The microbiota of pregnant women with SARS-CoV-2 and their infants

Abstract

Background: Infants receive their first bacteria from their birthing parent. This newly acquired microbiome plays a pivotal role in developing a robust immune system, the cornerstone of long-term health.

Results: We demonstrated that the gut, vaginal, and oral microbial diversity of pregnant women with SARS-CoV-2 infection is reduced, and women with early infections exhibit a different vaginal microbiota composition at the time of delivery compared to their healthy control counterparts. Accordingly, a low relative abundance of two Streptococcus sequence variants (SV) was predictive of infants born to pregnant women with SARS-CoV-2 infection.

Conclusions: Our data suggest that SARS-CoV-2 infections during pregnancy, particularly early infections, are associated with lasting changes in the microbiome of pregnant women, compromising the initial microbial seed of their infant. Our results highlight the importance of further exploring the impact of SARS-CoV-2 on the infant's microbiome-dependent immune programming. Video Abstract.

Fig. 1

Gut microbial diversity of pregnant women differs by SARS-CoV-2 infection. Gut microbiota alpha diversity at the sequence variant (SV) level, of pregnant women with a positive SARS-CoV-2 diagnosis during pregnancy (Positive) or healthy controls (HC) was estimated using the Shannon index. P values were calculated by applying a linear regression model with Shannon diversity indexes as the dependent variable and SARS-CoV-2 infection (and other covariates, see methods) as the independent variables

Fig. 2

Vaginal microbiota of pregnant women differs by SARS-CoV-2 infection. A Alpha diversity is shown using the Chao1 estimator comparing pregnant women with SARS-CoV-2 infection during pregnancy and pregnant healthy controls (HC). B Alpha diversity is shown using the Chao1 estimator comparing pregnant women with SARS-CoV-2 infection early, late, or active vs. HC. Significance was determined using Linear regression and pairwise comparison with estimated marginal means. C, D Beta diversity analyses by groups: C all pregnant women with SARS-CoV-2 infection during pregnancy compared to HC; D pregnant women with SARS-CoV-2 infection early, late, or active compared to HC. Beta diversity comparisons were performed using PERMANOVA analysis with pairwise comparisons and BETADISPER for dispersion analysis. For PERMANOVA and BETADISPER analyses we used Sørensen dissimilarities. P values were all adjusted by false discovery rate. E, F, G Bacterial taxa (at the sequence variant or SV) were selected by the random forest classification (RFC) and ranked according to their importance in the classification. RFC comparisons are shown in E pregnant women with SARS-CoV-2 infection during pregnancy vs. HC, F pregnant women with early SARS-CoV-2 infection vs. HC, G pregnant women with active SARS-CoV-2 infection vs. HC. Bars’ colors indicate the comparison group (i.e., SARS-CoV-2 or HC), and each bar indicates the importance by which the increase on an SV predicts a particular comparison group. The selection of the variables for RFC was performed with Boruta algorithm. We also used the Local Interpretable Model-agnostic Explanation (LIME) to estimate a threshold of the abundance of the SV selected with Boruta that predicts a particular comparison group. *Padj < 0.050, **Padj < 0.010

Fig. 3

Oral microbiota of pregnant women differs by SARS-CoV-2 infection. Beta diversity analyses by groups: A pregnant women with SARS-CoV-2 infection compared to HC or B pregnant women with early, late, or active SARS-CoV-2 infections compared to HC. Beta diversity comparisons were performed using PERMANOVA analysis with pairwise comparisons and BETADISPER for dispersion analysis. For PERMANOVA and BETADISPER analyses, we used Sørensen dissimilarities. P values were all adjusted by false discovery rate. C, D Bacterial taxa (at the sequence variant or SV) were selected by the random forest classification (RFC) and ranked according to their importance in the classification. RFC comparisons are shown in C pregnant women with SARS-CoV-2 infection compared to HC; D pregnant women with active SARS-CoV-2 infection compared to HC. Bar colors indicate the comparison group (i.e., SARS-CoV-2 or HC); and each bar indicates the importance by which the increase in an SV predicts a particular comparison group. The selection of the variables for RFC was performed using the Boruta algorithm. We also used the Local Interpretable Model-agnostic Explanation (LIME) to estimate a threshold of the abundance of the SV selected with Boruta that predicts a particular comparison group. *Padj < 0.050, **Padj < 0.010

Fig. 4

The oral microbiota of infants born to pregnant women with SARS-CoV-2 is altered. Beta diversity analyses by groups: A infants born to pregnant women infected with SARS-CoV-2 during pregnancy compared to infants born to pregnant healthy controls (HC); B infants born to pregnant women infected with SARS-CoV-2 early or late during pregnancy or with active infection, compared to HC. Beta diversity comparisons were performed using PERMANOVA analysis with pairwise comparisons and BETADISPER for dispersion analysis. For PERMANOVA and BETADISPER analyses, we used Sørensen dissimilarities. P values were all adjusted by false discovery rate. C Bacterial taxa (at the amplicon sequence variant or SV) were selected by the random forest classification (RFC) and ranked according to their importance in the classification for infants born to pregnant women with SARS-CoV-2 compared to HC. Bar colors indicate the comparison group (i.e., SARS-CoV-2 or HC); and each bar indicates the importance by which the increase in an SV predicts a particular comparison group. The Boruta algorithm was used to select variables for RFC. We also used the Local Interpretable Model-agnostic Explanation (LIME) to estimate a threshold of the abundance of the SV selected with Boruta that predicts a particular comparison group. *Padj < 0.050, **Padj < 0.010