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. 2026 May 26;12:161. doi: 10.1038/s41522-026-01009-4

Integrative analysis of oral microbiota and its gut transmission with host immunity in term pregnancy

Ting Huang 1,2, Xiaomei Tang 2,3, Huijuan Luo 2,3, Han Bao 2,3, Haishan Xie 2,3, Lulu Meng 2,3, Li Zhang 1,✉, Guocheng Liu 1,✉, Xiaomin Xiao 2,3,✉
PMCID: PMC13487304  PMID: 42191697

Abstract

This study aims to evaluate associations between the oral microbiota, metabolites, potential oral-gut transmitted microbes, and systemic immune alterations in term pregnancy. Oral and gut microbiomes, salivary metabolome, peripheral immune cells, and cytokines were profiled in term pregnant (n = 25) and non-pregnant women (n = 25). Species enriched in pregnancy, including Streptococcus anginosus and Prevotella denticola, correlated positively with the NK cell ratio, while Prevotella histicola and Prevotella micans correlated positively with CD56brightCD16− NK cells. Mediation analysis indicated that fatty acids in saliva may not mediate the impact of oral microbiota on peripheral immune changes during pregnancy. Several potential oral–gut transmitted microbes with a higher maternal transfer ratio were positively associated with CD56brightCD16− NK cells and the NK cell ratio, but negatively with Th1 cells and the Th1/Th2 ratio. These findings provide preliminary evidence that pregnancy-related immune shifts may be linked to oral microbiota alterations and microbial transmission.

Subject terms: Clinical microbiology, Microbiome

Introduction

To ensure successful embryo implantation and healthy fetal development, the maternal immune system undergoes a series of dynamic adjustments throughout pregnancy1. Disruptions in immune function can increase the risk of adverse outcomes, such as miscarriage, preterm birth, and preeclampsia2, highlighting the critical role of immune regulation in maintaining a healthy pregnancy. However, the specific mechanisms underlying immune changes during pregnancy remain incompletely understood. A pivotal study comparing germ-free pregnancy mice with conventional pregnant mice found that the number of viable offspring was significantly lower in the absence of microbiota3, underscoring the essential role of the commensal microbiota in shaping maternal immune responses. Among microbial communities, the gut microbiota is particularly recognized for its profound influence on host metabolism and immune modulation during pregnancy4,5. During gestation, the gut microbiota undergoes dynamic remodeling, characterized by decreased alpha diversity and altered microbial composition in the third trimester6. Growing evidence has linked gut dysbiosis to various pregnancy complications, including gestational diabetes, preeclampsia, and intrahepatic cholestasis of pregnancy7–10. These findings highlight the critical involvement of the gut microbiota in maternal adaptation and pregnancy outcomes. In contrast, the contribution of the oral microbiota, the body’s second-largest microbial community, to immune regulation during healthy pregnancy remains largely unexplored.

As early as the 1990s, researchers identified an association between periodontal infections and preterm low birth weight11. Subsequent studies revealed that periodontal disease increases the risk of adverse pregnancy outcomes, such as preterm birth, preeclampsia, and low birth weight12. Improving periodontal health either during or before pregnancy has been shown to effectively reduce these risks13. Early research detected periodontal pathogens like Porphyromonas gingivalis and Fusobacterium nucleatum in the placenta and amniotic fluid of women who experienced preterm birth, suggesting that oral microbes might invade the placenta, trigger localized or systemic inflammatory responses, and increase the risk of preterm labor14,15. A recent study found that the oral microbiota undergoes a pathogenic shift during pregnancy, which reverts to a healthy state postpartum16. The potential impact of these physiological changes in the oral microbiota on immune regulation during pregnancy warrants further investigation.

The oral cavity and the gut represent the two largest microbial ecosystems within the human body, each harboring a distinct microbial community shaped by their unique environmental and functional niches. Traditionally, it was believed that the translocation of oral microbes to the gut was a rare event, impeded by gastric juice, bile acids, and various intestinal defense mechanisms. However, this view has been challenged by emerging evidence elucidating potential mechanistic pathways through which oral microbes may reach and colonize the gut. First, oral bacteria can directly reach the gut via the gastrointestinal tract; a gavage study has demonstrated that salivary microbes from periodontitis patients can persist in the murine gut for up to 24 h, leading to intestinal inflammation and barrier disruption17. Second, oral pathogens can enter the bloodstream through routine activities such as brushing or chewing, particularly in the presence of gingival inflammation18–20, providing a hematogenous route to distal sites, including the gut. Third, certain oral bacteria can exploit host immune cells, such as dendritic cells or macrophages, as vehicles for dissemination from the oral mucosa to systemic sites20. These mechanistic insights support the concept that the oral cavity may serve as an endogenous and continuous reservoir for the gut microbiota21. Indeed, the ectopic colonization of oral microbes in the gut has been observed in various diseases, including hypertension22, rheumatoid arthritis23, and inflammatory bowel disease24. Even in healthy adults, this phenomenon may be more common than previously thought; Schmidt et al.25 estimated that approximately one-third of identifiable oral microbes could colonize the gut, accounting for at least 2% of the identifiable fecal microbial community. While these findings derive primarily from non-pregnant populations, pregnancy is characterized by physiological changes that could profoundly influence this oral-gut axis, including reduced gastric acidity, hormonal shifts that alter mucosal immunity, and increased intestinal permeability. Therefore, the oral-gut microbial axis may be particularly dynamic during pregnancy, with the potential to modulate the gut microbial community and, consequently, systemic immune responses. Despite this, research remains limited regarding whether oral-gut microbial transmission is enhanced in pregnancy, which specific oral microbes are most frequently transferred, and whether this process contributes to the modulation of maternal systemic immunity. Addressing these questions is important, as the unique physiological changes of pregnancy may promote oral-to-gut microbial translocation, with potential implications for maternal immune regulation and pregnancy outcomes. Moreover, elucidating this axis in healthy pregnancy will establish a foundational basis for future investigations into its potential role in the pathogenesis of pregnancy complications.

