Engagement of CD14 Sensitizes Primary Monocytes to IFN-γ to Produce IL-12/23p40 and IL-23 Through p38 Mitogen-Activated Protein Kinase and Independent of the Janus Kinase/Signal Transducers and Activators of Transcription Signaling
Bibliographic record
Abstract
Interferon (IFN)-γ is a potent stimulator of the IL-12 family Th1 cytokines, including IL-12/23p40 and IL-23, responsible for coordinating the innate and adaptive immune responses. Our results show that IFN-γ induced the production of IL-12/23p40 and IL-23p19 mRNA as well as IL-12p40 and IL-23 proteins in primary human monocytes isolated by positive selection through anti-CD14 microbeads. These results were confirmed by IFN-γ stimulation of CD14-activated monocytes resulting in IL-12/23p40 and IL-23 production. We investigated the signaling pathways governing the regulation of IL-23 and its subunits IL-23p40 and IL-23p19 following IFN-γ stimulation. We observed a differential regulation of IL-23p19, IL-12/23p40, and IL-23 following IFN-γ stimulation. IFN-γ-induced IL-23 and IL-12/23p40 expression was positively regulated by the p38 mitogen-activated protein kinases (MAPKs), independent of the Janus kinase (Jak)/signal transducers and activators of transcription (STAT) signaling. In contrast, IL-12 and IL-23 were negatively regulated by the Jak/STAT, phosphatidylinositol 3-kinase (PI3K), and the c-Jun-N-terminal kinase (JNK) MAPKs in IFN-γ-stimulated monocytes. Overall, our results suggest for the first time a differential positive regulation of IL-12p40 and IL-23 by p38 MAPKs independent of the Jak/STAT pathways and negative regulation by the Jak/STAT, JNK, and PI3K pathways in CD14-activated primary human monocytes stimulated with IFN-γ.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".