Hemocyanins Stimulate Innate Immunity by Inducing Different Temporal Patterns of Proinflammatory Cytokine Expression in Macrophages
Bibliographic record
Abstract
Hemocyanins induce a potent Th1-dominant immune response with beneficial clinical outcomes when used as a carrier/adjuvant in vaccines and nonspecific immunostimulant in cancer. However, the mechanisms by which hemocyanins trigger innate immune responses, leading to beneficial adaptive immune responses, are unknown. This response is triggered by a proinflammatory signal from various components, of which macrophages are an essential part. To understand how these proteins influence macrophage response, we investigated the effects of mollusks hemocyanins with varying structural and immunological properties, including hemocyanins from Concholepas concholepas, Fissurella latimarginata, and Megathura crenulata (keyhole limpet hemocyanin), on cultures of peritoneal macrophages. Hemocyanins were phagocytosed and slowly processed. Analysis of this process showed differential gene expression along with protein levels of proinflammatory markers, including IL-1β, IL-6, IL-12p40, and TNF-α. An extended expression analysis of 84 cytokines during a 24-h period showed a robust proinflammatory response for F. latimarginata hemocyanin in comparison with keyhole limpet hemocyanin and C. concholepas hemocyanin, which was characterized by an increase in the transcript levels of M1 cytokines involved in leukocyte recruitment. These cytokine genes included chemokines (Cxcl1, Cxcl3, Cxcl5, Ccl2, and Ccl3), ILs (Il1b and Ifng), growth factors (Csf2 and Csf3), and TNF family members (Cd40lg). The protein levels of certain cytokines were increased. However, every hemocyanin maintains downregulated key M2 cytokine genes, including Il4 and Il5 Collectively, our data demonstrate that hemocyanins are able to trigger the release of proinflammatory factors with different patterns of cytokine expression, suggesting differential signaling pathways and transcriptional network mechanisms that lead to the activation of M1-polarized macrophages.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".