In this study, we set out to explore the potential pathways through which the oral microbiota may influence immune function in pregnant women. First, we collected saliva, fecal, and peripheral blood samples from healthy pregnant women and healthy non-pregnant volunteers. We performed 16S rRNA gene sequencing on the saliva and fecal samples to analyze changes in the oral and gut microbiota, alongside assessing peripheral immune response in pregnant women. Then, we applied correlation analysis and mediation analysis to assess the associations between the oral microbiota, salivary fatty acids, peripheral immune cells, and cytokines. Additionally, we evaluated potentially translocated oral-gut microbes in the subjects and explored their association with peripheral immune changes.

Results

Clinical characteristics of participants

Between January and October 2023, a total of 25 healthy pregnant women (Healthy pregnancy group, HP group) and 25 healthy non-pregnant women (Healthy non-pregnant women group, HW group) were recruited to this study (Fig. 1). Details of the inclusion and exclusion criteria are provided in the Methods section and Supplementary Data Table 1. The prevalence of gingival bleeding was significantly higher in the HP group than in the HW group (p = 0.018), while there were no significant differences between the groups in other oral conditions, oral hygiene habits, age, height, weight, and BMI (Supplementary Data Table 1).

Fig. 1. Study flowchart of integrative microbiomics, metabolomics, and immune analyses in this study.

Fig. 1

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HP group Healthy pregnancy group, HW group Healthy non-pregnant women group. Elements in the figure were sourced from Icons8 (https://icons8.com), used under the Free Account license.

Comparison of immune status

Pregnant and non-pregnant women exhibit distinct immune profiles. As shown in Fig. 2, the proportion of Th1 cells was significantly reduced in the HP group (p = 0.037), accompanied by a significantly elevated CD56brightCD16− NK cell population (p = 0.018) and a reduced CD56dimCD16+ NK cell population (p = 0.034). The CD56brightCD16− NK/CD56dimCD16+ NK ratio in the HP group was also significantly higher than in the HW group (p = 0.008). CD56dimCD16+ NK cells exhibit strong cytotoxic functionality, whereas CD56brightCD16− NK cells have relatively weaker cytotoxicity26; a higher CD56brightCD16− NK/CD56dimCD16+ NK ratio may indicate a reduction in maternal cytotoxic attack on the fetus during pregnancy. Among serum cytokines, only the interleukin-6 (IL-6) levels were significantly higher in the HP group compared to the HW group (p = 0.009; Fig. 3).

Fig. 2. Comparison of peripheral immune cell levels between the HW group and the HP group.

Fig. 2

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HP group Healthy pregnancy group, HW group Healthy non-pregnant women group. *p < 0.05, **p < 0.01.

Fig. 3. Comparison of peripheral cytokine levels between the HW group and the HP group.

Fig. 3

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HP group Healthy pregnancy group, HW group Healthy non-pregnant women group. **p < 0.01.

Oral microbiome profile comparison

Previous research has demonstrated that the oral microbiota profiles of pregnant women differ from those of non-pregnant women16. Accordingly, the present study initially compared microbial composition at different taxonomic levels and microbial diversity between the HP and HW groups. At the phylum level, the dominant bacterial phyla in both the pregnant women and non-pregnant women were Bacillota, Pseudomonadota, Bacteroidota, Fusobacteriota, and Actinomycetota (Fig. 4A). The relative abundance of Actinomycetota and Campylobacterota was significantly higher in the HP group compared with the HW group (p = 0.047 and 0.034, respectively; Fig. 4A). At the genus level, Streptococcus, Listeria, Neisseria, and Haemophilus were the dominant genera in both the HW and HP groups (Fig. 4B). There were no significant differences in the Chao1, Observed species, Shannon, and Simpson indices between the HP and HW groups (p = 0.100, 0.100, 0.095, and 0.213, respectively; Fig. 4C–F). Principal component analysis (PCA) and principal coordinates analysis (PCoA) were conducted to compare the bacterial community structure between pregnant and non-pregnant women (Fig. 4G–H). Both Bray-Curtis distance-based PCA and Weighted UniFrac distance-based PCoA indicated significant differences in microbial community structure between pregnant and non-pregnant women (p = 0.025 and 0.036, respectively). In summary, compared with non-pregnant women, pregnant women showed no significant differences in alpha diversity of the oral microbiota, but the microbial community structure was significantly altered.

Fig. 4. Comparison of taxonomy composition and diversity of oral microbiota between pregnant and non-pregnant women.

Fig. 4

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A Comparison of oral microbiota between two groups at the phylum level. B Comparison of oral microbiota between two groups at the genus level. C–F Comparison of alpha diversity between two groups. G PCA analysis based on Bray-Curtis distance. H PCoA analysis based on Weighted Unifrac distance. HP group Healthy pregnancy group, HW group Healthy non-pregnant women group.

Since periodontal pathogens are typically analyzed at the species level, this study further focused on the differential microbial species between the HP and HW groups using STAMP with White’s non-parametric t-test. A total of 35 differential species were identified. Among these, 30 species, including Porphyromonas gingivalis, Prevotella denticola, Streptococcus anginosus, Prevotella histicola, Actinomyces graevenitzii, Scardovia wiggsiae, and Schaalia odontolytica, showed increased relative abundance in the HP group (Fig. 5 and Supplementary Data Table 2). In contrast, the relative abundances of Porphyromonas pasteri and Porphyromonas catoniae were reduced in the HP group (Supplementary Data Table 2). Porphyromonas pasteri is a species closely associated with oral health, frequently detected in the oral cavities of healthy individuals27–29. Porphyromonas catoniae, an early colonizer of the oral cavity that establishes itself on teeth with the eruption of the first tooth, may serve as a potential biomarker for evaluating oral health status30. These observations suggest that the abundance of commensal or beneficial oral microbes may decline during pregnancy.

