Defining Vaginal Community Dynamics: daily microbiome transitions, the role of menstruation, bacteriophages, and bacterial genes.
Daily variations
Dysbiosis
Menstrual cycle
Reproductive health
Vaginal microbiome
Journal
Microbiome
ISSN: 2049-2618
Titre abrégé: Microbiome
Pays: England
ID NLM: 101615147
Informations de publication
Date de publication:
19 Aug 2024
19 Aug 2024
Historique:
received:
06
06
2023
accepted:
09
07
2024
medline:
20
8
2024
pubmed:
20
8
2024
entrez:
19
8
2024
Statut:
epublish
Résumé
The composition of the vaginal microbiota during the menstrual cycle is dynamic, with some women remaining eu- or dysbiotic and others transitioning between these states. What defines these dynamics, and whether these differences are microbiome-intrinsic or mostly driven by the host is unknown. To address this, we characterized 49 healthy, young women by metagenomic sequencing of daily vaginal swabs during a menstrual cycle. We classified the dynamics of the vaginal microbiome and assessed the impact of host behavior as well as microbiome differences at the species, strain, gene, and phage levels. Based on the daily shifts in community state types (CSTs) during a menstrual cycle, the vaginal microbiome was classified into four Vaginal Community Dynamics (VCDs) and reported in a classification tool, named VALODY: constant eubiotic, constant dysbiotic, menses-related, and unstable dysbiotic. The abundance of bacteria, phages, and bacterial gene content was compared between the four VCDs. Women with different VCDs showed significant differences in relative phage abundance and bacterial composition even when assigned to the same CST. Women with unstable VCDs had higher phage counts and were more likely dominated by L. iners. Their Gardnerella spp. strains were also more likely to harbor bacteriocin-coding genes. The VCDs present a novel time series classification that highlights the complexity of varying degrees of vaginal dysbiosis. Knowing the differences in phage gene abundances and the genomic strains present allows a deeper understanding of the initiation and maintenance of permanent dysbiosis. Applying the VCDs to further characterize the different types of microbiome dynamics qualifies the investigation of disease and enables comparisons at individual and population levels. Based on our data, to be able to classify a dysbiotic sample into the accurate VCD, clinicians would need two to three mid-cycle samples and two samples during menses. In the future, it will be important to address whether transient VCDs pose a similar risk profile to persistent dysbiosis with similar clinical outcomes. This framework may aid interdisciplinary translational teams in deciphering the role of the vaginal microbiome in women's health and reproduction. Video Abstract.
Sections du résumé
BACKGROUND
BACKGROUND
The composition of the vaginal microbiota during the menstrual cycle is dynamic, with some women remaining eu- or dysbiotic and others transitioning between these states. What defines these dynamics, and whether these differences are microbiome-intrinsic or mostly driven by the host is unknown. To address this, we characterized 49 healthy, young women by metagenomic sequencing of daily vaginal swabs during a menstrual cycle. We classified the dynamics of the vaginal microbiome and assessed the impact of host behavior as well as microbiome differences at the species, strain, gene, and phage levels.
RESULTS
RESULTS
Based on the daily shifts in community state types (CSTs) during a menstrual cycle, the vaginal microbiome was classified into four Vaginal Community Dynamics (VCDs) and reported in a classification tool, named VALODY: constant eubiotic, constant dysbiotic, menses-related, and unstable dysbiotic. The abundance of bacteria, phages, and bacterial gene content was compared between the four VCDs. Women with different VCDs showed significant differences in relative phage abundance and bacterial composition even when assigned to the same CST. Women with unstable VCDs had higher phage counts and were more likely dominated by L. iners. Their Gardnerella spp. strains were also more likely to harbor bacteriocin-coding genes.
CONCLUSIONS
CONCLUSIONS
The VCDs present a novel time series classification that highlights the complexity of varying degrees of vaginal dysbiosis. Knowing the differences in phage gene abundances and the genomic strains present allows a deeper understanding of the initiation and maintenance of permanent dysbiosis. Applying the VCDs to further characterize the different types of microbiome dynamics qualifies the investigation of disease and enables comparisons at individual and population levels. Based on our data, to be able to classify a dysbiotic sample into the accurate VCD, clinicians would need two to three mid-cycle samples and two samples during menses. In the future, it will be important to address whether transient VCDs pose a similar risk profile to persistent dysbiosis with similar clinical outcomes. This framework may aid interdisciplinary translational teams in deciphering the role of the vaginal microbiome in women's health and reproduction. Video Abstract.
