miR-27b Targets KSRP to Coordinate TLR4-Mediated Epithelial Defense against Cryptosporidium parvum Infection
Bibliographic record
Abstract
Cryptosporidium is a protozoan parasite that infects the gastrointestinal epithelium and causes a diarrheal disease. Toll-like receptor (TLR)- and NF-κB-mediated immune responses from epithelial cells, such as production of antimicrobial peptides and generation of reactive nitrogen species, are important components of the host's defense against cryptosporidial infection. Here we report data demonstrating a role for miR-27b in the regulation of TLR4/NF-κB-mediated epithelial anti-Cryptosporidium parvum responses. We found that C. parvum infection induced nitric oxide (NO) production in host epithelial cells in a TLR4/NF-κB-dependent manner, with the involvement of the stabilization of inducible NO synthase (iNOS) mRNA. C. parvum infection of epithelial cells activated NF-κB signaling to increase transcription of the miR-27b gene. Meanwhile, downregulation of KH-type splicing regulatory protein (KSRP) was detected in epithelial cells following C. parvum infection. Importantly, miR-27b targeted the 3'-untranslated region of KSRP, resulting in translational suppression. C. parvum infection decreased KSRP expression through upregulating miR-27b. Functional manipulation of KSRP or miR-27b caused reciprocal alterations in iNOS mRNA stability in infected cells. Forced expression of KSRP and inhibition of miR-27b resulted in an increased burden of C. parvum infection. Downregulation of KSRP through upregulating miR-27b was also detected in epithelial cells following LPS stimulation. These data suggest that miR-27b targets KSRP and modulates iNOS mRNA stability following C. parvum infection, a process that may be relevant to the regulation of epithelial anti-microbial defense in general.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".