Biology subjects

Bebell, L. M.

Publications and source records attributed to Bebell, L. M..

3 recordsLinked to original sources

bioRxiv2021

Placental expression of ACE2 and TMPRSS2 in maternal SARS-CoV-2 infection: are placental defenses mediated by fetal sex?

BackgroundSex differences in vulnerability to and severity of SARS-CoV-2 infection have been described in non-pregnant populations. ACE2 and TMPRSS2, host molecules required for viral entry, are regulated by sex steroids and expressed in the placenta. We sought to investigate whether placental ACE2 and TMPRSS2 expression vary by fetal sex and in the presence of maternal SARS-CoV-2 infection. MethodsPlacental ACE2 and TMPRSS2 were quantified in 68 pregnant individuals (38 SARS-CoV-2 positive, 30 SARS-CoV-2 negative) delivering at Mass General Brigham from April to June 2020. Maternal SARS-CoV-2 status was determined by nasopharyngeal RT-PCR. Placental SARS-CoV-2 viral load was quantified. RTqPCR was performed to quantify expression of ACE2 and TMPRSS2 relative to the reference gene YWHAZ. Western blots were performed on placental homogenates to quantify protein levels. The impact of fetal sex and SARS-CoV-2 exposure on ACE2 and TMPRSS2 expression was analyzed by 2-way ANOVA. ResultsSARS-CoV-2 virus was undetectable in all placentas. Maternal SARS-CoV-2 infection impacted TMPRSS2 placental gene and protein expression in a sexually dimorphic fashion (2-way ANOVA interaction p-value: 0.002). We observed no impact of fetal sex or maternal SARS-CoV-2 status on placental ACE2 gene or protein expression. Placental TMPRSS2 expression was significantly correlated with ACE2 expression in males (Spearmans {rho}=0.54, p=0.02) but not females ({rho}=0.23, p=0.34) exposed to maternal SARS-CoV-2. ConclusionsSex differences in placental TMPRSS2 but not ACE2 were observed in the setting of maternal SARS-CoV-2 infection. These findings may have implications for offspring vulnerability to placental infection and vertical transmission.These findings may have implications for offspring vulnerability to placental infection and vertical transmission.

developmental biology↗

bioRxiv2021

Sexually dimorphic placental responses to maternal SARS-CoV-2 infection

There is a persistent male bias in the prevalence and severity of COVID-19 disease. Underlying mechanisms accounting for this sex difference remain incompletely understood. Interferon responses have been implicated as a modulator of disease in adults, and play a key role in the placental anti-viral response. Moreover, the interferon response has been shown to alter Fc-receptor expression, and therefore may impact placental antibody transfer. Here we examined the intersection of viral-induced placental interferon responses, maternal-fetal antibody transfer, and fetal sex. Placental interferon stimulated genes (ISGs), Fc-receptor expression, and SARS-CoV-2 antibody transfer were interrogated in 68 pregnancies. Sexually dimorphic placental expression of ISGs, interleukin-10, and Fc receptors was observed following maternal SARS-CoV-2 infection, with upregulation in males. Reduced maternal SARS-CoV-2-specific antibody titers and impaired placental antibody transfer were noted in pregnancies with a male fetus. These results demonstrate fetal sex-specific maternal and placental adaptive and innate immune responses to SARS-CoV-2.

immunology↗

bioRxiv2020

Vaginal Microbiome Topic Modelling of Laboring Ugandan Women With and Without Fever

The composition of the maternal vaginal microbiome may influence the duration of pregnancy, onset of labor and even neonatal outcomes. Maternal microbiome research in sub Saharan-Africa has focused on non-pregnant and postpartum composition of the vaginal microbiome. We examined the vaginal microbiome composition of 99 laboring Ugandan women using routine microbiology and 16S ribosomal DNA sequencing from two hypervariable regions (V1-V2 and V3-V4), using standard hierarchical methods. We then introduce Grades of Membership (GoM) modeling for the vaginal microbiome, a method often used in the text mining machine learning literature. Leveraging GoM models, we create a basis composed of a small number of microbial topics whose linear combination optimally represents each patient yielding more accurate associations. We identified relationships between defined communities and the presentation or absence of intrapartum fever. Using a random forest model we showed that by including novel microbial topic models we improved upon clinical variables to predict maternal fever. We also show by integrating clinical variables with a microbial topic model into this model found young maternal age, fever report earlier in the current pregnancy, and longer labors, as well as a more diverse, less Lactobacillus dominated microbiome were features of labor associated with intrapartum fever. These results better define relationships between presentation or absence of intrapartum fever, demographics, peripartum course, and vaginal microbial communities, and improve our understanding of the impact of the microbiome on maternal and neonatal infection risk.

genomics↗