CD47 in the Immune Response: Role of Thrombospondin and SIRP-α Reverse Signaling
Bibliographic record
Abstract
The past decades have been marked by spectacular progress towards understanding how dendritic cells (DCs) interact with T cells to elicit protective immune responses to fight infectious diseases and cancer. DCs that are lying at the interface between innate and adaptive immunity, are educated in peripheral tissues prior to their journey to the secondary lymphoid organs (SLO) whereby they dictate different classes of T cell responses. Uncontrolled or unwanted inflammatory responses are the price to pay to eliminate pathogens. However, if not self-limited, they may induce collateral damages that result in chronic inflammation often associated with autoimmune disorders. CD47 and its two ligands, i.e. thrombospondin 1 (TSP-1) and SIRP-alpha, were identified as a previously unappreciated inhibitory axis of DC and T cell functions. TSP-1 is predominantly a negative regulator of DC and T cell function while basal SIRP-alpha ligation on APC by CD47 enforces tolerance. Yet, CD47/SIRP-alpha interaction positively controls DC and innate cell transendothelial migration. Due to the promiscuity of the protein interactions for CD47 and its ligands, it is quite interesting to note that deletion of the CD47 gene in mice largely agrees with the in vitro data with human cells. In fact, the well-conserved tissue distribution of CD47 and SIRP-alpha across species may facilitate the transition from bench to bedside. We thus propose CD47/TSP-1/SIRP-alpha axis as an important sensor to maintain homeostasis and regulate innate and adaptive immune responses.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".