Exploring the female genital tract mycobiome in young South African women using metaproteomics

Abstract

Background: Female genital tract (FGT) diseases such as bacterial vaginosis (BV) and sexually transmitted infections are prevalent in South Africa, with young women being at an increased risk. Since imbalances in the FGT microbiome are associated with FGT diseases, it is vital to investigate the factors that influence FGT health. The mycobiome plays an important role in regulating mucosal health, especially when the bacterial component is disturbed. However, we have a limited understanding of the FGT mycobiome since many studies have focused on bacterial communities and have neglected low-abundance taxonomic groups, such as fungi. To reduce this knowledge deficit, we present the first large-scale metaproteomic study to define the taxonomic composition and potential functional processes of the FGT mycobiome in South African reproductive-age women.

Results: We examined FGT fungal communities present in 123 women by collecting lateral vaginal wall swabs for liquid chromatography-tandem mass spectrometry. From this, 39 different fungal genera were identified, with Candida dominating the mycobiome (53.2% relative abundance). We observed changes in relative abundance at the protein, genus, and functional (gene ontology biological processes) level between BV states. In women with BV, Malassezia and Conidiobolus proteins were more abundant, while Candida proteins were less abundant compared to BV-negative women. Correspondingly, Nugent scores were negatively associated with total fungal protein abundance. The clinical variables, Nugent score, pro-inflammatory cytokines, chemokines, vaginal pH, Chlamydia trachomatis, and the presence of clue cells were associated with fungal community composition.

Conclusions: The results of this study revealed the diversity of FGT fungal communities, setting the groundwork for understanding the FGT mycobiome. Video Abstract.

Keywords: Bacterial vaginosis; Female genital tract; Fungi; Metaproteomics; Mycobiome.

Fig. 1

Fungal relative abundance was determined using metaproteomics. Liquid chromatography-tandem mass spectrometry (LC–MS/MS) was used to evaluate the fungal metaproteome in lateral vaginal wall swabs from 123 women from Cape Town, South Africa. Raw MS files were processed with MaxQuant using a database that was created by manually curating a fungal pan proteome database and concatenated to a Metanovo database. A Taxonomic assignment of peptides. Taxonomic assignment was conducted using Unipept and the distribution of phyla identified is shown as a doughnut chart. The majority of fungal peptide sequences were assigned to the phylum Ascomycota. B Distribution of fungal genera using peptide sequences. Genera identified using Unipept, and distribution is depicted as a doughnut chart. The majority of fungal peptide sequences were assigned to the genus Aspergillus. C Relative abundance of fungal genera pre-normalization using summed total intensity-based absolute quantification (iBAQ) protein values for each genus. Candida was the most relatively abundant genus. D Relative abundance of fungal protein biological process gene ontology (GO) function. Biological process relative abundance was determined by aggregation of fungal protein iBAQ values assigned to the same GO term; n = 24. The most relatively abundant biological processes were histone H4 acetylation and membrane raft polarization. The relative abundance of biological processes is depicted as a doughnut chart

Fig. 2

Fungal relative abundance was determined using metaproteomics for each bacterial vaginosis (BV) group. Liquid chromatography-tandem mass spectrometry (LC–MS/MS) was used to evaluate the fungal metaproteome in lateral vaginal wall swabs from 123 women from Cape Town, South Africa. Raw MS files were processed with MaxQuant using a database that was created by manually curating a fungal pan proteome database and concatenated to a Metanovo database. The iBAQ values for fungal proteins were normalized, log₂-transformed, and imputed using the k-nearest neighbor method. BV groups were defined by the Nugent score. At visit 1, 57 patients were BV-positive, 47 were BV negative, and 7 were BV intermediate. At visit 2, 37 patients were BV-positive, 38 were BV-negative, and 5 were BV intermediate. Fungal relative abundance for each BV state was determined by aggregation of fungal proteins for each individual assigned to a specific BV state. A The total relative abundance of fungal genus by BV state at each visit was determined by summing total fungal iBAQ values of proteins assigned to each fungal genus. Genus assignments were determined using UniProt on protein groups. Candida was the most relatively abundant across all BV states. B Fungal community composition at the genus level across BV states at different visits. C Relative abundance of biological processes according to Gene Ontology (GO). GO determined using UniProt across BV states at different visits. Biological process relative abundance was determined by aggregation of fungal protein iBAQ values assigned to the same GO term. Histone H4 acetylation and membrane raft polarization were the most abundant biological processes across BV states

Fig. 3

Fungal proteins associated with bacterial vaginosis (BV) status. A Unsupervised hierarchical clustering was used to group samples based on the log2-transformed imputed intensity-based absolute quantification (iBAQ) values of fungal proteins at visit 1. Red indicates the highest expression, and blue indicates the lowest expression. The darker the red banding pattern, the higher the iBAQ value. The darker the blue banding pattern, the lower the iBAQ value. Samples showed some clustering according to BV status indicated by the top dendrogram. BV status was defined based on Nugent score values. B Principal component analysis (PCA) was used to group individuals based on the log2-transformed imputed iBAQ intensities of fungal proteins for visit 1. Grouping was based on BV status and each point represented an individual women. BV-positive and BV-negative individuals tended to cluster together into their respective groups, with a few outliers. C PCA was used to group individuals based on the log2-transformed imputed iBAQ intensities of fungal proteins for visit 2. Grouping was based on BV status and each point represented individual women. BV-positive and BV-negative individuals tended to cluster together into their respective groups, with a few outliers

Fig. 4

A The importance of identified fungal proteins in discriminating the BV-positive group from the BV-negative group was determined by an ENSEMBLE bagging decision tree. M5EKD5 (Malassezia sympodialis) was the most important fungal protein distinguishing the BV-positive from the BV-negative group. B An ENSEMBLE bagging decision tree using previously identified important fungal proteins between the BV-positive and the BV-negative state to predict BV status. Candida albicans (A0A1D8PFR4) was selected as the root feature of the decision tree. Each fungal protein on the tree has an iBAQ value unit for cutoff. Each node provides the percentage of the satisfied condition at the node and provides the probability of it not being the BV status of the node

Fig. 5

Correlations between differentially expressed common fungal (n = 10) and bacterial taxa associated with BV and inflammation from visit 1 samples (n = 113) using a Spearman’s correlation. Fungal taxa are highlighted in purple. Red circles represent negative correlations (negative r-value), and blue circles represent positive correlations (positive r-value). The size of the circle represents the strength of the correlation (r-value), darker colors, and circle size represent stronger correlations. M. sympodialis had the strongest positive correlation with the bacterium G. vaginalis. The strongest negative correlation was observed between C. albicans and G. vaginalis

Fig. 6

Bayesian network depicting the associations between differentially abundant common fungal taxa (n = 10) and optimal (green) and non-optimal bacterial (red) taxa linked with BV and inflammation (n = 11) at visit 1 (n = 113). The selected BV and inflammation-associated bacteria were obtained from the literature. Arrows represent the microbial taxa that have an association with other microbial taxa, with only associations with a strength > 0.8 depicted. Candida species are highlighted in yellow and the remainder of fungi are highlighted in white