MERS-CoV and SARS-CoV-2 infection in diverse human lung organoid-derived cultures
Bibliographic record
Abstract
Cell cultures are widely used to study infectious respiratory diseases and to test therapeutics; however, they do not faithfully recapitulate the architecture and complexity of the human respiratory tract. Lung organoids have emerged as an alternative model that partially overcomes this key disadvantage. Lung organoids can be cultured in various formats that offer potential for studying highly pathogenic viruses. However, the effects of these different formats on virus infection remain unexplored, leaving their relative value unclear. In this study, we generated primary lung organoids from human donor cells and used them to derive monolayers and air-liquid interface (ALI) cultures with the goal of comparing the replication kinetics of two circulating highly pathogenic coronaviruses, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Middle East respiratory syndrome coronavirus (MERS-CoV). Infection studies revealed that organoid-derived monolayers displayed limited infection, and the innate immune response was impaired against bacterial lipopolysaccharide (LPS) but not against virus-like double-stranded RNA (dsRNA) or poly(I:C). Meanwhile, organoids and organoid-derived ALI cultures retained viral permissivity, with ALI cultures displaying diverse antiviral immune responses against both coronaviruses. SARS-CoV-2 and MERS-CoV demonstrated differential replication kinetics in organoid and organoid-derived ALI cultures. Therefore, primary organoid-derived cells in two-dimensional monolayer or three-dimensional ALI formats influence virus infection and host antiviral responses. Our study informs the selection of culture conditions for organoid-based respiratory disease research and therapeutic testing. IMPORTANCE: The COVID-19 pandemic heralded the upsurge in human-derived lung organoid-based studies due to their cellular heterogeneity that partly emulates the cellular complexity of the respiratory tract. A major disadvantage of organoid models resides in their apical-in conformation that "hides" cells and proteins that are typically exposed to the air-liquid interface (ALI) in the airways and are targets of viruses. Here, we generated monolayers and ALI cultures to facilitate cell exposure to highly relevant pathogens and compared them to parental organoids. Organoids at the ALI captured infection and immune responses better than organoids and organoid-derived monolayer cultures. Organoids at the ALI are a viable approach to improve identification and characterization of virus infection, host responses, and therapeutic testing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".