0026rsquo;-CCTACGGGNGGCWGCAG-3\u0026rsquo;) and 805R (5\u0026rsquo;-GACTACHVGGGTATCTAATCC-3\u0026rsquo;). PCR product quality was verified by gel electrophoresis. A sequencing library was prepared, and samples were barcoded. Paired-end sequencing of 300-nucleotide fragments was performed using the MiSeq reagent kit V3 600 cycle on a MiSeq system (Illumina). Data analysis, including clustering into Operational Taxonomic Units (OTUs), was conducted using QIIME 2 with the SILVA database. Statistical comparisons and diversity analyses were performed to evaluate differences in microbial compositions between groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Quantification of Short-Chain Fatty Acids of Mouse Fecal Samples\u003c/h2\u003e \u003cp\u003eCecum feces were collected from mice, immediately sealed, and stored at -80\u0026deg;C. The samples were then freeze-dried using a freeze dryer (FDU-2110) connected to a drying chamber (DRC-1100, EYELA, Tokyo, Japan). After drying, the fecal samples were transferred to a disposable grinding chamber (MT 40, IKA, Staufen, Germany), ground into a fine powder using the IKA Tube Mill control, and stored at -30\u0026deg;C until analysis. For SCFAs quantification, approximately 20 mg of freeze-dried fecal powder was mixed with 1000 \u0026micro;L of 5 mmol/L sodium hydroxide and homogenized. After centrifugation at 13,200 \u0026times;g for 20 minutes at 4\u0026deg;C, the supernatant (333 \u0026micro;L) was mixed with 200 \u0026micro;L of water, 50 \u0026micro;L of hexanoic-6,6,6-d3 acid solution (internal standard), 200 \u0026micro;L of 2-methyl-1-propanol, 133 \u0026micro;L of pyridine, and 67 \u0026micro;L of isobutyl chloroformate. The resulting solution was shaken for 1 minute, then 0.3 mL of hexane was added and the mixture was shaken vigorously for 10 minutes. After centrifugation, the upper organic phase was transferred to a GC glass vial for analysis. Quantitative analysis of acetate, propionate, butyrate, and valerate was performed using an Agilent 7890A GC coupled with an Agilent 5975 inert mass spectrometer (Agilent Technologies), following the procedure reported by Ueyama et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Measurement of Cytokines of Mouse Serum Samples\u003c/h2\u003e \u003cp\u003eMaternal and fetal blood samples were collected from mice on E16. Maternal serum was separated by centrifugation and stored at -80\u0026deg;C until analysis. Due to the limited amount of fetal serum available, the serum from all fetuses in each litter was pooled and treated as a single sample. A bead-based multiplex assay (Bio-Rad Laboratories, Inc., Hercules, CA, USA) was employed to measure the levels of 23 cytokines, including interleukin (IL)-1α, IL-1β, IL-2, IL-3, IL-4, IL-5, IL-6, IL-9, IL-10, IL-12(p40), IL-12(p70), IL-13, IL-17; Eotaxin; granulocyte colony-stimulating factor (G-CSF); granulocyte-macrophage colony-stimulating factor (GM-CSF); interferon-gamma (IFN-γ); C-X-C motif chemokine ligand 1 (CXCL1); C-C motif chemokine ligand (CCL) 2, CCL3, CCL4, CCL5; and tumor necrosis factor-alpha (TNF-α). The assay was performed on the Luminex200 system (Luminex, Austin, TX, USA) according to previously described techniques(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Flowcytometry of Mouse Spleen Samples\u003c/h2\u003e \u003cp\u003eSpleen tissues of mice were collected and minced before being processed with a gentle MACS Dissociator (Miltenyi Biotec, San Diego, CA). After digestion, the homogenized tissues were washed and filtered through a cell strainer (Fisher Scientific, Durham, NC, USA). Cell suspensions were then centrifuged at 300 \u0026times;g for 5 minutes at 4\u0026deg;C. Cell debris was removed using Debris Removal Solution (Miltenyi Biotec, Bergisch Gladbach, Germany). This method was modified from a previously reported technique (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The following monoclonal antibodies were purchased from BioLegend (San Diego, CA): Brilliant Violet (BV) 785-conjugated anti-CD8 (53\u0026thinsp;\u0026minus;\u0026thinsp;6.7), Allophycocyanin (APC)- and Cy7-conjugated anti-mouse CD4 (GK1.5), Alexa Fluor (AF) 700-conjugated anti-CD3 (HIT3a), BV605-conjugated anti-CD25 (PC61), and Alexa Fluor 647 anti-mouse Foxp3 (MF-14). Cells were stained for surface markers (CD3, CD4, CD8, CD25) and with a fixable viability dye (LIVE/DEAD Fixable Blue Dead Cell Stain Kit; Thermo Fisher Scientific). Intracellular FOXP3 staining was performed using the BD Biosciences Intracellular Staining Kit (BD Biosciences). The fluorescence of the cells was measured using a FACS ARIA II instrument (BD Biosciences, San Jose, CA, USA), and data analysis was performed using FlowJo software (Tree Star, San Carlos, CA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Quantitative Real-Time PCR of Mouse Uterine Samples\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from mouse uterine tissues using the RNeasy Mini Kit (Qiagen). cDNA was synthesized using the ReverTra Ace\u0026reg; qPCR RT Master Mix (Toyobo, Osaka, Japan). Quantitative Real-Time PCR was conducted using Fast SYBR Green Reaction Mix (Applied Biosystems) on a QuantStudio\u0026reg; 3 Real-Time PCR System (Applied Biosystems). The expression levels of target genes were normalized to GAPDH. The primers used were as follows: GAPDH (Forward: 5\u0026prime;-TCAACAGCAACTCCCACTCTT-3\u0026prime;, Reverse: 5\u0026prime;-ACCCTGTTGCTGTAGCCGTAT-3\u0026prime;), TNFα (Forward: 5\u0026prime;-GTAGCCCACGTCGTAGCAAAC-3\u0026prime;, Reverse: 5\u0026prime;-CTGGCACCACTAGTTGGTTGTC-3\u0026prime;), and Cox2 (Forward: 5\u0026prime;-TGCCCAGCACTTCACCCATCA-3\u0026prime;, Reverse: 5\u0026prime;-AGTCCACTCCATGGCCCAGTCC-3\u0026prime;). Data were analyzed using the ΔΔCt method and are presented as fold changes relative to the controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Human Gut Microbiota Analysis in Pregnant Women\u003c/h2\u003e \u003cp\u003eThis prospective observational study was conducted at Nagoya University Hospital between