LncRNA Nostrill promotes interferon-γ-stimulated gene transcription and facilitates intestinal epithelial cell-intrinsic anti-Cryptosporidium defense
Bibliographic record
Abstract
Intestinal epithelial cells possess the requisite molecular machinery to initiate cell-intrinsic defensive responses against intracellular pathogens, including intracellular parasites. Interferons(IFNs) have been identified as cornerstones of epithelial cell-intrinsic defense against such pathogens in the gastrointestinal tract. Long non-coding RNAs (lncRNAs) are RNA transcripts (>200 nt) not translated into protein and represent a critical regulatory component of mucosal defense. We report here that lncRNA Nostrill facilitates IFN-γ-stimulated intestinal epithelial cell-intrinsic defense against infection by Cryptosporidium, an important opportunistic pathogen in AIDS patients and a common cause of diarrhea in young children. Nostrill promotes transcription of a panel of genes controlled by IFN-γ through facilitating Stat1 chromatin recruitment and thus, enhances expression of several genes associated with cell-intrinsic defense in intestinal epithelial cells in response to IFN-γ stimulation, including Igtp, iNos, and Gadd45g. Induction of Nostrill enhances IFN-γ-stimulated intestinal epithelial defense against Cryptosporidium infection, which is associated with an enhanced autophagy in intestinal epithelial cells. Our findings reveal that Nostrill enhances the transcription of a set of genes regulated by IFN-γ in intestinal epithelial cells. Moreover, induction of Nostrill facilitates the IFN-γ-mediated epithelial cell-intrinsic defense against cryptosporidial infections.
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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) | 0.003 | 0.001 |
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".