The Role of FcRn in Antigen Presentation
Bibliographic record
Abstract
Immunoglobulins are unique molecules capable of simultaneously recognizing a diverse array of antigens and themselves being recognized by a broad array of receptors. The abundance specifically of the IgG subclass and the variety of signaling receptors to which it binds render this an important immunomodulatory molecule. In addition to the classical Fcγ receptors that bind IgG at the cell surface, the neonatal Fc receptor (FcRn) is a lifelong resident of the endolysosomal system of most hematopoietic cells where it determines the intracellular fate of both IgG and IgG-containing immune complexes (IgG IC). Cross-linking of FcRn by multivalent IgG IC within antigen presenting cells such as dendritic cells initiates specific mechanisms that result in trafficking of the antigen-bearing IgG IC into compartments from which the antigen can successfully be processed into peptide epitopes compatible with loading onto both major histocompatibility complex class I and II molecules. In turn, this enables the synchronous activation of both CD4(+) and CD8(+) T cell responses against the cognate antigen, thereby bridging the gap between the humoral and cellular branches of the adaptive immune response. Critically, FcRn-driven T cell priming is efficient at very low doses of antigen due to the exquisite sensitivity of the IgG-mediated antigen delivery system through which it operates. FcRn-mediated antigen presentation has important consequences in tissue compartments replete with IgG and serves not only to determine homeostatic immune activation at a variety of sites but also to induce inflammatory responses upon exposure to antigens perceived as foreign. Therapeutically targeting the pathway by which FcRn enables T cell activation in response to IgG IC is thus a highly attractive prospect not only for the treatment of diseases that are driven by immune complexes but also for manipulating local immune responses against defined antigens such as those present during infections and cancer.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".