Abstract
Previous research has established that the formation of Gardnerella vaginalis (GV) biofilm is one of the primary reasons for bacterial vaginosis (BV) recurrence. This study was the first to explore the impact of Streptococcus agalactiae (group B Streptococcus, GBS) on GV biofilm in a co-culture scenario. The results revealed that GBS could significantly increased the GV biomass in 48-hours dual-species biofilms. The luxS gene of GBS was significantly higher in dual-species biofilm, while knockdown of the luxS gene resulted in a significant decrease in mono- and dual-species biofilms. Meanwhile, in vitro addition of AI-2 (product of luxS gene) substantially increased biofilm biomass. Furthermore, we found that the expression of two genes related to biofilm formation was notably elevated in GV after receiving AI-2 signals. Collectively, these findings suggest that GBS enhances GV biofilm formation via luxS/AI-2 in an in vitro co-culture model, which in turn may promotes recurrence of BV.
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Introduction
Bacterial vaginosis (BV) is the most common lower genital tract disorder in women of childbearing age1, affecting millions of women annually. It is associated with a disruption of the optimal vaginal microbiota, characterized by a dramatic depletion of Lactobacillus and significant overgrowth of a wide array of obligate or facultative anaerobic bacteria, including Gardnerella, Prevotella, Atopobium, Mobiluncus, Bifidobacterium, Sneathia, and Leptotrichia2. Currently, the recommended BV therapy by the Centers for Disease Control and Prevention (CDC) in 2021 includes oral metronidazole, vaginal clindamycin cream, or metronidazole gel3. However, previous research has established that BV recurs in 50–70% of women within 3–6 months and up to 80% of women with long-term recurrence4,5. Existing research suggests that the biofilm is a major factor in recurrent BV (RBV), preventing the penetration of antibiotics and aiding microorganisms in evading host immune defense mechanisms6,7. Biofilms are defined as microbial communities attached to surfaces or interfaces and embedded in self-produced extracellular polymeric substances (EPS), and the process of biofilm formation is considered a cooperative group behavior8,9. Among BV-associated bacteria (BVAB), G. vaginalis has the foremost virulence potential, exhibiting higher initial adhesion and cytotoxic effects, as well as a greater tendency to form biofilms10,11. Extensive research has shown that G. vaginalis and other BVAB interact synergistically. Christina A Muzny et al. have proposed an updated conceptual model of the pathogenesis of BV, focusing on the roles of virulent strains of G. vaginalis, as well as P. bivia and A. vaginae12,13. Interestingly, in this symbiotic relationship, additional strains promoted the growth of pre-formed G. vaginalis biofilms to varying degrees, irrespective of the bacterial species10,14,15,16.
A longitudinal, open-label study has suggested that RBV after metronidazole treatment is associated with the abundance of microbiota at diagnosis. The accumulation of rare or low-abundance taxa may play a greater role than dominant taxa in determining treatment outcome17. Similarly, in one of our cohort studies, we observed a significant increase in Streptococcus agalactiae (group B Streptococcus, GBS) in the vaginal flora of RBV patients compared to those without recurrence after 7 days of metronidazole treatment (not yet published). Furthermore, with the better recognition of aerobic vaginitis (AV), a considerable number of BV patients have been found to have a combination of mild to moderate AV in the clinic18,19. Several studies to date have reported multiple BV-associated mixed vaginitis and have indicated a high incidence of BV + AV20,21,22. Presently, it is believed that the microflora of AV consists of commensal aerobic microorganisms from the intestinal tract, with the most frequently encountered bacteria being GBS, E. coli, S. aureus, E. faecalis, and K. pneumoniae23,24,25). Notably, GBS is one of the leading causes of infections during pregnancy, preterm labor, and neonatal infections26,27,28.
Currently, there are few studies on the interaction between G. vaginalis and GBS, and only Gilbert NM et al. demonstrated that G. vaginalis was able to promote GBS vaginal colonization, enabling ascending uteroplacental infection in pregnant mice29. Considering all this evidence, it appears that the presence of GBS (a non-dominant bacterium) in the vaginas of BV patients may be associated with BV recurrence. Therefore, we hypothesize that GBS may contribute to the recurrence of BV by participating in the formation of Gardnerella vaginalis (GV) biofilms. In our study, we utilized the pre-conditioned biofilm model (where G. vaginalis formed the early biofilm that served as a scaffold for the adhesion of other species) developed by Rosca AS et al.30 for in vitro experiments. Subsequently, we further explored the potential interactions between GV and GBS through combined transcriptomic and proteomic analyses.
Results
GBS promoted the formation of GV biofilm
In our in vitro conditions, we observed that G. vaginalis and GBS were able to form mono-species biofilms, and no differences were noted in the total biomass between these two 48-hours biofilms. However, the addition of GBS to G. vaginalis lead to a significant enhancement of biomass in the 48-hours dual-species biofilms (Fig. 1A). SEM was employed to examine the structure and interactions in static biofilms formed by G. vaginalis and GBS. As illustrated in Fig. 1B, G. vaginalis biofilms were arranged in clusters, while those of GBS were diffuse in the mono-species biofilms. A closer view of the 48-hours dual-species biofilms revealed G. vaginalis remained arranged in clumps, with GBS tending to be arranged around G. vaginalis. It is noteworthy that a substantial amount of amorphous matrix, likely EPS (indicated by white arrows), was visible on its surface. To confirm the results obtained with the CV assay and visualize the spatial distribution and different architectures of the tested dual-species biofilms, we analyzed various z-stacks under diverse treatment conditions using FISH/CLSM. CLSM generally confirmed that the biofilm of GBS was thicker than GV, albeit without statistical significance (Fig. 1C). Notably, as shown in Fig. 1C, the presence of GBS promoted biomass in dual-species biofilms compared to G. vaginalis mono-species biofilms, consistent with the CV results. Throughout the co-culture period, the biomass of both GBS and GV in the dual-species biofilm significantly increased compared with mono-species biofilms. Consequently, we can conclude that GBS and GV may act as bidirectional promoters.
A Crystalline violet assay for the quantification of mono- and dual-species 48-hours biofilms. B The structure of mono- and dual-species 48-hours biofilms observed via scanning electron microscopy (3000×, 8000×, and 15,000× magnifications). The white arrow indicates EPS. C Mono- and dual-species 48-hours biofilm status and quantification as observed under confocal laser scanning microscopy (100× magnification). Data are shown as mean ± SEM (n = 9). Significant difference with p values determined using Student’s t-test and adjusted for the false discovery rate, are marked by * for p values <0.05, **for p values <0.01.
Transcriptomic analysis
Nine samples were sequenced using the Illumina HiSeqTM 2500, following the removal of adapters and low-quality reads. A total of 7.3 G and 7.1 G of valid data was obtained in the M vs. GBS and M vs. GV groups, respectively, with the sequencing error rate not exceeding 0.03%. In both analyses, Q20 exceeded 90%, and Q30 surpassed 85%, indicating that the samples were free from contamination and that the RNA-Seq quality was good (Tables S1 and S2). Tables S3 and S4 display the comparison between the sample and the reference genome, illustrating that all the multiple mapping rates were less than 10%. The transcriptomic data exhibited minor variations among replicates at intensity values (Fig. S1A, D), with the R2 between the samples consistently exceeding 92% (Fig. S1B, E), demonstrating a high reproducibility of transcriptome profiles. Furthermore, in the principal component analysis (PCA) analysis, the data from mono-species and dual-species biofilms were distinguishable (Fig. S1C, F).
