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Human placenta has no microbiome but can contain potential pathogens

An Author Correction to this article was published on 01 October 2019

This article has been updated


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.

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Fig. 1: Batch effect detection in metagenomic and 16S rRNA amplicon sequencing data, cohort 1 samples.
Fig. 2: Mode of delivery and detection of vaginal bacteria by 16S rRNA amplicon sequencing.
Fig. 3: Bacterial signals and adverse pregnancy outcome.
Fig. 4: Sources of bacterial signals detected in human placental samples.

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Data availability

The 16S rRNA gene sequencing datasets generated and analysed in this study are publicly available under European Nucleotide Archive (ENA) accession number ERP109246. The metagenomics datasets, which primarily contain human sequences, are available with managed access in the European Genome-phenome Archive (EGA) accession number EGAD00001004198.

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  • 01 October 2019

    An Amendment to this paper has been published and can be accessed via a link at the top of the paper.


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The work was supported by the Medical Research Council (UK; MR/K021133/1) and the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (Women’s Health theme). We thank L. Bibby, S. Ranawaka, K. Holmes, J. Gill, R. Millar and L. Sánchez Busó for technical assistance during the study. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.

Author information

Authors and Affiliations



G.C.S.S., D.S.C.-J., J.P. and S.J.P. conceived the experiments. G.C.S.S., D.S.C.-J., J.P., S.J.P. and S.L. designed the experiments. S.L. and M.C.d.G. optimized the experimental approach. S.L. and F.G. performed the experiments. M.C.d.G. analysed all of the sequencing data. U.S. matched cases and controls, performed statistical analyses and provided logistical support for patient and sample metadata. E.C. managed sample collection and processing and the biobank in which all sample were stored. All authors contributed in writing the manuscript and approved the final version.

Corresponding authors

Correspondence to Julian Parkhill or Gordon C. S. Smith.

Ethics declarations

Competing interests

J.P. reports grants from Pfizer, personal fees from Next Gen Diagnostics, outside the submitted work; S.J.P. reports personal fees from Specific, personal fees from Next Gen Diagnostics, outside the submitted work; D.S.C.-J. reports grants from GlaxoSmithKline Research and Development, outside the submitted work and non-financial support from Roche Diagnostics, outside the submitted work; G.C.S.S. reports grants and personal fees from GlaxoSmithKline Research and Development, personal fees and non-financial support from Roche Diagnostics, outside the submitted work; D.S.C.-J. and G.C.S.S. report grants from Sera Prognostics, non-financial support from Illumina, outside the submitted work. M.C.d.G., S.L., U.S., F.G. and E.C. have nothing to disclose.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Peer review information Nature thanks David N. Fredricks and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Fig. 1 Two cohorts of placental samples were analysed.

Cohort 1 (n = 80) contained only samples from pre-labour Caesarean section (CS) deliveries and S. bongori was added to the samples before DNA isolation as a positive control. Samples in cohort 1 were analysed by both metagenomics and16S rRNA amplicon sequencing. Cohort 2 (n = 498) contained placental samples from Caesarean section and vaginal deliveries. DNA was isolated twice from each placental sample with two different DNA extraction kits. Samples were analysed by 16S rRNA amplicon sequencing. Pre-eclampsia (PE) was defined using The American College of Obstetricians and Gynaecologists (ACOG) 2013 definition. Small for gestational age (SGA) was defined as a birth weight less than the fifth percentile using a customized reference. Preterm denotes birth before 37 weeks gestation.

Extended Data Fig. 2 Positive control experiment comparison between metagenomics and 16S amplicon sequencing.

a, b, Adding approximately 1,100 CFUs of S. bongori to the placental tissue before DNA isolation resulted in an average of 180 reads (s.d. 90 reads) by metagenomic sequencing (n = 80) (a) or on average of 54% of all 16S rRNA amplicon sequencing reads (approximately 33,000 reads) being identified as S. bongori (s.d. 13%; n = 79) (b). Box represents the interquartile range; whiskers represent the maximum and minimum values; centre lines denote the median.

Source data

Extended Data Fig. 3 Strain analysis of E. coli reads found by metagenomics.

All reads identified in all 80 samples by Kraken25 as E. coli were extracted and mapped together against the closest E. coli reference genome (GenBank: CP02409.1). Single nucleotide polymorphisms, shown in red, were consistent for all samples across the genome. Single nucleotide polymorphisms were rare, except in the fimbrial chaperone protein gene (EcpD) indicated in light red. Sequence differences that appear as short sporadic red lines represent sequencing errors. Strain variation would have resulted in dashed vertical lines.

Extended Data Fig. 4 Detailed heat map metagenomic data.

Heat map showing the abundance of all non-human reads as detected by metagenomics. Human reads remaining after filtering (89.8%; s.d. 1.5%) are not shown for scaling purposes. Most taxa (shown on the right) are found in higher abundance within groups 1 and/or 2 (indicated on the left with light blue and purple, respectively). The purple box highlights the samples and species associated with group 2. The lane ID of each sample is represented by the first number (x axis). All samples from lanes 4 and 5 form group 2, and all samples from lanes 8 and 9 form group 3 (see Fig. 1a, b).

