Human placenta has no microbiome but can contain potential pathogens.
Biopsy
Cohort Studies
DNA Contamination
DNA, Bacterial
/ analysis
Delivery, Obstetric
Female
Humans
Infant, Newborn
Male
Metagenomics
Obstetric Labor Complications
/ microbiology
Placenta
/ microbiology
Pregnancy
Pregnancy Complications, Infectious
/ microbiology
Pregnancy Outcome
RNA, Ribosomal, 16S
/ analysis
Reproducibility of Results
Sepsis
/ congenital
Sequence Analysis, DNA
Streptococcus agalactiae
/ isolation & purification
Journal
Nature
ISSN: 1476-4687
Titre abrégé: Nature
Pays: England
ID NLM: 0410462
Informations de publication
Date de publication:
08 2019
08 2019
Historique:
received:
16
01
2019
accepted:
28
06
2019
pubmed:
2
8
2019
medline:
18
12
2019
entrez:
2
8
2019
Statut:
ppublish
Résumé
We sought to determine whether pre-eclampsia, spontaneous preterm birth or the delivery of infants who are small for gestational age were associated with the presence of bacterial DNA in the human placenta. Here we show that there was no evidence for the presence of bacteria in the large majority of placental samples, from both complicated and uncomplicated pregnancies. Almost all signals were related either to the acquisition of bacteria during labour and delivery, or to contamination of laboratory reagents with bacterial DNA. The exception was Streptococcus agalactiae (group B Streptococcus), for which non-contaminant signals were detected in approximately 5% of samples collected before the onset of labour. We conclude that bacterial infection of the placenta is not a common cause of adverse pregnancy outcome and that the human placenta does not have a microbiome, but it does represent a potential site of perinatal acquisition of S. agalactiae, a major cause of neonatal sepsis.
Identifiants
pubmed: 31367035
doi: 10.1038/s41586-019-1451-5
pii: 10.1038/s41586-019-1451-5
pmc: PMC6697540
mid: EMS83569
doi:
Substances chimiques
DNA, Bacterial
0
RNA, Ribosomal, 16S
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
329-334Subventions
Organisme : Medical Research Council
ID : MR/K021133/1
Pays : United Kingdom
Commentaires et corrections
Type : CommentIn
Type : ErratumIn
Références
Brosens, I., Pijnenborg, R., Vercruysse, L. & Romero, R. The “Great Obstetrical Syndromes” are associated with disorders of deep placentation. Am. J. Obstet. Gynecol. 204, 193–201 (2011).
doi: 10.1016/j.ajog.2010.08.009
Aagaard, K. et al. The placenta harbors a unique microbiome. Sci. Transl. Med. 6, 237ra65 (2014).
doi: 10.1126/scitranslmed.3008599
Antony, K. M. et al. The preterm placental microbiome varies in association with excess maternal gestational weight gain. Am. J. Obstet. Gynecol. 212, 653.e1–653.e16 (2015).
doi: 10.1016/j.ajog.2014.12.041
Collado, M. C., Rautava, S., Aakko, J., Isolauri, E. & Salminen, S. Human gut colonisation may be initiated in utero by distinct microbial communities in the placenta and amniotic fluid. Sci. Rep. 6, 23129 (2016).
doi: 10.1038/srep23129
Perez-Muñoz, M. E., Arrieta, M. C., Ramer-Tait, A. E. & Walter, J. A critical assessment of the “sterile womb” and “in utero colonization” hypotheses: implications for research on the pioneer infant microbiome. Microbiome 5, 48 (2017).
doi: 10.1186/s40168-017-0268-4
Salter, S. J. et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 12, 87 (2014).
doi: 10.1186/s12915-014-0087-z
Jervis-Bardy, J. et al. Deriving accurate microbiota profiles from human samples with low bacterial content through post-sequencing processing of Illumina MiSeq data. Microbiome 3, 19 (2015).
doi: 10.1186/s40168-015-0083-8
de Goffau, M. C. et al. Recognizing the reagent microbiome. Nat. Microbiol. 3, 851–853 (2018).
doi: 10.1038/s41564-018-0202-y
Lauder, A. P. et al. Comparison of placenta samples with contamination controls does not provide evidence for a distinct placenta microbiota. Microbiome 4, 29 (2016).
doi: 10.1186/s40168-016-0172-3
Leiby, J. S. et al. Lack of detection of a human placenta microbiome in samples from preterm and term deliveries. Microbiome 6, 196 (2018).
doi: 10.1186/s40168-018-0575-4
Theis, K. R. et al. Does the human placenta delivered at term have a microbiota? Results of cultivation, quantitative real-time PCR, 16S rRNA gene sequencing, and metagenomics. Am. J. Obstet. Gynecol. 220, 267.e1–267.e39 (2019).
doi: 10.1016/j.ajog.2018.10.018
Leon, L. J. et al. Enrichment of clinically relevant organisms in spontaneous preterm delivered placenta and reagent contamination across all clinical groups in a large UK pregnancy cohort. Appl. Environ. Microbiol. 84, e00483-e18 (2018).
