Dataset Information

Cervicovaginal Microbiome and Urine Metabolome Paired Analysis Reveals Niche Partitioning of the Microbiota in Patients with Human Papilloma Virus Infections.

ABSTRACT: In this study, we evaluate the association between vaginal and cervical human papillomavirus infections high-risk types (HPV+H), negative controls (HPV-), the bacterial biota, and urinary metabolites via integration of metagenomics, metabolomics, and bioinformatics analysis. We recently proposed that testing urine as a biofluid could be a non-invasive method for the detection of cervical HPV+H infections by evaluating the association between cervical HPV types and a total of 24 urinary metabolites identified in the samples. As a follow-up study, we expanded the analysis by pairing the urine metabolome data with vaginal and cervical microbiota in selected samples from 19 Puerto Rican women diagnosed with HPV+H infections and HPV- controls, using a novel comprehensive framework, Model-based Integration of Metabolite Observations and Species Abundances 2 (MIMOSA2). This approach enabled us to estimate the functional activities of the cervicovaginal microbiome associated with HPV+H infections. Our results suggest that HPV+H infections could induce changes in physicochemical properties of the genital tract through which niche partitioning may occur. As a result, Lactobacillus sp. enrichment coincided with the depletion of L. iners and Shuttleworthia, which dominate under normal physiological conditions. Changes in the diversity of microbial species in HPV+H groups influence the capacity of new community members to produce or consume metabolites. In particular, the functionalities of four metabolic enzymes were predicted to be associated with the microbiota, including acylphosphatase, prolyl aminopeptidase, prolyl-tRNA synthetase, and threonyl-tRNA synthetase. Such metabolic changes may influence systemic health effects in women at risk of developing cervical cancer. Overall, even assuming the limitation of the power due to the small sample number, our study adds to current knowledge by suggesting how microbial taxonomic and metabolic shifts induced by HPV infections may influence the maintenance of microbial homeostasis and indicate that HPV+H infections may alter the ecological balance of the cervicovaginal microbiota, resulting in higher bacterial diversity.

SUBMITTER: Chorna N

PROVIDER: S-EPMC7022855 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Cervicovaginal Microbiome and Urine Metabolome Paired Analysis Reveals Niche Partitioning of the Microbiota in Patients with Human Papilloma Virus Infections.

Chorna Nataliya N Romaguera Josefina J Godoy-Vitorino Filipa F

Metabolites 20200115 1

In this study, we evaluate the association between vaginal and cervical human papillomavirus infections high-risk types (HPV+H), negative controls (HPV-), the bacterial biota, and urinary metabolites via integration of metagenomics, metabolomics, and bioinformatics analysis. We recently proposed that testing urine as a biofluid could be a non-invasive method for the detection of cervical HPV+H infections by evaluating the association between cervical HPV types and a total of 24 urinary metabolit ...[more]

PMID: 31952112

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Project description:Comprehensive analyses of multi-omics data may provide insights into interactions between different biological layers concerning distinct clinical features. We integrated data on the gut microbiota, blood parameters and urine metabolites of treatment-naive individuals presenting a wide range of metabolic disease phenotypes to delineate clinically meaningful associations. Trans-omics correlation networks revealed that candidate gut microbial biomarkers and urine metabolite feature were covaried with distinct clinical phenotypes. Integration of the gut microbiome, the urine metabolome and the phenome revealed that variations in one of these three systems correlated with changes in the other two. In a specific note about clinical parameters of liver function, we identified Eubacteriumeligens, Faecalibacteriumprausnitzii and Ruminococcuslactaris to be associated with a healthy liver function, whereas Clostridium bolteae, Tyzzerellanexills, Ruminococcusgnavus, Blautiahansenii, and Atopobiumparvulum were associated with blood biomarkers for liver diseases. Variations in these microbiota features paralleled changes in specific urine metabolites. Network modeling yielded two core clusters including one large gut microbe-urine metabolite close-knit cluster and one triangular cluster composed of a gut microbe-blood-urine network, demonstrating close inter-system crosstalk especially between the gut microbiome and the urine metabolome. Distinct clinical phenotypes are manifested in both the gut microbiome and the urine metabolome, and inter-domain connectivity takes the form of high-dimensional networks. Such networks may further our understanding of complex biological systems, and may provide a basis for identifying biomarkers for diseases. Deciphering the complexity of human physiology and disease requires a holistic and trans-omics approach integrating multi-layer data sets, including the gut microbiome and profiles of biological fluids. By studying the gut microbiome on carotid atherosclerosis, we identified microbial features associated with clinical parameters, and we observed that groups of urine metabolites correlated with groups of clinical parameters. Combining the three data sets, we revealed correlations of entities across the three systems, suggesting that physiological changes are reflected in each of the omics. Our findings provided insights into the interactive network between the gut microbiome, blood clinical parameters and the urine metabolome concerning physiological variations, and showed the promise of trans-omics study for biomarker discovery.

Dataset Information

Cervicovaginal Microbiome and Urine Metabolome Paired Analysis Reveals Niche Partitioning of the Microbiota in Patients with Human Papilloma Virus Infections.

Publications

Cervicovaginal Microbiome and Urine Metabolome Paired Analysis Reveals Niche Partitioning of the Microbiota in Patients with Human Papilloma Virus Infections.

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