Potent Stimulation of the Innate Immune System by a<i>Leishmania brasiliensis</i>Recombinant Protein
Bibliographic record
Abstract
The interaction of the innate immune system with the microbial world involves primarily two sets of molecules generally known as microbial pattern recognition receptors and microbial pattern recognition molecules, respectively. Examples of the former are the Toll receptors present particularly in macrophages and dendritic cells. Conversely, the microbial pattern recognition molecules are conserved protist homopolymers, such as bacterial lipopolysaccharides, lipoteichoic acids, peptidoglycans, glucans, mannans, unmethylated bacterial DNA, and double-strand viral RNA. However, for protists that lack most of these molecules, such as protozoans, the innate immune system must have evolved receptors that recognize other groups of microbial molecules. Here we present evidence that a highly purified protein encoded by a Leishmania brasiliensis gene may be one such molecule. This recombinant leishmanial molecule, a homologue of eukaryotic ribosomal elongation and initiation factor 4a (LeIF), strongly stimulates spleen cells from severe combined immunodeficient (SCID) mice to produce interleukin-12 (IL-12), IL-18, and high levels of gamma interferon. In addition, LeIF potentiates the cytotoxic activity of the NK cells of these animals. Because LeIF is a conserved molecule and because SCID mice lack T and B lymphocytes but have a normal innate immune system (normal reticuloendothelial system and NK cells), these results suggest that proteins may also be included as microbial pattern recognition molecules. The nature of the receptor involved in this innate recognition is unknown. However, it is possible to exclude the Toll receptor Tlr4 as a putative LeIF receptor because the gene encoding this receptor is defective in C3H/HeJ mice, the mouse strain used in the present studies.
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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 | 0.000 |
| 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".