Biofilm formation is a complex, dynamic process, typically involving surface attachment, biofilm maturation, and biofilm dispersion31. Subsequent analysis of the transcriptomes uncovered a substantial number of genes exhibiting differential expression in mono- and dual-species biofilms. Before screening DEG, we first conducted homologous alignment in mono- and dual-species biofilms to exclude conserved genes that may cause confounding factors. In comparison with GBS mono-biofilms, approximately 10.48% of the S. agalactiae genome (246 genes) displayed significant differences (P < 0.05 & FoldChange≥1.5) in dual-species biofilms, with 99 transcripts being upregulated and 147 downregulated (Fig. 2A). Moreover, DEGs were subjected to row and column clustering (q value ≤ 0.05), depicting the gene expression level and the similarity of expression patterns (Fig. S2A). Similarly, when compared with GV biofilms, a total of 437 DEGs were identified in dual-species biofilms, meeting the criteria of P value ≤ 0.05 and FoldChange ≥ 1.5. Among these, 206 genes were significantly up-regulated, while 231 genes were significantly down-regulated in their expression.
A Volcano plot illustrating the DEGs between M and GBS; B Volcano plot illustrating the DEGs between M and GV; C Volcano plot illustrating the DEPs between M and GBS; D Volcano plot illustrating the DEPs between M and GV; (The red, green, and gray denote upregulated, downregulated, and non-regulated genes or proteins, respectively).
To explore the function of these nonredundant DEGs during dual-species biofilm formation, we conducted a functional analysis using GO and KEGG pathway terms. The 246 DEGs in GBS were allocated to 257 GO terms, encompassing 152 biological processes (BP), 19 cellular components (CC), and 86 molecular functions (MF). The most prevalent groups identified in CC were associated with “membrane”. The top three BP categories observed were linked to “phosphoenolpyruvate-dependent sugar phosphotransferase system (PTS)”, “carbohydrate transport”, and “organic substance transport”. Furthermore, the predominantly enriched MF terms indicated that the transcripts are mainly affiliated with transmembrane transporter activity (especially carbohydrate, active, and organic anion transmembrane transporter activity) and protein-N(PI)-phosphohistidine-sugar phosphotransferase activity (Fig. 3A). Subsequently, the functional categorization of these 246 genes by their assigned KEGG classification is depicted in Fig. 3B. Parallel to the GO enrichment, the most abundant groups identified in the KEGG pathway were “PTS” and “Starch and sucrose metabolism”. Additionally, numerous annotated transcripts were clustered within groups related to signal communication, such as “Quorum sensing” (ToxE, PhzC, RhlC, etc) and “Two-component system” (PhoP, CusR, VicR, etc). The 437 DEGs in GV were associated with 279 GO terms, including 172 BPs, 18 CCs, and 86 MFs. In contrast to GBS, the GO enrichment analysis of GV primarily focused on ribosome-related BPs, biosynthetic processes, and signal transduction (Fig. 3C). The most notable GO terms included “structural constituent of ribosome”, “structural molecule activity”, and “ribosome”. Similarly, KEGG analysis indicated that DEGs were significantly enriched for those involved in “metabolic pathways”, “ribosome”, and “Biosynthesis of secondary metabolites” (Fig. 3D). Notably, GV also exhibited enrichment in both “Quorum sensing”(RpfB, Clp, ToxF, etc) and “Two-component system” (SenX3, Clp, LytR, etc). The enrichment analysis suggested substantial information exchange or mutual utilization of metabolites between GBS and GV during the dual-species biofilm reaction.
A GO analysis and B KEGG analysis of DEGs between M and GBS. C GO analysis and D KEGG analysis of DEGs between M and GV.
Proteomic analysis
The impact of co-culture on the proteome of GBS and GV was assessed using a liquid chromatography-mass spectrometry (LC-MS) approach. In the comparison of dual-species biofilms with GBS biofilms, a total of 5420 peptides and 919 proteins were identified. Similar to the findings in the transcriptomic data, the proteomes from the dual-species biofilm samples formed distinct clusters separate from the GBS biofilm proteomes (Fig. S3A). Moreover, the CV in Fig. S3B indicates high sample reproducibility. Likewise, a total of 9103 peptides and 1042 proteins were identified in dual-species biofilms compared with GV biofilms. The PCA analysis demonstrated significant differences in protein abundance between the two biofilms (Fig. S3C). Additionally, Fig. S3D illustrates good repeatability in this comparison. As depicted in Fig. S4A, a total of 672 proteins were detected in both the M group and GBS group, while 22 proteins were exclusively identified in GBS mono-species biofilms and 10 in dual-species biofilms. Similarly, a total of 914 proteins were detected in both the M group and GV group, with only 1 protein being detected in GV biofilm and 3 in dual-species biofilms.
Label-free quantification was utilized to assess the relative abundance of the proteome in mono- and dual-species biofilms. A 1.5-fold threshold and t-test (p < 0.05) were employed to determine valid protein changes. Among the proteins identified from dual-species biofilms and quantified, 134 were up-regulated and 89 were down-regulated in GBS (Fig. 2C). In comparison to GV biofilms, 61 were down-regulated and 54 were up-regulated in dual-species biofilms (Fig. 2D). Furthermore, the hierarchical clustering of significant proteins illustrated the arrangement of biological replicates under different biofilm conditions, indicating that GBS and G. vaginalis exhibited distinct proteomic patterns influenced by co-culture (Fig. S2C and S2D). Regarding the subcellular localization of differentially expressed proteins (DEPs), 41, 20, and 4 proteins in the comparison of GBS and M groups were predicted as cytoplasmic proteins, cell membrane proteins, and extracellular proteins, respectively (Fig. S4B). Similarly, in the comparison of GV and M groups, 15, 6, 4, and 1 proteins were predicted as cell membrane proteins, cytoplasmic proteins, extracellular proteins, and cell wall proteins, respectively (Fig. S4C). These predictions revealed that GBS DEPs were mainly located in the cytoplasm, while GV DEPs were primarily situated in the cell membrane.
According to the GO analysis using Blast2GO, the differentially expressed proteins were categorized into three cellular functions classified as BP (103 pathways), MF (85 pathways), and CC (3 pathways), which were confirmed by Fisher’s exact test. Specifically, 86 proteins were predicted to modulate cellular processes, and 76 proteins were implicated in regulating metabolic processes. In terms of MF, 20 proteins possessed a structural constituent of the ribosome, and 3 proteins exhibited glycerone kinase activity. Unlike the transcriptome, the most prevalent group identified in CC was the ribosome (Fig. 4A). Furthermore, enrichment analysis using the KEGG database revealed 46 pathways in which GBS differentially expressed proteins are involved in dual-species biofilms. The majority of pathways were related to metabolism or degradation, encompassing Glycerolipid, Tyrosine, Thiamine metabolism, Phenylalanine metabolism, and fatty acid, Chloroalkane and chloroalkene, Lysine, and Styrene degradation (Fig. 4B). The enrichment analysis indicated that GBS under two growth conditions triggered various BPs related to phenotypes.
A GO analysis and B KEGG analysis of DEPs between M and GBS. C GO analysis and D KEGG analysis of DEPs between M and GV.
We also conducted statistical analysis on the distribution of quantified proteins in the comparison of GV and M groups using GO secondary annotation classification, encompassing three major classes: BP (34), CC (10), and MF (34). The significant GO terms for BP included “macromolecule catabolic process” “cellular catabolic process” and “cellular macromolecule catabolic process” (Fig. 4C). Among the up-regulated proteins, the top three GO terms for CC comprised “integral component of membrane” “beta-galactosidase complex” and “membrane”. Based on KEGG pathway enrichment analysis, “mismatch repair” “nucleotide excision repair” and “ribosome enrichment” were most pronounced in G. vaginalis during co-culture (Fig. 4D). In this process, the most upregulated proteins were involved in the “Citrate cycle” “Sphingolipid metabolism” and “Galactose metabolism”, while the major downregulated proteins were associated with the “Ribosome” “Nucleotide excision repair” and “Acarbose and validamycin biosynthesis”. The functional diversity of the DEPs (not limited to the previous enumeration) was related to the complex regulatory network that controls biofilm formation.
