In-vitro Modulation of mTOR-HIF-1α Axis by TLR7/8 Agonist (Resiquimod) in B-Chronic Lymphocytic Leukemia
Bibliographic record
Abstract
Abstract Targeting toll-like receptors (TLRs), via TLR agonists, has been implicated in the regulation of immunometabolism. B-chronic lymphocytic leukemia (B-CLL) represents a suitable model for B-cell derived malignancies with shifted metabolic adaptations. Several signaling pathways have been found to be critical in metabolic reprogramming of CLL, including mechanistic target of rapamycin- hypoxia inducible factor-1α (mTOR- HIF-1α) pathway, the main metabolic regulator of glycolysis. Here, we investigated the effect of TLR7/8 agonist (Resiquimod) on the expression of mTOR and HIF-1α in patients with CLL. B cells were purified using Rosettesep Human B cell Enrichment Cocktail (Stem cell Technologies, Vancouver, BC, Canada#15,024) from peripheral venous blood of CLL patients (n = 20) and healthy individuals (n = 15). Isolated B cells were then cultured in both presence and absence of Resiquimod. Gene expression of mTOR and HIF-1α were assessed using qRT-PCR. Resiquimod significantly decreased mTOR and HIF-1α gene expression in both CLL (p < 0.001and p < 0.001, respectively) and Normal B cells (p = 0.004 and p = 0.001, respectively). Resiquimod may reprogram immunometabolism of malignant B-CLL cells via down-regulation of key glycolytic metabolic actors, mTOR and HIF-1α genes. Accordingly, Resiquimod may be an adjuvant as a therapeutic tool for CLL, which needs to be studied further.
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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".