Identifiants
pubmed: 39160615
doi: 10.1186/s40168-024-01870-5
pii: 10.1186/s40168-024-01870-5
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
153Subventions
Organisme : Science for Life Laboratory
ID : KAW 2020.0239
Organisme : Rigshospitalet
ID : E-22614-01, E-22614-02
Organisme : Vetenskapsrådet
ID : 2021-01683
Informations de copyright
© 2024. The Author(s).
Références
Wu S, Hugerth LW, Schuppe-Koistinen I, Du J. The right bug in the right place: opportunities for bacterial vaginosis treatment. NPJ Biofilms Microbiomes. 2022;8:34.
pubmed: 35501321
pmcid: 9061781
doi: 10.1038/s41522-022-00295-y
Gajer P, et al. Temporal dynamics of the human vaginal microbiota. Sci Transl Med. 2012;4:132ra52.
pubmed: 22553250
pmcid: 3722878
doi: 10.1126/scitranslmed.3003605
Haahr T, et al. Abnormal vaginal microbiota may be associated with poor reproductive outcomes: a prospective study in IVF patients. Hum Reprod. 2016;31:795–803.
pubmed: 26911864
doi: 10.1093/humrep/dew026
Brusselaers N, Shrestha S, van de Wijgert J, Verstraelen H. Vaginal dysbiosis and the risk of human papillomavirus and cervical cancer: systematic review and meta-analysis. Am J Obstet Gynecol. 2019;221:9-18.e8. Preprint at : https://doi.org/10.1016/j.ajog.2018.12.011 .
doi: 10.1016/j.ajog.2018.12.011
pubmed: 30550767
Tamarelle J, et al. The vaginal microbiota and its association with human papillomavirus, Chlamydia trachomatis, Neisseria gonorrhoeae and Mycoplasma genitalium infections: a systematic review and meta-analysis. Clin Microbiol Infecti. 2019;25:35–47. Preprint at : https://doi.org/10.1016/j.cmi.2018.04.019 .
doi: 10.1016/j.cmi.2018.04.019
Norenhag J, et al. The vaginal microbiota, human papillomavirus and cervical dysplasia: a systematic review and network meta-analysis. BJOG. 2020;127:171–80.
pubmed: 31237400
doi: 10.1111/1471-0528.15854
Gudnadottir U, et al. The vaginal microbiome and the risk of preterm birth: a systematic review and network meta-analysis. Sci Rep. 2022;12:7926.
pubmed: 35562576
pmcid: 9106729
doi: 10.1038/s41598-022-12007-9
Hakimjavadi H, et al. The vaginal microbiome is associated with endometrial cancer grade and histology. Cancer Res Commun. 2022;2:447–55.
pubmed: 35928983
pmcid: 9345414
doi: 10.1158/2767-9764.CRC-22-0075
France M, Alizadeh M, Brown S, Ma B, Ravel J. Towards a deeper understanding of the vaginal microbiota. Nat Microbiol. 2022;7:367–78.
pubmed: 35246662
pmcid: 8910585
doi: 10.1038/s41564-022-01083-2
Kwon MS, Lee HK. Host and Microbiome Interplay Shapes the Vaginal Microenvironment. Front Immunol. 2022;13:919728.
pubmed: 35837395
pmcid: 9273862
doi: 10.3389/fimmu.2022.919728
Amabebe E, Anumba DOC. Mechanistic Insights into Immune Suppression and Evasion in Bacterial Vaginosis. Curr Microbiol. 2022;79(3):1–13.
doi: 10.1007/s00284-022-02771-2
Amabebe E, Anumba DOC. The vaginal microenvironment: the physiologic role of Lactobacilli. Front Med. 2018;5:181.
doi: 10.3389/fmed.2018.00181
Cerca N, et al. Gardnerella vaginalis as a Cause of Bacterial Vaginosis: Appraisal of the Evidence From in vivo Models. Front Cell Infect Microbiol. 2020;1:168 https://www.frontiersin.org .