Fig. 5. Significantly altered species of the oral microbiota community in the pregnant women compared to the non-pregnant women.

Fig. 5

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The figure shows 15 differential species. Detailed information on differential oral bacteria can be found in Supplementary Data Table 2. HP group Healthy pregnancy group, HW group Healthy non-pregnant women group.

Association between oral microbiome and immune status

In the analysis above, we observed alterations in the relative abundance of certain oral microbes of pregnant women compared to non-pregnant women. To investigate how these changes are related to peripheral immune status, a Spearman correlation analysis was performed between the differential species and peripheral immune parameters. As illustrated in Fig. 6, Th1 cells showed a positive correlation with Solobacterium moorei and Haemophilus parainfluenzae, which were enriched in the HW group. Prevotella denticola, Streptococcus anginosus, Anaeroglobus geminatus, and Actinomyces gerencseriae were positively correlated with CD56brightCD16− NK/CD56dimCD16+ NK cells (Fig. 6). Prevotella histicola, Prevotella micans, and Megasphaera micronuciformis were positively correlated with CD56brightCD16− NK cells (Fig. 6). These bacteria showed significantly higher abundance in the HP group. These findings suggest that changes in peripheral blood immune status during pregnancy may be associated with certain specific oral bacteria.

Fig. 6. Correlation network diagram depicting associations between differential oral microbiota and differential peripheral blood immune parameters.

Fig. 6

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Circular nodes represent oral bacteria, while diamond-shaped nodes denote immune parameters. Red circular nodes represent bacteria with significantly higher abundance in the HP group, while green circular nodes represent bacteria with significantly higher abundance in the HW group. Correlations were calculated using Spearman’s rank correlation, and P-values were adjusted for multiple comparisons using the Benjamini–Hochberg false discovery rate (FDR) method. Only correlations with q < 0.05 are shown. HP group Healthy pregnancy group, HW group Healthy non-pregnant women group.

Association between oral microbiota, metabolites, and peripheral blood immune profile

It is reported that the microbiome accounts for only about 10% of the variation in cytokines levels within the human31. Some metabolites are capable of modulating cytokine production or exhibiting cytokine-like effects32. Emerging evidence suggests that the oral microbiota, which participates in the initial breakdown of ingested food, can produce a wide range of metabolites33. Therefore, this study further evaluated the estimated metabolic functions of the oral microbiota in two groups and investigated whether metabolites mediate the influence of oral microbiota on immune function. The PICRUSt functional prediction analysis suggested differences in the predicted metabolic functional profiles between the HW and HP groups (p = 0.002) (Supplementary Fig. 1A). The volcano plot of differential gene expression revealed a significant upregulation of K11533 (fatty acid synthase, bacteria type [EC:2.3.1.-]) and a significant downregulation of K13770 (fatty acid metabolism regulator protein) in the HP group (Supplementary Fig. 1B).

Observed differences were found in the expression of fatty acid-related genes predicted through microbiome functional analysis between the HW and HP groups. Based on these differences, this study measured short-chain and medium-chain fatty acids in the participants’ saliva. However, we found no statistically significant difference in salivary fatty acids between the two groups (p > 0.05; Supplementary Data Table 3). We further conducted a mediation analysis to explore whether saliva fatty acids might mediate the effect of the oral microbiota on host immunity. This approach established 11 mediation links, among which those related to IL-1β were the most numerous (Fig. 7A). For instance, acetic acid mediated the inhibitory effects of Clostridiales bacterium oral taxon 075 and Eubacterium sp. oral clone GI038 on IL-1β, respectively (Fig. 7B, C). Additionally, Leptotrichia wadei promoted an increase in IL-1β by inhibiting acetate levels (Fig. 7D). No evidence was found to support the role of fatty acids as mediators in the effects of oral bacteria on the differential immune parameters observed between the HW and HP groups. These findings suggest that the influence of oral bacteria on changes in maternal peripheral immune status is unlikely to be mediated through salivary fatty acids.

Fig. 7. Mediation pathways among oral microbiota, medium-chain and short-chain fatty acids in saliva, and peripheral blood immune markers.

Fig. 7

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A Sankey diagram depicting mediation effects among oral microbiota, medium-chain and short-chain fatty acids in saliva, and peripheral blood immune markers. Columns from left to right represent oral microbiota, medium-chain and short-chain fatty acids in saliva, and peripheral blood immune markers. Flow line colors represent different fatty acids, and the width of the flows indicates the magnitude of the mediation effect. B Acetate mediates the effect of Clostridiales bacterium oral taxon 075 on IL-1β (95%CI: [−0.283, −0.003]). The percentage in the middle of the ternary plot denotes the proportion of the total effect mediated. C Acetate mediates the effect of Eubacterium sp. Oral clone GI038 on IL-1β (95%CI: [−0.204, −0.020]). D Acetate mediates the effect of Leptotrichia wadei on IL-1β (95%CI: [0.006, 0.155]). AA acetic acid, IBA isobutyric acid, IVA isovaleric acid, 4-MVA 4-methylvaleric acid, HPA hexanoic acid.

Potential oral-gut microbial transmission in full-term pregnancy

To investigate whether oral-gut microbial transmission occurs in pregnant women, we calculated the Bray–Curtis distance between paired saliva and fecal samples for each subject in the HW and HP groups. The Bray–Curtis distance between the oral and gut microbiota of all subjects was less than 1, indicating that each individual had at least one amplicon sequence variant (ASV) shared between saliva and fecal samples (Fig. 8A). The Bray–Curtis distance between paired saliva and fecal samples in the HP group was significantly lower than that in the HW group (p = 0.018; Fig. 8B), suggesting a greater similarity between the oral and gut microbiota in the pregnant women. Fast expectation-maximization microbial source tracking (FEAST) analysis further suggested that oral-gut microbial transmission occurred in both full-term pregnant and non-pregnant women, with a significantly higher transfer ratio in pregnant women than in non-pregnant women (2.9% vs. 1.5%, p = 0.029; Fig. 8C).