pubmed: 29776928
pmcid: 6029081
Sovio, U., White, I. R., Dacey, A., Pasupathy, D. & Smith, G. C. S. Screening for fetal growth restriction with universal third trimester ultrasonography in nulliparous women in the Pregnancy Outcome Prediction (POP) study: a prospective cohort study. Lancet 386, 2089–2097 (2015).
doi: 10.1016/S0140-6736(15)00131-2
Hornef, M. & Penders, J. Does a prenatal bacterial microbiota exist? Mucosal Immunol. 10, 598–601 (2017).
doi: 10.1038/mi.2016.141
Leong, H. N. et al. The prevalence of chromosomally integrated human herpesvirus 6 genomes in the blood of UK blood donors. J. Med. Virol. 79, 45–51 (2007).
doi: 10.1002/jmv.20760
Ravel, J. et al. Vaginal microbiome of reproductive-age women. Proc. Natl Acad. Sci. USA 108 (suppl. 1), 4680–4687 (2011).
doi: 10.1073/pnas.1002611107
Glaser, P. et al. Genome sequence of Streptococcus agalactiae, a pathogen causing invasive neonatal disease. Mol. Microbiol. 46, 1499–1513 (2002).
doi: 10.1046/j.1365-2958.2002.03126.x
Abele-Horn, M., Scholz, M., Wolff, C. & Kolben, M. High-density vaginal Ureaplasma urealyticum colonization as a risk factor for chorioamnionitis and preterm delivery. Acta Obstet. Gynecol. Scand. 79, 973–978 (2000).
pubmed: 11081683
Schrag, S. J. et al. Group B streptococcal disease in the era of intrapartum antibiotic prophylaxis. N. Engl. J. Med. 342, 15–20 (2000).
doi: 10.1056/NEJM200001063420103
Pasupathy, D. et al. Study protocol. A prospective cohort study of unselected primiparous women: the pregnancy outcome prediction study. BMC Pregnancy Childbirth 8, 51 (2008).
doi: 10.1186/1471-2393-8-51
Gardosi, J., Mongelli, M., Wilcox, M. & Chang, A. An adjustable fetal weight standard. Ultrasound Obstet. Gynecol. 6, 168–174 (1995).
doi: 10.1046/j.1469-0705.1995.06030168.x
American College of Obstetricians and Gynecologists & Task Force on Hypertension in Pregnancy. Report of the American College of Obstetricians and Gynecologists’ Task Force on Hypertension in Pregnancy. Obstet. Gynecol. 122, 1122–1131 (2013).
doi: 10.1097/01.AOG.0000437382.03963.88
Lager, S. et al. Detecting eukaryotic microbiota with single-cell sensitivity in human tissue. Microbiome 6, 151 (2018).
doi: 10.1186/s40168-018-0529-x
Walker, A. W. et al. 16S rRNA gene-based profiling of the human infant gut microbiota is strongly influenced by sample processing and PCR primer choice. Microbiome 3, 26 (2015).
doi: 10.1186/s40168-015-0087-4
Wood, D. E. & Salzberg, S. L. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 15, R46 (2014).
doi: 10.1186/gb-2014-15-3-r46
Nurk, S. et al. Assembling single-cell genomes and mini-metagenomes from chimeric MDA products. J. Comput. Biol. 20, 714–737 (2013).
doi: 10.1089/cmb.2013.0084
Johnson, M. et al. NCBI BLAST: a better web interface. Nucleic Acids Res. 36, W5–W9 (2008).
doi: 10.1093/nar/gkn201
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
doi: 10.1093/bioinformatics/btp324
Carver, T., Harris, S. R., Berriman, M., Parkhill, J. & McQuillan, J. A. Artemis: an integrated platform for visualization and analysis of high-throughput sequence-based experimental data. Bioinformatics 28, 464–469 (2012).
doi: 10.1093/bioinformatics/btr703
Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K. & Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 79, 5112–5120 (2013).
doi: 10.1128/AEM.01043-13
Eren, A. M. et al. Oligotyping: differentiating between closely related microbial taxa using 16S rRNA gene data. Methods Ecol. Evol. 4, 1111–1119 (2013).
doi: 10.1111/2041-210X.12114
Schmieder, R. & Edwards, R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 27, 863–864 (2011).
doi: 10.1093/bioinformatics/btr026
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).
doi: 10.1093/nar/gks1219
Ludwig, W. et al. ARB: a software environment for sequence data. Nucleic Acids Res. 32, 1363–1371 (2004).
doi: 10.1093/nar/gkh293
Viera, A. J. & Garrett, J. M. Understanding interobserver agreement: the kappa statistic. Fam. Med. 37, 360–363 (2005).
pubmed: 15883903
Mackinnon, A. A spreadsheet for the calculation of comprehensive statistics for the assessment of diagnostic tests and inter-rater agreement. Comput. Biol. Med. 30, 127–134 (2000).
doi: 10.1016/S0010-4825(00)00006-8