Brady A, et al. Molecular basis of lysis-lysogeny decisions in gram-positive phages. Annu Rev Microbiol. 2021;75:563–81.
pubmed: 34343015
doi: 10.1146/annurev-micro-033121-020757
Erez Z, et al. Communication between viruses guides lysis–lysogeny decisions. Nature. 2017;541(7638):488–93.
pubmed: 28099413
pmcid: 5378303
doi: 10.1038/nature21049
Madere FS, et al. Transkingdom analysis of the female reproductive tract reveals bacteriophages form communities. Viruses. 2022;14:430.
pubmed: 35216023
pmcid: 8878565
doi: 10.3390/v14020430
Jakobsen RR, et al. Characterization of the Vaginal DNA Virome in Health and Dysbiosis. Viruses. 2020;12:1143.
pubmed: 33050261
pmcid: 7600586
doi: 10.3390/v12101143
Manhanzva MT, et al. Inflammatory and antimicrobial properties differ between vaginal Lactobacillus isolates from South African women with non-optimal versus optimal microbiota. Sci Rep. 2020;10(1):1–13.
doi: 10.1038/s41598-020-62184-8
Vaneechoutte M, et al. Emended description of Gardnerella vaginalis and description of gardnerella leopoldii sp. Nov., gardnerella piotii sp. Nov. and Gardnerella swidsinskii sp. nov., with delineation of 13 genomic species within the genus Gardnerella. Int J Syst Evol Microbiol. 2019;69:679–87.
pubmed: 30648938
doi: 10.1099/ijsem.0.003200
Hill JE, Albert AYK & Group, the V. R. Resolution and Cooccurrence Patterns of Gardnerella leopoldii, G. swidsinskii, G. piotii, and G. vaginalis within the Vaginal Microbiome. Infect Immun. 2019. https://doi.org/10.1128/IAI.00532-19 .
Holm JB, et al. Integrating compositional and functional content to describe vaginal microbiomes in health and disease. Microbiome. 2023;11:1–20.
doi: 10.1186/s40168-023-01692-x
France MT, et al. VALENCIA: a nearest centroid classification method for vaginal microbial communities based on composition. Microbiome. 2020;8:1–15.
doi: 10.1186/s40168-020-00934-6
Ravel J, et al. Daily temporal dynamics of vaginal microbiota before, during and after episodes of bacterial vaginosis. Microbiome. 2013;1:29.
pubmed: 24451163
pmcid: 3968321
doi: 10.1186/2049-2618-1-29
Krog MC, et al. The healthy female microbiome across body sites: effect of hormonal contraceptives and the menstrual cycle. Hum Reprod. 2022;37:1525–43.
pubmed: 35553675
pmcid: 9247429
doi: 10.1093/humrep/deac094
Theis KR, et al. Sneathia: an emerging pathogen in female reproductive disease and adverse perinatal outcomes. Crit Rev Microbiol. 2021;47:517–42.
pubmed: 33823747
pmcid: 8672320
doi: 10.1080/1040841X.2021.1905606
Gentile GL, et al. Identification of a Cytopathogenic Toxin from Sneathia amnii. J Bacteriol. 2020;202:e00162-20.
pubmed: 32291280
pmcid: 7283592
doi: 10.1128/JB.00162-20
Ali A, Jørgensen JS, Lamont RF. The contribution of bacteriophages to the aetiology and treatment of the bacterial vaginosis syndrome. Fac Rev. 2022;11:8.
pubmed: 35509673
pmcid: 9022730
doi: 10.12703/r/11-8
Carter KA, Fischer MD, Petrova MI, Balkus JE. Epidemiologic Evidence on the Role of Lactobacillus iners in Sexually Transmitted Infections and Bacterial Vaginosis: a series of systematic reviews and meta-analyses. Sex Transm Dis. 2023;50:224–35.
pubmed: 36729966
doi: 10.1097/OLQ.0000000000001744
Marantos A, Mitarai N, Sneppen K. From kill the winner to eliminate the winner in open phage-bacteria systems. PLoS Comput Biol. 2022;18:e1010400.
pubmed: 35939510
pmcid: 9387927
doi: 10.1371/journal.pcbi.1010400
Machado A, Jefferson KK, Cerca N. Interactions between Lactobacillus crispatus and bacterial vaginosis (BV)-associated bacterial species in initial attachment and biofilm formation. Int J Mol Sci. 2013;14:12004–12.
