Defining the relationship between vaginal and urinary microbiomes.
Adult
Burkholderiales
Case-Control Studies
Clostridiales
Discriminant Analysis
Escherichia
Female
Flavobacterium
Gardnerella
Humans
Lactobacillus
Linear Models
Microbiota
/ genetics
Middle Aged
Prevotella
RNA, Ribosomal, 16S
/ analysis
Streptococcus
Ureaplasma
Urinary Incontinence
Urinary Tract
/ microbiology
Urine
/ microbiology
Vagina
/ microbiology
Lactobacillus
mixed urinary incontinence
urinary microbiome
urologic conditions
vaginal microbiome
Journal
American journal of obstetrics and gynecology
ISSN: 1097-6868
Titre abrégé: Am J Obstet Gynecol
Pays: United States
ID NLM: 0370476
Informations de publication
Date de publication:
02 2020
02 2020
Historique:
received:
15
04
2019
revised:
24
07
2019
accepted:
02
08
2019
pubmed:
20
8
2019
medline:
8
5
2020
entrez:
18
8
2019
Statut:
ppublish
Résumé
Although the vaginal and urinary microbiomes have been increasingly well-characterized in health and disease, few have described the relationship between these neighboring environments. Elucidating this relationship has implications for understanding how manipulation of the vaginal microbiome may affect the urinary microbiome and treatment of common urinary conditions. To describe the relationship between urinary and vaginal microbiomes using 16S rRNA gene sequencing. We hypothesized that the composition of the urinary and vaginal microbiomes would be significantly associated, with similarities in predominant taxa. This multicenter study collected vaginal swabs and catheterized urine samples from 186 women with mixed urinary incontinence enrolled in a parent study and 84 similarly aged controls. Investigators decided a priori that if vaginal and/or urinary microbiomes differed between continent and incontinent women, the groups would be analyzed separately; if similar, samples from continent and incontinent women would be pooled and analyzed together. A central laboratory sequenced variable regions 1-3 (v1-3) and characterized bacteria to the genus level. Operational taxonomic unit abundance was described for paired vaginal and urine samples. Pearson's correlation characterized the relationship between individual operational taxonomic units of paired samples. Canonical correlation analysis evaluated the association between clinical variables (including mixed urinary incontinence and control status) and vaginal and urinary operational taxonomic units, using the Canonical correlation analysis function in the Vegan package (R version 3.5). Linear discriminant analysis effect size was used to find taxa that discriminated between vaginal and urinary samples. Urinary and vaginal samples were collected from 212 women (mean age 53±11 years) and results from 197 paired samples were available for analysis. As operational taxonomic units in mixed urinary incontinence and control samples were related in canonical correlation analysis and since taxa did not discriminate between mixed urinary incontinence or controls in either vagina or urine, mixed urinary incontinence and control samples were pooled for further analysis. Canonical correlation analysis of vaginal and urinary samples indicated that that 60 of the 100 most abundant operational taxonomic units in the samples largely overlapped. Lactobacillus was the most abundant genus in both urine and vagina (contributing on average 53% to an individual's urine sample and 64% to an individual's vaginal sample) (Pearson correlation r=0.53). Although less abundant than Lactobacillus, other bacteria with high Pearson correlation coefficients also commonly found in vagina and urine included: Gardnerella (r=0.70), Prevotella (r=0.64), and Ureaplasma (r=0.50). Linear discriminant analysis effect size analysis identified Tepidimonas and Flavobacterium as bacteria that distinguished the urinary environment for both mixed urinary incontinence and controls as these bacteria were absent in the vagina (Tepidimonas effect size 2.38, P<.001, Flavobacterium effect size 2.15, P<.001). Although Lactobacillus was the most abundant bacteria in both urine and vagina, it was more abundant in the vagina (linear discriminant analysis effect size effect size 2.72, P<.001). Significant associations between vaginal and urinary microbiomes were demonstrated, with Lactobacillus being predominant in both urine and vagina. Abundance of other bacteria also correlated highly between the vagina and urine. This inter-relatedness has implications for studying manipulation of the urogenital microbiome in treating conditions such as urgency urinary incontinence and urinary tract infections.
