Systematic profiling of the acetyl lysine machinery reveals a role for MAPKAPK2 in bromodomain inhibitor resistance
Bibliographic record
Abstract
Summary Bromodomain (BRD)-containing proteins are chemically tractable multi-domain scaffolding molecules involved in acetyl lysine (Kac) signaling. BRD inhibitors have shown promise in clinical oncology, including melanomas; however, their narrow therapeutic windows and issues with resistance in pre-clinical models highlight the need to better understand the functions of and interconnection between BRD-containing proteins. Here, we use complementary interaction-mapping techniques (affinity purification and proximity-dependent biotinylation) to map the interactions of 39 of the 42 BRD-containing proteins and 110 additional proteins that physically or functionally associate with them. We uncover 3,892 novel interactions and reveal the intricate connectivity of the Kac machinery. Chemical inhibition of multiple BRD classes revealed that inhibiting BETs—but not mSWI/SNF or CREBBP/EP300 proteins—dramatically rewired the interactome. Finally, we identified MAPKAPK2 activity as a critical determinant of BET inhibitor sensitivity in melanoma through its impact on chromatin composition remodeling. In Brief Kougnassoukou Tchara et al . generate a static protein interaction map of the human acetyl lysine machinery by coupling two complementary functional proteomics approaches (FLAG affinity purification and proximity-dependent biotinylation) to mass spectrometry. They also investigate network changes upon bromodomain inhibition, and describe a novel resistance mechanism mediated by the p38 stress signaling pathway that causes significant metabolic changes. Highlights Two complementary interaction proteomics analyses of the human acetyl lysine machinery were performed. Novel target- and compound-specific impacts of bromodomain inhibitors were identified. MAPKAPK2 was identified as a novel resistance gene to BET bromodomain inhibitors in melanoma. BET bromodomain inhibition leads to metabolic adaptation in melanoma.
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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".