Innate Immune Collectin Surfactant Protein D Enhances the Clearance of DNA by Macrophages and Minimizes Anti-DNA Antibody Generation
Bibliographic record
Abstract
Dying microbes and necrotic cells release highly viscous DNA that induces inflammation and septic shock, and apoptotic cells display DNA, a potential autoantigen, on their surfaces. However, innate immune proteins that mediate the clearance of free DNA and surface DNA-containing cells are not clearly established. Pulmonary surfactant proteins (SP-) A and D are innate immune pattern recognition collectins that contain fibrillar collagen-like regions and globular carbohydrate recognition domains (CRDs). We have recently shown that collectins SP-A, SP-D, and mannose binding lectin recognize DNA and RNA via their collagen-like regions and CRDs. Here we show that SP-D enhances the uptake of Cy3-labeled fragments of DNA and DNA-coated beads by U937 human monocytic cells, in vitro. Analysis of DNA uptake by freshly isolated mouse alveolar macrophages shows that SP-D, but not SP-A, deficiency results in reduced clearance of DNA, ex vivo. Analysis of bronchoalveolar lavage fluid shows that SP-D- but not SP-A-deficient mice are defective in clearing free DNA from the lung. Additionally, both SP-A- and SP-D-deficient mice accumulate anti-DNA Abs in sera in an age-dependent manner. Thus, we conclude that collectins such as SP-A and SP-D reduce the generation of anti-DNA autoantibody, which may be explained in part by the defective clearance of DNA from the lungs in the absence of these proteins. Our findings establish two new roles for these innate immune proteins and that SP-D enhances efficient pinocytosis and phagocytosis of DNA by macrophages and minimizes anti-DNA Ab generation.
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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".