Sections du résumé
BACKGROUND
Although the vaginal and urinary microbiomes have been increasingly well-characterized in health and disease, few have described the relationship between these neighboring environments. Elucidating this relationship has implications for understanding how manipulation of the vaginal microbiome may affect the urinary microbiome and treatment of common urinary conditions.
OBJECTIVE
To describe the relationship between urinary and vaginal microbiomes using 16S rRNA gene sequencing. We hypothesized that the composition of the urinary and vaginal microbiomes would be significantly associated, with similarities in predominant taxa.
STUDY DESIGN
This multicenter study collected vaginal swabs and catheterized urine samples from 186 women with mixed urinary incontinence enrolled in a parent study and 84 similarly aged controls. Investigators decided a priori that if vaginal and/or urinary microbiomes differed between continent and incontinent women, the groups would be analyzed separately; if similar, samples from continent and incontinent women would be pooled and analyzed together. A central laboratory sequenced variable regions 1-3 (v1-3) and characterized bacteria to the genus level. Operational taxonomic unit abundance was described for paired vaginal and urine samples. Pearson's correlation characterized the relationship between individual operational taxonomic units of paired samples. Canonical correlation analysis evaluated the association between clinical variables (including mixed urinary incontinence and control status) and vaginal and urinary operational taxonomic units, using the Canonical correlation analysis function in the Vegan package (R version 3.5). Linear discriminant analysis effect size was used to find taxa that discriminated between vaginal and urinary samples.
RESULTS
Urinary and vaginal samples were collected from 212 women (mean age 53±11 years) and results from 197 paired samples were available for analysis. As operational taxonomic units in mixed urinary incontinence and control samples were related in canonical correlation analysis and since taxa did not discriminate between mixed urinary incontinence or controls in either vagina or urine, mixed urinary incontinence and control samples were pooled for further analysis. Canonical correlation analysis of vaginal and urinary samples indicated that that 60 of the 100 most abundant operational taxonomic units in the samples largely overlapped. Lactobacillus was the most abundant genus in both urine and vagina (contributing on average 53% to an individual's urine sample and 64% to an individual's vaginal sample) (Pearson correlation r=0.53). Although less abundant than Lactobacillus, other bacteria with high Pearson correlation coefficients also commonly found in vagina and urine included: Gardnerella (r=0.70), Prevotella (r=0.64), and Ureaplasma (r=0.50). Linear discriminant analysis effect size analysis identified Tepidimonas and Flavobacterium as bacteria that distinguished the urinary environment for both mixed urinary incontinence and controls as these bacteria were absent in the vagina (Tepidimonas effect size 2.38, P<.001, Flavobacterium effect size 2.15, P<.001). Although Lactobacillus was the most abundant bacteria in both urine and vagina, it was more abundant in the vagina (linear discriminant analysis effect size effect size 2.72, P<.001).
CONCLUSION
Significant associations between vaginal and urinary microbiomes were demonstrated, with Lactobacillus being predominant in both urine and vagina. Abundance of other bacteria also correlated highly between the vagina and urine. This inter-relatedness has implications for studying manipulation of the urogenital microbiome in treating conditions such as urgency urinary incontinence and urinary tract infections.
