Associations Between Vaginal Bacteria and Bacterial Vaginosis Signs and Symptoms: A Comparative Study of Kenyan and American Women

Sexual transmission of urogenital bacteria: whole metagenome sequencing evidence from a sexual network study

Sexual transmission of the urogenital microbiota may contribute to adverse sexual and reproductive health outcomes. The extent of sexual transmission of the urogenital microbiota is unclear as prior studies largely investigated specific pathogens. We used epidemiologic data and whole metagenome sequencing to characterize urogenital microbiota strain concordance between participants of a sexual network study. Individuals who screened positive for genital Chlamydia trachomatis were enrolled and referred their sexual contacts from the prior 60-180 days. Snowball recruitment of sexual contacts continued for up to four waves. Vaginal swabs and penile urethral swabs were collected for whole metagenome sequencing. We evaluated bacterial strain concordance using inStrain and network analysis. We defined concordance as ≥99.99% average nucleotide identity over ≥50% shared coverage; we defined putative sexual transmission as concordance between sexual contacts with <5 single-nucleotide polymorphisms per megabase. Of 138 participants, 74 (54%) were female; 120 (87%) had genital chlamydia; and 43 (31%) were recruited contacts. We identified 115 strain-concordance events among 54 participants representing 25 bacterial species. Seven events (6%) were between sexual contacts including putative heterosexual transmission of Fannyhessea vaginae, Gardnerella leopoldii, Prevotella amnii, Sneathia sanguinegens, and Sneathia vaginalis (one strain each), and putative sexual transmission of Lactobacillus iners between female contacts. Most concordance events (108, 94%) were between non-contacts, including eight female participants connected through 18 Lactobacillus crispatus and 3 Lactobacillus jensenii concordant strains, and 14 female and 2 male participants densely interconnected through 52 Gardnerella swidsinskii concordance events.IMPORTANCEEpidemiologic evidence consistently indicates bacterial vaginosis (BV) is sexually associated and may be sexually transmitted, though sexual transmission remains subject to debate. This study is not capable of demonstrating BV sexual transmission; however, we do provide strain-level metagenomic evidence that strongly supports heterosexual transmission of BV-associated species. These findings strengthen the evidence base that supports ongoing investigations of concurrent male partner treatment for reducing BV recurrence. Our data suggest that measuring the impact of male partner treatment on F. vaginae, G. leopoldii, P. amnii, S. sanguinegens, and S. vaginalis may provide insight into why a regimen does or does not perform well. We also observed a high degree of strain concordance between non-sexual-contact female participants. We posit that this may reflect limited dispersal capacity of vaginal bacteria coupled with individuals' comembership in regional transmission networks where transmission may occur between parent and child at birth, cohabiting individuals, or sexual contacts.

A manifold-based framework for studying the dynamics of the vaginal microbiome

The vaginal microbiome plays a crucial role in our health. The composition of this community can be classified into five community state types (CSTs), four of which are primarily consisted of Lactobacillus species and considered healthy, while the fifth features non-Lactobacillus populations and signifies a disease state termed Bacterial vaginosis (BV), which is associated with various symptoms and increased susceptibility to diseases. Importantly, however, the exact mechanisms and dynamics underlying BV development are not yet fully understood, including specifically possible routes from a healthy to a BV state. To address this gap, this study set out to characterize the progression from healthy- to BV-associated compositions by analyzing 8026 vaginal samples and using a manifold-detection framework. This approach, inspired by single-cell analysis, aims to identify low-dimensional trajectories in the high-dimensional composition space. It further orders samples along these trajectories and assigns a score (pseudo-time) to each analyzed or new sample based on its proximity to the BV state. Our results reveal distinct routes of progression between healthy and BV states for each CST, with pseudo-time scores correlating with community diversity and quantifying the health state of each sample. Several BV indicators can also be successfully predicted based on pseudo-time scores, and key taxa involved in BV development can be identified using this approach. Taken together, these findings demonstrate how manifold detection can be used to successfully characterize the progression from healthy Lactobacillus-dominant populations to BV and to accurately quantify the health condition of new samples along the route of BV development.

Profiling of vaginal Lactobacillus jensenii isolated from preterm and full-term pregnancies reveals strain-specific factors relating to host interaction

Each year, 15 million infants are born preterm (<37 weeks gestation), representing the leading cause of mortality for children under the age of five. Whilst there is no single cause, factors such as maternal genetics, environmental interactions, and the vaginal microbiome have been associated with an increased risk of preterm birth. Previous studies show that a vaginal microbiota dominated by Lactobacillus is, in contrast to communities containing a mixture of genera, associated with full-term birth. However, this binary principle does not fully consider more nuanced interactions between bacterial strains and the host. Here, through a combination of analyses involving genome-sequenced isolates and strain-resolved metagenomics, we identify that L. jensenii strains from preterm pregnancies are phylogenetically distinct from strains from full-term pregnancies. Detailed analysis reveals several genetic signatures that distinguish preterm birth strains, including genes predicted to be involved in cell wall synthesis, and lactate and acetate metabolism. Notably, we identify a distinct gene cluster involved in cell surface protein synthesis in our preterm strains, and profiling the prevalence of this gene cluster in publicly available genomes revealed it to be predominantly present in the preterm-associated clade. This study contributes to the ongoing search for molecular biomarkers linked to preterm birth and opens up new avenues for exploring strain-level variations and mechanisms that may contribute to preterm birth.

