Bacterial amylases enable glycogen degradation by the vaginal microbiome

Nov 22, 2023

Nature Microbiology volume 8, pages 1641–1652 (2023)Cite this article

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The human vaginal microbiota is frequently dominated by lactobacilli and transition to a more diverse community of anaerobic microbes is associated with health risks. Glycogen released by lysed epithelial cells is believed to be an important nutrient source in the vagina. However, the mechanism by which vaginal bacteria metabolize glycogen is unclear, with evidence implicating both bacterial and human enzymes. Here we biochemically characterize six glycogen-degrading enzymes (GDEs), all of which are pullanases (PulA homologues), from vaginal bacteria that support the growth of amylase-deficient Lactobacillus crispatus on glycogen. We reveal variations in their pH tolerance, substrate preferences, breakdown products and susceptibility to inhibition. Analysis of vaginal microbiome datasets shows that these enzymes are expressed in all community state types. Finally, we confirm the presence and activity of bacterial and human GDEs in cervicovaginal fluid. This work establishes that bacterial GDEs can participate in the breakdown of glycogen, providing insight into metabolism that may shape the vaginal microbiota.

Dysbiosis within the human vaginal microbiota is associated with adverse health outcomes1. The bacterial community composition can be classified taxonomically into one of five community state types (CSTs)2. CST I–III and V are dominated by a single species of Lactobacillus: L. crispatus, L. gasseri, L. iners and L. jensenii, respectively. By contrast, CST IV consists of a diverse group of anaerobic and facultative anaerobic microbes, including species of Gardnerella, Prevotella, Mobiluncus and low levels of Lactobacillus. The Lactobacillus-dominated CSTs are associated with a vaginal pH below 4.5, low Nugent scores and reduced inflammation3, whereas CST IV is associated with a higher pH and several health sequelae, including HIV acquisition4, bacterial vaginosis5 and preterm birth6. However, it is important to note that CST IV is overrepresented in healthy Hispanic and Black women and is not necessarily indicative of dysbiosis7. Overall, it has become clear that vaginal microbiota composition alone is insufficient to predict health outcomes and that gaining a mechanistic understanding of this community requires deciphering vaginal bacterial functions1.

One function known to influence the composition and stability of host-associated bacterial communities is the liberation of carbohydrates from dietary or host-derived sources by glycoside hydrolases. While this is well established within the human gut microbiota8,9,10,11, carbohydrate metabolism in the vaginal environment is poorly understood. It is widely believed that glycogen released by exfoliated and lysed epithelial cells supports colonization of vaginal lactobacilli12,13 since glycogen levels in vaginal samples are associated with Lactobacillus dominance and low vaginal pH14. However, until recently, attempts to obtain vaginal Lactobacillus isolates capable of growth on glycogen were largely unsuccessful15,16, raising the question of whether and how vaginal bacteria access this carbon source.

Glycogen consists of linear chains of α-1,4-glycosidic-linked glucose units, with periodic α-1,6-glycosidic branches. Metabolism of glycogen requires extracellular glycoside hydrolases to release shorter glucose polymers (maltodextrins). Several vaginal lactobacilli use maltodextrins for growth, leading to an initial hypothesis that a non-Lactobacillus glycoside hydrolase in the vaginal environment releases these oligosaccharides17. The detection of human α-amylase in cervicovaginal lavage samples (CVLs) may support this proposal17,18. But how human amylase, which is produced predominantly in the pancreas and salivary glands17, is found in genital fluid has not yet been established.

In addition to human amylase, recent work identified other glycogen-degrading enzymes in vaginal fluid, including glucosidases from the parasite Trichomonas vaginalis and several uncharacterized bacterial enzymes detected via proteomics19,20,21. Most notably, a putative secreted Type 1 pullulanase (PulA, EEU28204.2) from L. crispatus has been suggested as a candidate glycogen-degrading enzyme (GDE) on the basis of strain-to-strain variation in its predicted signal peptide (SP), which correlates with growth on glycogen22,23.

Pullulanases hydrolyse the α-1,6-glycosidic bonds in pullulan and other branched oligosaccharides, releasing maltodextrins24. Homologues of PulA are encoded in various vaginal bacterial genomes25, suggesting that this enzyme is not limited to L. crispatus and highlighting the potential for bacterial competition for glycogen. Notably, proteomics studies have identified putative pullulanases from L. iners and Gardnerella vaginalis in CVLs, but their activity was not biochemically validated21. The predicted α-1,6-glycosidic bond specificity of pullulanases raises questions regarding the fate of the remaining glycogen backbone and how longer branches are hydrolysed. The identification of these bacterial enzymes also raises questions about the relative role of human amylase in the vaginal ecosystem. Clearly, biochemical characterization of vaginal bacterial enzymes is needed to enhance our understanding of glycogen metabolism in this environment.

Here we report the biochemical characterization of six PulA homologues from vaginal bacteria representing Lactobacillus-dominated CSTs (I and III) and the diverse CST IV. Our study reveals that despite a common annotation, these enzymes exhibit variability in their pH profiles, glycogen breakdown product profiles, substrate preferences and susceptibility to inhibitors. By analysing multi-omics datasets, we reveal that the genes encoding these GDEs are present and transcribed in all CSTs. Using activity-based protein profiling (ABPP)26 and a selective enzymatic assay, we demonstrate that both human and bacterial GDEs are present and active in cervical vaginal fluid (CVF). Overall, this work provides molecular insight into the bacterial metabolism of an abundant carbon source in the vaginal microbiota.

To identify candidate vaginal bacterial GDEs, we conducted a BLASTp search of 151 vaginal isolate genomes in the IMG database using the L. crispatus PulA (EEU28204.2) as a query sequence22, with a cut-off of 35% amino acid identity. Hits were further narrowed to those containing both a glycoside hydrolase domain and a signal peptide, since glycogen degradation occurs extracellularly27. A total of 62 homologues were identified in strains from 11 bacterial species (Supplementary File 1), including L. crispatus (7/9 strains in the database, average 99% amino acid identity to our query), L. iners (12/13, 45%), Mobiluncus mulieris (2/4, 43%), Prevotella bivia (2/2, 40%) and G. vaginalis (15/18, 37%). Gene neighbourhood analysis revealed another signal peptide-containing glycoside hydrolase (GH13) encoded next to the P. bivia pulA (25% identity to PulA), so this sequence was also included. Subsequent characterization efforts focused on this set of proteins. We also detected potential homologues with lower identity (Supplementary File 1), including one from Streptococcus agalacticae and one significantly smaller protein in G. vaginalis homologous to a recently reported α-glucosidase enzyme that is active on maltose and other oligosaccharides but does not degrade glycogen28.

PFAM domain analysis revealed that all six candidate GDEs contain an S-layer protein A domain (SlpA), a cell-wall binding domain (CWB) or transmembrane helices (TM), suggesting localization on the cell surface29,30,31. In addition, each protein contains at least one α-amylase catalytic domain (PF00128), a member of the glycoside hydrolase 13 enzyme family known to cleave various glycosidic bonds32. Interestingly, the G. vaginalis enzyme has two amylase domains. Several enzymes possess putative carbohydrate-binding domains common to bacterial enzymes in this class, including the pullulanase domain (PUD; PF03714)33. Additional carbohydrate-binding modules (CBMs) from the CAZy database found in these enzymes include CBM25 and CBM48, which are involved in binding different linear and cyclic α-glucans related to starch and glycogen32,34 and in multivalent binding to soluble amylopectin and pullulan35 (Fig. 1a).

a, Predicted domains in putative vaginal bacterial extracellular GDEs. α-amylase, α-amylase catalytic domain (GH13); C-malt, cyclomaltodextrinase domain. b, Growth of L. crispatus C0176A1 (pulA−, JAEDCG000000000) and MV-1A-US (pulA+) on different carbon sources. c, L. crispatus C0176A1 (pulA−) grown on oyster glycogen supplemented with 200 nM purified L. crispatus PulA. d, 24 h OD600 values of L. crispatus C0176A1 (pulA−) grown on glucose, maltose or glycogen supplemented with 200 to 400 nM purified protein (L. crispatus PulA vs M. mulieris PulA, P = 0.0084; L. crispatus PulA vs P. bivia PulA, P < 0.0001; L. crispatus PulA vs P. bivia GH13, P < 0.0001). All growth curves are representative of three experimental replicates (n = 3). Error bars represent one standard deviation above and below the mean of all data collected. A multiple comparisons (Tukey) one-way analysis of variance (ANOVA) was performed to determine statistical significance. **P ≤ 0.01, ****P ≤ 0.0001.

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To examine their ability to degrade glycogen, we heterologously expressed and purified each enzyme (Extended Data Fig. 1 and Supplementary Fig. 1), then tested whether it rescued growth of a pulA-deficient L. crispatus strain on glycogen (Fig. 1b,c and Extended Data Fig. 2). Addition of purified L. crispatus PulA to the medium restored growth, providing direct evidence that PulA is sufficient for L. crispatus glycogen metabolism22 (Fig. 1c and Extended Data Fig. 2). The other enzymes also supported growth (Fig. 1d and Extended Data Fig. 2), although the lower densities of cultures grown with the M. mulieris PulA and P. bivia enzymes suggest that they are not as efficient at glycogen degradation, or that their specific oligosaccharide products are not as accessible to L. crispatus.

