A naturally occurring transcript variant of MARCO reveals the SRCR domain is critical for function
Bibliographic record
Abstract
Macrophage receptor with collagenous structure (MARCO) is a class A scavenger receptor (cA-SR) that recognizes and phagocytoses a wide variety of pathogens. Most cA-SRs that contain a C-terminal scavenger receptor cysteine-rich (SRCR) domain use the proximal collagenous domain to bind ligands. In contrast, the role of the SRCR domain of MARCO in phagocytosis, adhesion and pro-inflammatory signaling is less clear. The discovery of a naturally occurring transcript variant lacking the SRCR domain, MARCOII, provided the opportunity to study the role of the SRCR domain of MARCO. We tested whether the SRCR domain is required for ligand binding, promoting downstream signaling and enhancing cellular adhesion. Unlike cells expressing full-length MARCO, ligand binding was abolished in MARCOII-expressing cells. Furthermore, co-expression of MARCO and MARCOII impaired phagocytic function, indicating that MARCOII acts as a dominant-negative variant. Unlike MARCO, expression of MARCOII did not enhance Toll-like receptor 2 (TLR2)-mediated pro-inflammatory signaling in response to bacterial stimulation. MARCO-expressing cells were more adherent and exhibited a dendritic-like phenotype, whereas MARCOII-expressing cells were less adherent and did not exhibit changes in morphology. These data suggest the SRCR domain of MARCO is the key domain in modulating ligand binding, enhancing downstream pro-inflammatory signaling and MARCO-mediated cellular adhesion.
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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) | 0.000 | 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) | 0.001 | 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".