Macrophage Migration Inhibitory Factor Has a MHC class I‐Like Motif and Function
Bibliographic record
Abstract
Macrophage migration inhibitory factor (MIF) is found in immune-privileged sites and inhibits cytotoxicity mediated by CD3-ve lymphokine-activated killer cells (LAK). The mechanism by which MIF attenuates LAK cytotoxicity is unknown. We provide evidence that MIF has a major histocompatibility complex (MHC) class I-like motif. A monoclonal antibody (OX18) that binds a conserved region of rat MHC class I proteins binds native MIF. Anti-MIF polyclonal antibodies bind MHC class I. Epitope mapping suggests OX18 binds a loop of MHC class I bound by several receptors for MHC class I. A sequence (PRPEG) within the proposed OX18-binding site on MHC class I exists with a short insertion in MIF. OX18 does not bind MIF that is denatured by SDS-PAGE. This suggests the OX18 epitope is dependent on higher order structure in MIF. Interestingly, MIF inhibits binding of tetramers of MHC class I (H2D(b)) to LAK cells, suggesting it may bind to receptors for MHC class I. MIF may be an example where small regions of MHC class I are used by endogenous and viral proteins to control cytotoxicity mediated by immune cells.
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 | 0.000 |
| 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".