The tropomyosin 3.1/3.2 inhibitor ATM-3507 alters B-cell actin dynamics and impairs the growth and motility of diffuse large B-cell lymphoma cell lines
Bibliographic record
Abstract
Introduction: By stabilizing actin filaments and recruiting non-muscle myosin II, the closely related tropomyosin (Tpm) isoforms Tpm3.1 and Tpm3.2 support actin-dependent processes including membrane dynamics, cell migration, and cytokinesis. Actin dynamics are essential for B cell function, but the roles of Tpm3.1 and 3.2 (collectively termed Tpm3.1/3.2) in B cells have not been explored. Moreover, new treatments are needed to limit the growth and dissemination of diffuse large B-cell lymphoma (DLBCL), the most prevalent B-cell malignancy. Methods: To test whether Tpm3.1/3.2 is essential for B-cell actin dynamics and could be a target for treating DLBCL, we employed ATM-3507, a compound that selectively interferes with Tpm3.1/3.2 function. Results: We show that ATM-3507 treatment inhibited B-cell receptor-induced formation of the peripheral ring of branched actin that drives cell spreading and also prevented the formation of actomyosin arcs at the inner face of the peripheral actin ring. Tpm3.1/3.2 localizes to these structures during B-cell spreading. Treating DLBCL cell lines with ATM-3507 inhibited cell growth and caused the cells to accumulate in the G2/M phase of the cell cycle. Furthermore, ATM-3507 markedly reduced CXCL12-stimulated chemotaxis and integrin-dependent motility of DLBCL cell lines on fibronectin. Conclusion: Tpm3.1/3.2 orchestrates key actin-driven processes in B cells, and drugs that target Tpm3.1/3.2 may be useful adjuncts for treating DLBCL.
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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) | 0.002 | 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".