Extended Data Fig. 5 Species associated with batch effects visualized by PCA also do not show signal reproducibility.

a, PCAs of selections of samples from cohort 2 (16S), or of all cohort 2 samples as shown here, allows for the identification of batch effects and allows for the identification of contaminating species associated with the use of specific DNA isolation methods, kits and/or other reagents. An analysis of all samples shows that principal components 3 (x axis) and 4 (y axis) are strongly correlated with the use of Qiagen or specific Mpbio DNA isolation kits. b, Examples of bacteria detected in high abundance and frequency when processed with the Qiagen (x axis) and/or Mpbio (y axis) DNA isolation kits. Patterns that lack positive correlation (compare with Fig. 2a) demonstrate that signals are not sample- but batch-associated.

Source data

Extended Data Fig. 6 Scatterplot representations of the abundance of Bradyrhizobium, Burkholderia, vaginal lactobacilli and vaginosis bacteria during 16S amplicon sequencing.

a, b, The abundance of Bradyrhizobium (a) or Burkholderia (b) with respect to sequencing run batch effects during 16S amplicon sequencing. Numbers in parentheses indicate the number of samples sequenced in a given run. Values of zero are not shown on the logarithmic axis. c, d, The abundance of vaginal lactobacilli (c) and vaginosis bacteria (d) with respect to the mode of delivery during 16S amplicon sequencing. *P < 0.05, ***P < 0.001, Mann–Whitney U-tests, where values below 1% are regarded as 0% (not biologically relevant).

Extended Data Fig. 7 Mode of delivery and the detection of bacterial signals.

a, b, The association of vaginal lactobacilli with the mode of delivery, as determined by the analysis of 466 samples by 16S amplicon sequencing that were successfully sequenced twice using the Mpbio (a) and Qiagen (b) DNA isolation methods. Comparisons of the Mpbio and Qiagen DNA isolation techniques highlight that the same patterns are observed in the associations with mode of delivery. Comparisons also show that the Qiagen DNA isolation was more sensitive, resulting in twice as many signals above the 1% threshold. ch, The association of bacterial groups with mode of delivery. Analyses were performed using all 498 placental samples with the highest value of either DNA isolation method for each bacterial group per sample. c, d, S. agalactiae was not associated with the mode of delivery irrespective of whether a 1% threshold was used (the minimum percentage considered to be potentially ecologically relevant) (c) or a 0.1% threshold was used (the 16S detection limit, relevant for detecting traces of contamination during delivery) (d). e, f, The Ureaplasma genus was significantly associated with the mode of delivery using the 0.1% threshold, similar to Fig. 2c, which describes the combination of all vaginosis-associated bacteria. g, h, F. nucleatum was not associated with the mode of delivery, irrespective of whether a 1% (g) or 0.1% (h) threshold was used. *P < 0.05, **P < 0.01, ***P < 0.001, Mann–Whitney U-tests.

Source data

Extended Data Fig. 8 Heat map of Spearman’s rho correlation coefficients of bacterial signals as found by 16S rRNA amplicon sequencing.

Sample-associated signals (red bar), are typically identified by increased kappa scores, as shown in Supplementary Table 4. Reagent contaminants are indicated by a blue bar. Vaginosis-associated bacteria (purple bar) show positive correlations (purple square) with each other, Lactobacillus iners and faecal bacteria (brown bar). Lactobacilli (yellow bar) show limited positive correlation with faecal bacteria. Reagent contaminants mainly associated with the Qiagen (light blue) or the Mpbio (green) kit form distinct clusters. Species that are strongly associated with sample collection contamination in 2012–2013 are indicated in orange. For each species the highest value (percentage) found using either the Qiagen or the Mpbio DNA isolation kit, was used as input (using all 498 samples).

Source data

Extended Data Fig. 9 Bacterial signals and adverse pregnancy outcome.

ad, Scatterplot representations of the non-significant associations of S. agalactiae with SGA (a), S. anginosus with SGA (b), and of the significant associations of L. iners with pre-eclampsia (c), and Ureaplasma with PTB (d). Samples with 0% signal are not shown on the logarithmic scale. Signals above 1% (dotted line) are regarded as positive for use in McNemar’s test (ac), and signals below 1% are considered as negative. The Mann–Whitney U-test was used for unpaired samples in d.

Source data

Supplementary information

Supplementary Information

This supplementary information file contains all six Supplementary Tables. It furthermore contains a Supplementary Discussion section and a Supplementary Methods section.

Reporting Summary

Supplementary Information

This supplementary information file contains detailed oligotype abundance information (16S rRNA amplicon sequencing results).

Source data

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de Goffau, M.C., Lager, S., Sovio, U. et al. Human placenta has no microbiome but can contain potential pathogens. Nature 572, 329–334 (2019).

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