January 2023 and October 2024 to investigate the relationship between sPTB and gut microbiota in pregnant women. Singleton pregnant women between 25 and 31 weeks of gestation were enrolled, as gestational ages earlier than 25 weeks are often associated with severe intrauterine infection (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), which we aimed to avoid in order to focus on other etiologies of sPTB. The inclusion criteria required a singleton pregnancy, absence of clinical signs of infection or active labor or rupture of membranes, no history of antibiotic use during pregnancy, and willingness to cooperate with the study protocol. Women were excluded if they developed hypertensive disorders of pregnancy (HDP), gestational diabetes mellitus, placenta previa, or uterine malformations, or if they had major congenital fetal anomalies, maternal malignancy, or iatrogenic PTB. Although no formal power calculation was performed, we aimed to collect samples with an expected ratio of approximately 1:2 between sPTB and term birth groups, based on feasibility and clinical incidence, to allow for meaningful group comparisons.\u003c/p\u003e \u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, a total of 35 eligible pregnant women were initially recruited, including 10 healthy participants and 25 asymptomatic women with a shortened cervix. Following enrollment, pregnancy management was left to the discretion of each participant's attending physician. Two participants from the healthy group and one from the short cervix group were excluded due to the development of HDP, resulting in 32 women completing the study protocol.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFecal samples were collected using the Mykinso fecal collection kit\u0026reg; (Cykinso, Inc.), which contains guanidine thiocyanate solution. Participants collected the fecal samples themselves according to the manufacturer\u0026rsquo;s instructions. All samples were collected prior to any antibiotic administration and were promptly transported at room temperature to the Medical Laboratory of Cykinso, Inc. for processing. DNA extraction, 16S rRNA gene sequencing, and taxonomic classification (via the SILVA database) were performed using the QIIME2 pipeline. The sequencing and analysis were commercially conducted by Cykinso, Inc. All participants completed a lifestyle questionnaire covering smoking habits, alcohol consumption, exercise routines, constipation, sleep duration, and dietary patterns during pregnancy. The cohort consisted exclusively of Japanese women to minimize ethnic variability in gut microbiota. Maternal characteristics and dietary information are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. BMI and other lifestyle factors were comparable between groups.\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\u003eMaternal characteristics and dietary life of the study groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePreterm birth (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTerm birth (n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMaternal characteristics\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eGA at birth (weeks)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e32.7 (28.8 to 34.3)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e38.7 (37.9 to 39.4)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eMaternal age (years)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e28.0 (27.3 to 32.3)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e33.5 (29.0 to 37.8)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e0.025\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimiparity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (54.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.6 (20.3 to 22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.0 (20.2 to 23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInfertility treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRace/Nationality (Asian/Japanese)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmoking during pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlcohol consumption during pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegular exercise during pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleeping hours (hours)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.0 (6.3 to 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.0 (6.0 to 7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConstipation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (68.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGA at feces collection (weeks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.3 (27.6 to 29.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.5 (25.7 to 30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eCL at feces collection (mm)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e10.0 (6.0 to 10.8)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e18.5 (15.3 to 33.8)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e0.016\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHistory of preterm birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMaternal dietary life\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRoot vegetable (times per week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0 (2.0 to 5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.3 (2.0 to 5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFruit vegetable (times per week)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2.0 (2.0 to 5.0)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e4.6 (2.0 to 6.5)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e0.052\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeaf vegetable (times per week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (5.0 to 5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.6 (5.0 to 7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eFruits (times per week)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e2.0 (2.0 to 5.0)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e4.8 (2.0 to 6.5)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e0.040\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eMeat (times per week)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e5.0 (2.0 to 5.0)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e5.5 (5.0 to 7.0)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e0.026\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFish (times per week)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2.0 (2.0 to 2.0)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e4.0 (2.0 to 5.0)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e0.083\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEgg (times per week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5 (2.0 to 5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.0 (2.0 to 5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eDairy product (times per week)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e2.0 (0.0 to 2.0)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e4.3 (2.0 to 5.0)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e0.015\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFermented food (times per week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5 (2.0 to 7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.4 (2.0 to 5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoy product (times per week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0 (0.5 to 4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.8 (2.0 to 4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeaweed (times per week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0 (0.5 to 2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0 (2.0 to 2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMushroom (times per week)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2.0 (0.0 to 2.0)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2.8 (2.0 to 5.0)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e0.057\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSugar-sweetened beverage (times per week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (0.5 to 5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.1 (2.0 to 5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eData are presented as medians (interquartile ranges) or n (%). GA: gestational age; CL: cervical length.\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=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Statistics\u003c/h2\u003e \u003cp\u003eContinuous variables were evaluated using the nonparametric Mann\u0026ndash;Whitney U test for two-group comparisons. For comparisons involving three groups, one-way ANOVA was applied, followed by Tukey\u0026rsquo;s post-hoc test to identify specific group differences. The chi-squared test or Fisher\u0026rsquo;s exact test was used to compare categorical variables. The Kaplan\u0026ndash;Meier method was employed to evaluate the rate of continuing pregnancy in mice, with comparisons made using the log-rank test. The relationships between the combined \u003cem\u003eLachnospiraceae\u003c/em\u003e and \u003cem\u003eRuminococcaceae\u003c/em\u003e relative abundances and gestational age were analyzed using Spearman's rank correlation. The association between the combined \u003cem\u003eLachnospiraceae\u003c/em\u003e and \u003cem\u003eRuminococcaceae\u003c/em\u003e relative abundances and sPTB outcomes was evaluated using a receiver operating characteristic (ROC) curve. All the statistical analyses were performed using SPSS (version 29) (IBM SPSS Statistics for Windows, Armonk, NY, USA). The principal component analysis (PCA) was conducted using GraphPad Prism 9 (GraphPad Software, Inc., La Jolla, CA, USA). Statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12. Study approval\u003c/h2\u003e \u003cp\u003e The experiment using human fecal samples was approved by the Institutional Review Board and Ethics Committee of the Nagoya University Graduate School of Medicine (approval number 20210353), and written informed consent was obtained from all participants. All experimental studies involving animals were approved by the Nagoya University Graduate School of Medicine (approval number: M240021) and performed according to the guidelines and regulations therein described.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Effects of Antibiotic Treatment on Maternal Gut Microbiota Composition\u003c/h2\u003e \u003cp\u003eTo assess the effects of antibiotic treatment on maternal gut microbiota, we analyzed fecal samples from C57BL/6J pregnant mice treated with vancomycin or polymyxin B, along with control group, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. Microbiota composition at the class level showed substantial shifts in the vancomycin group compared to the control, whereas polymyxin B-treated mice retained a profile similar to the control (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The PCA highlights a distinct divergence in the microbial community structure of the vancomycin group from the other two groups, demonstrating the extent of dysbiosis induced by vancomycin treatment. Despite similar fecal DNA concentrations across groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), the vancomycin group exhibited a marked reduction in Operational Taxonomic Units (OTUs) at the genus level (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eE) and a lower Shannon diversity index (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eF), confirming the specific dysbiotic effect of vancomycin. Polymyxin B did not significantly alter OTUs richness or Shannon diversity compared to the control. Analysis at the family level revealed distinct shifts in bacterial taxa, with 11 taxa significantly reduced and 9 taxa increased in vancomycin-treated mice compared to both control and polymyxin B-treated groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e; only