Surfactant Protein D Interacts with α2-Macroglobulin and Increases Its Innate Immune Potential
Bibliographic record
Abstract
Surfactant protein D (SP-D) is an innate immune collectin that recognizes microbes via its carbohydrate recognition domains, agglutinates bacteria, and forms immune complexes. During microbial infections, proteases, such as elastases, cleave the carbohydrate recognition domains and can inactivate the innate immune functions of SP-D. Host responses to counterbalance the reduction of SP-D-mediated innate immune response under these conditions are not clearly understood. We have unexpectedly identified that SP-D could interact with protein fractions containing ovomucin and ovomacroglobulin. Here, we show that SP-D interacts with human alpha(2)-macroglobulin (A2M), a protease inhibitor present in the lungs and serum. Using enzyme-linked immunosorbent assays, surface plasmon resonance, and carbohydrate competition assays, we show that SP-D interacts with A2M both in solid phase (K(D) of 7.33 nM) and in solution via lectin-carbohydrate interactions under physiological calcium conditions. Bacterial agglutination assays further show that SP-D x A2M complexes increase the ability of SP-D to agglutinate bacteria. Western blot analyses show that SP-D, but not A2M, avidly binds bacteria. Interestingly, intact and activated A2M also protect SP-D against elastase-mediated degradation, and the cleaved A2M still interacts with SP-D and is able to enhance its agglutination abilities. We also found that SP-D and A2M can interact with each other in the airway-lining fluid. Therefore, we propose that SP-D utilizes a novel mechanism in which the collectin interacts with protease inhibitor A2M to decrease its degradation and to concurrently increase its innate immune function. These interactions particularly enhance bacterial agglutination and immune complex formation.
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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 | 0.001 |
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".