Monocyte-to-Macrophage Differentiation
Bibliographic record
Abstract
Although monocyte- and macrophage-derived molecules are known to promote extracellular matrix (ECM) disruption and destabilization, it is less appreciated that they also synthesize molecules contributing to ECM formation, stabilization, and function. We have identified and characterized the synthesis of proteoglycans and related proteins, some not previously known to be associated with macrophages. Proteoglycan extracts of [(35)S]sulfate- and (35)S-trans amino acid-radiolabeled culture media from THP-1 monocytes induced to differentiate by treatment with phorbol myristate acetate revealed three major proteins of ~25, 90, and 100 kDa following chondroitin ABC lyase digestion. The 25-kDa protein was predominant for monocytes, whereas the 90- and 100-kDa proteins were predominant for macrophages. Tandem mass spectrometry identified (i) the 25-kDa core protein as serglycin, (ii) the 90-kDa core protein as inter-α-inhibitor heavy chain 2 (IαIHC2), and (iii) the 100-kDa core as amyloid precursor-like protein 2 (APLP2). Differentiation was also associated with (i) a >500-fold increase in mRNA for TNF-stimulated gene-6, an essential cofactor for heavy chain-mediated matrix stabilization; (ii) a >800-fold increase in mRNA for HAS2, which is responsible for hyaluronan synthesis; and (iii) a 3-fold increase in mRNA for versican, which interacts with hyaluronan. Biochemical evidence is also presented for an IαIHC2-APLP2 complex, and immunohistochemical staining of human atherosclerotic lesions demonstrates similar staining patterns for APLP2 and IαIHC2 with macrophages, whereas serglycin localizes to the underlying glycosaminoglycan-rich region. These findings indicate that macrophages synthesize many of the molecules participating in ECM formation and function, suggesting a novel role for these molecules in the differentiation of macrophages in the development of atherosclerosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 0.000 |
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 teacher head, 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".