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Bone Marrow Stromal Cells Induce Bortezomib Resistance in Multiple Myeloma Cells through Downregulation of miRNA-101-3p Targeting Survivin

2015· article· en· W2579787262 on OpenAlexaff
Jahangir Abdi, Yijun Yang, Patrick Meyer-Erlach, Hong Chang

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsBortezomibStromal cellCancer researchPanobinostatBone marrowSurvivinDownregulation and upregulationCell cultureBiologyMultiple myelomamicroRNATetherinStem cellMolecular biologyImmunologyCell biology

Abstract

fetched live from OpenAlex

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.038
GPT teacher head0.281
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations3
Published2015
Admission routes1
Has abstractyes

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