Macrophage-augmented intestinal organoids model virus-host interactions in enteric viral diseases and facilitate therapeutic development
Bibliographic record
Abstract
The pathogenesis of enteric viral infections is attributed to both viral replication and the resultant immune-inflammatory response. To recapitulate this complex pathophysiology, we engineer macrophage-augmented organoids (MaugOs) by integrating human macrophages into primary intestinal organoids. Echovirus 1, echovirus 6, rotavirus, seasonal coronavirus OC43 and SARS-CoV-2- known to directly invade the intestine- are used as disease modalities. We demonstrate that these viruses efficiently propagate in MaugOs and stimulate the host antiviral response. However, rotavirus, coronavirus OC43 and SARS-CoV-2, but not the two echoviruses, trigger inflammatory responses. Acetate, a microbial metabolite abundantly present in the intestine, potently inhibits virus-induced inflammatory responses in MaugOs, while differentially affecting viral replication in macrophages and organoids. Furthermore, we provide a proof-of-concept of combining antiviral agent with either anti-inflammatory regimen or acetate to simultaneously inhibit viral infection and inflammatory response in MaugOs. Collectively, these findings demonstrate that MaugOs are innovative tools for studying the complex virus-host interactions and advancing therapeutic development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".