Amygdalobacter indicium gen. nov., sp. nov., and Amygdalobacter nucleatus sp. nov., gen. nov.: novel bacteria from the family Oscillospiraceae isolated from the female genital tract

Four obligately anaerobic Gram-positive bacteria representing one novel genus and two novel species were isolated from the female genital tract. Both novel species, designated UPII 610-JT and KA00274T, and an additional isolate of each species were characterized utilizing biochemical, genotypic and phylogenetic analyses. All strains were non-motile and non-spore forming, asaccharolytic, non-cellulolytic and indole-negative coccobacilli. Fatty acid methyl ester analysis for UPII 610-JT and KA00274T and additional isolates revealed C16 : 0, C18 : 0, C18:1ω9c and C18:2ω6,9c to be the major fatty acids for both species. UPII 610-JT had a 16S rRNA gene sequence similarity of 99.4 % to an uncultured clone sequence (AY724740) designated as Bacterial Vaginosis Associated Bacterium 2 (BVAB2). KA00274T had a 16S rRNA gene sequence similarity of 96.5 % to UPII 610-JT. Whole genomic DNA mol% G+C content was 42.2 and 39.3 % for UPII 610-JT and KA00274T, respectively. Phylogenetic analyses indicate these isolates represent a novel genus and two novel species within the Oscillospiraceae family. We propose the names Amygdalobacter indicium gen. nov., sp. nov., for UPII 610-JT representing the type strain of this species (=DSM 112989T, =ATCC TSD-274T) and Amygdalobacter nucleatus gen. nov., sp. nov., for KA00274T representing the type strain of this species (=DSM 112988T, =ATCC TSD-275T).

A Manifold-Based Framework for Studying the Dynamics of the Vaginal Microbiome

The vaginal bacterial community plays a crucial role in preventing infections. The composition of this community can be classified into five main groups, termed community state types (CSTs). Four of these CSTs, which are primarily consisted of Lactobacillus species, are considered healthy, while the fifth, which is composed of non-Lactobacillus populations, is considered less protective. This latter CST is often considered to represent a state termed Bacterial vaginosis (BV) - a common disease condition associated with unpleasant symptoms and increased susceptibility to sexually transmitted diseases. However, the exact mechanisms underlying BV development are not yet fully understood, including specifically, the dynamics of the vaginal microbiome in BV, and the possible routes it may take from a healthy to a BV state. This study aims to identify the progression from healthy Lactobacillus-dominant populations to symptomatic BV by analyzing 8,026 vaginal samples and using a manifold-detection framework. This approach is inspired by single-cell analysis and aims to identify low-dimensional trajectories in the high-dimensional composition space. This framework further order samples along these trajectories and assign a score (pseudo-time) to each sample based on its proximity to the BV state. Our results reveal distinct routes of progression between healthy and BV state for each CST, with pseudo-time scores correlating with community diversity and quantifying the health state of each sample. BV indicators, including Nugent score, positive Amsel's test, and several Amsel's criteria, can also be successfully predicted based on pseudo-time scores. Additionally, Gardnerella vaginalis can be identified as a key taxon in BV development using this approach, with increased abundance in samples with high pseudo-time, indicating an unhealthier state across all BV-development routes on the manifold. Taken together, these findings demonstrate how manifold detection can be used to successfully characterizes the progression from healthy Lactobacillus-dominant populations to BV and to accurately quantify the health condition of new samples along the route of BV development.

Vaginal Microbiome Metagenome Inference Accuracy: Differential Measurement Error according to Community Composition