bacterial taxa with \u0026gt;\u0026thinsp;1% relative abundance are displayed). Furthermore, no significant changes were observed in gut microbiota composition before and after delivery across all groups, underscoring the stability of microbial communities throughout the perinatal period (Supplemental Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Vancomycin-Induced Dysbiosis Potentiates PTB Following Anti-CD3ε Administration\u003c/h2\u003e \u003cp\u003eNext, to examine the relationship between dysbiosis and sPTB and to investigate the underlying mechanisms, we developed the first in vivo model of Dysbiosis-induced PTB. In this model, immune activation was induced by anti-CD3ε antibody administration. The anti-CD3ε antibody, commonly administered at doses up to 10 \u0026micro;g per mouse to induce PTB (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), was used at a subclinical dose of 2 \u0026micro;g per mouse on E16 to assess whether vancomycin-induced dysbiosis potentiates the effects of anti-CD3ε (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The findings indicate that only the combination of vancomycin-induced dysbiosis with anti-CD3ε administration significantly increased PTB rates, with a notable percentage (43.3%) of pregnant mice delivering prematurely before E18.5 compared to the control and polymyxin B-treated groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Neither the control nor the polymyxin B groups exhibited any PTB occurrences, regardless of anti-CD3ε exposure. Furthermore, pregnant mice in the vancomycin group receiving anti-CD3ε exhibited significantly shorter gestational lengths and a marked decrease in fetal survival, with survival rates dropping to 50%. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). These results validate the efficacy of our model, establishing a Dysbiosis-induced PTB mouse model using vancomycin combined with a subclinical anti-CD3ε dose. Therefore, we refer to this as \u0026ldquo;Dysbiosis-induced PTB mouse model\u0026rdquo; henceforth. Although fetal weights were significantly lower in the polymyxin B and vancomycin groups compared to controls at E16 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eF, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eG), no significant differences were observed across groups in placental weight (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eH), pups count (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eI), maternal blood pressure, or heart rate (Supplemental Fig.\u0026nbsp;2),\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Diminished Abundance of Lachnospiraceae and Ruminococcaceae in Women with Spontaneous Preterm Birth\u003c/h2\u003e \u003cp\u003eWe examined the association between maternal gut microbiota and sPTB by analyzing fecal samples from 35 pregnant women. A total of 32 participants were included in the analysis, comprising 10 women who experienced sPTB and 22 who delivered at term (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes participant characteristics, with a significant difference in average gestational age at delivery between groups (32.7 weeks for the sPTB group vs. 38.7 weeks for the term group). The timing of fecal sample collection was comparable across both groups (median of 28.3 weeks for the sPTB group vs. 26.5 weeks for the term group). Participants in the sPTB group were notably younger at delivery and presented with shorter cervical lengths. Other variables, such as gravidity, body mass index, lifestyle habits (smoking, alcohol, exercise, sleep duration), constipation, and previous sPTB history, showed no significant differences.\u003c/p\u003e \u003cp\u003eMicrobial profiling at the class level revealed \u003cem\u003eBacteroidia\u003c/em\u003e as the predominant class in both groups; however, \u003cem\u003eClostridia\u003c/em\u003e were significantly reduced in the sPTB group, and \u003cem\u003eGammaproteobacteria\u003c/em\u003e were elevated, albeit at low proportions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). At the family level, we only display the distribution of bacterial taxa with a relative abundance of at \u0026gt;\u0026thinsp;1% in either group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Both \u003cem\u003eLachnospiraceae\u003c/em\u003e and \u003cem\u003eRuminococcaceae\u003c/em\u003e, which belong to the \u003cem\u003eClostridia\u003c/em\u003e, were significantly diminished in the sPTB group, while no families within \u003cem\u003eGammaproteobacteria\u003c/em\u003e exhibited notable differences between groups. Moreover, combined abundance of \u003cem\u003eLachnospiraceae\u003c/em\u003e and \u003cem\u003eRuminococcaceae\u003c/em\u003e was markedly reduced in the sPTB group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) and correlated positively with gestational age at delivery (r: 0.585, 95% CI: 0.290\u0026ndash;0.778, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). ROC analysis revealed that the combined abundance of these taxa serves as a predictive marker for sPTB, yielding an AUC of 0.823 (95% CI: 0.659\u0026ndash;0.86, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). To further assess whether the reduced abundance of \u003cem\u003eLachnospiraceae\u003c/em\u003e and \u003cem\u003eRuminococcaceae\u003c/em\u003e was independently associated with sPTB, we performed multivariate logistic regression analysis including CL at fecal sampling, GA at fecal collection, maternal age, and the combined relative abundance of \u003cem\u003eLachnospiraceae\u003c/em\u003e and \u003cem\u003eRuminococcaceae\u003c/em\u003e as explanatory variables. The analysis demonstrated that the microbial abundance remained a significant independent predictor of sPTB (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019), while CL showed a trend toward significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.077) (Supplemental Table\u0026nbsp;1). PCA illustrated a slight separation in microbial community composition between sPTB and term groups along PC2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). A dietary survey of all participants (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below) showed that sPTB group consumed vegetables, fruits, dairy, and mushrooms less frequently, suggesting potential dietary influence on gut microbiome composition.