Targeting a Highly Deregulated eIF4F Translation Initiation Complex Sensitises IM-Resistant Cells to Tyrosine Kinase Inhibitors and Effectively Suppresses BCR-ABL1 Protein Expression
Bibliographic record
Abstract
Background We previously reported that Abelson helper integration site-1 (AHI-1) is highly deregulated in chronic myeloid leukemia (CML) leukemic stem cells (LSCs) and mediates tyrosine kinase inhibitor (TKI) resistance. AHI-1 physically interacts with BCR-ABL1 and its SH3 domain interacts with BCR-ABL1 substrates, like dynamin-2, which regulate leukemic properties of CML LSCs. However, the molecular and biological roles of AHI-1 and its interacting partners in mediating TKI resistance remain largely unknown. Objective To investigate the molecular functions of AHI-1 and its SH3 domain in regulation of TKI resistance, using advanced antibody microarray analysis. Methods A high content antibody microarray was performed in BCR-ABL1+ cells co-transduced with wild-type (WT) AHI-1 or the deletion of AHI-1 SH3 domain (SH3Δ) with or without imatinib (IM). Changes in antibody signals for protein expression or phosphorylation were determined using limma and pathway enrichment analysis by g:Profiler. eIF4G1 genetic inhibition by lentiviral-mediated shRNA or pharmacological inhibition by SBI-756, was performed in TKI-resistant cells to assay translation initiation activity by proximity ligation assay (PLA), o-propargyl-puromycin (OPP), and polysome profiling. Results The antibody microarray analysis revealed that WT AHI-1 cells have the greatest number of changes in the phospho-proteome and proteome compared to BCR-ABL1+ cells and AHI-1 SH3Δ cells with and without IM. Pathway enrichment analysis identified that the targets with significantly increased differential antibody signal after IM treatment in WT AHI-1 cells were related to the regulation of translation initiation complex (p < 0.0001). Interestingly, our RNA-seq dataset analysis further identified several eukaryotic initiation factor 4F (eIF4F) complex members to be significantly increased in CD34+ CML patient cells compared to normal bone marrow, particularly eIF4G1, the scaffold protein of the eIF4F complex (2-fold, p = 0.001). WT AHI-1 cells also showed increased expression and phosphorylation of eIF4G1 (>2-fold) and eIF4B (>2-fold), a cofactor that regulates the helicase activity of the eIF4F complex, and cyclin D3 (a downstream protein of eIF4F translational activity) as compared to BCR-ABL1+ cells, by immunoblotting. These results were similarly demonstrated in IM-resistant cells as compared to IM-sensitive cells (2-5-fold, p < 0.05). Mechanistically, eIF4G1 knockdown by shRNA impaired survival (5-fold, p < 0.0001) and increased TKI sensitivity in IM-resistant cells (p < 0.0021). These cells showed reduced levels of eIF4F complex formation by PLA assay (p = 0.013) and reduced protein expression of cyclin D3 (40%, p = 0.0036) and BCR-ABL1 (70%). Similarly, IM-resistant cells were more sensitive to SBI-756 treatment, an eIF4G1 inhibitor (50% reduction, p = 0.0001) than IM-sensitive cells (30% reduction); these effects were enhanced by a combination of SBI-756 and IM (80%, p < 0.02). Furthermore, SBI-756 treatment reduced PLA foci formation (p = 0.014) and global protein synthesis rates (p < 0.0001). Polysome profiling demonstrated that IM-resistant cells had higher levels of translation activity, which was inhibited by SBI-756 treatment. Most interestingly, SBI-756 treatment reduced protein expression of BCR-ABL1 (70%) and cyclin D3 (40%) in these cells. Conclusion We have uncovered that the eIF4F complex, the key regulator of the mRNA-ribosome recruitment phase of translation initiation, has increased activity in IM-resistant cells. eIF4G1 inhibitor treatment sensitises IM-resistant cells to TKI and reduces BCR-ABL1 protein expression, providing potential treatment strategies to overcome TKI resistance.
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 | 0.001 |
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".