We next measured the kinetics of breakdown of a variety of glucose polymers to determine each enzyme’s substrate preference and specificity for different glycosidic linkages (Table 1 and Extended Data Fig. 3). In addition to glycogen, we tested amylose, which consists solely of α-1,4-glycosidic bonds and pullulan, which consists of maltotriose units connected by α-1,6-glycosidic bonds. All enzymes were active on glycogen (Table 1 and Extended Data Fig. 3). Interestingly, mutants of the G. vaginalis enzyme with either of the two amylase domains inactivated retained only 5% of wild-type activity, suggesting that the two domains may act synergistically (Extended Data Fig. 4). All enzymes were active on pullulan, suggesting that they cleave α-1,6-glycosidic bonds but differed in their activity towards the α-1,4-glycosidic bonds in amylose, with L. crispatus, L. iners, G. vaginalis and P. bivia GH13 enzymes showing activity, while M. mulieris and P. bivia PulA enzymes were inactive (Table 1 and Extended Data Fig. 3).

The measured kinetic parameters of these GDEs were broadly consistent with those of other bacterial enzymes that process these substrates (glycogen36,37,38, amylose39,40, pullulan39,41). Comparing the specificity constants (kcat/Km) for each substrate revealed that glycogen is the preferred substrate for the G. vaginalis and L. iners enzymes. The L. crispatus PulA had similar specificity constants for both pullulan and glycogen, with activity towards amylose. P. bivia PulA and M. mulieris PulA had higher specificity constants for pullulan compared with glycogen and amylose, while the P. bivia GH13 enzyme preferred amylose (Table 1 and Extended Data Fig. 3). Overall, these enzymes varied in their substrate preference despite sharing homology with L. crispatus PulA.

Lactobacillus-dominant CSTs are typically associated with a low vaginal pH (<4.5)42 due to production of lactic acid14. We therefore hypothesized that GDEs from lactobacilli may have evolved to maintain activity at a lower pH than those from other vaginal bacteria. We measured GDE activity on glycogen over a pH range of 2.5–8.0 (Fig. 2). Five of the GDEs exhibited maximum activity between pH 5.5 and 6.0, which is consistent with other characterized bacterial pullulanases and amylopullulanases43. P. bivia PulA exhibited maximum activity at a slightly lower pH between 4.5 and 5 (Fig. 2). Most of the enzymes from vaginal anaerobes—G. vaginalis PulA, M. mulieris PulA and P. bivia GH13—showed almost no activity at pH 4.0. Critically, however, the L. crispatus, L. iners and P. bivia PulAs displayed 34%, 51% and 97% of their maximal activity at pH 4.0, suggesting that they are better adapted to a low pH environment. This activity may explain how vaginal lactobacilli can utilize host-derived glycogen under low pH conditions, potentially contributing to their dominance.

pH profiles of six extracellular glycogen-degrading enzymes. Buffer systems consisted of glycine (pH = 2.5–3.3), sodium acetate (pH = 4.0–5.5), MES (pH = 6.0–6.5) and HEPES (pH = 7.0–8.0). Data are representative of three independent experiments over 2 d (n = 3).

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We next sought to characterize and quantify the specific oligosaccharide products of each enzyme (Fig. 3). Both human amylase and the P. bivia GH13 produced predominantly glucose disaccharides (G2) and a small amount of glucose (G1) from glycogen and amylose. In contrast, the enzymes annotated as Type I Pullulanases produced longer oligosaccharides in addition to G2, including glucose trisaccharides (G3) and in some cases glucose tetrasaccharides (G4). These results resemble those observed for previously characterized bacterial amylopullulanses44,45. G4 was not detected in assays with G. vaginalis PulA and was only detected at a low level in assays with the M. mulieris and P. bivia enzymes. However, the Lactobacillus-derived PulA enzymes produced a higher relative amount of G4 when acting on amylose or glycogen. During incubation with pullulan, all bacterial enzymes produced predominantly G3, whereas the human salivary amylase was inactive. The sole production of G3 is common among pullulan-degrading enzymes41,43,44,46. Notably, pullulanase activity appears unique to vaginal bacterial GDEs and is not exhibited by the human enzyme (Table 1 and Fig. 3).

Polymer breakdown products generated following overnight incubation with purified enzyme. LC–MS analysis is representative of three independent experiments performed over 3 d (n = 3). Error bars represent one standard deviation above and below the mean. G1, glucose; G2, maltose and isomers; G3, maltotriose and isomers; G4, maltotetraose and isomers; G5, maltopentaose and isomers.

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The specific G3 products of pullulan degradation were identified using thin-layer chromatography (TLC). Every enzyme except P. bivia GH13 produced maltotriose, confirming their ability to cleave α-1,6-glycosidic bonds. P. bivia GH13 produced panose, suggesting that this enzyme cleaves only α-1,4-glycosidic bonds in pullulan (Extended Data Fig. 5). These data, paired with the kinetic analyses (Table 1), demonstrate that both Lactobacillus PulA enzymes and the G. vaginalis PulA enzyme can cleave the α-1,4 and α-1,6-glycosidic bonds found in glycogen and support their reassignment as type II pullulanases or amylopullulanases (EC. 3.2.1.1/41, reviewed in ref. 45). P. bivia GH13 only cleaves α-1,4-glycosidic linkages (including within pullulan), identifying this enzyme as a pullulan hydrolase type I or neopullulanase (EC 3.2.1.135, reviewed in ref. 47). In contrast, the lack of activity of the M. mulieris and P. bivia PulA enzymes towards amylose identifies them as type I pullulanases (EC 3.2.1.41) and may explain their reduced ability to complement L. crispatus growth on glycogen (Figs. 1d and 3, Table 1 and Extended Data Fig. 5).

Given their role in enabling growth on glycogen and the biochemical distinctions between different GDEs, we hypothesized that these enzymes may be targets for possible therapeutic intervention aimed at establishing a Lactobacillus-dominant community. Of four clinically used amylase inhibitors, only acarbose and acarviosin showed any activity towards the GDEs (Extended Data Fig. 6a). Acarbose inhibited G. vaginalis PulA, P. bivia PulA and P. bivia GH13 enzymes, with half-maximum inhibitory concentration values (IC50) of 120 ± 30 μΜ, 420 ± 90 μΜ and 0.84 ± 0.05 μΜ, respectively, while the L. crispatus, L. iners and M. mulieris enzymes were largely unaffected (Extended Data Fig. 6b).

Since acarbose selectively inhibited GDEs from CST IV-associated microbes, we characterized its effect on bacterial growth as a first step towards testing its utility for community modulation. While acarbose inhibited G. vaginalis growth on glycogen (IC50 = 0.2 μM), it also inhibited L. crispatus growth on maltose (IC50 = 22 μM) and glycogen (IC50 = 0.1 μM) even though L. crispatus PulA was not affected in vitro. Interestingly, L. crispatus growth was not affected when glucose was the primary carbon source (Extended Data Fig. 6c). These data suggest that acarbose inhibits additional L. crispatus enzymes involved in maltodextrin metabolism. Despite potently inhibiting the P. bivia GH13 in vitro, acarbose had no impact on P. bivia growth on any substrate (Extended Data Fig. 6c). This suggests that P. bivia PulA, which was less susceptible to inhibition in vitro, is likely the predominant GDE in this organism and that intracellular maltodextrin catabolism in P. bivia is not affected by acarbose. Overall, although acarbose is not a suitable candidate for community modulation due to its broad target spectrum, these results highlight differences between the GDEs that may potentially be targeted for selective inhibition.

Having identified bona fide vaginal bacterial GDEs, we next sought to understand the presence and expression of genes encoding these enzymes in the vaginal environment. While other searches have detected putative bacterial amylases in clinical samples using proteomics21, metagenomics and metatranscriptomics23,48, the activities of these enzymes were not biochemically verified. We employed shortBRED (Short, Better Representative Extract Dataset) to identify biochemically characterized GDEs in a dataset of 178 paired vaginal metagenomes and metatranscriptomes from 40 non-pregnant, reproductive-age women who self-collected vaginal swabs over 10 weeks48,49. ShortBRED is a computational tool that identifies and quantifies unique amino acid sequences that are distinct to a query protein (85% identity cut-off). In contrast to previous studies, all enzymes queried are predicted to be extracellular and degrade glycogen in vitro, increasing confidence that any hits also possess this activity.