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Reduction of SCFAs and Inflammatory Changes in the Dysbiosis-Induced Preterm Birth Model\u003c/h2\u003e \u003cp\u003eTo clarify the role of specific gut bacteria in sPTB, we compared gut microbiota profiles in our Dysbiosis-induced PTB mouse model with those of fecal samples from pregnant women. \u003cem\u003eLachnospiraceae\u003c/em\u003e and \u003cem\u003eRuminococcaceae\u003c/em\u003e, representative SCFAs-producing bacteria (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) involved in fiber degradation, were significantly reduced in both PTB groups, highlighting their potential role in sPTB risk. Analysis of SCFAs levels in the Dysbiosis-induced PTB model showed significant reductions in acetate, propionate, butyrate, and valeric acid, with butyrate exhibiting the largest decline (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Butyrate plays a critical role in immune modulation, particularly by promoting immune tolerance through Treg expansion (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), which informed our analysis of Treg (CD3\u0026thinsp;+\u0026thinsp;CD4\u0026thinsp;+\u0026thinsp;CD25\u0026thinsp;+\u0026thinsp;FOXP3+) and CD8 T cell (CD3\u0026thinsp;+\u0026thinsp;CD8+) populations via flow cytometry (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). In the Dysbiosis-induced PTB model, Treg levels were reduced, and CD8 T cell proportions remained consistent across groups, indicating compromised immune tolerance (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eD, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eE, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Following anti-CD3ε treatment, elevated expression of inflammatory markers Cox2 and TNF-α was observed in uterine tissues (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eG, \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eH), suggesting increased local inflammation. Systemic inflammation is linked to sPTB (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Therefore, we measured systemic inflammatory markers in maternal serum. The Dysbiosis-induced PTB model displayed elevated levels of several cytokines, including IL-6, GM-CSF, IFN-γ, CCL3, and TNF-α in maternal serum (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Unexpectedly, no such increase in inflammatory mediators was observed in fetal serum (Supplemental Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Butyrate Supplementation Restores Immune Tolerance and Reduces Dysbiosis-Induced Preterm Birth\u003c/h2\u003e \u003cp\u003eIn the Dysbiosis-induced PTB mouse model, we examined the preventive potential of butyrate supplementation by comparing a standard AIN93G diet with 10% HAMS to an AIN93G diet with 10% B-HAMS. Butyrate-enriched diets significantly reduced PTB rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e8\u003c/span\u003eC), extended gestation (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e8\u003c/span\u003eD and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e8\u003c/span\u003eE), and increased live pup births (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e8\u003c/span\u003eF). Although CD8 T cell proportions remained consistent across groups, butyrate supplementation significantly elevated Treg populations (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e8\u003c/span\u003eG, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e8\u003c/span\u003eH, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e8\u003c/span\u003eI), indicating restored immune tolerance. These findings suggest that enhanced butyrate levels in the colon may facilitate immune tolerance, effectively mitigating dysbiosis-induced inflammatory responses. Supporting this, Treg cell implantation in the Dysbiosis-induced PTB model yielded similar improvements, reducing PTB rates (Supplemental Fig.\u0026nbsp;4B), extending pregnancy (Supplemental Fig.\u0026nbsp;4C), and trending toward higher live pup birth rates (Supplemental Fig.\u0026nbsp;4D). These findings indicated butyrate\u0026rsquo;s potential role in promoting immune tolerance and reducing sPTB risk within the dysbiosis context.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study indicates the potential influence of maternal gut microbiota in maintaining pregnancy and modulating the risk of sPTB. By establishing a Dysbiosis-induced PTB model in mice through vancomycin administration, we provide a foundational tool for investigating how specific bacterial taxa influence pregnancy outcomes. This model revealed reductions in \u003cem\u003eLachnospiraceae\u003c/em\u003e and \u003cem\u003eRuminococcaceae\u003c/em\u003e, consistent with fecal analyses from pregnant women, suggesting that these taxa may serve as cross-species biomarkers of healthy gestation.\u003c/p\u003e \u003cp\u003eAlthough gut dysbiosis has previously been associated with pregnancy complications such as gestational diabetes mellitus and pre-eclampsia (\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), direct evidence connecting dysbiosis to sPTB remains limited. The use of vancomycin, a non-absorbable antibiotic, was instrumental in selectively altering gut microbiota without systemic absorption, resulting in a dysbiotic state that enhanced both systemic and local inflammation\u0026mdash;important contributors to sPTB. These microbial shifts mirrored those observed in human sPTB cases and underscore the value of this model for elucidating microbiota-related mechanisms in pregnancy. Our findings that \u003cem\u003eLachnospiraceae\u003c/em\u003e and \u003cem\u003eRuminococcaceae\u003c/em\u003e were reduced in the sPTB cohort aligns with previous findings (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), although some inconsistencies across studies, likely due to variations in methodologies and populations (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Notably, \u003cem\u003eLachnospiraceae\u003c/em\u003e abundance declines physiologically near term, correlating with decreased butyrate levels (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The accelerated reduction observed here may reflect early perturbations in this protective microbial community, potentially impairing immune regulation and increasing susceptibility to sPTB.