Correlation analysis
The Pearson correlation coefficients between gene expression and protein abundance for GBS and GV were 0.034 and 0.064, respectively (Fig. S5). This result indicated that, under the given conditions (mono- or dual-species biofilms), there is essentially no correlation between genes and proteins that are down-regulated or up-regulated in the same direction between the transcriptome and proteome datasets. This phenomenon has often been observed in other studies, and Kumar et al. explain that weak ribosome binding site, regulatory proteins, codon usage bias, half-life difference between protein and mRNA may lead to weak correlations between measured RNA and proteins32. Furthermore, we identified a number of genes related to GBS and GV biofilm formation, epithelial cell adhesion, and virulence factors from the sequencing results and compared the trends of these genes in the transcriptome and proteome analyses (Table 1 and Table 2).
AI-2 increased significantly in dual-species biofilms
To validate the transcriptomic and proteomic results, GBS selected 17 virulence factors, while GV selected 16, which may be associated with biofilm formation and epithelial cell adhesion for RT-qPCR. The gene IDs and primers of these proteins are shown in Tables S5 and S6. The RT-qPCR results showed that the expression of most genes related to biofilm formation and cell adhesion was not significantly elevated in GBS and GV. Additionally, the expression trends of the proteome and transcriptome in both groups were generally consistent with the RT-qPCR results. Notably, both RT-qPCR and proteomic results showed a substantial up-regulation of luxS in the dual-species biofilm (Fig. 5A), but no significant change was observed in the RNA-seq. The quantitative difference between qPCR and RNA-seq gene expression may be related to the experimental principles: the quantification by RT-qPCR is measured in a localized region of the gene, and the quantification by RNA-seq is measured in the full-length region of the gene.
RNA-Seq and proteomic validation of selected DEGs of GBS (A) and GV (B) in dual-species biofilms by RT-qPCR. Data are shown as mean ± SEM (n = 3).
Next, we constructed the luxS mutant strain (ΔGBS) of GBS. As depicted in Fig. S6A, the upstream (Line 1) and downstream (Line 2) regions of the luxS gene, as well as erm (Line 3) and pSET4s (Line 4), were amplified by PCR. Similarly, the recombinant plasmid (plasmid pSET4s-luxS) was verified using the primers M13F and M13R. Lines 1, 2, 4, and 5 (2483 bp) in Fig. S6B represent the recombinant plasmids in this study. Finally, the validation of luxS mutants is shown in Fig. S6C, where 1 and 4 (483 bp) correspond to wild strains and 2, 3, and 5 represent mutant strains.
AI-2 activities in cell-free supernatants from batch cultures of GBS wild type, ΔGBS, GV, GV + GBS (M), and (GV + ΔGBS) ΔM were measured using the V. harveyi AI-2 bioluminescence induction assay, which provides an indication of AI-2 concentration. AI-2 activity was detected in the cell-free supernatant of wild-type GBS, GV, ΔM, and M groups after 48 hours of biofilm formation (Fig. 6A). It is also notable that the fluorescence intensity of all groups decreased in the first 3 hours. The trend of decreasing fluorescence intensity of the M group and the positive control remained consistent, with the M group exhibiting significantly higher fluorescence intensity than the other groups (refer to Fig. 6B). At the point of lowest fluorescence intensity for the negative control (3.5 hours), the fluorescence intensity of GBS, GV, and ΔM was greater than the negative control but less than the positive control, indicating that the samples being tested contained AI-2, albeit at a concentration lower than that of the positive control. Simultaneously, we observed that no AI-2 was detected in the ΔGBS group, while the concentrations of AI-2 in the GV and ΔM groups were comparable, suggesting that GV did not produce AI-2 in the co-culture model (Fig. 6B).
A Fluorescence intensity of the 48-hours supernatant under different culture modes. B Relative fluorescence intensity of the 48-hours supernatant under different culture modes. Data are shown as mean ± SEM (n = 3).
LuxS/AI-2 of GBS promotes GV biofilm formation
To evaluate the viability of the luxS gene in biofilm development, a CV assay was employed to assess the thickness of biofilms under different culture conditions. As depicted in Fig. 7A, the biomass of the ΔGBS biofilm decreased significantly following the knockout of the luxS gene (P < 0.05).
A Crystalline violet assay for quantification of mono- and dual-species 48-hours biofilms. B Crystal violet quantification of biofilm with ΔGBS and added gradient AI-2 (from 0.76 nM to 0.76 µM). C Mono-species 48-hours biofilm observed under SEM; c1-3 represent ΔGBS; c4-6 represent ΔGBS + 0.76 µM AI-2. D Mono-species 48-hours biofilm observed under CLSM; d1-3 represent ΔGBS, the addition of 76 nM and 0.76μm AI-2, respectively (100× magnification). Data are shown as mean ± SEM (n = 9). Significant differences with p values determined using Student’s t-test and adjusted for the false discovery rate, are marked by * for p value < 0.05, ** for p value < 0.01 and *** for p value < 0.001.
Concurrently, we observed that wild-type GBS uniformly covered most of the surface with biofilm (refer to Fig. 1 c2). In contrast, the biofilm formed by the luxS mutant GBS, as examined by a three-dimensional confocal stack, appeared to cover a smaller surface area (Fig. 7c1). The same phenomenon can be seen in Fig. 7 d1–d3, where the bacterial arrangement in the ΔGBS biofilm was sparse and loose. To provide evidence for the involvement of AI-2 in biofilm formation, various concentrations ranging from 0.76 nM to 7.6 µM of exogenous AI-2 molecules were used to complement the luxS mutant. As illustrated in Fig. 7B, the biofilm biomass of the luxS mutant reached the same level as that of wild-type GBS when the exogenous AI-2 concentration reached 76 nM. Figure 7c2 and c3 depict ΔGBS with the addition of 76 nM and 0.76 µM AI-2, respectively. As the concentration of AI-2 increased (0.76 µM), ΔGBS aligned more tightly and the biofilm thickness increased significantly, accompanied by the production of a small amount of EPS (Fig. 7 d4–d6).
In the dual-species biofilm culture system, the biofilm biomass of the ΔGBS co-cultured with GV also decreased significantly compared to that of the M group (Fig. 8B). This observation is further corroborated by the CLSM images in Fig. 8A, where both GBS and GV in ΔM exhibited much thinner biofilms than those in the M group (Fig. S7). Similarly, when the concentration of exogenous AI-2 reached 0.76 µM, the total biofilm biomass of ΔM reached the same level as that of the M group (Fig. 8B). Furthermore, the biofilm formed by adding 7.6 µM AI-2 to ΔM was significantly thicker than the 48-hours biofilm of the M group (Fig. 8 a4–6). The same result can be observed in SEM (Fig. 8C). These results indicate that AI-2 not only promoted the growth of GBS biofilm but also facilitated GV biofilm development in the co-culture model. To further verify the effect of AI-2 on GV, exogenous AI-2 was added to GV and GV + ΔGBS biofilms, and genes in GV that might be related to biofilm formation were verified by RT-qPCR. We can see from Fig. 8D that two genes, murG and GAVG_RS05105, were significantly increased after the addition of AI-2. Therefore, we speculate that exogenous AI-2 could lead to elevated murG and GAVG_RS05105 expression in GV, thus affecting GV biofilm formation and may leading to recurrence of BV.
