EtMIC3 and its receptors BAG1 and ENDOUL are essential for site-specific invasion of Eimeria tenella in chickens
Bibliographic record
Abstract
Avian coccidian parasites exhibit a high degree of site specificity in different Eimeria species. Although the underlying mechanism is unclear, an increasing body of evidence suggests that site specificity is due to the interaction between microneme proteins (MICs) and their receptors on the surface of target host cells. In this study, the binding ability of E. tenella MICs (EtMICs) to different intestinal tissue was observed by immunofluorescence to identify the key surface molecule on the parasite responsible for the site specificity. Subsequently, we identified the corresponding host-cell receptors by yeast two-hybrid screening and glutathione-S-transferase pull-down experiments and the distribution of these receptors was observed by immunofluorescence in chicken intestinal tissues. Finally, we evaluated the efficacy of receptor antiserum against the infection of E. tenella in chickens. The results showed that EtMIC3 could only bind to the caecum while EtMIC1, EtMIC2, and EtAMA1 did not bind to any other intestinal tissues. Anti-serum to EtMIC3 was able to block the invasion of sporozoites with a blocking rate of 66.3%. The receptors for EtMIC3 were BCL2-associated athanogene 1 (BAG1) and Endonuclease polyU-specific-like (ENDOUL), which were mainly distributed in the caecum. BAG1 and ENDOUL receptor antiserum reduced weight loss and oocyst output following E. tenella infection, showing partial inhibition of E. tenella infection. These data elucidate the mechanism of site specificity for Eimeria infection and reveal a potential therapeutic avenue.
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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".