Characteristics of study participants
Participants who indicated a reproductive tract infection and recent antibiotic treatment on the questionnaire were removed from the microbiome study. In total, 41 SA patients and 19 controls were included for further analysis. The general characteristics of the study participants, including age, pre-pregnancy body mass index (BMI), education level, smoking status, alcohol-drinking status, reproductive tract infection, antibodies screen and medical history are presented in Table 1. No observable differences in these characteristics were found between the SA and control groups.
Decreased bacterial diversity in fecal microbiota associated with SA patients
Alpha diversity (α-diversity) of the samples, which reflects the local scale of the microbial species, was measured by Chao1 estimators and the Shannon index. Chao 1 is an index of species richness, unrelated to abundance and evenness [20]. The Shannon index is related to not only species richness but also species evenness. Both Chao1 estimators and Shannon index were significantly decreased in the SA group relative to the control group (p < 0.001 and p < 0.01, respectively), indicating a lower richness and evenness of gut bacteria in SA patients (Fig. 1A). We then analyzed the beta diversity of the two groups. Both the unweighted and weighted Principal coordinate analysis (PCoA) plots revealed that the gut microbiota in subjects with SA clustered significantly compared to that of controls (Fig. 1B and C). These results indicate that the diversity of gut microbiota is significantly lower with different microbiota profile, in SA patients compared with controls.
Alterations in the composition of fecal microflora associated with SA
The filtered data set contained 60 samples (41 cases and 19 controls). The 16s rRNA gene targeted sequencing yielded between 14982 and 36470 valid tags with average lengths ranging from 423.18 to 433.71 bp. Clustering of these 16s sequence tags produced between 92 and 1083 operational taxonomic units (OTUs)/sample. The identified OTUs belong to 16 phyla, 30 classes, 55 orders, 96 families, and 268 genera. The relative abundance of 1029 OTUs, 7 phyla, 9 classes, 14 orders, 18 families, and 57 genera were significantly changed in the SA group compared to the control group. Bacteroidetes was the most predominant phylum, accounting for 53.3% and 51.9% of the OTUs in the SA and control groups, respectively. In addition, Firmicutes was enriched in the SA group compared to the control group, whereas Proteobacteria was enriched in the control group (Fig. 2A). Given that an upregulated Firmicutes/Bacteroidetes ratio has been suggested as an indicator of several pathological conditions [21], the ratio was of 0.65 in controls and 0.80 in cases (p = 0.039), indicating a pathological change occurred in SA patients (Fig. 2B).
We further compared the differences in fecal microflora between the two groups. At the phylum level, Spirochaetae (p < 0.001), Fibrobacteres (p < 0.001), and Tenericutes (p < 0.001) were significantly more abundant in the control group than in the SA group (Fig. 2C). Fifty-seven genera of bacteria changed in abundance in SA patients (Supplementary Table S1). Specifically, the relative abundance of 55 genera of bacteria, including Prevotella_1, Prevotellaceae_UCG_003, Roseburia, and Selenomonas_1, were significantly reduced in the SA group, and Helicobacter and Lachnospiraceae_UCG_001 were markedly increased (Fig. 2D). Considering that this discriminant analysis did not distinguish the predominant taxon, Linear discriminant analysis coupled with effect size measurements (LEfSe) was used to generate a cladogram to identify the specific bacteria associated with SA (Fig. 3A). It was shown that several opportunistic pathogens including Prevotellaceae_NK3B31_group, Bacteroidales_S24_7_group, and Eubacterium ruminantium_group were all significantly overrepresented (all LDA scores (log10) > 3.0) in the feces of SA patients, whereas Prevotellaceae, Prevotella_1, and Gammaproteobacteria were the most abundant microbiota in the control group (LDA scores (log10) > 4.0) (Fig. 3B). These results indicate that the alterations in the composition of fecal microflora were associated with SA.
Fecal metabolomics profiles were altered in SA patients
The fecal metabolome is a functional readout of the gut microbiome. Fecal metabolic profiling is a novel tool for exploring links between microbiome composition and host phenotypes [22]. Liquid chromatography/mass spectrometry (LC/MS) analysis was used to obtain the fecal metabolic profiles of the 20 subjects. The quality control (QC) samples in the Principle component analysis (PCA) score plot overlapped, which indicates that samples behaved stably for the duration of the run. Using PCA and (orthogonal) partial least-squares-discriminant analysis (OPLS-DA), we found that the SA group was completely separated from the control group (R2Y (cum) = 0.996, Q2 (cum) = 0.428), demonstrating that metabolic disturbances exist in these two groups. Significant shifts in the compositions of fecal metabolites were observed in the SA and control groups (Fig. 4A). The permutation test indicated that the analytical platform exhibited excellent stability and repeatability (R2 = 0.945, Q2 = -0.136), and can be utilized in subsequent metabolomics research (Fig. 4B). In total, 23706 metabolites were detected in these samples. Based on the differential screening strategy, 239 discriminating metabolites were found in the SA group compared with the control group (Fig. 4C and Supplementary Table S2). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses indicated that these differentially present metabolites were related to 1) bile secretion (5alpha-androstane-3alpha-ol-17-one sulfate, Deoxycholic acid 3-glucuronide, TXB2, L-Carnitine, and acetylcholine); 2) histidine metabolism (1,4-Methylimidazoleacetic acid, ergothioneine, and imidazolepropionic acid); 3) glycerophospholipid metabolism (acetylcholine, lysoPC(22:1(13Z)), and sn-3-O-(geranylgeranyl)glycerol 1-phosphate); 4) arachidonic acid metabolism pathways (TXB2, 15-Deoxy-d-12,14-PGJ2, and 12(S)-HETE), and 5) steroid hormone biosynthesis (5alpha-androstane-3alpha-ol-17-one sulfate, cortisone, and 7a-Hydroxydehydroepiandrosterone) (Fig. 4D, p < 0.05).