Identifiants
pubmed: 31421123
pii: S0002-9378(19)31010-5
doi: 10.1016/j.ajog.2019.08.011
pmc: PMC6995424
mid: NIHMS1542357
pii:
doi:
Substances chimiques
RNA, Ribosomal, 16S
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
154.e1-154.e10Subventions
Organisme : NICHD NIH HHS
ID : UG1 HD069013
Pays : United States
Organisme : NICHD NIH HHS
ID : UG1 HD069006
Pays : United States
Organisme : NICHD NIH HHS
ID : UG1 HD069025
Pays : United States
Organisme : NIA NIH HHS
ID : R03 AG060082
Pays : United States
Organisme : NICHD NIH HHS
ID : UG1 HD069010
Pays : United States
Organisme : NIDDK NIH HHS
ID : K23 DK110417
Pays : United States
Organisme : NICHD NIH HHS
ID : UG1 HD054214
Pays : United States
Organisme : NIDDK NIH HHS
ID : K12 DK100024
Pays : United States
Organisme : NIA NIH HHS
ID : P30 AG028716
Pays : United States
Organisme : NCATS NIH HHS
ID : KL2 TR001448
Pays : United States
Organisme : NICHD NIH HHS
ID : UG1 HD041261
Pays : United States
Organisme : NICHD NIH HHS
ID : U24 HD069031
Pays : United States
Informations de copyright
Copyright © 2019 Elsevier Inc. All rights reserved.
Références
Int Urogynecol J. 2018 Dec;29(12):1765-1771
pubmed: 30116843
Trends Microbiol. 2017 Sep;25(9):729-740
pubmed: 28550944
Sci Transl Med. 2012 May 2;4(132):132ra52
pubmed: 22553250
Int Urogynecol J. 2018 Dec;29(12):1785-1795
pubmed: 29909556
Trends Microbiol. 2017 Mar;25(3):182-191
pubmed: 27914761
Am J Obstet Gynecol. 2015 Sep;213(3):347.e1-11
pubmed: 26210757
Front Microbiol. 2019 Feb 14;10:193
pubmed: 30837959
Microbiol Spectr. 2016 Dec;4(6):
pubmed: 28087949
Int Urogynecol J. 2016 Oct;27(10):1479-90
pubmed: 27287818
J Clin Microbiol. 2014 Mar;52(3):871-6
pubmed: 24371246
Sex Transm Infect. 2018 Mar;94(2):117-123
pubmed: 28947665
Clin Infect Dis. 2019 May 2;68(10):1675-1683
pubmed: 30407498
Arch Gynecol Obstet. 2014 Mar;289(3):479-89
pubmed: 24170161
J Infect Dis. 2019 Aug 30;220(7):1099-1108
pubmed: 30715405
Microbiome. 2013 Dec 02;1(1):29
pubmed: 24451163
PLoS One. 2012;7(11):e50267
pubmed: 23189192
J Infect Dis. 2016 Aug 15;214 Suppl 1:S21-8
pubmed: 27449870
Menopause. 2018 Nov;25(11):1321-1330
pubmed: 30358729
Clin Microbiol Rev. 2016 Apr;29(2):223-38
pubmed: 26864580
Proc Natl Acad Sci U S A. 2011 Mar 15;108 Suppl 1:4680-7
pubmed: 20534435
Res Microbiol. 2017 Nov - Dec;168(9-10):826-836
pubmed: 28951208
Clin Infect Dis. 2011 May;52(10):1212-7
pubmed: 21498386
PLoS One. 2011;6(6):e20956
pubmed: 21738596
Int Urogynecol J. 2018 Dec;29(12):1797-1805
pubmed: 30267143
J Clin Microbiol. 2002 Jun;40(6):2147-52
pubmed: 12037079
PLoS One. 2011;6(12):e27310
pubmed: 22194782
Res Microbiol. 2017 Nov - Dec;168(9-10):811-825
pubmed: 28851670
Int Urogynecol J. 2017 May;28(5):711-720
pubmed: 27738739
Nat Commun. 2018 Apr 19;9(1):1557
pubmed: 29674608
Obstet Gynecol. 2014 Dec;124(6):1147-1156
pubmed: 25415166
PLoS One. 2014 Apr 08;9(4):e93827
pubmed: 24714158
Genome Med. 2016 Apr 01;8(1):35
pubmed: 27036316
Microbiome. 2016 Nov 1;4(1):58
pubmed: 27802830