Analysis of intestinal epithelial cell responses to IFN-γ during <i>Cryptosporidium</i> infection
Bibliographic record
Abstract
Abstract Cryptosporidium species are intracellular parasites that infect intestinal epithelial cells (IEC) and the rapid cycle of growth, cell lysis and reinfection every 12 hours can cause severe enteric disease in young or immunocompromised patients. Interferon-γ (IFN-γ) has a crucial role in protective immunity and mice with an IEC-specific deletion of the IFN-γ receptor (IFN-γRΔIEC) are highly susceptible to infection. However, the dynamics of IFN-γ signalling to IEC and how this leads to parasite control remain poorly understood. Based on the use of a reporter mouse for IFN signalling in vivo, while macrophages showed a transient response to treatment with IFN-γ, IEC exhibited a sustained response to IFN-γ that peaks at 24h. Indeed, the treatment of mice with recombinant IFN-γ transiently limits parasite shedding but takes 24h to show protective activity. Single-cell RNA sequencing of enterocytes from naïve and infected mice identified upregulation of several IFN-γ-inducible effectors but highlighted that induction of these genes was comparable in infected and bystander IEC. Together, these results suggest that while Cryptosporidium-infected cells are responsive to IFN-γ signalling, the protective effects of IFN-γ may be mediated through activation of uninfected bystander enterocytes. These studies provide new insights into the dynamics and function of IFN-γ signalling on IEC that are broadly relevant to other IEC-restricted pathogens. Supported by grants from NIH (R01 AI148249) and post-doctoral fellowships from the Canadian Institutes for Health Research and Fonds de Recherche du Québec – Santé.
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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".