EGCG Prevents the Transcriptional Reprogramming of an Inflammatory and Immune-Suppressive Molecular Signature in Macrophage-like Differentiated Human HL60 Promyelocytic Leukemia Cells
Bibliographic record
Abstract
Background: The promyelocytic leukemia cell differentiation process enables recapitulation of the polarized M1 or M2 macrophage-like phenotype with inflammatory and immune-suppressive properties. While evidence supports the anti-inflammatory effect of dietary-derived epigallocatechin-3-gallate (EGCG), its impact on the onset of immune phenotype molecular signature remains unclear. Methods: Human HL60 promyelocytic cells grown in suspension were differentiated into CD11bHigh/CD14Low adherent macrophages with phorbol 12-myristate 13-acetate (PMA). Gelatin zymography was used to assess the levels of matrix metalloproteinase (MMP)-9, and total RNA was isolated for RNAseq and RT-qPCR assessment of differentially expressed gene levels involved in inflammation and immunity. Protein lysates were used to assess the phosphorylation status of signaling intermediates involved in macrophage-like cell differentiation. Results: Cell adhesion and induction of MMP-9 were indicative of HL60 cell differentiation into a macrophage-like phenotype. The extracellular signal-regulated kinase (ERK), glycogen synthase kinase (GSK)-3, p90 ribosomal S6 kinases (RSK), and cAMP-response-element-binding protein (CREB) were all phosphorylated, and EGCG reduced such phosphorylation status. Increases in inflammation and immunity genes included, among others, CCL22, CSF1, CSF2, IL1B, and TNF, which inductions were prevented by EGCG. This was corroborated by unbiased transcriptomic analysis which further highlighted the capacity of EGCG to downregulate the hematopoietic stem cell regulator CBFA2T3. Conclusion: EGCG inhibits inflammatory signaling crosstalk and prevents the onset of an immune phenotype in macrophage-like differentiated cells.
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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".