\u003c/p\u003e \u003cp\u003eWhile butyrate has been shown to exert a wide range of pharmacological activities\u0026mdash;including anti-inflammatory, antioxidant, and metabolic effects\u0026mdash;we focused our investigation on its immunomodulatory properties, particularly its role in promoting Tregs induction. Mechanistically, butyrate promotes Tregs differentiation through inhibition of histone deacetylases (HDACs) and activation of G protein-coupled receptors such as GPR43 and GPR41 (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). This immunoregulatory function is especially relevant during pregnancy, as Tregs play a pivotal role in maintaining maternal\u0026ndash;fetal immune tolerance and preventing fetal rejection (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). \u003cem\u003eLachnospiraceae\u003c/em\u003e and \u003cem\u003eRuminococcaceae\u003c/em\u003e, recognized as representative butyrate-producing bacteria (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), may therefore contribute to pregnancy maintenance by supporting Tregs homeostasis. We hypothesize that a reduction in these taxa leads to decreased butyrate production, weakening immune tolerance and promoting a pro-inflammatory state that raises sPTB risk. This hypothesis is supported by our findings in the dysbiosis-induced PTB mouse model, in which reduced butyrate levels coincided with diminished Tregs populations. Furthermore, butyrate supplementation restored Tregs numbers and successfully prevented sPTB, while adoptive transfer of Tregs alone also rescued the PTB phenotype, underscoring the central role of this immune axis. In addition, we observed elevated levels of pro-inflammatory cytokines such as TNF-α and IL-6 in maternal serum, reflecting a systemic inflammatory state. This cytokine profile aligns with clinical observations in women experiencing preterm labor (\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Since maternal inflammation is known to disrupt immune tolerance at the maternal\u0026ndash;fetal interface (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), it is plausible that elevated circulating cytokines contribute to a cascade of immune activation leading to premature labor. Collectively, these findings suggest that gut microbiota composition\u0026mdash;through its impact on butyrate production, Tregs regulation, and systemic inflammation\u0026mdash;can modulate immune homeostasis and significantly influence pregnancy outcomes.\u003c/p\u003e \u003cp\u003eTo achieve targeted delivery of butyrate to the colon, we employed a specialized feed formulation resistant to small-intestine digestion. This approach effectively promoted Tregs expansion, improved immune tolerance, reduced inflammatory responses, extended gestation, and increased live birth rates in our dysbiosis model. To our knowledge, this is the first study to demonstrate that targeted butyrate supplementation under dysbiosis conditions can mitigate inflammation-induced PTB by restoring Tregs population and immune balance during pregnancy. Our findings thereby underscore butyrate\u0026rsquo;s potential as a therapeutic agent for pregnancy maintenance, offering a multifaceted strategy to bolster immune tolerance and avert inflammation-related pregnancy complications. Additionally, our results build on prior research showing butyrate\u0026rsquo;s therapeutic potential in pregnancy complications, which reported benefits in preeclampsia models through sodium butyrate administration, such as reductions in blood pressure and inflammation markers, IL-1β and IL-6, alongside enhanced gut microbiota diversity and intestinal barrier function (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Unlike these studies, however, our approach utilized a butyrate-enriched feed that ensures delivery to the colon, offering a novel method that may more effectively support immune balance and pregnancy maintenance.\u003c/p\u003e \u003cp\u003eThe dysbiosis model treated with anti-CD3ε antibody exhibited both systemic and local inflammation, as evidenced by elevated levels of TNF-α and COX2 in uterine tissues and increased inflammatory cytokines in maternal serum. The specific increases in TNF-α and COX2 underscore their roles in promoting uterine contractions and cervical ripening, both critical for initiating labor (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Our findings imply that a reduction in butyrate-producing bacteria alone does not directly trigger PTB but instead primes the maternal immune system, rendering it more susceptible to inflammatory responses. This inflammation-prone state, coupled with even a subclinical inflammatory trigger like anti-CD3ε in this study, readily induced systemic and localized uterine inflammation, ultimately leading to PTB. This mechanism suggests that dysbiosis may contribute to sPTB by diminishing immune tolerance, thereby creating an environment conducive to inflammatory activation and increasing sPTB risk.