IgM Promotes the Clearance of Small Particles and Apoptotic Microparticles by Macrophages
Bibliographic record
Abstract
BACKGROUND: Antibodies are often involved in enhancing particle clearance by macrophages. Although the mechanisms of antibody-dependent phagocytosis have been studied for IgG in greater detail, very little is known about IgM-mediated clearance. It has been generally considered that IgM does not support phagocytosis. Recent studies indicate that natural IgM is important to clear microbes and other bioparticles, and that shape is critical to particle uptake by macrophages; however, the relevance of IgM and particle size in their clearance remains unclear. Here we show that IgM has a size-dependent effect on clearance. METHODOLOGY/PRINCIPAL FINDINGS: We used antibody-opsonized sheep red blood cells, different size beads and apoptotic cells to determine the effect of human and mouse IgM on phagocytosis by mouse alveolar macrophages. Our microscopy (light, epifluorescence, confocal) and flow cytometry data show that IgM greatly enhances the clearance of small particles (about 1-2 micron) by these macrophages. There is an inverse relationship between IgM-mediated clearance by macrophages and the particle size; however, macrophages bind and internalize many different size particles coated with IgG. We also show that IgM avidly binds to small size late apoptotic cells or bodies (2-5 micron) and apoptotic microparticles (<2 µm) released from dying cells. IgM also promotes the binding and uptake of microparticle-coated beads. CONCLUSIONS/SIGNIFICANCE: Therefore, while the shape of the particles is important for non-opsonized particle uptake, the particle size matters for antibody-mediated clearance by macrophages. IgM particularly promotes the clearance of small size particles. This finding may have wider implications in IgM-mediated clearing of antigens, microbial pathogens and dying cells by the host.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".