Fig. 8. Increased oral-gut bacterial transmission in pregnant women.

Fig. 8

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A Histogram of Bray-Curtis distances between paired saliva and fecal samples for each participant. B Comparison of Bray–Curtis distances between salivary and fecal paired samples in the HW group and the HP group. C FEAST analysis calculates the proportion of oral microbiota origins in gut microbiota between the HW and HP groups. HP group Healthy pregnancy group, HW group Healthy non-pregnant women group. *p < 0.05.

To identify potential oral–gut microbial transmission events, we employed the method described by Kageyama et al.34. In this approach, ASVs detected in both salivary and gut microbiota were defined as shared ASVs for each subject, and transfer ratios were calculated as the proportion of individuals harboring identical ASVs across both sites for each salivary ASV. This ASV-level resolution enabled reliable detection of identical variants that are potentially transmitted between oral and gut niches. In the HW group, 173 oral-gut shared ASVs were identified, with a median transfer ratio of 4.0% (range: 4.0%–84.0%; Supplementary Fig. 2, Supplementary Data Table 4). In the HP group, 202 oral-gut shared ASVs were identified, with a median transfer ratio of 4.2% (range: 4.2%–91.7%; Supplementary Fig. 2 and Supplementary Data Table 4). Of these, 89 ASVs were common to both groups, while 84 were unique to the HW group and 113 to the HP group, indicating that both pregnant and non-pregnant women may have experienced varying degrees of oral-gut bacterial transmission (Supplementary Fig. 2 and Supplementary Data Table 4).

To enhance the biological interpretability of these ASV-level findings, we next annotated the transmitted ASVs to the genus and species level and then summarized the putative transmitted taxa, thereby providing biologically meaningful insights into the microbes that may be involved in oral–gut microbial transmission. At the species level, Schaalia odontolytica, TM7 phylum sp. oral clone FR058, Veillonella atypica, and Streptococcus anginosus showed relatively high transfer ratios in both groups, with numerically higher ratios in the HP group than in the HW group (Supplementary Fig. 3 and Supplementary Data Table 5). At the genus level, Streptococcus, Haemophilus, Veillonella, TM7x, Rothia, Actinomyces, and Gemella showed relatively high transfer ratios in both groups (Supplementary Fig. 4 and Supplementary Data Table 6). With the exception of Haemophilus, transfer ratios of these genera were consistently higher in the HP group. Streptococcus, Rothia, Actinomyces, and TM7x consistently exhibited significantly higher relative abundances in saliva than in feces across both groups (p < 0.001; Supplementary Data Table 7). Moreover, their fecal abundances were significantly higher in the HP group than in the HW group (p < 0.05; Supplementary Data Table 8), highlighting that these four genera undergo more extensive dissemination during pregnancy.

Association between potential oral-gut transmitted microbes and peripheral blood immune profile

To investigate the relationship between potential oral–gut transmitted microbes and host immunity, we conducted a correlation analysis between shared ASVs with a transfer ratio exceeding 10% in the gut and peripheral immune indicators in both the HP and HW groups (Fig. 9). In the HW group, there were 37 significant correlations between shared ASVs and immune indicators, while in the HP group, there were 57 such associations, suggesting a greater and closer association between shared ASVs and immunity in pregnant women.

Fig. 9. Correlation analysis network diagram of oral-gut shared ASVs with transfer rates greater than 10% in pregnant and non-pregnant women, and their associations with peripheral blood immune parameters.

Fig. 9

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Diamond nodes represent peripheral blood immune parameters, circular nodes represent oral-gut shared ASVs with higher transfer rates in the HP group, and triangular nodes represent ASVs with higher transfer rates in the HW group. The shading of circular nodes indicates the magnitude of ASV transfer rates in the HP group, with darker colors indicating higher transfer rates in pregnant women. Lines between nodes represent relationships between ASVs and peripheral blood immune parameters: red lines indicate positive correlations, and blue lines indicate negative correlations. Dashed lines denote relationships between ASVs in the HW group and immune parameters, while solid lines denote relationships in the HP group. Correlations were calculated using Spearman’s rank correlation, and P-values were adjusted for multiple comparisons using the Benjamini–Hochberg false discovery rate (FDR) method. Only correlations with q < 0.05 are shown. HP group Healthy pregnancy group, HW group Healthy non-pregnant women group.

In the HP group, several ASVs were positively correlated with CD56brightCD16− NK cells, including ASV1 (Streptococcus), ASV297 (Actinomyces massiliensis), ASV1458 (Streptococcus mutans), ASV526 (Weissella), ASV825 (Streptococcus), ASV7 (Streptococcus), and ASV115 (Streptococcus). Among these, ASV297 (Actinomyces massiliensis) and ASV1458 (Streptococcus mutans) were also positively correlated with the CD56brightCD16−/CD56dimCD16+ NK cell ratio (Fig. 9). Notably, both ASV297 (Actinomyces massiliensis) and ASV844 (Streptococcus) were shared ASVs unique to the HP group. ASV297 was positively correlated with NK cell ratio, whereas ASV844 was negatively correlated with Th1 cells and the Th1/Th2 ratio (Fig. 9). These results suggest that alterations in peripheral immune indicators at full-term pregnancy may be associated with oral–gut transmitted microbes.

We further investigated the association between pregnancy-specific oral-gut transmitted ASVs and immune status. ASV2371 (Scardovia wiggsiae) was positively correlated with Treg cells but negatively correlated with IL-5, IFN-γ, and the IFN-γ/TNF-α ratio (Fig. 9). ASV338 (Rothia) showed a negative correlation with TNF-α and IL-8 (Fig. 9). Taken together, these findings indicate that pregnancy-specific oral–gut transmitted ASVs may contribute to an anti-inflammatory state in the peripheral immune system.

