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Deficient Spindle Assembly Checkpoint in Multiple Myeloma

Overview of attention for article published in PLOS ONE, November 2011
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Title
Deficient Spindle Assembly Checkpoint in Multiple Myeloma
Published in
PLOS ONE, November 2011
DOI 10.1371/journal.pone.0027583
Pubmed ID
Authors

Elena Díaz-Rodríguez, Stela Álvarez-Fernández, Xi Chen, Bruno Paiva, Ricardo López-Pérez, Juan Luis García-Hernández, Jesús F. San Miguel, Atanasio Pandiella

Abstract

Multiple myeloma (MM) is a hematological disease characterized by an abnormal accumulation of plasma cells in the bone marrow. These cells have frequent cytogenetic abnormalities including translocations of the immunoglobulin heavy chain gene and chromosomal gains and losses. In fact, a singular characteristic differentiating MM from other hematological malignancies is the presence of a high degree of aneuploidies. As chromosomal abnormalities can be generated by alterations in the spindle assembly checkpoint (SAC), the functionality of such checkpoint was tested in MM. When SAC components were analyzed in MM cell lines, the RNA levels of most of them were conserved. Nevertheless, the protein content of some key constituents was very low in several cell lines, as was the case of MAD2 or CDC20 in RPMI-8226 or RPMI-LR5 cells. The recovery of their cellular content did not substantially affect cell growth, but improved their ability to segregate chromosomes. Finally, SAC functionality was tested by challenging cells with agents disrupting microtubule dynamics. Most of the cell lines analyzed exhibited functional defects in this checkpoint. Based on the data obtained, alterations both in SAC components and their functionality have been detected in MM, pointing to this pathway as a potential target in MM treatment.

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The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 2%
France 1 2%
Unknown 41 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 26%
Student > Ph. D. Student 7 16%
Student > Master 7 16%
Other 4 9%
Student > Bachelor 4 9%
Other 7 16%
Unknown 3 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 37%
Biochemistry, Genetics and Molecular Biology 10 23%
Medicine and Dentistry 8 19%
Mathematics 1 2%
Immunology and Microbiology 1 2%
Other 1 2%
Unknown 6 14%