Innate immune signaling in trophoblast and decidua organoids defines differential antiviral defenses at the maternal-fetal interface
- Duke University, United States
- University of Pittsburgh, United States
- Cornell University, United States
Abstract
Infections at the maternal-fetal interface can directly harm the fetus and induce complications that adversely impact pregnancy outcomes. Innate immune signaling by both fetal-derived placental trophoblasts and the maternal decidua must provide antimicrobial defenses at this critical interface without compromising its integrity. Here, we developed matched trophoblast and decidua organoids from human placentas to define the relative contributions of these cells to antiviral defenses at the maternal-fetal interface. We demonstrate that trophoblast and decidua organoids basally secrete distinct immunomodulatory factors, including the constitutive release of the antiviral type III interferon IFN-λ2 from trophoblast organoids, and differentially respond to viral infections through the induction of organoid-specific factors. Lastly, we define the differential susceptibility and innate immune signaling of trophoblast and decidua organoids to human cytomegalovirus (HCMV) and develop a co-culture model of trophoblast and decidua organoids which showed that trophoblast-derived factors protect decidual cells from HCMV infection. Our findings establish matched trophoblast and decidua organoids as ex vivo models to study vertically transmitted infections and highlight differences in innate immune signaling by fetal-derived trophoblasts and the maternal decidua.
Data availability
Sequence data have been deposited into Sequence Read Archives SUB11885513.
Article and author information
Author details
Funding
National Institute of Allergy and Infectious Diseases (NIHAI145828)
- Carolyn B Coyne
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2022, Yang et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Innate immune signaling in trophoblast and decidua organoids defines differential antiviral defenses at the maternal-fetal interface
eLife 11:e79794.
https://doi.org/10.7554/eLife.79794
Further reading
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- Immunology and Inflammation
- Microbiology and Infectious Disease
Coronavirus disease 2019 (COVID-19) is a respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that displays great variability in clinical phenotype. Many factors have been described to be correlated with its severity, and microbiota could play a key role in the infection, progression, and outcome of the disease. SARS-CoV-2 infection has been associated with nasopharyngeal and gut dysbiosis and higher abundance of opportunistic pathogens. To identify new prognostic markers for the disease, a multicentre prospective observational cohort study was carried out in COVID-19 patients divided into three cohorts based on symptomatology: mild (n = 24), moderate (n = 51), and severe/critical (n = 31). Faecal and nasopharyngeal samples were taken, and the microbiota was analysed. Linear discriminant analysis identified Mycoplasma salivarium, Prevotella dentalis, and Haemophilus parainfluenzae as biomarkers of severe COVID-19 in nasopharyngeal microbiota, while Prevotella bivia and Prevotella timonensis were defined in faecal microbiota. Additionally, a connection between faecal and nasopharyngeal microbiota was identified, with a significant ratio between P. timonensis (faeces) and P. dentalis and M. salivarium (nasopharyngeal) abundances found in critically ill patients. This ratio could serve as a novel prognostic tool for identifying severe COVID-19 cases.
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- Epidemiology and Global Health
- Microbiology and Infectious Disease
Background:
In many settings, a large fraction of the population has both been vaccinated against and infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Hence, quantifying the protection provided by post-infection vaccination has become critical for policy. We aimed to estimate the protective effect against SARS-CoV-2 reinfection of an additional vaccine dose after an initial Omicron variant infection.
Methods:
We report a retrospective, population-based cohort study performed in Shanghai, China, using electronic databases with information on SARS-CoV-2 infections and vaccination history. We compared reinfection incidence by post-infection vaccination status in individuals initially infected during the April–May 2022 Omicron variant surge in Shanghai and who had been vaccinated before that period. Cox models were fit to estimate adjusted hazard ratios (aHRs).
Results:
275,896 individuals were diagnosed with real-time polymerase chain reaction-confirmed SARS-CoV-2 infection in April–May 2022; 199,312/275,896 were included in analyses on the effect of a post-infection vaccine dose. Post-infection vaccination provided protection against reinfection (aHR 0.82; 95% confidence interval 0.79–0.85). For patients who had received one, two, or three vaccine doses before their first infection, hazard ratios for the post-infection vaccination effect were 0.84 (0.76–0.93), 0.87 (0.83–0.90), and 0.96 (0.74–1.23), respectively. Post-infection vaccination within 30 and 90 days before the second Omicron wave provided different degrees of protection (in aHR): 0.51 (0.44–0.58) and 0.67 (0.61–0.74), respectively. Moreover, for all vaccine types, but to different extents, a post-infection dose given to individuals who were fully vaccinated before first infection was protective.
Conclusions:
In previously vaccinated and infected individuals, an additional vaccine dose provided protection against Omicron variant reinfection. These observations will inform future policy decisions on COVID-19 vaccination in China and other countries.
Funding:
This study was funded the Key Discipline Program of Pudong New Area Health System (PWZxk2022-25), the Development and Application of Intelligent Epidemic Surveillance and AI Analysis System (21002411400), the Shanghai Public Health System Construction (GWVI-11.2-XD08), the Shanghai Health Commission Key Disciplines (GWVI-11.1-02), the Shanghai Health Commission Clinical Research Program (20214Y0020), the Shanghai Natural Science Foundation (22ZR1414600), and the Shanghai Young Health Talents Program (2022YQ076).