Discussion

In this study, we observed that the oral microbiota of pregnant women exhibited a distinct community structure compared with that of non-pregnant women. Alterations in bacterial abundance during pregnancy may be associated with changes in peripheral immune status, while salivary fatty acids did not appear to mediate the influence of oral bacteria on pregnancy-related immune modulation. Furthermore, we observed an increase in oral–gut microbial transmission at full term, and the potentially transmitted microbes showed close associations with host immunity. These findings provide preliminary insights into the interactions between the oral microbiome and the host immune system during healthy pregnancy.

The oral microbiota is closely associated with the host immune system and can directly or indirectly influence systemic immunity35. Hormonal changes during pregnancy increase gingival vascularization and permeability, potentially enhancing interactions between the oral microbiota, its microbial components, and the host immune system. To explore the relationship between systemic immune changes and the oral microbiota during pregnancy, we conducted a correlation analysis between oral microbiota and peripheral blood immune cells and cytokines. Specifically, we observed that Streptococcus anginosus and Prevotella denticola, which were enriched in the pregnant women, were positively correlated with both CD56brightCD16− NK cells and the CD56brightCD16−/CD56dimCD16+ NK cell ratio. These species, typically colonizing the oral cavity, have been reported to translocate to extraoral sites under specific conditions and induce infections36,37. CD56brightCD16− NK cells respond to infections by secreting cytokines to help to control pathogen spread and minimize tissue damage38. The positive correlation between Streptococcus anginosus and Prevotella denticola and the CD56brightCD16−/CD56dimCD16+ NK cell ratio may reflect the immune system’s efforts to maintain immune homeostasis. In contrast, Solobacterium moorei, which showed reduced abundance in the pregnant group, was positively correlated with Th1 cells. This finding aligns with previous reports that Solobacterium moorei can upregulate pro-inflammatory cytokines in vitro39, supporting its potential role in promoting Th1-mediated immune responses. The decline of Solobacterium moorei during pregnancy, together with reduced Th1 cell proportions, may contribute to the Th1-to-Th2 shift that supports pregnancy maintenance. However, these findings require further validation through additional clinical and experimental studies.

Certain metabolites have the ability to regulate cytokine production or exhibit cytokine-like effects32. Therefore, we examined salivary short-chain fatty acid (SCFA) and medium-chain fatty acid levels in the participants and their associations with immune markers. No significant differences in salivary fatty acid levels were observed between pregnant and non-pregnant women in this study. Although the abundance of certain SCFA-producing bacteria, such as Prevotella species and Porphyromonas gingivalis40,41, increased in the oral cavity of pregnant women, and the expression of fatty acid synthase genes was upregulated, these findings only indicate changes in specific biological pathways. These changes may have been balanced or regulated through other metabolic pathways, such as the downregulation of fatty acid metabolism regulatory proteins in pregnant women. Increased SCFA production in the oral cavity is typically attributed to microbial dysbiosis, and high SCFA levels are often associated with periodontitis42. Since all participants in this study were healthy, this may partially explain why no statistically significant differences in fatty acid levels were detected between the two groups. Additionally, in biomedical research, the absence of statistically significant differences may merely indicate that under the chosen sample size and experimental conditions, the differences were not detectable. It does not rule out the possibility of new discoveries with larger sample sizes. Future studies with expanded sample sizes could enhance statistical power and reduce the impact of biological variability among individuals. In this study, while no associations were found between immune modulation during pregnancy and salivary fatty acid levels, a mediation effect model suggested that a specific salivary fatty acid may mediate the effects of oral microbiota on other immune parameters. For example, Clostridiales bacterium oral taxon 075 and Eubacterium sp. oral clone GI038 may suppress serum IL-1β levels by upregulating acetate production. Both of these bacteria belong to the family Clostridiaceae, some members of which are known acetate producers43. Acetate can reduce IL-1β levels by binding to GPR41 and inhibiting the ERK/JNK/NF-κB pathway44. These findings align with previous studies. However, this study was a cross-sectional observational study. Although we employed a directed mediation effect causal inference model to explore the interactions among the microbiota, metabolites, and immune responses, these analyses did not establish causality. Further longitudinal and mechanistic studies are necessary to validate and extend these findings.

This study observed a considerable overlap of oral and gut microbiota in both pregnant and non-pregnant women, with a higher number of shared oral-gut ASVs in the pregnant group, suggesting that microbial transmission between the oral cavity and gut occurs more frequently during pregnancy. We further calculated the transfer ratio of taxa corresponding to these shared ASVs. At the genus level, Streptococcus, Haemophilus, and Veillonella exhibited higher oral-gut transfer rates in both pregnant and non-pregnant women, with even higher transfer rates observed in the pregnant group. A previous study also reported that Streptococcus, Haemophilus, and Veillonella are common genera in oral-gut microbial transmission34, consistent with our findings. At the species level, Chen et al.22 identified 16 oral-gut transmitting species using metagenomic techniques, including Veillonella atypica and Haemophilus sputorum, which were also among the species with high transfer rates in this study. During pregnancy, hormonal changes, alterations in oral pH and salivary composition, and shifts in the oral microbiome may create favorable conditions for the transmission of certain bacteria between the oral cavity and the gut. Moreover, factors such as slowed gastrointestinal motility, changes in gut barrier function, and immune system modulation during pregnancy may increase the likelihood of bacterial migration from the oral cavity to the gut.

