NFATc1 Mediates Toll-Like Receptor-Independent Innate Immune Responses during Trypanosoma cruzi Infection
Bibliographic record
Abstract
Host defense against the intracellular protozoan parasite Trypanosoma cruzi depends on Toll-like receptor (TLR)-dependent innate immune responses. Recent studies also suggest the presence of TLR-independent responses to several microorganisms, such as viruses, bacteria, and fungi. However, the TLR-independent responses to protozoa remain unclear. Here, we demonstrate a novel TLR-independent innate response pathway to T. cruzi. Myd88(-/-)Trif(-/-) mice lacking TLR signaling showed normal T. cruzi-induced Th1 responses and maturation of dendritic cells (DCs), despite high sensitivity to the infection. IFN-gamma was normally induced in T. cruzi-infected Myd88(-/-)Trif(-/-) innate immune cells, and further was responsible for the TLR-independent Th1 responses and DC maturation after T. cruzi infection. T. cruzi infection induced elevation of the intracellular Ca(2+) level. Furthermore, T. cruzi-induced IFN-gamma expression was blocked by inhibition of Ca(2+) signaling. NFATc1, which plays a pivotal role in Ca(2+) signaling in lymphocytes, was activated in T. cruzi-infected Myd88(-/-)Trif(-/-) innate immune cells. T. cruzi-infected Nfatc1(-/-) fetal liver DCs were impaired in IFN-gamma production and DC maturation. These results demonstrate that NFATc1 mediates TLR-independent innate immune responses in T. cruzi 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 | 0.001 |
| 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".