Combined reads from the six GDEs were more abundant in CST I metagenomes compared with CST II and CST IV metagenomes (Fig. 4a). This increased abundance in CST I samples is due to L. crispatus pulA (Fig. 4b), which was detected in 84.6% of the metagenomes and 89.7% of the metatrascriptomes from CST I participants. M. mulieris pulA was not detected in these individuals and the other four GDEs were detected in fewer than 11% of metagenomes and metatranscriptomes (Extended Data Fig. 7). In CST III samples, which were dominated by L. iners, L. iners pulA was detected in 41.9% and 32.3% of the metagenomes and metatranscriptomes, respectively. Interestingly, genes encoding other GDEs (L. crispatus PulA, G. vaginalis PulA, both P. bivia GDEs) were detected in >20% of CST III metagenomes. However, the detection of these genes in the metatranscriptomes was highly variable (6.45%–38.7%).

a, Metagenomic analysis of 178 participant samples using ShortBRED analysis of biochemically characterized GDEs stratified by CST. Only samples encoding a bacterial GDE were plotted. % encoding represents the percentage of samples that contain >0 genes per bacterial genome. A multiple comparisons (Dunnett) one-way ANOVA was performed to determine statistically significant differences compared to CST I abundance (CST IV, ****P < 0.0001; CST V, *P = 0.0196; NSP > 0.05,) The box represents 1.5× the interquartile range and the whiskers represent the minimum to the maximum of the dataset. The centerline denotes the median. b, Heat map of metagenomic presence and abundance detected using ShortBRED within each sample. NP, not present. c, ABPP analysis identifies bacterial GDEs and human proteins (α-amylase and GAA) in CVF supernatants. ND, not detected; GAA, lysosomal α-glucosidase. d, Human CVF contains distinctly bacterial pullulanase activity at pH 5.5. Data are representative of three experimental replicates over 2 d and the error bars are one standard deviation above and below the mean. A multiple comparisons (Dunnett) one-way ANOVA was performed to determine statistically significant differences compared to the no-CVF sample (blue) (S003, ****P < 0.0001; S011, ****P < 0.0001).

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All GDEs were detected in CST IV metagenomes at frequencies of 16.9%–36.1%. While L. crispatus pulA was detected in 39.8% of CST IV metatranscriptomes, transcripts from other GDEs were detected in only 3.6%–10.8% of the datasets (Extended Data Fig. 7). Overall, this analysis demonstrates that characterized bacterial GDEs are present in vaginal metagenomes and expressed in various vaginal bacterial CSTs, with CST III and CST IV communities in particular harbouring GDEs from multiple species.

We next sought to detect the activity of bacterial GDEs and human amylase in clinical samples. We initially analysed 20 CVL sample supernatants spanning a range of Nugent scores (0–8), comparing total amylase activity to the concentration of human amylase determined by enzyme-linked immunosorbent assay (ELISA) (Supplementary Fig. 2 and Extended Data Fig. 8). Activity assays with a fluorescent starch substrate were conducted across a range of pHs, spanning the healthy vaginal environment (4.4) to the optimum for the human amylase (6.8). At all pH values, there was a statistically significant correlation between these measurements (Extended Data Fig. 8), suggesting that the majority of the amylase activity is human. However, it is interesting to note that as pH decreased, the correlation coefficient was reduced, perhaps suggesting increased contribution from other enzymes at lower pH (Extended Data Fig. 8). Comparing these results to Nugent scores (low Nugent 0–3, high Nugent 7–10), we found no difference in amylase activity or human amylase levels (Supplementary Fig. 3). To confirm the specificity of the ELISA for the human enzyme, we assayed our purified bacterial enzymes using the same kit and found no cross-reactivity at enzyme concentrations as high as 1 μM (Supplementary Fig. 2). Together, these results indicate that the contribution of human amylase to glycogen degradation should not be overlooked, despite the existence of bacterial enzymes with related activities.

We next attempted to determine whether bacterial GDEs were active in CVF samples by applying activity-based protein profiling (ABPP)50 using probes targeting amylase (Amy-ABP) and glucosidase (Glc-ABP) enzymes. After confirming that the purified bacterial GDEs reacted with at least one probe (Supplementary Figs. 4 and 5), CVF supernatants were labelled with biotin-tagged probes, followed by pull-down, tryptic digest and liquid chromatography tandem mass spectrometry (LC–MS/MS) identification of the peptides. The Amy-ABP probe identified L. crispatus PulA, L. iners PulA and G. vaginalis AmyA in these samples (Fig. 4c and Extended Data Fig. 9). L. crispatus PulA and L. iners PulA were mutually exclusive, consistent with previous studies finding their co-occurrence uncommon7. In one sample (S004), we observed co-occurrence of two bacterial enzymes: L. iners PulA and G. vaginalis AmyA. Although we did not characterize G. vaginalis AmyA in this study (because of its low amino acid similarity to L. crispatus PulA), it was recently shown to degrade glycogen51. We also detected human α-amylase (AMY1) in the majority of samples (Fig. 4c and Extended Data Fig. 9). In contrast to the Amy-ABP-enriched proteins, all Glc-ABP-enriched proteins were human in origin. The protein with the highest intensity was lysosomal α-glucosidase, which is canonically localized to the lysosome but has been detected previously in CVF (Fig. 4c, Extended Data Fig. 9 and Supplementary File 1)52,53,54. Overall, these data demonstrate that bacterial GDEs are present and active in the vaginal environment, including L. crispatus PulA, L. iners PulA and G. vaginalis AmyA.

To further validate the activity of bacterial GDEs in these samples, we used an LC–MS-based assay to detect pullulan degradation, leveraging the observation that all characterized GDEs metabolize pullulan, whereas the human amylase does not (Fig. 3). Every CVF sample showed activity in this assay (Extended Data Fig. 10). Notably, the two most active samples, S003 and S011, had the highest intensity of L. crispatus PulA in the ABPP experiment (Fig. 4c) and generated significantly increased levels of G3 compared with a no-CVF control (Fig. 4d). These results further demonstrate that vaginal bacterial GDEs are active in clinical samples and validate a simple, accessible assay for bacterial GDEs that does not depend on proteomic workflows.

In this study, we biochemically characterized six GDEs from vaginal bacteria. Our results demonstrate that in addition to relying on human amylase, some vaginal bacteria possess alternative enzymes for accessing glycogen. These findings are further validated by a separate report of the glycogen-degrading activity of L. crispatus PulA (GlgU, 99% identity)55. Critically, despite sharing a common annotation, we find that the substrate preferences and breakdown products of bacterial GDEs are quite distinct. This is consistent with the unique carbohydrate-binding modules found in each protein and may suggest adaptation to process structurally distinct glucose polymers in the vaginal environment. Further, since the oligosaccharides produced from glycogen breakdown are released extracellularly and may act as ‘public goods’56, the differences in the product distributions of these enzymes may suggest that differential availability of glycogen-derived oligosaccharides between CSTs supports the growth of distinct non-glycogen-degrading bacteria via cross-feeding11. A better understanding of the structure of glycogen within the vaginal environment and whether it differs among CSTs is needed to further evaluate this possibility.

Our work also suggests a potential mechanism supporting L. crispatus growth and dominance. Specifically, we discovered that L. crispatus PulA is active at the low pH values (~3.5–4) associated with vaginal health. This enzymatic activity may therefore enable L. crispatus to access glycogen under conditions where the human amylase is minimally active and the growth of competing bacteria is inhibited. Critically, the pH profiles, substrate preferences and breakdown products of amylases cannot be predicted from primary sequence analysis, further highlighting the need for biochemical characterization to support bioinformatic interrogations of bacterial metabolism within the human microbiome.

The high prevalence of the L. crispatus PulA in CST I metagenomes (85%) further suggests an important role for this enzyme. Notably, in the L. iners-dominated CST III samples, the homologous PulA enzyme is much less prevalent (42%), perhaps suggesting that L. iners is less dependent on glycogen or relies more on other enzymes. Moreover, glycogen degradation has never been reported for L. gasseri or L. jensennii and we found no PulA homologues encoded in their genomes, leaving open questions about glycogen metabolism in CST II and V.

A major challenge in characterizing glycogen metabolism in clinical samples has stemmed from difficulties distinguishing human and bacterial amylase activity21. Our use of ABPP categorically identifies active enzymes in these complex samples. In addition, our simple LC–MS-based assay for pullulanase activity rapidly identifies bacterial GDE activity. Our results and those from other recent efforts17,21 show that bacterial GDE activity is highly variable, highlighting a need to test larger numbers of better-characterized clinical samples. We anticipate the pullulanase activity assay will find broad utility in the analysis of such samples and enable further study of the biological roles of bacterial GDEs57.

Overall, the insights gained from this investigation highlight the need to complement bioinformatic analysis with detailed biochemical characterization of vaginal bacterial enzymes. This improved understanding of the activities of vaginal bacterial GDEs will enable future exploration of bacterial glycogen metabolism in the vaginal microbiome and its contribution to community composition, stability and dysbiosis.

This work complied with all relevant ethical regulations, and we obtained informed consent from all donors. The study protocols were approved by Massachusetts General Hospital (IRB: 2014P001066) and Seattle University Affiliates (IRB: FY2022‐002).

Homologues of PulA in L. crispatus22 (EEU28204.2) were identified by BLASTp searches of genomes from vaginal isolates in the IMG database58 using an E-value cut-off of 1 × 10−5. The IMG database contains 151 vaginal isolate genomes from the Human Microbiome Project with a sample body subsite of ‘vaginal’ (Supplementary File 1). Hits with no predicted signal peptide were removed (SignalP v.5.0 (ref. 59)). Six candidates with >35% amino acid identity from microbes associated with health or disease were selected. Genomic DNA was extracted from the encoding strains with a DNeasy UltraClean microbial kit (Qiagen). Genes were amplified via PCR removing the signal peptide (Supplementary Fig. 1) and cloned into the E. coli expression vector pET28a (Novagen) via Gibson assembly to generate an N-terminal His6-tagged gene. Plasmids were then transformed into the expression host BL21 (DE3) (P. bivia enzymes) or ArcticExpress (DE3) (all other enzymes) for expression and purification. Complete lists of plasmids and primers are provided in Supplementary Table 2 and Supplementary File 1, respectively.