A Dual-species 48-hours biofilm status as observed under CLSM; a1-3 represent ΔM, a4-6 represent ΔM + 7.6μm AI-2 (100× magnification). B Crystal violet quantification of biofilm with ΔM and added gradient AI-2 (from 7.6 nM to 7.6 µM). C Dual-species 48-hours biofilm observed under SEM; c1-3 represent ΔM; c4-6 represent ΔM + 7.6 µM AI-2. Data are shown as mean ± SEM (n = 9). Significant differences with p values determined using Student’s t-test and adjusted for the false discovery rate, are marked by * for p value < 0.05, ** for p value < 0.01 and *** for p value < 0.001. D RT-qPCR results of biofilm-related genes after adding exogenous AI-2 to GV. Data are shown as mean ± SEM (n = 3).
Discussion
The general population prevalence of BV is high globally, ranging from 20% to 60% across regions, with the highest prevalence in sub-Saharan Africa33,34. Several reports have indicated that the prevalence of BV among Black and Hispanic women in North America was significantly higher (33% and 31%, respectively) compared to other racial and ethnic groups (White people, 23%; Asians, 11%). Additionally, results from the 2001 to 2004 U.S. National Health Survey show that although the prevalence of BV varied across age, race or ethnicity, education, and poverty, nearly one-third of women (29%) tested positive for BV35,36.
The recurrence mechanism of BV remains unclear, and the existing mechanisms include G. vaginalis biofilm formation, an imbalance of vaginal flora, immunological abnormalities of the vaginal mucosa, and genetic variations of bacterial genes. Recent studies suggest that the reason for RBV is closely related to biofilm formation37. The cells produce EPS and are held together by its sugary molecular strands, allowing them to develop complex three-dimensional, resilient, attached communities. Bacterial biofilm formation is a well-regulated, multi-step process involving (1) reversible attachment, (2) monolayer and irreversible adhesion, (3) microcolony formation, (4) biofilm maturation, and (5) biofilm detachment and dispersion38. As highlighted in the literature review, biofilms play an important role in disease-causing processes as they can evade immune responses and are highly resistant to conventional antimicrobials, thus impacting human health and healthcare systems39. The National Institutes of Health (NIH) estimates that 3 out of 4 bacterial infections are biofilm-based40. The diversity of biofilm-related diseases is increasing over time, and they may involve multiple human organs, such as the heart (native valve endocarditis, prosthetic valve endocarditis), lungs (cystic fibrosis pneumonia), and oral cavity (periodontitis)41. More recently, biofilms have been linked to vaginal infections. In addition to G. vaginalis associated with RBV, a strong relationship between Candida spp. biofilm and recurrent vulvovaginal candidiasis (VVC) has been reported in the literature42. Bacteria in biofilms exhibit phenotypic distinctions from their planktonic counterparts, particularly in terms of growth rate and gene expression. Microbial cells within biofilms can demonstrate a thousand-fold resistance compared to planktonic cells40. For instance, in GV-dominated biofilms of BV, Patterson et al. developed an in vitro model of G. vaginalis mono-species biofilm formation and demonstrated that biofilms were 5-fold and 4–8-fold more tolerant to H2O2 and lactate than planktonic cultures, respectively43. In reviewing the literature, increased antibiotic resistance in biofilms is influenced by several factors (1) polymeric matrices that can limit antibiotic diffusion, (2) interaction of antibiotics with biofilm matrices, (3) enzyme-mediated resistance, (4) changes in metabolic activity within the biofilm, (v) genetic adaptations, (5) efflux pumps, and (6) the presence of outer membrane structure44. Biofilm communities can be formed by one or more species of bacteria, and more than 500 species of bacteria have been identified in a typical dental plaque biofilm45. Cell-cell interactions are pivotal to the formation and development of multispecies biofilms, involving co-adhesion and co-aggregation, which facilitate the mutual exchange between adjacent cells in a biofilm. In multispecies biofilms, each microbial species has its unique characteristics, offering certain evolved and distinct functions not present in their mono-species counterparts46. Multispecies biofilms are more common clinically, typically found on a wide range of medical devices, and have been associated with a significant number of human bacterial infections, posing a serious concern for human health and placing an economic burden on healthcare systems47.
AV was not fully understood until 2002, when Donders et al. defined it as a vaginal infection caused by aerobic bacteria48. However, in our clinical practice, mild to moderate AV is often ignored and even misdiagnosed as other forms of vaginitis. The role played by trace amounts of AV-associated aerobic bacteria in the relapse process among some patients with RBV remains unknown. Concerning the role of GBS on GV, Gilbert et al. demonstrated in animal models that the presence of GBS promotes the colonization of GV in the vagina from 18% to 38%29. Based on this conjecture, we designed the present in vitro experiments and showed that GBS can promote the formation of GV biofilm. GBS, a beta-hemolytic, Gram-positive bacterium, is one of many that inhabit the human body and are usually found in the intestines or lower genital tract. While this bacterium typically does not cause serious illness nor is it a sexually transmitted infection (STI), it can cause serious illnesses such as septicaemia, pneumonia, and meningitis in newborns49. The pathogenic process of GBS involves various virulence factors, including adhesion factors (Lmb, DltA, BibA, FbsA, FbsB, and pili), invasion factors (HylE, CspA, and Gap), and immune evasion factors (CpsA, ScpB, and sialic acid)50. Like other Gram-positive bacteria, GBS can form three-dimensional biofilm-like structures. Donlan and Costerton’s study found GBS bacteria on intrauterine devices, related to other known biofilm formers such as Staphylococcus aureus and Staphylococcus epidermidis. Quorum sensing (QS) is a bacterial cell-cell communication process that detects and responds to cell population density through gene regulation51. QS relies on the production, release, detection, and response to extracellular signaling molecules, called autoinducers (AIs). Interestingly, QS is not only present in both Gram-negative and Gram-positive species but can also exist within and between species. Previous studies have indicated that QS within species can coordinate disease progression, while interspecies QS can lead to both competition and cooperation. For instance, gut bacteria in the human gut microbiome coordinate with each other to combat harmful bacteria, while some bacteria can enhance each other’s virulence and form biofilms that protect pathogens from antibiotics52,53. Moreover, QS is involved in various biological processes, primarily including bioluminescence, biofilm formation, sporulation, antibiotic production, and virulence factor secretion54. QS also promote horizontal gene transfer between bacteria, thereby triggering the spread of bacterial resistance, leading to drug resistance in various infectious diseases. Additionally, QS allows for symbiotic interactions between the host and pathogenic bacteria. For example, QS activates host immune signaling and prolongs host survival by limiting bacterial uptake of nutrients such as tryptophan, which can be further converted to serotonin55. In our in vitro co-culture model, luxS gene expression of GBS was elevated, and we confirmed the increased concentration of AI-2 in the co-culture model through bioluminescence induction assay. AI-2 is an essential QS synthesized by a large cohort of Gram-negative and Gram-positive bacteria, mediating communication at intraspecies and interspecies levels56. The enzyme luxS, which plays a key role in QS and bacterial growth regulation, catalyzes the production of AI-2 signaling molecules. Various studies have shown that luxS/AI-2 affects growth characteristics, biofilm formation, antibiotic production, virulence, and metabolism of different strains57. Moreover, the luxS gene is highly conserved in GBS, and several studies have shown the role of luxS/AI-2 in different GBS serotypes58,59.
Another important finding is that two genes associated with GV biofilm formation, murG and GAVG_RS05105, were significantly elevated in the presence of AI-2. MurG is an essential bacterial glycosyltransferase. Studies have shown that E. coli murG encodes UDP-N-acetylglucosamine, which is involved in the membrane step of peptidoglycan biosynthesis60. Similarly, the murG gene was found in Pseudomonas aeruginosa with the same function61. GAVG_RS05105, SpaH/EbpB family LPXTG-anchored major pilin, is a cell surface protein reported in Enterococcus faecalis, Actinobacillus, Streptococcus62. The above mechanism may partially explain the factors that BV patients co-infected with AV pathogens are more likely to relapse in the clinic, whereas the mechanisms for other aerobic bacteria between GV need to be further verified.
