Transcriptional profiling of zebrafish intestines identifies macrophages as host cells for human norovirus infection
Bibliographic record
Abstract
Human noroviruses (HuNoVs) are a major cause of diarrheal disease, yet critical aspects of their biology, including cellular tropism, remain unclear. Although research has traditionally focused on the intestinal epithelium, the hypothesis that HuNoV infects macrophages has been recurrently discussed and is investigated here using a zebrafish larval model. Through single-cell RNA sequencing of dissected zebrafish intestines, we unbiasedly identified macrophages as host cells for HuNoV replication, with all three open reading frames mapped to individual macrophages. Notably, HuNoV preferentially infects actively phagocytosing inflammatory macrophages. HuNoV capsid proteins and double-stranded RNA colocalized within intestinal macrophages of infected zebrafish larvae, and the negative-strand RNA intermediate was detected within FACS-sorted macrophages. Flow cytometry confirmed viral replication within these macrophages, constituting approximately 23% of HuNoV's host cells. Identifying macrophages as host cells prompts a reevaluation of their role in HuNoV pathogenesis, offering new directions for understanding and controlling this infection.
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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 | 0.000 |
| 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".