Cultures containing expression plasmids were grown in LB medium containing 50 μg ml−1 kanamycin to an optical density at 600 nm (OD600) of 0.6–0.8, then cooled to 15 °C and induced with 250 μM isopropyl β-D-1 thiogalactopyranoside (IPTG). After 16 h at 15 °C, cells were collected and the pellets were stored at −20 °C until use. Pellets were resuspended in 98% buffer A (50 mM HEPES, 300 mM KCl, 10% glycerol, pH 7.8) and 2% buffer B (50 mM HEPES, 300 mM KCl, 10% glycerol, 500 mM imidazole, pH 7.8) supplemented with EDTA-free protease inhibitor cocktail (Sigma). Cells were lysed via homogenization (3 ×15,000 psi, Emulsiflex-C3, Avestin) and lysates were clarified (16,000 × g for 45 min at 4 °C) before being loaded onto a 5 ml HisTrap column (GE Healthcare). This was followed by one column volume (c.v.) of 2% buffer B and 2 c.v. of 10% buffer B. Protein was eluted using a linear gradient from 10 to 100% buffer B over 20 c.v. Protein-containing fractions and purity were determined by SDS–PAGE. Amylase-containing fractions were pooled, concentrated to a volume of ~1 ml in a spin concentrator (Millipore) and purified by size exclusion chromatography (GE Healthcare, Superdex 200) in 100% buffer A.

Fractions were again analysed by SDS–PAGE and protein-containing fractions were pooled, concentrated (Millipore, Amicon 30 kDa), flash frozen in liquid nitrogen and stored at −80 °C until use. Protein concentration was determined using a Bradford assay.

MRS broth containing glucose (BD Difco) was prepared according to manufacturer protocol. For growth assays on different carbon sources, MRS broth without glucose (Food Check Systems, pH 6.5–6.6) was prepared according to the manufacturer’s recipe and supplemented with either 2% d-glucose (Sigma), 2% maltose monohydrate (VWR) or 5% glycogen from oyster (Sigma). Each medium type was filter sterilized (0.2 μm) and left inside an anaerobic chamber with an atmosphere of 2.5% H2, 5% CO2 and 92.5% N2 (Coy Labs) overnight for equilibration. Starter cultures of L. crispatus C0176A1 and L. crispatus MV-1A-US were inoculated into MRS media (BD Difco) in Hungate tubes and incubated overnight at 37 °C. The next day, purified protein was thawed and added to 5% glycogen MRS media to a concentration ranging between 200–400 nM. The medium was again filter sterilized before use. As a negative control, protein boiled at 100 °C for 15 min was included. Of each medium type, 50 μl was aliquoted into a 384-well tissue culture-treated clear microplate (Corning). Overnight culture (1 μl) was used to inoculate each well. The plate was sealed and growth was monitored in a plate reader (Biotek) inside an anaerobic chamber (Coy Labs) at 37 °C for 24 h by measuring OD600 every 15 min.

Kinetic analysis of GDEs was performed using a reducing sugar assay41, modified for a 96-well format. Reactions (300 μl) were set up containing substrate (0.0048–10 mg ml−1 glycogen; 0.0012–1.25 mg ml−1 Pullulan (Megazyme); or 0.0048–1.25 mg ml−1 amylose in a final concentration of 2% dimethyl sulfoxide), 0.8–700 nM enzyme and reaction buffer (20 mM sodium acetate, pH 5.5, 0.5 mM CaCl2). Reaction mixtures were incubated at 37 °C for 15 min and 50 μl aliquots were removed (2, 5, 7.5, 10, 15 min) into 125 μl of the BCA stop solution (0.4 M sodium carbonate, pH 10.7, 2.5 mM CuSO4, 2.5 mM 4,4’-dicarboxy-1,2’-biquinoline, 6 mM l-serine). After 30 min incubation at 80 °C, absorbances were read at 540 nm and compared to a maltose standard curve (0.000610–0.625 mg ml−1) to quantify activity. Initial velocities were calculated via linear regression, selecting the data points that produced the highest initial rate, utilizing at least three data points. KM and kcat parameters were determined by fitting the Michaelis–Menten equation using nonlinear regression (Graphpad Prism 8).

A 1 μl volume from the GDE reaction mixtures were spotted onto a TLC plate and run for ~5 h in 3:2:1 butanol:acetic acid:water. The plate was removed, dried for 10 min with a heat gun and sprayed with a 1:19 sulfuric acid:ethanol solution. The plate was developed by heating for 15 min with a heat gun until spots appeared. Identity of products was confirmed by co-running pure standards.

Reactions were conducted using the reducing sugar assay (see above) with 1.25 mg ml−1 glycogen, 0.9–850 nM enzyme and assay buffer ranging in pH from 2.5 to 8.0 (pH 2.5–3.3: 20 mM glycine, 0.5 mM CaCl2; pH 4.0–5.5: 20 mM sodium acetate, 0.5 mM CaCl2; pH 6.0–6.5: 20 mM MES, 0.5 mM CaCl2; pH 7.0–8.0: 20 mM HEPES, 0.5 mM CaCl2). G. vaginalis PulA active site mutants were constructed using a multifragment Gibson assembly amplified from the wild-type expression vector pETpullGV using the primers listed in Supplementary File 1. pETpullGV-AS1 (∆AS1) and pETpullGV-AS2 (∆AS2) contained a D233A and D1317A mutation, respectively, designed to inactivate the catalytic aspartate of the amylase domains of these proteins. pETpullGV-DM (∆DBL) contained both mutations. Specific activities of G. vaginalis PulA active site mutants were determined at pH 5.5.

Reactions were set up containing 10 mg ml−1 substrate and 500 nM enzyme, all dissolved in reaction buffer and incubated at 37 °C overnight. Samples were quenched by 10-fold dilution into 90% acetonitrile. The plates were centrifuged (3,220 × g for 10 min, 4 °C) and the samples were diluted 1,000-fold in acetonitrile before analysis by UHPLC–MS using a Xevo TQ-S (Waters) with electrospray ionization (ESI). Sample (5 μl) was injected onto an Acquity BEH/Amide UPLC column (Waters, 1.7 µm, 130 Å, 2.1 mm × 50 mm) heated to 40 °C. A flow rate of 0.5 ml min−1 was used, with the following gradient: 0–1.0 min at 97% B (acetonitrile with 0.1% formic acid) and 3% A (H2O with 0.1% formic acid) isocratic, 1.0–4.0 min 97–30% B, 4.0–5.0 min at 30% B isocratic, 5.0–5.1 min at 30–97% B, 5.1–7.0 min at 97% B isocratic. Carbohydrate products were detected by ESI in positive mode (capillary voltage 3.10 kV; cone voltage 42 V; source offset voltage 50 V; desolvation temperature 500 °C; desolvation gas flow 1,000 l h−1; cone gas flow 150 l h−1; nebulizer 7.0 bar). See Supplementary Information for compound-specific detection parameters (Supplementary Table 3). For quantification of the oligosaccharides and their isomers, standards of glucose, maltose (VWR), maltotriose (Carbosynth), maltotetraose (Carbosynth) and maltopentaose (Carbosynth) were prepared ranging from 0.001–10 μg ml−1 in 9:1 acetonitrile:water. Oligosaccharide peak areas were quantified using the standard curve and the data were normalized to a no-enzyme control to account for non-enzymatic substrate breakdown (Waters MassLynx).

Growth inhibition assays were performed in an anaerobic chamber (Coy Labs) with an atmosphere of 2.5% H2, 5% CO2 and 92.5 N2. Bacteria were inoculated from single colonies into a peptone-yeast extract base broth (PYTs, pH 7.0–7.2) consisting of proteose peptone (20 g l−1), yeast extract (10 g l−1), MgSO4 (0.008 g l−1), K2HPO4 (0.04 g l−1), KH2PO4 (0.04 g l−1), NaHCO3 (0.4 g l−1), vitamin K (0.0025 g l−1), hemin (0.005 g l-1), l-cysteine • HCl (0.25 g l−1), Tween 80 (0.25 ml l−1), horse serum (50 ml l−1) and glucose (2 g l−1) and incubated at 37 °C for ~24 h. Cultures were adjusted to OD600 of 0.4–0.5, subcultured at a 1:50 dilution into fresh PYTs (without glucose), with the indicated carbohydrates added to a final concentration of 2 g l−1. Glycogen was from oyster (Sigma, G8751). Assays were performed in duplicate in 384-well plates sealed with BreathEasy gas permeable membranes (Diversified Biotech) under anaerobic conditions. Bacterial growth was monitored by measuring the OD600 at 1 h intervals for 48 h in a BioTek Epoch2 plate reader. Data were normalized to blank (uninoculated) media. For inhibition assays, bacteria were cultivated as above, with the addition of acarbose at the indicated concentrations. The extent of inhibition was determined by normalizing OD600 for each treatment to an untreated control at the time the control reached stationary phase, then IC50 values were calculated using least-squares regression (GraphPad Prism 8).

