Macrophage Migration Inhibitory Factor: A Downregulator of Early T Cell-Dependent IFN-γ Responses in <i>Plasmodium chabaudi adami</i> (556 KA)-Infected Mice
Bibliographic record
Abstract
Neutralization of macrophage migration inhibitory factor (MIF) increases anti-tumor cytotoxic T cell responses in vivo and IFN-γ responses in vitro, suggesting a plausible regulatory role for MIF in T cell activation. Considering that IFN-γ production by CD4(+) T cells is pivotal to resolve murine malaria and that secretion of MIF is induced by Plasmodium chabaudi adami parasites, we investigated the effect of MIF deficiency on the infection with this pathogen. Infections with P. c. adami 556 KA parasites were more efficiently controlled in MIF-neutralized and MIF-deficient (knockout [KO]) BALB/c mice. The reduction in parasitemia was associated with reduced production of IL-4 by non-T/non-B cells throughout patent infection. At day 4 postinfection, higher numbers of activated CD4(+) cells were measured in MIF KO mice, which secreted more IFN-γ, less IL-4, and less IL-10 than did CD4(+) T cells from wild-type mice. Enhanced IFN-γ and decreased IL-4 responses also were measured in MIF KO CD4(+) T cells stimulated with or without IL-12 and anti-IL-4 blocking Ab to induce Th1 polarization. However, MIF KO CD4(+) T cells efficiently acquired a Th2 phenotype when stimulated in the presence of IL-4 and anti-IL-12 Ab, indicating normal responsiveness to IL-4/STAT6 signaling. These results suggest that by promoting IL-4 responses in cells other than T/B cells during early P. c. adami infection, MIF decreases IFN-γ secretion in CD4(+) T cells and, additionally, has the intrinsic ability to render CD4(+) T cells less capable of acquiring a robust Th1 phenotype when stimulated in the presence of IL-12.
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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) | 0.001 | 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".