Effects of SP-2509 and OG-L002 on lipophagy using target or off-target molecules in glycolysis-suppressed pancreatic ductal adenocarcinoma cells
Bibliographic record
Abstract
Abstract Although increased aerobic glycolysis is common in cancers, pancreatic ductal adenocarcinoma (PDAC) cells can survive glycolysis suppression. We aimed to identify potential therapeutic targets in glycolysis-suppressed PDAC cells. By screening anticancer metabolic compounds, we identified SP-2509, a selective lysine-specific demethylase (LSD) 1 inhibitor. SP-2509 lowered the viability of three distinct human PDAC cell lines (PANC-1, PK-1, and KLM-1 cells) under glycolysis suppression. The effects of three other LSD1 inhibitors (OG-L002, iadademstat, and T-3775440) on PDAC cell viability were investigated; OG-L002, but not iadademstat or T-3775440, lowered PDAC cell viability under glycolysis suppression, similar to SP-2509. However, knockdown of LSD1/LSD2 failed to lower the viability of PDAC cells subjected to glycolysis suppression. SP-2509 and OG-L002 lowered PDAC cell viability even when given to cells which already been depleted of LSD-1, subjected to glycolysis suppression. Proteomic analyses implied that glucose-starvation causes PDAC cells to switch to mitochondrial oxidative phosphorylation. We observed that fatty acid metabolism is important for the survival of PDAC cells following the suppression of glycolysis. SP-2509 and OG-L002 promoted lipid droplet accumulation in PDAC cells under glycolysis suppression by inhibiting lipophagy. This indicates the significant potential of SP-2509 and OG-L002 to impair oncogenic cell proliferation through regulation of lipophagic fluxes. SP-2509 showed anti-tumor effects of PDAC in 2-DG-treated mice with lipid droplet accumulation and alteration of the tumor microenvironment. Hence, there is potentially new therapeutic strategies for PDAC in the presence of dual inhibition of glycolysis and fatty acids metabolism.
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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 | 0.001 |
| 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".