Previous studies on the oral-gut axis have primarily focused on the potential pathogenic mechanisms of ectopic colonizing bacteria. This study expands the scope of this field by exploring the microbial transmission between the oral cavity and gut, as well as its physiological significance, in healthy pregnant women. We observed that pregnant women exhibited more numerous and stronger associations between potential oral-gut transmitted ASVs and immune indicators compared to non-pregnant women. This finding suggests the existence of specific patterns of interaction between oral-gut transmitted bacteria and the immune system during pregnancy. Previous research has highlighted that the immune system undergoes adaptive adjustments during pregnancy to support fetal growth and development45. Our study further suggests that these adjustments may be associated with the transmission of microbiota between the oral cavity and the gut. For example, the transfer ratio of Actinomyces massiliensis increased in the pregnancy group and was positively correlated with CD56brightCD16− NK cells and the CD56brightCD16−/CD56dimCD16+ NK cell ratio. Notably, a previous study reported that, compared with healthy individuals, Actinomyces massiliensis derived from the oral microbiota was enriched in the gut of patients with systemic lupus erythematosus46. This finding provides independent evidence that Actinomyces massiliensis is capable of oral–gut transmission and may affect host immune function, which supports our observation in pregnant women. Additionally, this study explored the relationship between host immunity and oral-gut transmitted microbes specific to healthy pregnancies, such as Scardovia wiggsiae. To date, most research has focused on the role of Scardovia wiggsiae in the oral environment, particularly its potential association with dental caries development47,48. However, its function in the gut environment remains largely unexplored. In this study, Scardovia wiggsiae was positively correlated with the proportion of Tregs and negatively correlated with serum levels of pro-inflammatory cytokines IL-5 and IFN-γ, suggesting a potential anti-inflammatory role. Scardovia wiggsiae is an acid-tolerant anaerobe that may survive the acidic gastric environment and thrive in the anaerobic gut. Under both acidic and alkaline conditions, it metabolizes sugars via the fructose-6-phosphate pathway, producing acetate as a metabolic end product48. SCFAs like acetate and propionate are known to induce the differentiation and function of colonic Tregs by activating G protein-coupled receptors (GPCRs)49. After entering the bloodstream, SCFAs can also enhance Treg differentiation and functionality by promoting the acetylation of the forkhead box transcription factor P3 (FoxP3)50. Based on these findings, we hypothesize that the increased abundance of Scardovia wiggsiae in the gut during pregnancy may regulate immune responses by producing SCFAs, contributing to the modulation of pregnancy-associated immunity.

Although this study provides preliminary evidence for correlations between oral microbes, potential oral–gut transmitted microbes, and immune markers during pregnancy, several limitations should be acknowledged. First, the use of 16S rRNA gene sequencing, while effective for broad microbial profiling, offers limited resolution at the species or strain level. To mitigate this, transmission inference was conducted at the ASV level, as identical sequence variants across both oral and gut samples provide a more robust signal of potential microbial transfer compared to species-level classifications. Where possible, ASVs were further annotated to the lowest confidently assigned taxonomic rank and summarized at the species level to enhance biological interpretation. Future studies employing metagenomics or metatranscriptomics will offer opportunities to complement and extend our findings, potentially validating species- or strain-level oral–gut microbial transmission. Moreover, the use of the SILVA database for taxonomic annotation may lead to the omission of certain oral taxa; future studies may consider integrating oral-specific databases, such as the Human Oral Microbiome Database (HOMD), to obtain more comprehensive and accurate results. Second, as a cross-sectional study, this research has inherent limitations, including the inability to fully control for potential confounding factors, such as diet. Third, while we proposed hypotheses regarding possible biological mechanisms by integrating microbiome, metabolome, and immune data, causality remains to be established. The dynamic interplay among the microbiome, metabolism, and immunity during pregnancy warrants further investigation through large-scale, multicenter clinical cohorts and mechanistic experimental studies. Finally, despite being powered by preliminary data, the relatively small sample size may reduce statistical robustness and generalizability. Future studies with larger, diverse populations are needed to confirm and extend these findings.

Methods

Study design

The sample size for this study was determined based on preliminary data obtained from a pilot experiment analyzing salivary samples from 10 pregnant women and 10 non-pregnant women. Using methods adapted from Climent-Casals, Pascual et al.51, we performed a power analysis to estimate the required sample size for detecting meaningful differences in alpha diversity. Based on preliminary results for the Shannon and Simpson diversity indices, our analysis indicated that approximately 18–20 participants per group would be necessary to achieve 80% power at a significance level of 0.05. Taking into account potential challenges such as participant dropout, difficulties in recruitment, and budgetary constraints, we decided to increase the sample size to 25 participants per group. This study finally recruited 25 healthy full-term pregnant women who were undergoing regular prenatal checkups at the First Affiliated Hospital of Jinan University, along with 25 healthy non-pregnant women as the control group. The inclusion criteria for pregnant women were as follows: (1) age between 18 and 34 years; (2) singleton pregnancy at or beyond 37 weeks of gestation without onset of labor; (3) pre-pregnancy BMI within the normal range (18.5–23.9 kg/m²). The exclusion criteria for pregnant women included: (1) pregnancy complications, such as gestational diabetes or hypertension; (2) history of spontaneous abortion; (3) history of dental procedures in the six months prior to pregnancy or during pregnancy, such as scaling, fillings, or orthodontics; (4) history of periodontal disease, gingivitis, or dental caries before or during pregnancy; (5) history of gastrointestinal diseases, hypertension, diabetes, hyperthyroidism, systemic lupus erythematosus, or other chronic diseases; (6) history of blood transfusion, organ transplantation, or immunotherapy; and (7) use of antibiotics or probiotic supplements during pregnancy. For non-pregnant women, the inclusion criteria were: (1) healthy women without chronic diseases; (2) age between 18 and 34 years; (3) BMI within the range of 18.5–23.9 kg/m²; (4) regular menstrual cycles. Exclusion criteria for non-pregnant women included: (1) history of dental procedures within the past 6 months, such as scaling, fillings, or orthodontics; (2) history of spontaneous abortion; (3) history of periodontal disease, gingivitis, or dental caries within the past 6 months; (4) use of antibiotics or probiotic supplements within the past 6 months; (5) history of gastrointestinal diseases, hypertension, diabetes, hyperthyroidism, systemic lupus erythematosus, or other chronic diseases; (6) history of blood transfusion, organ transplantation, or immunotherapy. The complete inclusion and exclusion criteria, along with participant demographic and clinical characteristics, are summarized in Supplementary Data Table 1.