Clustering and multivariate analyses reveal distinct metabolites in the SA and control groups
The hierarchical clustering analysis (HCA) of the metabolites that differed between the SA and control groups revealed four large clusters: (i) glycerophosphplipids and prenol lipids, which showed higher abundances in the control group than in the SA group, (ii) steroids and steroid derivatives, (iii) amino acids and derivatives, and (iv) alkaloids, drugs and other metabolites, which showed higher abundances in the SA group than in the control group (Fig. 5A). Variable importance in the projection (VIP) values, which were obtained by OPLS-DA analysis, indicate the importance of metabolites for interpreting the differences. The presence of several metabolites, such as hyocholic acid, methyl dihydrophaseate, cholanoic acid, 3-keto petromyzonol, hydeoxycholic acid, oic acid, oxocholanoic acid, THA, isolithocholoic acid, and chenodeoxycholic acid sulfate were able to differentiate SA patients from control subjects (Fig. 5B). Specifically, the abundances of hyocholic acid, methyl dihydrophaseate, cholanoic acid, oic acid, oxocholanoic acid, and chenodeoxycholic acid sulfate were significantly higher in SA patients than in controls (Fig. 5C). Taken together, our data clearly demonstrated that SA patients with a unique fecal metabolome, suggesting that there are gut microbiota profiles and metabolites that are associated with SA.
Correlation analysis of fecal microbiota, proinflammatory cytokines, and metabolites
Multiplex analysis revealed markedly increased serum levels of IL-2, IL-17A, IL-17F, tumour necrosis factor-α (TNF-α), and IFN-γ in SA patients as compared to control patients (Fig. 6A). Pearson analysis indicates that the Chao1 index was negatively associated with the changes in IL-17A, and IFN-γ, and the Shannon index was negatively associated with the changes in IL-17A (Fig. 6B). Moreover, the Bacteroides abundances were positively associated with the changes in IL-2; the Helicobacter abundances were positively associated with the changes in IFN-γ; the Prevotella_1 and Prevotellaceae_UCG_003 abundances were negatively associated with the changes in IL-17A and IFN-γ, and Selenomonas_1 was negatively associated with the changes in IL-17A, TNF-α, and IFN-γ (Fig. 6C). As shown in the association network (Fig. 6D, and Table 2), the Bacteroides abundances were positively associated with the changes in THA, lucidenic acid J, and ergothioneine; both the Prevotella_1 and Prevotellaceae_UCG_003 abundances were positively associated with the changes in 7-Hydroxy-3-oxocholanoic acid, and cortisone, and negatively correlated with those of 1,4-Methylimidazoleacetic acid, and imidazolepropionic acid; the Selenomonas_1 was positively associated with the changes in 7-Hydroxy-3-oxocholanoic acid, 16,16-dimethyl-6-keto Prostaglandin E1, and cortisone, and negatively associated with the changes in 1,4-Methylimidazoleacetic acid, imidazolepropionic acid, adrenic acid, and chenodeoxycholic acid sulfate.
In addition, we identified some microbe-associated metabolites which were significantly enriched in the bile secretion, histidine metabolism, and arachidonic acid metabolism pathways in SA group, suggesting a correlation with an imbalance in gut microflora or increased serum cytokines. Correlation analysis between these metabolites and cytokines demonstrated that hyodeoxycholic acid, isolithocholic acid, 7-Hydroxy-3-oxocholanoic acid, TXB2, sn-3-O-(geranylgeranyl) glycerol 1-phosphate, 15-Deoxy-d-12,14-PGJ2, and cortisone, which were decreased in SA patients were negatively associated with the changes in serum levels of IL-17A, IL-17F, TNF-α, and IFN-γ, while the increased fecal chenodeoxycholic acid sulfate, 1,4-Methylimidazoleacetic acid, imidazolepropionic acid, adrenic acid, L-Carnitine, acetylcholine, ergothioneine, and D-Urobilinogen in SA patients were positively associated with the changes in IL-17A, IL-17F, TNF-α, and IFN-γ (Fig. 6E-F, and Table 3). A receiver operating characteristic (ROC) curve analysis indicated that imidazolepropionic acid (area under the curve (AUC), 0.911; Fig. 6G) and 1, 4-Methylimidazoleacetic acid (AUC, 0.930; Fig. 6H) were significantly associated with SA samples.
To investigate whether patients with certain characteristics of gut microbiome and their metabolites are more susceptible to repeated pregnancy loss or infertility, we conducted a follow up survey. Our follow-up results demonstrated that 12 participants exhibited with a RPL or unsuccessful pregnancy. The ROC analysis also showed higher AUCs for imidazolepropionic acid (0.814; Fig. 6I) and 1, 4-Methylimidazoleacetic acid (0.813; Fig. 6J) for RPL. Thus, these results reveal a link between the distinct metabolites (e.g., imidazolepropionic acid and 1, 4-Methylimidazoleacetic acid) and Th17 immunity in SA patients. Furthermore, our data indicate potential roles of these metabolites as 1) biomarkers to identify women potential risk for recurrent miscarriage and 2) potential targets for prophylactics and intervention.