Wnt5a Skews Dendritic Cell Differentiation to an Unconventional Phenotype with Tolerogenic Features
Bibliographic record
Abstract
Dendritic cells (DCs) are critical regulators of immune responses that integrate signals from the innate and adaptive immune system and orchestrate T cell responses toward either immunity or tolerance. Growing evidence points to the Wnt signaling pathway as a pivotal piece in the immune balance and focuses on DCs as a direct target for their immunoregulatory role. Our results show that the increase in Wnt5a signaling during the differentiation of human DCs from monocytes alters their phenotype and compromises their subsequent capacity to mature in response to TLR-dependent stimuli. These Wnt5a-DCs produce scant amounts of IL-12p70 and TNF-α but increased levels of IL-10. Consequently, these Wnt5a-DCs have a reduced capacity to induce Th1 responses that promote IL-10 secretion by CD4 T cells. Changes in the transcriptional profile of Wnt5a-DCs correlate with their unconventional phenotype caused presumably by increased IL-6/IL-10 signaling during the process of DC differentiation. The effect of Wnt5a is not a consequence of β-catenin accumulation but is dependent on noncanonical Ca(2+)/calmodulin-dependent protein kinase II/NF-κB signaling. Our results therefore suggest that under high levels of Wnt5a, typical of the inflammatory state and sepsis, monocytes could differentiate into unconventional DCs with tolerogenic features.
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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".