ShortBRED was used to quantify the abundance of the six biochemically characterized bacterial GDEs in previously sequenced vaginal metagenomes and metatranscriptomes49. First, ShortBRED-Identify was used to create markers for all 6 PulA sequences using UniRef90 2017 as a reference list (Supplementary File 1) and an 85% cluster ID setting. Markers were used in ShortBRED-Quantify to determine the abundance of pulA genes and transcripts in paired metagenome and metatranscriptome databases (Bioproject PRJNA797778). The scripts used for processing the datasets have been previously described48. The output from ShortBRED-Quantify is reads per million reads per kilobase million (RPKM) and this was normalized to counts per microbial genome using the average genome sizes (AGS) of each metagenome sample, calculated using MicrobeCensus60. We normalized the output from ShortBRED using the previously derived equation shown below61.

Sample metadata were used to bin the results by community state type (CST I n = 39, CST II n = 16, CST III n = 31, CST IV n = 83, CST V n = 9). The fraction of samples positive for a bacterial GDE gene in a given CST was calculated by dividing the number of samples that contained a hit (reads >0) by the total number of samples with the corresponding CST.

CVLs were obtained from Dr Caroline Mitchell at Massachusetts General Hospital (IRB: 2014P001066). All metadata associated with this cohort are reported (Supplementary File 1). CVLs were collected using 3 ml of sterile saline washed over the cervix and vaginal walls with a transfer pipette and then re-aspirated. Samples were centrifuged (10,000 × g for 10 min at 4 °C) and the supernatants were decanted and used in the assay. Purified proteins were diluted in buffer A (50 mM HEPES, 300 mM KCl, 10% glycerol, pH 7.8) to 1 μM, then used in the assay. Human salivary amylase was purchased from Sigma Aldrich (A1031-1KU). Human amylase was detected in CVLs using an ELISA for human pancreatic amylase (Abcam ab137969) according to manufacturer instructions.

Amylase activity of CVL supernatants was determined using the EnzCheck Ultra Amylase Assay kit (Thermo Fisher, E33651). The substrate was prepared according to the kit instructions using three different buffers (20 mM sodium acetate, 0.5 mM CaCl2, pH 4.4; 20 mM sodium acetate, 0.5 mM CaCl2, pH 5.5; 20 mM MES, 0.5 mM CaCl2, pH 6.8). CVL supernatant (10 μl) was added to each well of a black clear-bottom 96-well plate and then diluted with 40 μl of pH-adjusted buffer. The reactions were initiated with 50 μl of substrate and incubated for 30 min at 37 °C. The pH-adjusted buffer made up 90% of the reaction volume and each kit reagent was dissolved in the corresponding buffer. Fluorescence was measured at 485/528 nm. Initial rates were calculated in the plate reader software (Biotek) by determining the highest slope that covered at least 5 data points.

CVF samples were collected from Seattle University Affiliates (IRB: FY2022‐002). The participants were not compensated for their inclusion in the study. All metadata are reported in Supplementary Information (Supplementary Table 4). Donors self-collected a sample by inserting a Soft Disc and then waiting 1–4 h before removing the disc and placing it into a 50 ml conical vial. Within 1 h of collection, CVF was removed from the disc through the addition of 1 ml PBS and centrifugation at 200 × g for 8 min. Samples were then frozen in 0.1 ml aliquots at −70 °C.

Biotinylated and fluorescent probes for α-amylases (CYR1114 and CYR232, respectively)26 and α-glucosidases (JJB384 and JJB383, respectively)62 were kindly provided by Dr Hermann Overkleeft and Dr Gideon Davies (Leiden University). Before use, CVF samples were spun down (10,000 × g for 5 min) to remove mucins. CVF samples were normalized to a protein concentration (bicinchoninic acid assay) of 1 mg ml−1 using sterile PBS, and EDTA-free protease inhibitor cocktail (Roche) was added. CVF supernatant was then incubated with the fluorescent amylase probe at a final concentration of 25 μM or the fluorescent glucosidase probe at a final concentration of 10 μM. Negative controls of vehicle (1% v/v dimethyl sulfoxide in water) and heat-shock controls were included to identify background fluorescence or off-target labelling. Samples were incubated for 4 h at 37 °C. Proteins were then separated on a 4–20% PAGE gel (Bio-Rad) and probe fluorescence was visualized (Azure C600).

Active amylases were enriched using ABPP and identified via LC–MS/MS as previously described, with slight modifications50. CVF supernatant prepared as above was divided into three 400 μl aliquots. Biotinylated amylase probe (final concentration 25 μM), biotinylated glucosidase probe (final concentration 10 μM) or an equal volume of vehicle (1% dimethyl sulfoxide in water) was added and samples were incubated for 4 h at 37 °C. After labelling, 400 μl of ice-cold methanol was added and samples were stored at −70 °C overnight to precipitate proteins. Precipitated protein was collected via centrifugation (10,000 × g for 10 min), redissolved in 500 μl 1.2% SDS in PBS and heated at 95 °C for 2 min. Samples were then centrifuged (14,000 × g for 5 min) to remove insoluble proteins.

Streptavidin agarose resin (100 μl, Thermo Fisher) was prepared by washing with 0.5% w/v SDS in PBS (3×), 6 M urea in 25 mM NH4HCO3 (3×) and PBS (3×) using a vacuum manifold. Washed resin in 2 ml of PBS was then added to protein samples and samples were incubated, rotating at 37 °C for 1 h. Samples were then transferred to columns (Bio-Rad Poly-Prep) on a vacuum manifold and washed with 1 ml volumes of 0.5% w/v SDS in PBS (3×), 6 M urea in 25 mM NH4HCO3 (3×), ultrapure water (3×), PBS (9×) and 25 mM NH4HCO3 (5×). Resin was then transferred in 6 M urea in 25 mM NH4HCO3 to low-bind Eppendorf tubes and reduced with 5 mM DTT at 37 °C for 30 min, followed by alkylation with 10 mM iodoacetamide at 50 °C for 1 h. Samples were washed with PBS (9×) and 25 mM NH4HCO3 (5×). Resin was then transferred to new low-bind Eppendorf tubes, resuspended in 200 μl 25 mM NH4HCO3 and 0.4 μl 0.25 μg μl−1 trypsin (Promega, proteomics grade) in 25 mM HEPES was added. Samples were incubated overnight at 37 °C with rotation. Supernatants were collected followed by an additional resin wash with 150 μl 25 mM NH4HCO3, which was added to the original supernatant. The peptides were then dried down (Speed-vac) before further analysis.

Except for S010, LC–MS/MS analysis was performed with a Thermo Scientific Easy1200 nLC (Thermo Scientific) coupled to a tribrid Orbitrap Eclipse (Thermo Scientific) mass spectrometer. In-line desalting was accomplished using a reversed-phase trap column (100 μm × 20 mm) packed with Magic C18AQ (5 μm 200 Å resin; Michrom Bioresources), followed by peptide separations on a reversed-phase column (75 μm × 270 mm) packed with ReproSil-Pur C18AQ (3 μm 120 Å resin; Dr Maisch) directly mounted on the electrospray ion source. A 60 min gradient using a two-mobile-phase system consisted of 0.1% formic acid in water (A) and 80% acetonitrile in 0.1% formic acid in water (B). The chromatographic separation was achieved over a 60 min gradient from 8 to 30% B over 57 min, 30 to 45% B for 10 min, 45 to 60% B for 3 min, 60 to 95% B for 2 min and held at 95% B for 11 min at a flow rate of 300 nl min−1. A spray voltage of 2,300 V was applied to the electrospray tip in line with a FAIMS source using varied compensation voltages of –40, –60 and –80 while the Orbitrap Eclipse instrument was operated in the data-dependent mode, MS survey scans were in the Orbitrap (normalized AGC target value 300%, resolution 240,000 and maximum injection time 50 ms) with a 1 s cycle time, and MS/MS spectra acquisition were detected in the linear ion trap (normalized AGC target value of 50% and injection time 35 ms) using HCD activation with a normalized collision energy of 27%. Selected ions were dynamically excluded for 60 s after a repeat count of 1. For S010, peptide samples were disolved in 2% acetonitrile in 0.1% formic acid (20 μl) and analysed (18 μl) by LC/ESI MS/MS with a Thermo Scientific Easy-nLC 1000 (Thermo Scientific) coupled to a tribrid Orbitrap Fusion (Thermo Scientific) mass spectrometer. In-line desalting was accomplished using a reversed-phase trap column (100 μm × 20 mm) packed with Magic C18AQ (5 μm 200 Å resin; Michrom Bioresources), followed by peptide separations on a reversed-phase column (75 μm × 250 mm) packed with ReproSil-Pur 120 C18AQ (3 μm 120 Å resin Dr Maisch) directly mounted on the electrospray ion source. A 90-min gradient from 2% to 35% acetonitrile in 0.1% formic acid at a flow rate of 300 nl min−1 was used for chromatographic separations. A spray voltage of 2,200 V was applied to the electrospray tip and the Orbitrap Fusion instrument was operated in the data-dependent mode, MS survey scans were in the Orbitrap (AGC target value 500,000, resolution 120,000 and injection time 50 ms) with a 3 s cycle time and MS/MS spectra acquisition were detected in the linear ion trap (AGC target value of 10,000 and injection time 35 ms) using HCD activation with a normalized collision energy of 27%. Selected ions were dynamically excluded for 20 s after a repeat count of 1.

