Impact of diet‐derived polyphenol EGCG on neurotensin response upon HL‐60 promyelocytic leukemia cells acquisition of a macrophage‐like phenotype
Bibliographic record
Abstract
Background The activation of a neurotensin (NTS)/neurotensin receptor (NTSR) axis in T lymphocytes, macrophages, and dendritic cells suggests that NTS could modulate immune response and trigger prosurvival signaling in malignant human B lymphocytes. Such axis can therefore represent a potential therapeutic target in chronic B cell leukemia. Methods In this study, analysis of NTS and NTSR1‐3 expression in acute myeloid leukemia (AML) patients using Kaplan‐Meyer plots specifically highlighted NTS and NTSRs as poor clinical prognosis biomarkers. NTSRs gene and protein expression was also explored in undifferentiated and upon phorbol 12‐myristate 13‐acetate (PMA)‐mediated differentiation of HL‐60 suspension cells, an acute promyelocytic leukemia subset cell line model from AML, into CD11b + /CD14 − adherent macrophages. Results We found that high NTS internalization correlated with increased matrix metalloproteinase‐9 expression, and basal macrophage cell migration. Whereas NTS dose‐dependently enhanced undifferentiated HL‐60 cells chemotaxis, it rather inhibited cell migration in differentiated macrophages through decreased Erk, MEK and Src signaling pathways. Epigallocatechin‐3‐gallate (EGCG), a diet‐derived polyphenol with anti‐inflammatory and chemopreventive properties, prevented PMA‐induced CD11b expression and NTS internalization. Conclusions Our study evidences specific regulation and role for NTS receptors in HL‐60 differentiated macrophages, which could in part account for their contribution to immune evasion or tumor‐associated inflammation, and be prevented by the anti‐cancer property of diet‐derived polyphenols. Support or Funding Information Funding: UQAM Foundation and Chaire in Cancer Prevention and Treatment This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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".