It is worth mentioning that in our experiments, we did demonstrate that the addition of AI-2 promotes biofilm formation. So far, three AI-2 receptors have been identified, including the LuxP receptor found only in Vibrio spp63, LsrB widely present in enteric bacteria64,65 and PctA and TlpQ receptors containing the dCACHE structural domain in Pseudomonas aeruginosa66. Unfortunately, we did not annotate the LsrB and LuxP genes, and genes containing the dCACHE structural domain, in the G. vaginalis genome (ATCC 14018). In addition, we predicted six histidine kinase receptor proteins in GV and failed to find surface proteins that bind to AI-2 (supplementary note 1). Therefore, we speculate that AI-2 may affect GV biofilm formation in other ways, for example, a recent study suggests that AI-2 may affect biofilm formation by influencing metabolism67. At the same time, our experimental setup has several limitations. Firstly, we only proved the promotion of GBS on GV in the in vitro model, which may not be consistent with the actual mechanism in the human body. Secondly, the number of GBS in the vagina was not as dense as in the in vitro experiment, which may have influenced some data interpretations in our experiment. Thirdly, we did not explore the AI-2 receptor of GV, and therefore could not fully explain the entire pathway of action. Finally, in this study, ΔGBS and GV dual-species biofilms are significantly thicker than mono-species biofilms, demonstrating that other mechanisms besides AI-2 contribute to biofilm formation in the co-culture model. We can further explore the mechanism of EPS increase in co-culture mode.
This study was the first to investigate the role of aerobic bacteria (GBS) on the biofilm formation of G. vaginalis in RBV patients. In summary, the present study showed that GBS can promote the formation of GV biofilm through luxS/AI-2, which leads to the recurrence of BV (Fig. 9). However, further confirmation through animal models is necessary, along with the identification of a threshold range of GBS that could influence recurrence in a cohort of RBV. Our ultimate goal is to identify the factors that may influence BV recurrence, enabling early screening of this population to guide medication use.
Interaction mechanism in GBS and GV dual-species biofilm (SAM: S-Adenosyl methionine): In GBS and GV dual-species biofilms, GBS enhanced the expression of luxS genes and produced large amounts of AI-2 by sensing the increase in GV density. GV, upon receiving the AI-2 signals, up-regulate the expression of murG and GAVG_RS05105 genes, which promotes biofilm formation, thus affecting the recurrence of BV.
Methods
Bacterial strains and growth conditions
The study utilized G. vaginalis strain ATCC 14018 and S. agalactiae strain ATCC 13813. Each inoculum was grown in New York City III (NYC III) broth supplemented with 10% (v/v) inactivated horse serum for 24 hours at 37°C under anaerobic conditions, as optimized68.
Biofilm formation and quantification
For all biofilm formation experiments, the initial bacterial concentration was adjusted using optical density (OD). Briefly, the two strains were cultured separately in NYC III broth until reaching the exponential phase, which yielded an OD value of 0.5 at 600 nm (Genesy 30, Thermo Fisher, USA). Mono-species biofilms of G. vaginalis and S. agalactiae were incubated for 24 hours at 37°C under anaerobic conditions in 96-well tissue culture plates (Corning, 3596, United States), fresh medium added to the respective wells after the first 24 hours of biofilm formation. Similarly, dual-species biofilms were initiated by G. vaginalis, then the supernatant was removed, fresh medium and GBS were added, and incubated for a further 24 hours under the same conditions. Additionally, the dual-species biofilms, referred to as the GV plus GBS group, were denoted as the “M” group, while the mono-species biofilms of GV and GBS were labeled accordingly.
Crystal violet (CV)
To quantify the biomass of mono- and dual-species biofilms, the CV method was employed. After fixation in 100% (v/v) methanol for 20 minutes, biofilms were stained with a 1% (v/v) CV solution for 20 minutes. Each well was rinsed twice with PBS (Phosphate buffer solution), and the bound CV was then released with 33% (v/v) acetic acid. The OD of the resulting solution was measured at 570 nm to estimate the total biofilm biomass. Biofilm assays were repeated at least three times on different days, with four technical replicates evaluated each time.
Confocal laser scanning microscopy (CLSM)
To analyze the bacterial distribution of mono- and dual-species biofilms, the biofilm structure was assessed using CLSM and the fluorescent in situ hybridization (FISH) method. The GBS and GV 16S ribosomal RNA (rRNA)-targeted probes utilized in this experiment were 5’-GTAAACACCAAACMTCAGCG-3’ and 5’-CCACTAAACACTTTCCCAACAAGA-3’, respectively. These probes were synthesized and fluorescently labeled GBS with 5-carboxyfluorescein (5-FAM) and GV with 5-TAMRA (5-carboxytetramethylrhodamine). For this experiment, biofilms were formed on 15 mm glass bottom cell culture dishes (Nest, Wuxi, China) at 37 °C under anaerobic conditions for 48 hours, with the replacement of NYC III medium at 24 hours of growth and the addition of GBS. The CLSM images were captured using an Olympus™ FluoView FV1200 (Olympus, Tokyo, Japan) confocal scanning laser microscope, using a 100× objective. Biofilm volume quantification was performed using the COMSTAT function of Imaris 9.3.1 software. All assays were repeated three independent times with three technical replicates.
Scanning electron microscopy (SEM)
Morphological differences between the mono- and dual-species biofilms were observed using SEM. The biofilm formation method was as described previously. After the biofilm was gently washed three times with PBS buffer, it was fixed with 2.5% glutaraldehyde (Servicebio, Wuhan, China) for 12 hours and then immersed in gradient concentrations (50, 70, 80, 95, and 100%) of ethanol (Sigma Aldrich, Saint Louis, USA) for 20 minutes each. Subsequently, the carriers were observed by SEM (Hitachi SU8100, Tokyo, Japan) after treatment with air drying and gold palladium coating (Polaron SC7640; United Kingdom).
RNA-seq and data analysis
Mono- and dual-species biofilms were cultured in 12-well plates as described previously. After two days of incubation, the medium containing unattached planktonic bacteria was removed, and the cells were washed twice with PBS to eliminate any remaining planktonic cells. Attached biofilms were then scraped off the plate using a cell scraper. Total RNA from the biofilm was extracted according to the manufacturer’s instructions (CWBIO, RNApure Bacteria Kit, China). Subsequently, RNA integrity was assessed using the RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, CA, USA). Following the testing and qualification of the extracted RNA, mRNA was obtained by removing rRNA from the total RNA. The obtained mRNA was then randomly fragmented into short fragments by adding fragmentation buffer, and libraries were constructed according to strand-specific library construction69. After a qualified library check, different libraries were pooled based on the required effective concentration and target data volume for Illumina sequencing. Sequenced fragments were converted into sequence data (reads) by CASAVA base recognition of image data measured by high-throughput sequencers. Subsequent to raw data filtering, sequencing error rate checking, and GC content distribution checking, clean reads for subsequent analysis were obtained. For organisms with high gene density such as bacteria, we used Bowtie2 software70 to analyze the filtered sequenced sequences for genomic localization. This was followed by new transcript prediction, quantitative analysis, and differential analysis. differentially expressed gene (DEGs) were identified using criteria with Padj ≤ 0.05 and a FoldChange value ≥ 1.5. To perform Gene Ontology (GO) functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, etc., on the differential gene sets, we used clusterProfiler software.