\u003c/p\u003e \u003cp\u003eThe clinical implications of these findings are notable. Dietary habits are known to influence gut microbiota, and our cohort analysis revealed considerable differences in diet between sPTB and term groups. Previous research has reported links between dietary intake and sPTB risk. Ito et al. found that higher consumption of fermented foods, including yogurt and fermented soybeans, before pregnancy was associated with a lower risk of early sPTB (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). In our study, sPTB cases showed lower intake of vegetables, fruits, and dairy products\u0026mdash;all of which are known to promote beneficial gut bacteria, including \u003cem\u003eLachnospiraceae\u003c/em\u003e and \u003cem\u003eRuminococcaceae\u003c/em\u003e (\u003cspan additionalcitationids=\"CR42 CR43\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). These findings highlight the value of dietary interventions that foster a microbiota composition conducive to pregnancy maintenance. Given diet\u0026rsquo;s dynamic influence on gut health, dietary modifications before conception or early in pregnancy could help support a balanced microbiome, potentially lowering sPTB risk.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, our use of 16S rRNA sequencing limits species-level resolution, constraining our ability to analyze specific microbial contributors to sPTB. More advanced metagenomic or metatranscriptomic analyses could provide deeper insights into species-specific microbial interactions and metabolic pathways relevant to pregnancy. Although direct measurement of SCFAs in human fecal samples could further strengthen the link between microbial composition and immune tolerance in pregnancy, such analysis was not performed in the present study due to technical and logistical constraints. Accurate SCFA quantification requires immediate freezing of stool samples following defecation, as emphasized by Ueyama et al., who demonstrated that this step is critical for preserving SCFA stability (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). While feasible under controlled laboratory conditions, such handling precision is difficult to achieve in clinical settings. Given these considerations, we selected SCFA-producing bacterial taxa as a practical and biologically relevant surrogate marker. Future studies incorporating direct SCFA measurements under optimized clinical protocols will be important to further validate these associations. Additionally, our mouse model does not replicate the dynamic shifts in gut microbiota observed in humans throughout pregnancy, particularly the substantial changes from the first to third trimesters (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). While our cohort consisted exclusively of Japanese women, thereby reducing inter-individual variation due to ethnic or dietary differences, this homogeneity may limit the generalizability of our findings to more diverse populations. It is well established that gut microbiota composition varies across ethnic groups, largely due to differences in dietary patterns, lifestyle, and environmental exposures (\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Therefore, future studies involving multi-ethnic cohorts are warranted to validate the broader applicability of our findings and to elucidate potential ethnic-specific microbial signatures related to pregnancy maintenance and PTB risk. Lastly, the differences in gut microbiota composition and dietary response between mice and humans warrant caution when applying these findings to human populations.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eOur study advances the understanding of dysbiosis\u0026rsquo;s importance on immune tolerance and inflammation during pregnancy, identifying butyrate as a crucial modulator of these processes. Future research should further investigate the therapeutic potential of SCFAs or dietary strategies supporting pregnancy outcomes through gut microbial balance. By establishing a clear link between microbiota composition and sPTB risk, our findings indicate the significance of gut health in maternal-fetal medicine and paves the way for innovative preventive strategies targeting the gut microbiome.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAIN93G (American Institute of Nutrition 1993 Growth Diet), ANOVA (Analysis of Variance), AUC (Area Under the Curve), B-HAMS (Butyrate-Enriched High Amylose Maize Starch), BP (Blood Pressure), CCL (C-C Motif Chemokine Ligand), CD (Cluster of Differentiation), CXCL1 (C-X-C Motif Chemokine Ligand 1), DMSO (Dimethyl Sulfoxide), DNA (Deoxyribonucleic Acid), E (Embryonic Day), GC (Gas Chromatography), GM-CSF (Granulocyte-Macrophage Colony-Stimulating Factor), GPR (G Protein-Coupled Receptor), HAMS (High Amylose Maize Starch), HDAC (Histone Deacetylase), IFN-\u0026gamma; (Interferon-Gamma), IL (Interleukin), KSPD (Kyoto Scale of Psychological Development), OTU (Operational Taxonomic Unit), PCA (Principal Component Analysis), PBS (Phosphate-Buffered Saline), PTB (Preterm Birth), qPCR (Quantitative Polymerase Chain Reaction), rRNA (Ribosomal RNA), ROC (Receiver Operating Characteristic), SCFAs (Short-Chain Fatty Acids), sPTB (Spontaneous Preterm Birth), SPSS (Statistical Package for the Social Sciences), Tregs (Regulatory T Cells), TNF-\u0026alpha; (Tumor Necrosis Factor-Alpha).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to Junichi Takahara from the Research and Development Department at Sanwa Starch Co., Ltd., for his invaluable support and expertise in providing the B-HAMS and untreated HAMS used in our study. We are also deeply grateful to Dr. Chieko Aoki for her valuable insights and guidance throughout the research process. Additionally, we would like to thank Cykinso, Inc. for 16S rRNA gene sequencing, and Editage for the English language editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAzusa Uchida: Sample Collection, Methodology, Investigation, Data curation, Writing-original draft. Kenji Imai: Conceptualization, Methodology, Investigation, Data curation, Funding acquisition, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. Rika Miki, and Tomonari Hamaguchi: Methodology, Investigation, Data curation. Hiroshi Nishiwaki, Mikako Ito, Jun Ueyama, and Satomi Hattori: Methodology, Data curation. Sho Tano, Kazuya Fuma, Seiko Matsuo, Takafumi Ushida, Kinji Ohno, and Hiroaki Kajiyama: Methodology, Investigation, Data curation. Tomomi Kotani: Conceptualization, Funding acquisition, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported in part by JSPS KAKENHI Grant No.21K16812, the Hori Sciences And Arts Foundation, and the Aichi Health Promotion Foundation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe BioSample metadata for bacterial 16S rRNA gene information is available in the DDBJ BioSample database under accession numbers DRR623374-DRR623456. For other data, restrictions imposed by the Ethics Committee of Nagoya University prevent the authors from making the minimal dataset publicly available. Researchers who wish to access the data must meet the criteria for accessing confidential data as set by Nagoya University. For inquiries, please contact [email protected].