Samples were analysed with FragPipe IonQuant enabled63,64,65,66. Spectra were matched to a database containing UniProt human reference proteins; UniRef90 proteins for L. crispatus, L. iners, L. gasseri, L. jensenii, G. vaginalis, A. vaginae, P. bivia and M. mueleris; common contaminants; and reverse protein sequences as decoys for false discovery rate (FDR) estimation (accessed 25 May 2022). Raw data are available in Supplementary File 1. Abundance data were analysed using Perseus67. Abundance data were log2 transformed and normalized using width adjustment. For S010, protein groups present in two of three replicates were averaged and the data tables were combined. Proteins with at least a 2-fold increased abundance relative to the No Probe control in one biological sample, 2 spectral counts across all samples and a ProteinProphet probability >0.95 (corresponding to ~2% FDR) were searched for CAZyme domains using dbCAN 2 (ref. 68).

CVF fluid (5 μl, not centrifuged) was added to 95 μl 10 mg ml−1 pullulan (Megazyme) in reaction buffer. The reaction mixtures were incubated at 37 °C and timepoints at 3, 5, 8 and 24 h were taken by diluting 100-fold into 9:1 acetonitrile:water. Samples were further diluted 1,000-fold in acetonitrile and analysed by LC–MS as described above. Samples were normalized to a no-enzyme control.

The inhibitory effect of a panel of four small-molecule inhibitors was determined using a modification of the amylase activity assay in CVLs used above. For initial screening, enzymes were preincubated with 1 mM acarbose (Abcam), acarviosin (Toronto Research Products), voglibose (Spectrum Chemical) or miglitol (Tokyo Chemical) for 15 min at room temperature. For IC50 analysis, enzyme (2.5–50 nM) was preincubated with acarbose ranging from 0.366 μM to 3,000 μM in a total volume of 50 μl. The reactions were initiated with 50 μl of substrate and incubated for 30 min at 37 °C, monitoring fluorescence at 485/528 nm. Initial rates were calculated by determining the highest slope that covered at least 8 data points. Percent activity was calculated by normalizing the activity to a no-inhibitor control. IC50 values were calculated using nonlinear fitting of the data to the inhibitor vs normalized response function (GraphPad Prism 8). Error associated with the IC50 values represents 95% confidence intervals.

No statistical method was used to predetermine sample size for any of the statistical comparisons. However, our sample size was similar to previous work on this topic21. For all statistical tests, data distribution was assumed to be normal, but this was not formally tested. Randomization was not relevant to this study because we did not place participants into groups. A ROUTE test was used to identify and remove outliers in the activity analysis of the CVL samples (Extended Data Fig. 8). The researchers performing the activity analysis of the clinical samples were blinded to the metadata during the course of the study.

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

The protein identification number in the NCBI database for each enzyme characterized is as follows: L. crispatus PulA (EEU28204.2), L. iners PulA (EFQ51965.1), G. vaginalis PulA (EPI56559.1), M. mulieris PulA (EEZ90738.1), P. bivia PulA (WP_061450340.1), P. bivia GH13 (WP_036862728.1). The L. crispatus C0176A1 (PulA−) genome can be found under accession number JAEDCG000000000. The metagenomic and metatranscriptomic datasets used in this study can be found under Bioproject PRJNA797778. The proteomics data from this study can be accessed in the PRIDE database using accession code PXD042917. Protein domain annotations were from the Pfam and CAZy databases. All data that support the findings of this study are available in a data repository at synapse.org and can be accessed at https://www.synapse.org/#!Synapse:syn51422003. Source data are provided with this paper.

Kalia, N., Singh, J. & Kaur, M. Microbiota in vaginal health and pathogenesis of recurrent vulvovaginal infections: a critical review. Ann. Clin. Microbiol. Antimicrob. 19, 5 (2020).

PubMed PubMed Central Google Scholar

Ravel, J. et al. Vaginal microbiome of reproductive-age women. Proc. Natl Acad. Sci. USA 108, 4680–4687 (2011).

CAS PubMed Google Scholar

Anahtar, M. N. et al. Inflammatory responses in the female genital tract. Immunity 42, 965–976 (2016).

Google Scholar

Gosmann, C. et al. Lactobacillus-deficient cervicovaginal bacterial communities are associated with increased HIV acquisition in young South African women. Immunity 46, 29–37 (2017).

CAS PubMed PubMed Central Google Scholar

Dols, J. A. M. et al. Microarray-based identification of clinically relevant vaginal bacteria in relation to bacterial vaginosis. Am. J. Obstet. Gynecol. 204, 305.e1–305.e7 (2011).

PubMed Google Scholar

Hočevar, K. et al. Vaginal microbiome signature is associated with spontaneous preterm delivery. Front. Med. 6, 201 (2019).

Google Scholar

Gajer, P. et al. Temporal dynamics of the human vaginal microbiota. Sci. Transl. Med. 4, 132ra52 (2012).

PubMed PubMed Central Google Scholar

Foster, K. R., Schluter, J., Coyte, K. Z. & Rakoff-Nahoum, S. The evolution of the host microbiome as an ecosystem on a leash. Nature 548, 43–51 (2017).

CAS PubMed PubMed Central Google Scholar

Rakoff-Nahoum, S., Foster, K. R. & Comstock, L. E. The evolution of cooperation within the gut microbiota. Nature 533, 255–259 (2016).

CAS PubMed PubMed Central Google Scholar

Porter, N. T. & Martens, E. C. The critical roles of polysaccharides in gut microbial ecology and physiology. Annu. Rev. Microbiol. 71, 349–369 (2017).

CAS PubMed Google Scholar

Rakoff-Nahoum, S., Coyne, M. J. & Comstock, L. E. An ecological network of polysaccharide utilization among human intestinal symbionts. Curr. Biol. 24, 40–49 (2014).

CAS PubMed Google Scholar

Cruickshank, R. & Sharman, A. The biology of the vagina in the human subject. BJOG 41, 208–226 (1934).

Google Scholar

Amabebe, E. & Anumba, D. O. C. The vaginal microenvironment: the physiologic role of Lactobacilli. Front. Med. 5, 181 (2018).

Google Scholar

Mirmonsef, P. et al. Free glycogen in vaginal fluids is associated with Lactobacillus colonization and low vaginal pH. PLoS ONE 9, 26–29 (2014).

Google Scholar

Stewart-Tull, D. E. S. Evidence that vaginal lactobacilli do not ferment glycogen. Am. J. Obstet. Gynecol. 88, 676–679 (1964).

CAS PubMed Google Scholar

Wylie, J. G. & Henderson, A. Identity and glycogen-fermenting ability of lactobacilli isolated from the vagina of pregnant women. J. Med. Microbiol. 2, 363–366 (1969).

CAS PubMed Google Scholar

Spear, G. T. et al. Human α-amylase present in lower-genital-tract mucosal fluid processes glycogen to support vaginal colonization by Lactobacillus. J. Infect. Dis. 210, 1019–1028 (2014).

CAS PubMed PubMed Central Google Scholar

Spear, G. T. et al. Effect of pH on cleavage of glycogen by vaginal enzymes. PLoS ONE 10, e0132646 (2015).

PubMed PubMed Central Google Scholar

Huffman, R. D., Nawrocki, L. D., Wilson, W. A. & Brittingham, A. Digestion of glycogen by a glucosidase released by Trichomonas vaginalis. Exp. Parasitol. J. 159, 151–159 (2015).

CAS Google Scholar

Smith, R. W., Brittingham, A. & Wilson, W. A. Purification and identification of amylases released by the human pathogen Trichomonas vaginalis that are active towards glycogen. Mol. Biochem. Parasitol. 210, 22–31 (2016).

CAS PubMed Google Scholar

Nunn, K. L. et al. Amylases in the human vagina. mSphere https://doi.org/10.1128/msphere.00943-20 (2020).

van der Veer, C. et al. Comparative genomics of human Lactobacillus crispatus isolates reveals genes for glycosylation and glycogen degradation: implications for in vivo dominance of the vaginal microbiota. Microbiome 7, 49 (2019).

PubMed PubMed Central Google Scholar

Hertzberger, R. et al. Genetic elements orchestrating Lactobacillus crispatus glycogen metabolism in the vagina. Int. J. Mol. Sci. 23, 5590 (2022).

CAS PubMed PubMed Central Google Scholar

Qiao, Y. et al. Gene cloning and enzymatic characterization of alkali-tolerant type I pullulanase from Exiguobacterium acetylicum. Lett. Appl. Microbiol. 60, 52–59 (2015).

CAS PubMed Google Scholar

Ma, B. et al. A comprehensive non-redundant gene catalog reveals extensive within-community intraspecies diversity in the human vagina. Nat. Commun. 11, 940 (2020).

CAS PubMed PubMed Central Google Scholar

Chen, Y. et al. Activity-based protein profiling of retaining α-amylases in complex biological samples. J. Am. Chem. Soc. 143, 2423–2432 (2021).

CAS PubMed PubMed Central Google Scholar

von Heijne, G. The signal peptide. J. Membr. Biol. 115, 195–201 (1990).

