DRAM1 promotes the stability of lysosomal VAMP8 to enhance autolysosome formation and facilitates the extravasation
Bibliographic record
Abstract
Autophagy classically functions to protect cells and organisms during stressful conditions by catabolizing intracellular components to maintain energy homeostasis. Lysosome-autophagosome fusion is a critical step in emptying degraded unwanted contents. However, the mechanism of autophagosome fusion with lysosomes is still not fully understood. Here, we report that DNA Damage-Regulated Autophagy Modulator 1 (DRAM1) interacts with Vesicle Associated Membrane Protein 8 (VAMP8) to mediate the fusion of autophagosomes with lysosomes. This DRAM1-VAMP8 interaction is enhanced upon stimulation of autophagy. However, DRAM1 preferentially mediates the fusion between autophagosomes and lysosomes by enhancing the assembly of the STX17-SNAP29-VAMP8 complex. Moreover, we reveal that DRAM1 specifically promotes the stability of lysosomal VAMP8 via inhibiting VAMP8 degradation by CHIP mediating ubiquitination. We also identify that DRAM1 inhibits the ubiquitination of VAMP8 at Lys 68,72, and 75 via competitively binding with CHIP. Furthermore, we demonstrate that DRAM1 promotes the extravasation of Hepatocellular Carcinoma (HCC) cells, and this process relies on enhanced autophagosome degradation. Our study reveals a mechanism for regulating autolysosome formation by DRAM1-VAMP8 association and suggests a potential strategy to inhibit the extravasation of HCC. DNA Damage Regulated Autophagy Modulator 1 (DRAM1) plays pivotal roles in autophagy regulation. Here, the authors show that DRAM1 stabilizes lysosomal VAMP8, thereby promoting autolysosome maturation. Furthermore, they establish that DRAM1–VAMP8 axis enhances autophagic flux and facilitates hepatocellular carcinoma extravasation, as validated in both zebrafish xenograft and mouse metastatic models.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".