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Bone Marrow Microenvironment Affects The Pathogenesis Of Multiple Myeloma Through Downregulation Of Alternative Splicing Factor Fox2 In Myeloma Cells

2013· article· en· W2338464704 on OpenAlexaff
Weihua Song, Chaolin Zhang, Yiguo Hu, Μαρία Γκοτζαμανίδου, Parantu K. Shah, Weisong Shan, Guang Yang, Yu‐Tzu Tai, Yi Li, Adam S. Sperling, Naim U. Rashid, Florence Magrangeas, Stéphane Minvielle, Hervé Avet‐Loiseau, Kenneth C. Anderson, Cheng Li, Nikhil C. Munshi

Bibliographic record

VenueBlood · 2013
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsRNA splicingAlternative splicingStromal cellSplicing factorDownregulation and upregulationCancer researchBiologyExonGene expressionCell cultureMolecular biologyBone marrowRNAGeneImmunologyGenetics

Abstract

fetched live from OpenAlex

Abstract Alternate splicing is an important post translational change that alters specificity of gene function. Misregulation of alternative splicing has been implicated in number of disease processes including cancer. We have analyzed alternate splicing in myeloma using high throughput GeneChip Human Exon 1.0 ST Arrays in 170 uniformly treated patients and identified pattern of splicing as well as their impact on both overall and event free survival in myeloma. We have now further analyzed this data and identified Fox2, a RNA alternative splicing regulator, as one of the most important genes predicting clinical outcome in these patients. We observe that the expression level of Fox2 correlates with the frequency of RNA splicing and disease prognosis in MM patients. We have now further investigated the molecular role of Fox2 in myeloma. Fox2 expression was detected in all 10 MM cell lines tested at both RNA and protein levels. Immunohistochemistry staining showed a predominant nuclear localization of Fox2. We next evaluated impact of IL-6 on Fox2 expression in MM1S and RPMI8226 MM cell lines and observed dose-dependent reduction in Fox2 expression. Importantly, MM cell - bone marrow stromal cells (BMSC) interaction also led to significant inhibition of Fox2 expression in MM1S and RPMI8226 cells. Similar response was also observed using BMSC supernatants. On the other hand, IGF-1 stimulation showed slight upregulation of Fox2 in MM cell lines. We have also evaluated impact of IL-6 on Fox2 and splicing using genomewide RNA-seq and confirmed the results. Fox2 was downregulated 33% in MM1S and 37% in RPMI8226 at gene expression level. To study its role in MM, we knocked down the expression of Fox2 in MM1S and RPMI8226 cell lines by using Fox2-directed siRNA. Compared to control cell lines, Fox2 knockdown in MM cell lines did not affect the cell proliferation and survival, as measured by cell titer glo luminescent cell viability assay and annexin V and PI staining respectively. Since Fox2 has been described to plays a role in the maintenance of cell cytoskeleton, we therefore evaluated whether Fox2 might influence the migration and adhesion in MM cells. Transwell migration assay showed enhanced migration rate of Fox2-knocking down- MM1S and RPMI8226 cells versus controls. We also observed the increased cell adhesion to fibronetin in both cell lines upon Fox2 knockdown. Actin polymerization evaluated by Alexa488-conjugated phalloidin staining and confocal microscope analysis showed Fox2 knocking down cells with increased actin polymerization in both MM1S and RPMI8226 cell lines. Currently, RNA seq data following Fox2 knock down in MM cell lines is being evaluated to define the molecular mechanisms of bone marrow microenvironment-mediated Fox2-regualted alternative splicing events in MM. In summary, our results identify Fox2 as a biologically important splicing factor with essential function and potential clinical implications in multiple myeloma. Disclosures: No relevant conflicts of interest to declare.

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 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 score0.005

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 integrity0.0000.000
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 head0.018
GPT teacher head0.254
Teacher spread0.235 · 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".

Quick stats

Citations2
Published2013
Admission routes1
Has abstractyes

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