Proteomic analysis and data analysis
The biofilm was collected as described above, then dissolved by adding the appropriate amount of SDT protein lysate (4% sodium dodecyl sulfate [SDS], 10 mM dithiothreitol [DTT], 100 mM cetyltrimethylammonium bromide [TEAB]), followed by shaking and mixing. Subsequently, it was ultrasonicated for 5 minutes in an ice-water bath to fully lyse the biofilm. The supernatant was centrifuged at 12,000 g for 15 minutes at 4°C. A final concentration of 10 mM DTT was added to the supernatant and allowed to react at 56°C for 1 hour. Following this, a sufficient amount of IAM was added, and the reaction carried out at room temperature in low light for 1 hour. Four times the volume of -20°C pre-cooled acetone was added, and the precipitate was left to settle at -20°C for at least 2 hours, after which it was centrifuged at 12,000 g for 15 minutes at 4°C. The resulting precipitate was collected. Afterward, 1 ml of -20°C pre-cooled acetone was added to resuspend and wash the precipitate, followed by centrifugation at 12,000 g for 15 minutes at 4°C. The precipitate was collected, air-dried, and the appropriate amount of proteolytic solution (8 M urea, 100 mM TEAB, pH=8.5) was added to dissolve the protein precipitate. Protein detection was performed using the Bradford Protein Quantification Kit, followed by proteolysis and mass spectrometry. The databases used for protein identification and quantification were GBS-ATCC13813-NCBI.fasta _handled.fasta (2010 sequences) and GV-ATCC10418-NCBI.fasta _handled.fasta (1245 sequences), respectively. Maxquant (2.0.3.0) was used to search all the resulting spectra according to the selected protein database. The search parameters were set as follows: 10 ppm mass tolerance for precursor ions, 0.02 Da mass tolerance for fragment ions, alkylation of cysteine for immobilization, oxidation of methionine for variable modifications, acetylation at the N-terminal end, and allowance for up to 2 missed cleavage sites.
Real-time quantitative PCR (RT-qPCR) validation
To validate the data generated from the RNA-seq and proteomic experiments, 17 genes of GBS and 16 genes of GV were selected for further analysis via qRT-PCR. After the formation of mono- or dual-species biofilms for 48 hours, RNA molecules were directly extracted from the biofilm by washing three times with PBS. The processed sample was placed into a pre-sterilized agate mortar, and liquid nitrogen was added to quickly grind the sample into a thick liquid. RNA isolation was carried out using the RNApure Bacteria Kit (DNase I) (CWBIO, China) as per the manufacturer’s instructions. The primers are listed in Table S5 and S6. cDNA was synthesized using ReverTra Ace qPCR RT Master Mix with gDNA Remover (TOYOBO Biotech, China). Subsequently, qRT-PCR was performed using UltraSYBR Mixture (CWBIO, China) with the Bio-Rad CFX96 system (Bio-rad, USA). GV gene expression was normalized to 16S rRNA transcript abundance, while GAPDH was used as a housekeeping gene for GBS. The relative difference in mRNA levels was calculated using the 2-ΔΔCt method71. Each qRT-PCR analysis was conducted in two independent experiments with three technical replicates.
AI-2 bioassay
The method described by Taga ME, et al. (2011)72 was utilized to assess AI-2 activity in the solution. The supernatant of the 48-hours mono- and dual-species biofilm was centrifuged at 4°C at 12,000 g for 10 minutes and then filtered through a 0.22-μm filter (Millipore, USA). Briefly, the V. harveyi BB170 reporter strain was cultured for 16 hours in AB medium at 30°C. For testing, fresh AB medium served as the negative control, overnight sterile Vibrio harveyi BB170 supernatant was the positive control, and fresh NYC-III medium was the medium control.
The cell-free supernatant was mixed with a V. harveyi BB170 culture diluted 5000-fold at a ratio of 10% (v/v), and the mixture was added to a white, flat-bottomed 96-well microtiter plate (Corning, USA), followed by shaking in a rotary shaker at 200 rpm, at 30°C. Luminescence was measured every 30 minutes for 7 hours using a fluorescent microtiter plate reader (Synergy H1, BioTeK, USA). AI-2 activity was determined by the relative fluorescence intensity, which was calculated based on the time point at which the fluorescence intensity of the negative control reached the minimum value within 0–6 hours.
Construction of bacterial mutants
The SnapGene software was utilized to design homologous primers for the gene of interest based on the gene sequence of the international standard strain of ATCC 13813 (NZ_GL636070) as published in GenBank. The luxS gene was deleted from the chromosome of S. agalactiae following a previously described procedure73. The primers used to construct the mutant are presented in Table 3. In brief, the erm resistance gene (erm, 965 bp) was amplified from the pJDC9 vector using the Erm-F and Erm-R primers74, while the flanking regions of the luxS gene cluster were amplified by PCR. The luxS-upF/luxS-upR and luxS-downF/luxS-downR primer pairs were employed to amplify the upstream (luxS-up, 701 bp) and downstream (luxS-down, 719 bp) fragments, respectively. In addition, the pSET4s vector75 was also amplified using pSET4s-F/pSET4s-R (Fig. S6). Furthermore, four fragments with 20 bp overlap were assembled using NEBuilder HiFi DNA Assembly Master Mix (NEB, MA, USA) to generate the plasmid pSET4s-luxS. The E. coli strain DH5α was transformed with the ligated plasmid pSET4s-luxS, after which plasmid DNA (pSET4s-luxS) was extracted from a resistant colony following expansion. Plasmid DNA extraction was carried out using a Vazyme FastPure Plasmid Mini Kit (Vazyme, China). Subsequently, the resulting plasmid was transformed into the wild type GBS ATCC13813 via electroporation. The mutant strains were screened as previously described.
Exogenous AI-2 complementation
The AI-2 complementation assay was conducted as previously described76. To verify the relationship between AI-2 communication and biofilm formation, the synthesized AI-2 molecule (S) 4, 5-dihydroxy-2, 3-pentanedione (DPD) (ISOREAG, Shanghai ZZBIO, China) was introduced to mutant GBS strains and the dual-species biofilm (luxS mutant GBS and GV) system (10-fold serial dilutions of DPD from 0.76 nM to 7.6 µM). Subsequently, the biomass of the 48-hours biofilm treated with various concentrations of DPD was quantified using CV and CLSM assays.
To investigate whether the significantly higher AI-2 in the co-culture mode had an effect on biofilm-related genes in GV. 48-hours biofilms of GV, GV + 0.76 µM AI-2, GV + ∆GBS, and GV + ∆GBS + 0.76 µM AI-2 were cultured according to the above methods, and then verified by RT-qPCR (relative genes and primers in Table S6).
Statistical analyses
All experimental data were presented as the mean ± standard deviation (n = 3), and statistical analyses were conducted using GraphPad Prism software (La Jolla, CA, USA). Data from multiple groups were analyzed using one-way analysis of variance (ANOVA) with Dunnett’s multiple comparison test, while the unpaired Student’s t-test was applied to compare two groups. Values with p < 0.05 were considered statistically significant.
Data availability
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: [https://www.ncbi.nlm.nih.gov/sra/PRJNA1129332] and [https://www.ncbi.nlm.nih.gov/sra/PRJNA1129296].
References
Unemo, M. et al. Sexually transmitted infections: challenges ahead. Lancet Infect. Dis. 17, e235–e279 (2017).
Fredricks, D. N., Fiedler, T. L. & Marrazzo, J. M. Molecular identification of bacteria associated with bacterial vaginosis. N. Engl. J. Med. 353, 1899–1911 (2005).
Workowski, K. A. et al. Sexually transmitted infections treatment guidelines, 2021. MMWR Recommendations Rep. 70, 1 (2021).
Bradshaw C. S. et al. High recurrence rates of bacterial vaginosis over the course of 12 months after oral metronidazole therapy and factors associated with recurrence. J. Infect. Dis. https://doi.org/10.1086/503780 (2006).
Sobel, J. D., Schmitt, C. & Meriwether, C. Long-term follow-up of patients with bacterial vaginosis treated with oral metronidazole and topical clindamycin. J. Infect. Dis. 167, 783–784 (1993).