This study was approved by the Ethics Committee of the First Affiliated Hospital of Jinan University (Approval No. KY-2023-144) and was conducted in accordance with the ethical principles of the Declaration of Helsinki and the International Ethical Guidelines for Biomedical Research Involving Human Subjects. All participants provided written informed consent.

Sample collection

Participants were instructed to fast after 10:00 PM the night before sampling and to avoid brushing their teeth on the morning of sampling. Pregnant women provided samples during their routine prenatal hospital visits, with a mean gestational age of 37.31 weeks (range: 37.00–38.28 weeks). Non-pregnant control participants collected samples on the morning of day 14 of their menstrual cycle. Participants rinsed their mouths with water for approximately 10 s. They were instructed to pool saliva under the tongue, then use a sterile plastic dropper to collect 1 mL of saliva, which was transferred to a sterile cryogenic tube and stored at −80 °C.

For fecal sample collection, participants were asked to provide a fecal sample on a sterile collection pad, ensuring that contamination with vaginal secretions or urine was avoided. Using a sterile spoon, they collected 3–5 g of the inner portion of the fecal sample and placed it in a sterile collection tube. The sample was transported to the laboratory on ice within 30 min and subsequently stored at −80°C. Both saliva and fecal samples were shipped on dry ice to Beijing Novogene Co., Ltd. for sequencing.

On the same day of saliva collection, a professional nurse collected 4 mL of peripheral venous blood into a sodium heparin anticoagulant tube and 2 mL into a serum-separator tube from each pregnant and non-pregnant participant. The blood in the sodium heparin tube was used to isolate peripheral blood mononuclear cells for flow cytometry analysis. The blood in the serum-separator tube was allowed to clot for 30 min, then centrifuged at 2000 × g for 10 min at 4 °C. The supernatant was aliquoted into cryotubes and stored at −80 °C for subsequent multiplex flow assay.

We recorded participants’ age, height, current weight, pre-pregnancy weight for pregnant women, perinatal conditions, and pregnancy outcomes. On the day of enrollment, participants completed an oral hygiene questionnaire, as daily oral hygiene habits may influence the oral microbiota. Participant characteristics, including age, BMI, gestational age, and pregnancy outcomes, are summarized in Supplementary Data Table 1.

16S rRNA gene sequencing and taxonomy annotation

Genomic DNA from all samples (saliva and feces) was extracted using the TIANamp Soil Genomic DNA Extraction Kit (centrifuge column type, catalog number DP336; TIANGEN Biotech, Beijing, China) according to the manufacturer’s instructions. The quality and concentration of the DNA were then assessed using agarose gel electrophoresis. Subsequently, an appropriate amount of the sample was transferred into a centrifuge tube and diluted to a concentration of 1 ng/μL using sterile water. The diluted genomic DNA was used as a template for PCR amplification. The targeted regions were the V3 and V4 regions, with primer sequences of CCTAYGGGRBGCASCAG and GGACTACNNGGGTATCTAAT. PCR amplification was performed using the Phusion® High-Fidelity PCR Master Mix with GC Buffer from New England Biolabs, ensuring high efficiency and fidelity with the use of efficient and high-fidelity enzymes. The DNA library was constructed using the NEBNext® Ultra™ DNA Library Prep Kit for Illumina from New England Biolabs. After library construction, quantification was performed using Qubit, and the quality of the library was checked to ensure it met the required standards. The library was then sequenced using the NovaSeq 6000 with PE250. The raw sequencing data were processed through concatenation, quality control, and chimeric sequence removal, resulting in the generation of effective tags. These effective tags were further analyzed using QIIME2 (Version QIIME2-202202)52 to perform DADA2 denoising, which produced ASVs for each deduplicated sequence. Taxonomic annotation of ASVs was performed using QIIME2’s classify-sklearn with a pre-trained Naive Bayes classifier. The SILVA 138.1 database (https://www.arb-silva.de/) was used as the reference.

Functional prediction of the oral microbiome

The gene functions of microbial communities were predicted using PICRUSt253 based on 16S rRNA gene sequencing data. PICRUSt2 can provide comprehensive functional predictions, including metabolic pathways, enzyme annotations, and gene family abundances. This study focuses on the prediction of metabolic pathways. Differentially expressed genes were identified using the DESeq2 (version 1.48.1) package54 for statistical analysis. The criteria for selecting differentially expressed genes were an adjusted P-value below 0.05 and an absolute Log2 (FC) greater than 1. A volcano plot was used to visualize the differential genes.

Medium and short-chain fatty acids analysis using GCMS

Gas Chromatography-Mass Spectrometry (GC-MS) is one of the most widely utilized and well-researched methods for detecting fatty acids, known for its high sensitivity, stability, and exceptional separation capability. In this study, GC-MS was used to quantify 17 specific medium- and short-chain fatty acids in saliva, including acetic acid (AA), propionic acid (PA), butyric acid (BA), valeric acid (VA), caproic acid (CA), heptanoic acid (HPA), octanoic acid (OA), decanoic acid (DEA), nonanoic acid (NOA), isobutyric acid (IBA), isovaleric acid (IVA), isocaproic acid (4-MVA), isoheptanoic acid (5-MCA), isodecanoic acid (IDEA), 2-methylbutyric acid (2-BA), 2-ethylcaproic acid (2-ECA), and 3,5,5-trimethylhexanoic acid (INA). The fatty acids mentioned above were detected using MetWare (http://www.metware.cn/) based on the Agilent 7890B-7000D GC-MS/MS platform.