pubmed: 23739678
pmcid: 3709769
doi: 10.3390/ijms140612004
Teixeira GS, et al. Characteristics of Lactobacillus and Gardnerella vaginalis from women with or without bacterial vaginosis and their relationships in gnotobiotic mice. J Med Microbiol. 2012;61:1074–81.
pubmed: 22539000
doi: 10.1099/jmm.0.041962-0
Happel AU, et al. Presence and persistence of putative lytic and temperate bacteriophages in vaginal metagenomes from south african adolescents. Viruses. 2021;13:2341.
pubmed: 34960611
pmcid: 8708031
doi: 10.3390/v13122341
Dufour A, Hindré T, Haras D, Le Pennec JP. The biology of lantibiotics from the lacticin 481 group is coming of age. FEMS Microbiol Rev. 2007;31:134–67.
pubmed: 17096664
doi: 10.1111/j.1574-6976.2006.00045.x
Teixeira GS, et al. Antagonism and synergism in Gardnerella vaginalis strains isolated from women with bacterial vaginosis. J Med Microbiol. 2010;59:891–7.
pubmed: 20466841
doi: 10.1099/jmm.0.019794-0
Krog MC, et al. The microbiome in reproductive health: protocol for a systems biology approach using a prospective, observational study design. Hum Reprod Open. 2022;2022:hoac015.
pubmed: 35441092
pmcid: 9014536
doi: 10.1093/hropen/hoac015
Hugerth LW, et al. Assessment of In Vitro and In Silico Protocols for Sequence-Based Characterization of the Human Vaginal Microbiome. mSphere. 2020;5:e01253-20.
pubmed: 33361132
pmcid: 7763557
Hugerth LW, et al. DegePrime, a program for degenerate primer design for broad-taxonomic-range PCR in microbial ecology studies. Appl Environ Microbiol. 2014;80:5116–23.
pubmed: 24928874
pmcid: 4135748
doi: 10.1128/AEM.01403-14
Callahan BJ, et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.
pubmed: 27214047
pmcid: 4927377
doi: 10.1038/nmeth.3869
Quast C, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.
pubmed: 23193283
doi: 10.1093/nar/gks1219
Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol. 2019;20:1–13.
doi: 10.1186/s13059-019-1891-0
Davis NM, Proctor DM, Holmes SP, Relman DA, Callahan BJ. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome. 2018;6:226.
pubmed: 30558668
pmcid: 6298009
doi: 10.1186/s40168-018-0605-2
Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.
pubmed: 24642063
doi: 10.1093/bioinformatics/btu153
Bushnell B, Rood J, Singer E. BBMerge – Accurate paired shotgun read merging via overlap. PLoS One. 2017;12:e0185056.
pubmed: 29073143
pmcid: 5657622
doi: 10.1371/journal.pone.0185056
Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. MetaSPAdes: A new versatile metagenomic assembler. Genome Res. 2017;27:824–34.
pubmed: 28298430
pmcid: 5411777
doi: 10.1101/gr.213959.116
Alneberg J, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6.
pubmed: 25218180
doi: 10.1038/nmeth.3103
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.
pubmed: 25977477
pmcid: 4484387
doi: 10.1101/gr.186072.114
Price MN, Dehal PS, Arkin AP. FastTree 2 – approximately maximum-likelihood trees for large alignments. PLoS One. 2010;5:e9490.
pubmed: 20224823
pmcid: 2835736
doi: 10.1371/journal.pone.0009490
Page AJ, et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics. 2015;31:3691–3.
pubmed: 26198102
pmcid: 4817141
doi: 10.1093/bioinformatics/btv421
Tonkin-Hill G, et al. Producing polished prokaryotic pangenomes with the Panaroo pipeline. Genome Biol. 2020;21:1–21.
doi: 10.1186/s13059-020-02090-4
Brynildsrud O, Bohlin J, Scheffer L, Eldholm V. Rapid scoring of genes in microbial pan-genome-wide association studies with Scoary. Genome Biol. 2016;17:1–9.
Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction. Nat Commun. 2020;11(1):1–11.
doi: 10.1038/s41467-020-17041-7
Harris PA, et al. The REDCap consortium: Building an international community of software platform partners. J Biomed Inform. 2019;95:103208.
pubmed: 31078660
pmcid: 7254481
doi: 10.1016/j.jbi.2019.103208