Verstraelen, H. & Swidsinski, A. The biofilm in bacterial vaginosis: implications for epidemiology, diagnosis and treatment: 2018 update. Curr. Opin. Infect. Dis. 32, 38–42 (2019).
Muzny, C. A. & Schwebke, J. R. Biofilms: an underappreciated mechanism of treatment failure and recurrence in vaginal infections. Clin. Infect. Dis. 61, 601–606 (2015).
Rather, M. A., Gupta, K. & Mandal, M. Microbial biofilm: formation, architecture, antibiotic resistance, and control strategies. Braz. J. Microbiol 52, 1701–1718 (2021).
Sauer, K. et al. The biofilm life cycle: expanding the conceptual model of biofilm formation. Nat. Rev. Microbiol 20, 608–620 (2022).
Machado, A., Jefferson, K. K. & Cerca, N. Interactions between Lactobacillus crispatus and bacterial vaginosis (BV)-associated bacterial species in initial attachment and biofilm formation. Int J. Mol. Sci. 14, 12004–12012 (2013).
Alves, P., Castro, J., Sousa, C., Cereija, T. B. & Cerca, N. Gardnerella vaginalis outcompetes 29 other bacterial species isolated from patients with bacterial vaginosis, using in an in vitro biofilm formation model. J. Infect. Dis. 210, 593–596 (2014).
Muzny, C. A. et al. An updated conceptual model on the pathogenesis of bacterial vaginosis. J. Infect. Dis. 220, 1399–1405 (2019).
Schwebke, J. R., Muzny, C. A. & Josey, W. E. Role of Gardnerella vaginalis in the pathogenesis of bacterial vaginosis: a conceptual model. J. Infect. Dis. 210, 338–343 (2014).
Machado, A. & Cerca, N. Influence of biofilm formation by Gardnerella vaginalis and other anaerobes on bacterial vaginosis. J. Infect. Dis. 212, 1856–1861 (2015).
Castro, J., Rosca, A. S., Muzny, C. A. & Cerca, N. Atopobium vaginae and prevotella bivia are able to incorporate and influence gene expression in a pre-formed gardnerella vaginalis Biofilm. Pathogens 10, 247 (2021).
Castro, J., Rosca, A. S., Cools, P., Vaneechoutte, M. & Cerca, N. Gardnerella vaginalis Enhances Atopobium vaginae Viability in an in vitro Model. Front Cell Infect. Microbiol 10, 83 (2020).
Gustin, A. T. et al. Recurrent bacterial vaginosis following metronidazole treatment is associated with microbiota richness at diagnosis. Am. J. Obstet. Gynecol. 226, 225.e1–225.e15 (2022).
Jahic, M., Mulavdic, M., Nurkic, J., Jahic, E. & Nurkic, M. Clinical characteristics of aerobic vaginitis and its association to vaginal candidiasis, trichomonas vaginitis and bacterial vaginosis. Med Arch. 67, 428–430 (2013).
Pacha-Herrera, D. et al. Vaginal microbiota evaluation and lactobacilli quantification by qPCR in pregnant and non-pregnant women: a pilot study. Front Cell Infect. Microbiol 10, 303 (2020).
Fan, A. et al. Aerobic vaginitis and mixed infections: comparison of clinical and laboratory findings. Arch. Gynecol. Obstet. 287, 329–335 (2013).
Wang, H., Huang, Z., Wu, Z., Qi, X. & Lin, D. An epidemiological study on vaginitis in 6,150 women of reproductive age in Shanghai. N. Microbiol 40, 113–118 (2017).
Liang, Q. et al. High-dose nifuratel for simple and mixed aerobic vaginitis: A single-center prospective open-label cohort study. J. Obstet. Gynaecol. Res 42, 1354–1360 (2016).
Prasad, D., Parween, S., Kumari, K. & Singh, N. Prevalence, etiology, and associated symptoms of vaginal discharge during pregnancy in women seen in a tertiary care hospital in Bihar. Cureus 13, e12700 (2021).
Elliyas, S., Gaind, R., Kanwal, S. K., Singh, S. & Arya, S. Bacterial colonization of vagina in indian women during labor and its association with puerperal and neonatal sepsis: a tertiary hospital study. Cureus 13, e13943 (2021).
Wang, C. et al. Vaginal bacterial profiles of aerobic vaginitis: a case-control study. Diagn. Microbiol Infect. Dis. 96, 114981 (2020).
Yadeta, T. A. et al. Maternal group B Streptococcus recto vaginal colonization increases the odds of stillbirth: evidence from Eastern Ethiopia. BMC Pregnancy Childbirth 18, 410 (2018).
Lawn, J. E., Gravett, M. G., Nunes, T. M., Rubens, C. E. & Stanton, C. GAPPS Review Group. Global report on preterm birth and stillbirth (1 of 7): definitions, description of the burden and opportunities to improve data. BMC Pregnancy Childbirth 10, S1 (2010).
Hillier, S. L., Krohn, M. A., Kiviat, N. B., Watts, D. H. & Eschenbach, D. A. Microbiologic causes and neonatal outcomes associated with chorioamnion infection. Am. J. Obstet. Gynecol. 165, 955–961 (1991).
Gilbert, N. M. et al. Gardnerella vaginalis promotes group B Streptococcus vaginal colonization, enabling ascending uteroplacental infection in pregnant mice. Am. J. Obstet. Gynecol. 224, 530.e1–530.e17 (2021).
Rosca, A. S., Castro J., França, Â., Vaneechoutte, M. & Cerca N. Gardnerella vaginalis dominates multi-species biofilms in both pre-conditioned and competitive in vitro biofilm formation models. Microb Ecol. https://doi.org/10.1007/s00248-021-01917-2 (2021).
Toyofuku, M. et al. Environmental factors that shape biofilm formation. Biosci. Biotechnol. Biochem 80, 7–12 (2016).
Kumar, D. et al. Integrating transcriptome and proteome profiling: Strategies and applications. Proteomics 16, 2533–2544 (2016).
Bautista, C. T. et al. Bacterial vaginosis: a synthesis of the literature on etiology, prevalence, risk factors, and relationship with chlamydia and gonorrhea infections. Mil. Med Res. 3, 4 (2016).
Koumans, E. H. et al. The prevalence of bacterial vaginosis in the United States, 2001-2004; associations with symptoms, sexual behaviors, and reproductive health. Sex. Transm. Dis. 34, 864–869 (2007).
Peebles, K., Velloza, J., Balkus, J. E., McClelland, R. S. & Barnabas, R. V. High global burden and costs of bacterial vaginosis: a systematic review and meta-analysis. Sex. Trans. Dis. 46, 304–311 (2019).
Allsworth, J. E. & Peipert, J. F. Prevalence of bacterial vaginosis: 2001-2004 National Health and Nutrition Examination Survey data. Obstet. Gynecol. 109, 114–120 (2007).
Machado, D., Castro, J., Palmeira-de-Oliveira, A., Martinez-de-Oliveira, J. & Cerca, N. Bacterial vaginosis biofilms: challenges to current therapies and emerging solutions. Front Microbiol 6, 1528 (2016).
Vasudevan, R. Biofilms: microbial cities of scientific significance. JMEN. https://doi.org/10.15406/jmen.2014.01.00014 (2014).
Costerton, J. W., Stewart, P. S. & Greenberg, E. P. Bacterial biofilms: a common cause of persistent infections. Science 284, 1318–1322 (1999).
Musk, D. J. & Hergenrother, P. J. Chemical countermeasures for the control of bacterial biofilms: effective compounds and promising targets. Curr. Med. Chem. https://doi.org/10.2174/092986706777935212 (2006).
Lebeaux, D., Chauhan, A., Rendueles, O. & Beloin, C. From in vitro to in vivo models of bacterial biofilm-related infections. Pathogens 2, 288–356 (2013).