Several studies have compared metagenome inference performance in different human body sites; however, none specifically reported on the vaginal microbiome. Findings from other body sites cannot easily be generalized to the vaginal microbiome due to unique features of vaginal microbial ecology, and investigators seeking to use metagenome inference in vaginal microbiome research are "flying blind" with respect to potential bias these methods may introduce into analyses. We compared the performance of PICRUSt2 and Tax4Fun2 using paired 16S rRNA gene amplicon sequencing and whole-metagenome sequencing data from vaginal samples from 72 pregnant individuals enrolled in the Pregnancy, Infection, and Nutrition (PIN) cohort. Participants were selected from those with known birth outcomes and adequate 16S rRNA gene amplicon sequencing data in a case-control design. Cases experienced early preterm birth (<32 weeks of gestation), and controls experienced term birth (37 to 41 weeks of gestation). PICRUSt2 and Tax4Fun2 performed modestly overall (median Spearman correlation coefficients between observed and predicted KEGG ortholog [KO] relative abundances of 0.20 and 0.22, respectively). Both methods performed best among Lactobacillus crispatus-dominated vaginal microbiotas (median Spearman correlation coefficients of 0.24 and 0.25, respectively) and worst among Lactobacillus iners-dominated microbiotas (median Spearman correlation coefficients of 0.06 and 0.11, respectively). The same pattern was observed when evaluating correlations between univariable hypothesis test P values generated with observed and predicted metagenome data. Differential metagenome inference performance across vaginal microbiota community types can be considered differential measurement error, which often causes differential misclassification. As such, metagenome inference will introduce hard-to-predict bias (toward or away from the null) in vaginal microbiome research. IMPORTANCE Compared to taxonomic composition, the functional potential within a bacterial community is more relevant to establishing mechanistic understandings and causal relationships between the microbiome and health outcomes. Metagenome inference attempts to bridge the gap between 16S rRNA gene amplicon sequencing and whole-metagenome sequencing by predicting a microbiome's gene content based on its taxonomic composition and annotated genome sequences of its members. Metagenome inference methods have been evaluated primarily among gut samples, where they appear to perform fairly well. Here, we show that metagenome inference performance is markedly worse for the vaginal microbiome and that performance varies across common vaginal microbiome community types. Because these community types are associated with sexual and reproductive outcomes, differential metagenome inference performance will bias vaginal microbiome studies, obscuring relationships of interest. Results from such studies should be interpreted with substantial caution and the understanding that they may over- or underestimate associations with metagenome content.

Global Meta-analysis of Urine Microbiome: Colonization of Polycyclic Aromatic Hydrocarbon-degrading Bacteria Among Bladder Cancer Patients

BACKGROUND

The application of next-generation sequencing techniques has enabled characterization of urinary tract microbiome. Although many studies have demonstrated associations between the human microbiome and bladder cancer (BC), these have not always reported consistent results, thereby necessitating cross-study comparisons. Thus, the fundamental questions remain how we can utilize this knowledge.

OBJECTIVE

The aim of our study was to examine the disease-associated changes in urine microbiome communities globally utilizing a machine learning algorithm.

DESIGN, SETTING, AND PARTICIPANTS

Raw FASTQ files were downloaded for the three published studies in urinary microbiome in BC patients, in addition to our own prospectively collected cohort.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Demultiplexing and classification were performed using the QIIME 2020.8 platform. De novo operational taxonomic units were clustered using the uCLUST algorithm and defined by 97% sequence similarity and classified at the phylum level against the Silva RNA sequence database. The metadata available from the three studies included were used to evaluate the differential abundance between BC patients and controls via a random-effect meta-analysis using the metagen R function. A machine learning analysis was performed using the SIAMCAT R package.

RESULTS AND LIMITATIONS

Our study includes 129 BC urine and 60 healthy control samples across four different countries. We identified a total of 97/548 genera to be differentially abundant in the BC urine microbiome compared with that of healthy patients. Overall, while the differences in diversity metrics were clustered around the country of origin (Kruskal-Wallis, p < 0.001), collection methodology was a driver of microbiome composition. When assessing dataset from China, Hungary, and Croatia, data demonstrated no discrimination capacity to distinguish between BC patients and healthy adults (area under the curve [AUC] 0.577). However, inclusion of samples with catheterized urine improved the diagnostic accuracy of prediction for BC to AUC 0.995, with precision-recall AUC = 0.994. Through elimination of contaminants associated with the collection methodology among all cohorts, our study identified increased abundance of polycyclic aromatic hydrocarbon (PAH)-degrading bacteria Sphingomonas, Acinetobacter, Micrococcus, Pseudomonas, and Ralstonia to be consistently present in BC patients.

CONCLUSIONS

The microbiota of the BC population may be a reflection of PAH exposure from smoking, environmental pollutants, and ingestion. Presence of PAHs in the urine of BC patients may allow for a unique metabolic niche and provide necessary metabolic resources where other bacteria are not able to flourish. Furthermore, we found that while compositional differences are associated with geography more than with disease, many are driven by the collection methodology.

PATIENT SUMMARY

The goal of our study was to compare the urine microbiome of bladder cancer patients with that of healthy controls and evaluate any potential bacteria that may be more likely to be found in patients with bladder cancer. Our study is unique as it evaluates this across multiple countries, to find a common pattern. After we removed some of the contamination, we were able to localize several key bacteria that are more likely to be found in the urine of bladder cancer patients. These bacteria all share their ability to break down tobacco carcinogens.