Google Scholar

Bhandari, P., Tingley, J. P., Palmer, D. R. J., Abbott, D. W. & Hill, J. E. Characterization of an a-glucosidase enzyme conserved in Gardnerella spp. isolated from the human vaginal microbiome. J. Bacteriol. 203, e0021321 (2021).

PubMed Google Scholar

Boot, H. J., Kolen, C. P. A. M. & Pouwels, P. H. Identification, cloning, and nucleotide sequence of a silent S-layer protein gene of Lactobacillus acidophilus ATCC 4356 which has extensive similarity with the S-layer protein gene of this species. J. Bacteriol. 177, 7222–7230 (1995).

CAS PubMed PubMed Central Google Scholar

Willing, S. E. et al. Clostridium difficile surface proteins are anchored to the cell wall using CWB2 motifs that recognise the anionic polymer PSII. Mol. Microbiol. 96, 596–608 (2015).

CAS PubMed PubMed Central Google Scholar

Krogh, A., Larsson, B., von Heijne, G. & Sonnhammer, E. L. L. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol. 305, 567–580 (2001).

CAS PubMed Google Scholar

Cantarel, B. I. et al. The Carbohydrate-Active EnZymes database (CAZy): an expert resource for glycogenomics. Nucleic Acids Res. 37, 20894 (2009).

Google Scholar

El-Gebali, S. et al. The Pfam protein families database in 2019. Nucleic Acids Res. 47, D427–D432 (2019).

CAS PubMed Google Scholar

Boraston, A. B. et al. A structural and functional analysis of α-glucan recognition by family 25 and 26 carbohydrate-binding modules reveals a conserved mode of starch recognition. J. Biol. Chem. 281, 587–598 (2006).

CAS PubMed Google Scholar

Wiatrowski, H. A. et al. Mutations in the Gal83 glycogen-binding domain activate the Snf1/Gal83 kinase pathway by a glycogen-independent mechanism. Mol. Cell. Biol. 24, 352–361 (2004).

CAS PubMed PubMed Central Google Scholar

Michelin, M. et al. Purification and biochemical characterization of a thermostable extracellular glucoamylase produced by the thermotolerant fungus Paecilomyces variotii. J. Ind. Microbiol. Biotechnol. 35, 17–25 (2008).

CAS PubMed Google Scholar

da Silva, T. M. et al. Purification and biochemical characterization of a novel α-glucosidase from Aspergillus niveus. Int. J. Gen. Mol. Microbiol. 96, 569–578 (2009).

Google Scholar

Li, Y., Zhang, L., Ding, Z., Gu, Z. & Shi, G. Engineering of isoamylase: improvement of protein stability and catalytic efficiency through semi-rational design. J. Ind. Microbiol. Biotechnol. 43, 3–12 (2016).

CAS PubMed Google Scholar

Jung, J. H. et al. Characterization of a novel extracellular α-amylase from Ruminococcus bromii ATCC 27255 with neopullulanase-like activity. Int. J. Biol. Macromol. 130, 605–614 (2019).

CAS PubMed Google Scholar

Lee, H. W. et al. Characterization and application of BiLA, a psychrophilic α-amylase from Bifidobacterium longum. J. Agric. Food Chem. 64, 2709–2718 (2016).

CAS PubMed Google Scholar

Marie S. Møller, A. et al. An extracellular cell-attached pullulanase confers branched α-glucan utilization in human gut Lactobacillus acidophilus. Appl. Environ. Microbiol. 83, e00402-17 (2017).

PubMed PubMed Central Google Scholar

Amsel, R. et al. Nonspecific vaginitis. Diagnostic criteria and microbial and epidemiologic associations. Am. J. Med. 74, 14–22 (1983).

CAS PubMed Google Scholar

Lin, F. P. & Leu, K. L. Cloning, expression, and characterization of thermostable region of amylopullulanase gene from Thermoanaerobacter ethanolicus 39E. Appl. Biochem. Biotechnol. 97, 33–44 (2002).

CAS PubMed Google Scholar

Nisha, M. & Satyanarayana, T. Characterization of recombinant amylopullulanase (gt-apu) and truncated amylopullulanase (gt-apuT) of the extreme thermophile Geobacillus thermoleovorans NP33 and their action in starch saccharification. Appl. Microbiol. Biotechnol. 97, 6279–6292 (2013).

CAS PubMed Google Scholar

Nisha, M. & Satyanarayana, T. Recombinant bacterial amylopullulanases: developments and perspectives. 4, 388–400 (2013).

Wang, M. et al. Discovery of a new microbial origin cold-active neopullulanase capable for effective conversion of pullulan to panose. Int. J. Mol. Sci. 23, 6928 (2022).

CAS PubMed PubMed Central Google Scholar

Hii, S. L., Tan, J. S., Ling, T. C. & Ariff, A. Bin Pullulanase: role in starch hydrolysis and potential industrial applications. Enzym. Res. 2012, 921362 (2012).

Google Scholar

France, M. T. et al. Insight into the ecology of vaginal bacteria through integrative analyses of metagenomic and metatranscriptomic data. Genome Biol. 23, 66 (2022).

CAS PubMed PubMed Central Google Scholar

Kaminski, J. et al. Structure, function, and diversity of the healthy human microbiome with ShortBRED. Nature 486, 207–214 (2012).

Google Scholar

Garcia, W. L. et al. Profiling how the gut microbiome modulates host xenobiotic metabolism in response to benzo[a]pyrene and 1-nitropyrene exposure. Chem. Res. Toxicol. 35, 585–596 (2022).

CAS PubMed PubMed Central Google Scholar

Bhandari, P., Tingley, J., Abbott, D. W. & Hill, J. E. Glycogen-degrading activities of catalytic domains of a-amylase and a-amylase-pullulanase enzymes conserved in Gardnerella spp. from the vaginal microbiome. J. Bacteriol. 205, e0039322 (2023).

PubMed Google Scholar

Andersch-Björkman, Y., Thomsson, K. A., Holmén Larsson, J. M., Ekerhovd, E. & Hansson, G. C. Large-scale identification of proteins, mucins and their O-glycosylation in the endocervical mucus during the menstrual cycle. Mol. Cell. Proteom. 6, 708–716 (2007).

Google Scholar

Pereira, L. et al. Identification of novel protein biomarkers of preterm birth in human cervical−vaginal fluid. J. Proteome Res. 6, 1269–1276 (2007).

CAS PubMed Google Scholar

Kim, Y. E. et al. Quantitative proteomic profiling of cervicovaginal fluid from pregnant women with term and preterm birth. Proteome Sci. 19, 3 (2021).

CAS PubMed PubMed Central Google Scholar

Zhang, J., Li, L., Zhang, T. & Zhong, J. Characterization of a novel type of glycogen‑degrading amylopullulanase from Lactobacillus crispatus. Appl. Microbiol. Biotechnol. 106, 4053–4064 (2022).

Coyte, K. Z. & Rakoff-Nahoum, S. Understanding competition and cooperation within the mammalian gut microbiome. Curr. Biol. 29, R538–R544 (2019).

CAS PubMed PubMed Central Google Scholar

Lithgow, K. V, Cochinamogulos, A., Muirhead, K. & Oluoch, L. M. Resolving glycogen and related enzymes reveals correlates of Lactobacillus crispatus dominance in a cohort of young African women. Preprint at Res. Square https://doi.org/10.21203/rs.3.rs-1679828/v1 (2022).

Markowitz, V. M. et al. IMG: the integrated microbial genomes database and comparative analysis system. Nucleic Acids Res. 40, 115–122 (2012).

Google Scholar

Almagro Armenteros, J. J. et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat. Biotechnol. 37, 420–423 (2019).

CAS PubMed Google Scholar

Nayfach, S. & Pollard, K. S. Average genome size estimation improves comparative metagenomics and sheds light on the functional ecology of the human microbiome. Genome Biol. 16, 51 (2015).

PubMed PubMed Central Google Scholar

Levin, B. J. et al. A prominent glycyl radical enzyme in human gut microbiomes metabolizes trans-4-hydroxy-L-proline. Science 355, eaai8386 (2017).

PubMed PubMed Central Google Scholar

Jiang, J. et al. Detection of active mammalian GH31 α-glucosidases in health and disease using in-class, broad-spectrum activity-based probes. ACS Cent. Sci. 2, 351–358 (2016).

CAS PubMed PubMed Central Google Scholar

Yu, F., Haynes, S. E. & Nesvizhskii, A. I. IonQuant enables accurate and sensitive label-free quantification with FDR-controlled match-between-runs. Mol. Cell. Proteom. 20, 100077 (2021).

CAS Google Scholar

Keller, A., Nesvizhskii, A. I., Kolker, E. & Aebersold, R. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 74, 5383–5392 (2002).

CAS PubMed Google Scholar

Nesvizhskii, A. I., Keller, A., Kolker, E. & Aebersold, R. A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 75, 4646–4658 (2003).

CAS PubMed Google Scholar

Kong, A. T., Leprevost, F. V., Avtonomov, D. M., Mellacheruvu, D. & Nesvizhskii, A. I. MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics. Nat. Methods 14, 513–520 (2017).

CAS PubMed PubMed Central Google Scholar

Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740 (2016).

CAS PubMed Google Scholar

Zhang, H. et al. DbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 46, W95–W101 (2018).