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experiment using human fecal samples was approved by the Institutional Review Board and Ethics Committee of the Nagoya University Graduate School of Medicine (approval number 20210353), and written informed consent was obtained from all participants. All experimental studies involving animals were approved by the Nagoya University Graduate School of Medicine (approval number: M240021) and performed according to the guidelines and regulations therein described.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors acknowledge and consent to the paper\u0026rsquo;s content and are included as co-authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOtsuka N, Imai K, Tano S, Matsuo S, Ushida T, Nomoto M, et al. Possible Efficacy of Vaginal Progesterone on Asymptomatic Women with a Short Cervix after 24 Weeks of Gestation: A Historical Cohort Study in Japan. 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Cell Genom. 2022;2(12):100219.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Dysbiosis, Preterm birth, Short-chain fatty acid, Inflammation, Treg","lastPublishedDoi":"10.21203/rs.3.rs-5770845/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5770845/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePreterm birth (PTB) is a major contributor to neonatal morbidity, mortality, and long-term health complications. Despite advances in perinatal care, PTB rates remain high, and its multifactorial etiology is not fully understood. Increasing evidence suggests that maternal gut microbiota plays a critical role in pregnancy maintenance, potentially through modulation of immune responses. However, the underlying causal mechanisms remain unclear. We hypothesized that dysbiosis disrupts immune tolerance and promotes PTB, and that butyrate (short-chain fatty acid produced by specific gut bacteria) may counteract this effect by enhancing regulatory T cell (Treg)-mediated immune regulation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe established a dysbiosis-induced PTB mouse model using vancomycin treatment combined with subclinical immune activation via anti-CD3ε antibody. Pregnant mice were fed either a standard or butyrate-enriched diet. Outcomes included gestational length, PTB incidence, live pup rates, and Treg cell levels assessed by flow cytometry. Parallelly, 16S rRNA gene sequencing was performed on fecal samples from 32 pregnant women to compare gut microbial composition between spontaneous PTB and term birth groups. Multivariate logistic regression and correlation analyses were conducted to assess associations with gestational outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVancomycin-induced dysbiosis in mice significantly reduced Treg cell populations and increased PTB rates (43.3% in dysbiosis vs. 0% in controls; p \u0026lt; 0.05), while butyrate supplementation reduced PTB incidence (p = 0.03), prolonged gestation (p = 0.01), and restored Treg counts (p \u0026lt; 0.001). In human samples, significant reductions in Lachnospiraceae and Ruminococcaceae, representative butyrate-producing bacteria, were seen in PTB cases. Their combined abundance was independently associated with sPTB risk (p = 0.019) and positively correlated with gestational age (r = 0.59, p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur findings demonstrate that maternal dysbiosis increases PTB risk via impaired immune tolerance, and that butyrate supplementation effectively reverses this effect in vivo. Human data support the translational relevance of butyrate-producing microbiota in pregnancy maintenance. These results highlight butyrate as a promising target for dietary interventions aimed at reducing PTB incidence by restoring immune homeostasis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e","manuscriptTitle":"Butyrate-Producing Bacteria in Pregnancy Maintenance: Mitigating Dysbiosis-Induced Preterm Birth","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-28 11:15:08","doi":"10.21203/rs.3.rs-5770845/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accept","date":"2025-04-24T23:20:55+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-04-20T16:43:10+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-20T16:41:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-18T11:18:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2025-04-18T01:41:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1464b14f-a358-49ba-b999-66778f24a746","owner":[],"postedDate":"April 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-19T16:05:24+00:00","versionOfRecord":{"articleIdentity":"rs-5770845","link":"https://doi.org/10.1186/s12967-025-06534-y","journal":{"identity":"journal-of-translational-medicine","isVorOnly":false,"title":"Journal of Translational Medicine"},"publishedOn":"2025-05-12 15:57:13","publishedOnDateReadable":"May 12th, 2025"},"versionCreatedAt":"2025-04-28 11:15:08","video":"","vorDoi":"10.1186/s12967-025-06534-y","vorDoiUrl":"https://doi.org/10.1186/s12967-025-06534-y","workflowStages":[]},"version":"v1","identity":"rs-5770845","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5770845","identity":"rs-5770845","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}