NK Cells Are Critical for Optimal Immunity to Experimental <i>Trypanosoma congolense</i> Infection
Bibliographic record
Abstract
Abstract NK cells are key innate immune cells that play critical roles in host defense. Although NK cells have been shown to regulate immunity to some infectious diseases, their role in immunity to Trypanosoma congolense has not been investigated. NK cells are vital sources of IFN-γ and TNF-α; two key cytokines that are known to play important roles in resistance to African trypanosomes. In this article, we show that infection with T. congolense leads to increased levels of activated and functional NK cells in multiple tissue compartments. Systemic depletion of NK cells with anti-NK1.1 mAb led to increased parasitemia, which was accompanied by significant reduction in IFN-γ production by immune cells in the spleens and liver of infected mice. Strikingly, infected NFIL3−/− mice (which genetically lack NK cell development and function) on the normally resistant background were highly susceptible to T. congolense infection. These mice developed fulminating and uncontrolled parasitemia and died significantly earlier (13 ± 1 d) than their wild-type control mice (106 ± 26 d). The enhanced susceptibility of NFIL3−/− mice to infection was accompanied by significantly impaired cytokine (IFN-γ and TNF-α) response by CD3+ T cells in the spleens and liver. Adoptive transfer of NK cells into NFIL3−/− mice before infection rescued them from acute death in a perforin-dependent manner. Collectively, these studies show that NK cells are critical for optimal resistance to T. congolense, and its deficiency leads to enhanced susceptibility in infected mice.
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) | 0.002 | 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".