Rodríguez-Cerdeira, C. et al. Biofilms and vulvovaginal candidiasis. Colloids Surf. B: Biointerfaces 174, 110–125 (2019).
Patterson, J. L., Girerd, P. H., Karjane, N. W. & Jefferson, K. K. Effect of biofilm phenotype on resistance of Gardnerella vaginalis to hydrogen peroxide and lactic acid. Am. J Obstetr. Gynecol. https://doi.org/10.1016/j.ajog.2007.02.027 (2007).
Abebe, G. M. The role of bacterial biofilm in antibiotic resistance and food contamination. Int. J. Microbiol. https://doi.org/10.1155/2020/1705814 (2020).
Rosan, B. & Lamont, R. J. Dental plaque formation. Microbes Infect. 2, 1599–1607 (2000).
Flemming, H. C. et al. Biofilms: an emergent form of bacterial life. Nat. Rev. Microbiol 14, 563–575 (2016).
Kvich, L., Burmølle, M. & Bjarnsholt, T. & Lichtenberg, M. Do Mixed-Species Biofilms Dominate in Chronic Infections?-Need for in situ Visualization of Bacterial Organization. Front. Cellular Infection Microbiol. https://doi.org/10.3389/fcimb.2020.00396 (2020).
Donders, G. G. G. et al. Definition of a type of abnormal vaginal flora that is distinct from bacterial vaginosis: aerobic vaginitis. BJOG 109, 34–43 (2002).
Le Doare, K. & Heath, P. T. An overview of global GBS epidemiology. Vaccine 31, D7–12 (2013).
Shabayek, S. & Spellerberg, B. Group B streptococcal colonization, molecular characteristics, and epidemiology. Front Microbiol 9, 437 (2018).
Lupp, C., Urbanowski, M., Greenberg, E. P. & Ruby, E. G. The Vibrio fischeri quorum-sensing systems ain and lux sequentially induce luminescence gene expression and are important for persistence in the squid host. Mol. Microbiol 50, 319–331 (2003).
Mukherjee, S. & Bassler, B. L. Bacterial quorum sensing in complex and dynamically changing environments. Nat. Rev. Microbiol 17, 371–382 (2019).
How Quorum Sensing Works. ASM.org. Accessed December 3. https://asm.org:443/Articles/2020/June/How-Quorum-Sensing-Works (2023).
Rutherford, S. T. & Bassler, B. L. Bacterial quorum sensing: its role in virulence and possibilities for its control. Cold Spring Harb. Perspect. Med 2, a012427 (2012).
Jugder, B. E. et al. Vibrio cholerae high cell density quorum sensing activates the host intestinal innate immune response. Cell Rep. 40, 111368 (2022).
Miller, S. T. et al. Salmonella typhimurium recognizes a chemically distinct form of the bacterial quorum-sensing signal AI-2. Mol. Cell 15, 677–687 (2004).
Zhang, B. et al. The AI-2/luxS quorum sensing system affects the growth characteristics, biofilm formation, and virulence of haemophilus parasuis. Front Cell Infect. Microbiol 9, 62 (2019).
Ma, Y. et al. LuxS/AI-2 in Streptococcus agalactiae reveals a key role in acid tolerance and virulence. Res Vet. Sci. 115, 501–507 (2017).
Detection of quorum-sensing pathway and construction of LuxS gene deletion mutants of group B streptococcus. Nan Fang Yi Ke Da Xue Xue Bao. 2007;27:225.
Mengin-Lecreulx, D., Texier, L., Rousseau, M. & van Heijenoort, J. The murG gene of Escherichia coli codes for the UDP-N-acetylglucosamine: N-acetylmuramyl-(pentapeptide) pyrophosphoryl-undecaprenol N-acetylglucosamine transferase involved in the membrane steps of peptidoglycan synthesis. J. Bacteriol. 173, 4625–4636 (1991).
Lokhande, K. B. et al. Screening of potential phytomolecules against MurG as drug target in nosocomial pathogen Pseudomonas aeruginosa: perceptions from computational campaign. J. Biomol. Struct. Dyn. 42, 495–508 (2024).
Fischetti, V. A. Surface Proteins on Gram-Positive Bacteria. Microbiol Spectr; 7, https://doi.org/10.1128/microbiolspec.GPP3-0012-2018 (2019).
Chen, X. et al. Structural identification of a bacterial quorum-sensing signal containing boron. Nature 415, 545–549 (2002).
Miranda, V., Torcato, I. M., Xavier, K. B. & Ventura, M. R. Synthesis of d-desthiobiotin-AI-2 as a novel chemical probe for autoinducer-2 quorum sensing receptors. Bioorg. Chem. 92, 103200 (2019).
Pereira, C. S., de Regt, A. K., Brito, P. H., Miller, S. T. & Xavier, K. B. Identification of functional LsrB-like autoinducer-2 receptors. J. Bacteriol. 191, 6975–6987 (2009).
Zhang, L. et al. Sensing of autoinducer-2 by functionally distinct receptors in prokaryotes. Nat. Commun. 11, 5371 (2020).
Li, J., Liu, H., Zhao, C., Zhang, J. & He, W. Autoinducer-2 quorum sensing regulates biofilm formation and chain elongation metabolic pathways to enhance caproate synthesis in microbial electrochemical system. Chemosphere 344, 140384 (2023).
Rosca, A. S., Castro, J. & Cerca, N. Evaluation of different culture media to support in vitro growth and biofilm formation of bacterial vaginosis-associated anaerobes. PeerJ 8, e9917 (2020).
Parkhomchuk, D. et al. Transcriptome analysis by strand-specific sequencing of complementary DNA. Nucleic Acids Res 37, e123 (2009).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 25, 402–408 (2001).
Taga, M. E. & Xavier, K. B. Methods for analysis of bacterial autoinducer‐2 production. Current Protocols Microbiol. https://doi.org/10.1002/9780471729259.mc01c01s23 (2011).
Zhang, D. et al. Capsular polysaccharide of Streptococcus agalactiae is an essential virulence factor for infection in Nile tilapia (Oreochromis niloticus Linn. J. Fish Dis. 42, 293–302 (2019).
Chen, J. D. & Morrison, D. A. Construction and properties of a new insertion vector, pJDC9, that is protected by transcriptional terminators and useful for cloning of DNA from Streptococcus pneumoniae. Gene 64, 155–164 (1988).
Takamatsu, D., Osaki, M. & Sekizaki, T. Thermosensitive suicide vectors for gene replacement in Streptococcus suis. Plasmid 46, 140–148 (2001).
Xue, T., Zhao, L. & Sun, B. LuxS/AI-2 system is involved in antibiotic susceptibility and autolysis in Staphylococcus aureus NCTC 8325. Int J. Antimicrob. Agents 41, 85–89 (2013).
Acknowledgements
This research is partially supported by National Natural Science Foundation of China, Key instrument Research and development Project (61927819); Key Laboratory of Precision Medicine, Tsinghua University (20219990012); Beijing Cultivation Program (PX2022039).
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M.L. and Z.Z. designed and performed the study. Y.L. and J.L. performed data collection. X.W., S.Z. and S.L. analyzed the data; H.W., Kim and K.Z. performed the Software. M.L., Q.J., Y.W., and W.H. wrote the original draft. L.Z. and Q.L. conducted project administration and Wrote review & editing. All authors contributed to the manuscript and approved the version as submitted.
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Li, M., Zeng, Z., Wang, X. et al. Mechanisms of S. agalactiae promoting G. vaginalis biofilm formation leading to recurrence of BV. npj Biofilms Microbiomes 10, 138 (2024). https://doi.org/10.1038/s41522-024-00601-w
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DOI: https://doi.org/10.1038/s41522-024-00601-w