CAS PubMed PubMed Central Google Scholar

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We thank A. Woo for help in cloning several of the bacterial PulA homologues, B. Fu for critical reading of the manuscript, and A. Bergerat-Thompson at Massachusetts General Hospital for providing CVL samples; H. Overkleeft (Leiden University) and G. Davies (University of York) for the generous gift of the ABPP probes; all members of the Bill and Melinda Gates Foundation Vaginal Microbiome Research Consortium for the helpful conversations about the work; the study participants for the donation of the CVL and CVF samples; and D. Relman (Stanford University) for the gift of the L. crispatus C0176A1. Financial support for this study was provided by the Bill and Melinda Gates Foundation (award number OPP1189211 to E.P.B). Additional support was provided by the Proteomics and Metabolomics Shared Resource of the Fred Hutch/University of Washington Cancer Consortium (award number P30 CA05704 to C.W). E.P.B. is a Howard Hughes Medical Institute Investigator. M.I.H.-P. was supported as a Fellow in the Pediatric Scientist Development Program (Award No. HD000850) from the Eunice Kennedy Shriver National Institute of Child Health and Human Development and through a Physician Scientist Fellowship from the Doris Duke Charitable Foundation (Grant No. 2019129). S.R.-N. was supported by a Career Award for Medical Scientists from the Burroughs Wellcome Fund, a Pew Biomedical Scholarship, a Basil O’Connor Starter Scholar Award from the March of Dimes (1K08AI130392-01) and by the NIGMS/NIH (award DP2GM136652). C.W., A.K.N. and M.Q.P. were supported by the M.J. Murdock Charitable Trust (award NS‐201913756) and the Seattle University College of Science and Engineering. P.P. was supported by the National Science Foundation Graduate Research Fellowship (NSF-GRFP). M.T.F. and J.R. were supported by the Bill and Melinda Gates Foundation (Award No. OPP1189217).

These authors contributed equally: Dominick J. Jenkins, Benjamin M. Woolston.

Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA

Dominick J. Jenkins, Benjamin M. Woolston, Paula Pelayo & Emily P. Balskus

Department of Chemical Engineering, Northeastern University, Boston, MA, USA

Benjamin M. Woolston

Division of Infectious Diseases and Division of Gastroenterology, Department of Pediatrics, Boston Children’s Hospital, Boston, MA, USA

M. Indriati Hood-Pishchany & Seth Rakoff-Nahoum

Department of Microbiology, Harvard Medical School, Boston, MA, USA

M. Indriati Hood-Pishchany & Seth Rakoff-Nahoum

Department of Chemistry, Seattle University, Seattle, WA, USA

Alyssa N. Konopaski, M. Quinn Peters & Christopher Whidbey

Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA

Michael T. France & Jacques Ravel

Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD, USA

Michael T. France & Jacques Ravel

Vincent Center for Reproductive Biology, Massachusetts General Hospital, Boston, MA, USA

Caroline M. Mitchell

Harvard Medical School, Boston, MA, USA

Caroline M. Mitchell

Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA

Emily P. Balskus

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E.P.B., B.M.W., S.R.-N. and M.I.H.-P. conceived the study. D.J.J. and B.M.W. designed and conducted enzyme purification and biochemical characterization experiments. E.P.B., D.J.J., B.M.W., C.W., S.R.-N. and M.I.H.-P. wrote the manuscript. D.J.J. and M.I.H. designed and conducted bacterial growth experiments. C.W., A.N.K. and M.Q.P. designed and conducted ABPP experiments. D.J.J., P.P. and E.P.B. designed bioinformatic analysis of metagenomic and metatranscriptomic data. P.P. conducted bioinformatic analysis of multi-omics sequencing data. M.T.F. and J.R. assisted with access, analysis and interpretation of the metagenomic and metatranscriptomic data. C.M.M. provided CVL samples and corresponding metadata. All authors contributed to the interpretation of data, were involved in the revision of the manuscript, and approved the final manuscript.

Correspondence to Seth Rakoff-Nahoum, Christopher Whidbey or Emily P. Balskus.

C.M.M. has served as a consultant for Scynexis Inc, Ferring Pharmaceuticals and has received research funding from Scynexis, Inc. J.R. is co-founder of LUCA Biologics, a biotechnology company focusing on translating microbiome research into live biotherapeutics drugs for women’s health. The other authors declare no competing interests.

Nature Microbiology thanks Nicole Koropatkin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

2 μg of purified protein was loaded onto a Biorad (2-14% Bis-Tris) SDS page gel. From left to right, Precision Plus Protein All Blue Standards (BioRad); L. crispatus PulA, 137kD; L. iners PulA, 192kD; M. mulieris PulA, 151kD; G. vaginalis PulA, 219kD; P. bivia GH 13, 70.4kD; P. bivia PulA, 72.8kD; Precision Plus Protein All Blue Standards (BioRad). Molecular weights of heterologously expressed proteins were predicted in EXPASY. This experiment was repeated twice demonstrating similar results (n = 2).

Source data

a. Growth of L. crispatus C0176A1 (pulA—) containing all relevant controls for protein complementation assays b. Growth of L. crispatus MV-1A-US (pulA+) containing all relevant controls for protein complementation assays. Glycogen, maltose, and glucose conditions are derived from the same data across all graphs within each panel. All growth data consists of three independent experiments performed over three days (n = 3).

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a. Michaelis–Menten kinetic analysis for assays with glycogen. b. Michaelis–Menten kinetic analysis for assays with pullulan c. Michaelis–Menten kinetic analysis for assays with amylose. All data are representative of three experimental replicates performed over two days. Nonlinear fitting was performed in Graphpad Prism 8.

Source data

a. 2 μg of active site mutants and wild type G. vaginalis PulA was loaded onto a Biorad (2-14% Bis-Tris) SDS page gel. This experiment was repeated twice demonstrating similar results (n = 2). b. Specific activity of active site mutants on glycogen. Inset graph shows specific activity data for G. vaginalis mutants. ∆AS1, ∆AS2, and ∆DBL represents D233A, D1317A, and both mutations respectively. Data is representative of three experimental replicates over two days. Error bars represent one standard deviation above and below the mean.

Source data

1 μL of each standard (10 mg/mL) and enzymatic reaction mixture was spotted onto a TLC plate (20 cm by 20 cm, Analtech Silica gel HLF). TLC was run for approximately 5 h in 3:2:1 butanol: acetic acid: water and stained with 1:19 sulfuric acid: ethanol. These results are representative of two trials showing similar findings (n = 2).

a. Effects of known amylase inhibitors (1 mM) on the activities of purified GDEs toward a BODIPY fluorescent starch substrate (n = 1). b. Inhibitory activity of acarbose toward purified extracellular amylases. A BODIPY fluorescent starch substrate was used and activity was normalized to a no inhibitor control. Data are representative of three experimental replicates over two days. c. Bacteria were grown in the presence of the indicated concentrations of acarbose in media containing either glucose, maltose or glycogen as the primary carbohydrate source. Growth in the presence of inhibitor was normalized to the untreated control. IC50 values were calculated using a least-squares regression of the normalized values. ND (Not determined) is indicated when the resulting curve fit was poor and an IC50 value could not be confidently determined, or the overall growth inhibition was less than 10%. Data are representative of at least two biological replicates performed over two days.

Source data

% Positive represents the percentage of samples that contained reads mapping to our query proteins (reads > 0). The sample size is as follows: CST I, n = 39; CST II, n = 16; CST III, n = 31; CST IV, n = 83; CST V, n = 9.

Source data

Outliers were determined and removed using a ROUT test. A two-tailed Pearson test was used to determine correlation (pH 4.4, r = 0.8556, 95% CI 0.6472 to 0.9450, p <0.0001; pH 5.5, r = 0.9611, 95% CI 0.8966 to 0.9857, p <0.0001; pH 6.8, r = 0.9713, 95% CI 0.9252 to 0.9891, p <0.0001) Activity data represents the mean of three experiments over two days and the ELISA detection is the mean of two experiments over two days. Error bars represent one standard deviation above and below the mean.

Source data

Spectral counts indicate the sum of total spectra assignable to a protein across all six individual samples. Abbreviations: Li PulA, L. iners PulA; Lc PulA, L. crispatus PulA; Gv AmyA, G. vaginalis AmyA; GAA, human lysosomal α-glucosidase; AMY1, human salivary amylase; SP, signal peptide; SlpA, surface layer protein A; GH, glycoside hydrolase domain PUD, bacterial pullulanase-associated domain; CBM, Carbohydrate binding module.

Timepoints from CVF reactions were removed and frozen at 3, 5, 8, and 24 hours. These data are derived from three experimental replicates over two days.

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Supplementary Figs. 1–5 and Tables 1–4.

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Unprocessed SDS–PAGE gel.

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Unprocessed SDS–PAGE gel.

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Jenkins, D.J., Woolston, B.M., Hood-Pishchany, M.I. et al. Bacterial amylases enable glycogen degradation by the vaginal microbiome. Nat Microbiol 8, 1641–1652 (2023). https://doi.org/10.1038/s41564-023-01447-2

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Received: 08 July 2021

Accepted: 11 July 2023

Published: 10 August 2023

Issue Date: September 2023

DOI: https://doi.org/10.1038/s41564-023-01447-2

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