Bone Marrow Stromal Cells Induce Bortezomib Resistance in Multiple Myeloma Cells through Downregulation of miRNA-101-3p Targeting Survivin
Bibliographic record
Abstract
Abstract INTRODUCTION It is not yet fully understood how bone marrow microenvironment components especially bone marrow stromal cells (BMSCs) induce drug resistance in multiple myeloma (MM). This form of drug resistance has been suggested to pave the way for intrinsic (de novo) resistance to therapy in early stages of the disease and contribute to acquired drug resistance in the course of treatment. Hence, deciphering the molecular mechanisms involved in induction of above resistance will help identify potential therapeutic targets in MM combined treatments. Our previous work showed that BMSCs (normal and MM patient-derived) induced resistance to bortezomib (BTZ) compared with MM cells in the absence of stroma. This resistance was associated with modulation of a transcriptome in MM cells, including prominent upregulation of oncogenes c-FOS, BIRC5 (survivin) and CCND1. However; whether these oncogenes mediate BTZ resistance in the context of BMSCs through interaction with miRNAs is not known. METHODS Human myeloma cell lines, 8226, U266 and MM.1s, were co-cultured with MM patient-derived BMSCs or an immortalized normal human line (HS-5) in the presence of 5nM BTZ for 24 h. MM cell monocultures treated with 5nM BTZ were used as controls. Co-cultures were then applied to magnetic cell separation (EasySep, Stem Cell Technologies) to isolate MM cells for downstream analyses (western blotting and qPCR). Total RNA including miRNAs was isolated from MM cell pellets (QIAGEN miRNeasy kit), cDNAs were synthesized (QIAGEN miScript RT II kit) and applied to miScript miRNA PCR Array (SABioscience, MIHS-114ZA). After normalization of all extracted Ct values to 5 different housekeeping genes, fold changes in miRNA expression were analyzed in co-cultures compared to MM cell monocultures using the 2-ΔΔCt algorithm. Moreover, survivin gene was silenced in MM cells using Ambion® Silencer® Select siRNA and Lipofectamine RNAiMAX transfection reagent. Survivin-silenced cells were then seeded on BMSCs and exposed to BTZ. Percent apoptosis of gated CD138+ MM cells was determined using FACS. For our overexpression and 3'UTR reporter experiments, we transiently transfected MM cells with pre-miR-101-3p, scrambled miRNA or pEZX-3'UTR constructs using Endofectin reagent (all from GeneCopoeia). RESULTS BMSCs upregulated survivin gene / protein (a member of inhibitors of apoptosis family) and modulated an array of miRNAs in MM cells compared to MM cells in the absence of stroma. The more noticeably downregulated miRNAs were hsa-miR-101-3p, hsa-miR-29b-3p, hsa-miR-32-5p, hsa-miR-16-5p (4-30 fold) and highly upregulated ones included hsa-miR-221-3p, hsa-miR-409-3p, hsa-miR-193a-5p, hsa-miR-125a-5p (80-330 fold). We focused on miRNA-101-3p as it showed the highest level of downregulation (30 fold) and has been shown to function as an important tumor suppressor in other malignancies. Real time RT-PCR confirmed downregulation of miRNA-101-3p. Moreover, microRNA Data Integration Portal (mirDIP) identified miRNA-101-3p as a putative target for survivin and Luciferase activity assays confirmed binding of miRNA-101-3p to 3'UTR of survivin. In addition, overexpression of miRNA-101-3p downregulated survivin and sensitized MM cells to BTZ-induced apoptosis. Furthermore, silencing of survivin upregulated miRNA-101-3p and increased BTZ-induced apoptosis in MM cell lines both in the absence of BMSCs (Apoptosis range in BTZ-treated conditions: 57.65% ± 4.91 and 28.66% ± 0.78 for si-survivin and scrambled control, respectively, p<0.05) and in the presence of BMSCs (41.23% ± 1.43 and 14.8% ± 0.66, for si-survivin and scrambled control, respectively, p<0.05). CONCLUSION Our results indicate that BMSCs downregulated miRNA-101-3p and upregulated survivin in MM cells compared to MM cells in the absence of stroma. Silencing of survivin or overexpression of miRNA-101-3p sensitized MM cells to BTZ in the presence of BMSCs. These findings suggest that miRNA-101-3p mediates BTZ response of MM cells in the presence of BMSCs by targeting survivin and disclose a role of survivin-miRNA-101-3p axis in regulation of BMSCs-induced BTZ resistance in MM cells, thus provide a rationale to further investigate the anti-myeloma activity of miRNA-101-3p in combination with BTZ as a potential novel therapeutic strategy in MM. Disclosures No relevant conflicts of interest to declare.
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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".