Defining Vaginal Community Dynamics: daily microbiome transitions, the role of menstruation, bacteriophages and bacterial genes

What do we already know about this subject?

The vaginal microbiota plays a key role in defending against pathogens, maintaining mucosal immunity, and supporting reproductive health. Dominance by Lactobacillus species, especially L. crispatus, maintains a low pH and inhibits pathogenic colonisation² . Dysbiosis — defined by a loss of lactobacilli and overgrowth of anaerobic species such as Gardnerella or Prevotella — is associated with increased risks of bacterial vaginosis (BV), preterm birth³ , infertility⁴, sexually transmitted infections, human papillomavirus (HPV) infections and gynaecological cancers⁵ . Previous studies have shown that hormonal changes, menstruation, and sexual intercourse can influence the composition of the vaginal microbiome⁶. Many of these studies relied on infrequent sampling and lacked resolution to assess short-term fluctuations or determine the drivers of transitions between eubiosis and dysbiosis. The contributions of viral dynamics and functional bacterial genes have remained largely unexplored.

What are the main insights from the study?

This study introduces the concept of VCDs, offering a new framework for classifying microbiome behaviour across the menstrual cycle. Unlike community state types (CSTs), which describe static microbiome compositions, VCDs capture temporal patterns that may better reflect microbiome resilience and vulnerability. The four VCDs — constant eubiotic, constant dysbiotic, menses-related dysbiosis, and unstable dysbiotic — represent distinct profiles of microbial stability. Women in the constant eubiotic group maintained Lactobacillus dominance throughout the cycle, while those with constant dysbiosis had persistent BV-associated communities. Menses-related dysbiosis was characterised by temporary shifts during menstruation, often reverting mid-cycle, whereas the unstable group experienced abrupt fluctuations after exposures like sexual intercourse, suggesting greater ecological fragility.

One of the key findings was that instability in the vaginal microbiome is associated with increased bacteriophage activity and a higher prevalence of L. iners. This species is frequently linked to transitional or less stable states, and phage abundance may reflect active lytic cycles that destabilise dominant bacteria via “kill-the-winner” dynamics. Additionally, women with transient dysbiosis showed increased abundance of potential pathogens such as Sneathia spp. during and after menstruation, pointing to specific periods of vulnerability.

Strain-level analysis revealed differences in bacterial gene content, including bacteriocins produced by Gardnerella leopoldii that may inhibit lactobacilli. These genes were more prevalent in unstable and dysbiotic VCDs, supporting a possible mechanistic role in shaping community structure. Although these genetic findings require further validation, they highlight the importance of moving beyond species-level classification to understand microbial function and its impact on host health.

What are the consequences for clinical practice?

This study underscores the need to rethink how vaginal health is assessed and monitored in clinical practice. The recognition that vaginal microbiota are dynamic — and that stability patterns differ markedly between women — has implications for diagnostics, risk assessment, and therapeutic strategies. Sampling at a single time point, especially during menstruation, may fail to capture meaningful fluctuations or misrepresent a woman’s baseline microbial state. Clinicians should consider collecting samples at multiple points in the cycle to better assess microbiome behavior, particularly in patients with recurrent symptoms or reproductive concerns.

The limitations of CST-based classification are evident in this study. Two women with the same CST may exhibit entirely different VCDs, one with stable eubiosis and the other with frequent dysbiosis. Incorporating VCD assessment could enable more personalised interventions, such as recommending prophylactic microbiome support for women with unstable patterns or targeting high-risk windows (e.g. post-menses) for infection screening.

The identification of phage-driven instability and strain-level bacterial traits opens avenues for precision medicine. Future therapies may need to address microbial function — such as biofilm formation or bacteriocin production — rather than composition alone. Understanding the dynamics of vaginal bacteriophages could also inform novel microbiome stabilisation strategies.

Figure 1. Vaginal time series can be classified into four categories (Vaginal Community Dynamics) according to their proportions of eubiotic samples

a. A decision tree can separate a time series of samples into dynamic groups, based on the community state types (CSTs). Input from the user determines which CSTs are considered eubiotic (here: I, II, and V) and which days are to be considered free from the influence of menses (here: cycle day 9 to cycle day 25). Time series with ≥80% eubiotic samples are considered constant eubiotic; conversely, those with >80% dysbiotic samples are considered constant dysbiotic. For those in the 20–80% range, a second assessment is done on the days free of menses: if they are >80% eubiotic, the time series is considered menses-related dysbiotic, and otherwise unstable (changing from eubiosis to dysbiosis without a clear temporal pattern). b. A colour map with one individual per row and one day per column. The colour of each intersection depicts CST. Coloured bars on the left side show the vaginal community dynamics of each woman. c. Additional colour bars show the inferred vaginal community dynamics of each participant when using fewer samples for classification. Reproduced from Hugerth LW, et al. Microbiome 2024, 12, 1531 (doi:10.1186/s40168- 024-01870-5) under a CC-BY 4.0 license (creativecommons.org/licenses/by/4.0). No changes have been made to the figure.

The classification tool VALODY, designed to assign VCD categories based on VALENCIA CST assignments, is available on GitHub at https://github.com/ctmrbio/valody.