Detection of immune cells and cytokines

The Cytokine Multiplex Detection Kit (Flow Cytometry) from Hangzhou CellGer Biotechnology Co., Ltd was used to measure the levels of serum cytokines, including interleukin-1β (IL-1β), interleukin-2 (IL-2), interleukin-4 (IL-4), interleukin-5 (IL-5), IL-6, interleukin-8 (IL-8), interleukin-10 (IL-10), interleukin-12p70 (IL-12p70), interleukin-17A (IL-17A), interferon-alpha (IFN-α), interferon-gamma (IFN-γ), and TNF-α. Sample and standard preparation were performed according to the manufacturer’s instructions provided in the kit manual.

The detection of immune cells in peripheral blood using flow cytometry was performed by Wuhan Purui Medical Laboratory Co., Ltd. The following antibodies were used: CD3-PerCP-Cy5.5 (BD Biosciences, #560835), CD4-BV605 (BD Biosciences, #562658), CD8-FITC (Biolegend, #301050), CD16-APC-H7 (BD Biosciences, #560195), CD56-BV510 (BD Biosciences, #563041), CD25-PE (BD Biosciences, #555432), CD127-BV421 (BD Biosciences, #562436), CD183 (CXCR3)-AF488 (BD Biosciences, #558047), CD196 (CCR6)-BV510 (BD Biosciences, #563241), CD185 (CXCR5)-AF647 (BD Biosciences, #558113), CD194 (CCR4)-BV421 (BD Biosciences, #562579). The data were processed using FlowJo (version 10.8.1) software.

Microbial diversity analysis

Alpha diversity was evaluated using the Chao1 index, Observed Species, Shannon index, and Simpson index. Beta diversity was analyzed through PCA based on Bray-Curtis distances and PCoA based on Weighted UniFrac distances. To assess the significance of differences in community structure between groups, Analysis of Similarities (ANOSIM) and Permutational Multivariate Analysis of Variance (PERMANOVA) were employed.

Multi-omics analysis

The correlations between differential oral bacterial species and peripheral blood immune parameters were evaluated using Spearman correlation analysis. To account for multiple comparisons, P-values were adjusted using the false discovery rate (FDR) correction with the Benjamini–Hochberg procedure, and an adjusted p < 0.05 was considered statistically significant. The significant correlations were subsequently visualized in Cytoscape (version 1.7.1) to construct network maps illustrating the relationships among oral microbiota and immune indicators.

Mediation analysis is a statistical model used to explore how an independent variable indirectly affects a dependent variable through a mediator. This approach is widely employed in medical and biological research to understand complex relationships between variables, such as how metabolites mediate interactions between the microbiome, immune function, or disease phenotypes. Previous studies have shown that metabolites may act as mediators in these interactions55–57, offering valuable insights into underlying mechanisms. In this study, we utilize this approach to explore the role of metabolites in the relationship between the microbiome and immune response in pregnancy. Mediation analysis was conducted using the bruceR (version 2023.8.23) package in R (version 4.1.2), with age and BMI included as covariates to minimize their potential confounding effects. The Bootstrap method was employed to directly test mediation effects, with 1000 bootstrap resamples. A mediation effect was considered significant if the confidence interval (CI) of the indirect effect did not include zero58. To visualize the connections between microbes, fatty acids, and immune indicators, Sankey diagrams were constructed using R (version 4.1.2) with the ggplot2 (version 3.4.3) and ggalluvial (version 0.12.5) packages.

Microbial source tracking and identification of potential oral-gut transmitted microbes

The FEAST method was used to predict the contribution of oral microbiota to gut microbiota59. Potential oral-gut transmissible bacteria and their transfer rates were analyzed following the approach described by Kageyama et al.34. ASVs detected in both saliva and fecal samples of the same subject were defined as shared ASVs, representing potential oral-gut transmissible bacteria. The transfer rate was calculated as the proportion of individuals with paired fecal and saliva samples containing the same species relative to the total number of study participants. Additionally, the transfer rates of shared ASVs were calculated at both the species and genus levels to identify taxa with high transfer rates.

Statistical analysis

Continuous data with a normal distribution were compared between groups using the t-test, whereas non-normally distributed continuous data were analyzed using the Wilcoxon rank-sum test. Categorical variables were compared between groups using the chi-square test. All statistical analyses were conducted using RStudio (Version 2023.06.1 + 524) or GraphPad Prism (Version 8.0.2). The differences in microbiota composition at the species level between groups were analyzed using STAMP software (v2.1.3). P-values were adjusted using the Benjamini-Hochberg FDR correction. Statistical significance was set at p < 0.05.

Supplementary information

Supplementary information (1.1MB, pdf)

Acknowledgements

We thank the Biobank of the First Affiliated Hospital of Jinan University for sample storage. We thank the National Natural Science Foundation of China for funding this research. This study was supported by the National Natural Science Foundation of China (81771664). The icons used in Fig. 1 were sourced from Icons8 and are freely available at https://icons8.com/. We gratefully acknowledge the use of these resources in the creation of the figure.

Author contributions

T.H. and X.M.X. designed the study. T.H., X.M.T., and H.J.L. recruited participants. T.H. and H.B. collected samples. T.H., L.Z., and G.C.L. analyzed the data. All authors reviewed the data and manuscript drafts. T.H., L.Z., G.C.L., and X.M.X. interpreted results and wrote the final manuscript.

Data availability

Raw sequencing data have been publicly deposited and are available at the NCBI Sequence Read Archive, with BioProject accession no. PRJNA1184325.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Li Zhang, Email: 413788598@qq.com.

Guocheng Liu, Email: hnliuguocheng@126.com.

Xiaomin Xiao, Email: xiaoxiaomin55@163.com.

Supplementary information

The online version contains supplementary material available at 10.1038/s41522-026-01009-4.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary information (1.1MB, pdf)

Data Availability Statement

Raw sequencing data have been publicly deposited and are available at the NCBI Sequence Read Archive, with BioProject accession no. PRJNA1184325.

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