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1.
Mol Biol Cell ; 35(6): ar84, 2024 Jun 01.
Artigo em Inglês | MEDLINE | ID: mdl-38598297

RESUMO

The spindle is a bipolar microtubule-based machine that is crucial for accurate chromosome segregation. Spindle bipolarity is generated by Eg5 (a kinesin-5), a conserved motor that drives spindle assembly by localizing to and sliding apart antiparallel microtubules. In the presence of Eg5 inhibitors (K5Is), KIF15 (a kinesin-12) can promote spindle assembly, resulting in K5I-resistant cells (KIRCs). However, KIF15 is a less potent motor than Eg5, suggesting that other factors may contribute to spindle formation in KIRCs. Protein Regulator of Cytokinesis 1 (PRC1) preferentially bundles antiparallel microtubules, and we previously showed that PRC1 promotes KIF15-microtubule binding, leading us to hypothesize that PRC1 may enhance KIF15 activity in KIRCs. Here, we demonstrate that: 1) loss of PRC1 in KIRCs decreases spindle bipolarity, 2) overexpression of PRC1 increases spindle formation efficiency in KIRCs, 3) overexpression of PRC1 protects K5I naïve cells against the K5I S-trityl-L-cysteine (STLC), and 4) PRC1 overexpression promotes the establishment of K5I resistance. These effects are not fully reproduced by a TPX2, a microtubule bundler with no known preference for microtubule orientation. These results suggest a model wherein PRC1-mediated bundling of microtubules creates a more favorable microtubule architecture for KIF15-driven mitotic spindle assembly in the context of Eg5 inhibition.


Assuntos
Cinesinas , Microtúbulos , Fuso Acromático , Cinesinas/metabolismo , Fuso Acromático/metabolismo , Microtúbulos/metabolismo , Humanos , Proteínas de Ciclo Celular/metabolismo , Proteínas Associadas aos Microtúbulos/metabolismo , Mitose/fisiologia , Células HeLa , Segregação de Cromossomos
2.
Cancer Cell ; 32(3): 310-323.e5, 2017 09 11.
Artigo em Inglês | MEDLINE | ID: mdl-28867147

RESUMO

A genome-wide association study identified LMO1, which encodes an LIM-domain-only transcriptional cofactor, as a neuroblastoma susceptibility gene that functions as an oncogene in high-risk neuroblastoma. Here we show that dßh promoter-mediated expression of LMO1 in zebrafish synergizes with MYCN to increase the proliferation of hyperplastic sympathoadrenal precursor cells, leading to a reduced latency and increased penetrance of neuroblastomagenesis. The transgenic expression of LMO1 also promoted hematogenous dissemination and distant metastasis, which was linked to neuroblastoma cell invasion and migration, and elevated expression levels of genes affecting tumor cell-extracellular matrix interaction, including loxl3, itga2b, itga3, and itga5. Our results provide in vivo validation of LMO1 as an important oncogene that promotes neuroblastoma initiation, progression, and widespread metastatic dissemination.


Assuntos
Carcinogênese/patologia , Proteínas de Ligação a DNA/metabolismo , Proteínas com Domínio LIM/metabolismo , Proteína Proto-Oncogênica N-Myc/metabolismo , Neuroblastoma/metabolismo , Neuroblastoma/patologia , Fatores de Transcrição/metabolismo , Animais , Animais Geneticamente Modificados , Carcinogênese/metabolismo , Linhagem Celular Tumoral , Movimento Celular/genética , Matriz Extracelular/metabolismo , Regulação Neoplásica da Expressão Gênica , Humanos , Hiperplasia , Modelos Biológicos , Invasividade Neoplásica , Metástase Neoplásica , Neuroblastoma/genética , Transdução de Sinais/genética , Transgenes , Peixe-Zebra
3.
Int J Mol Sci ; 18(1)2016 Dec 27.
Artigo em Inglês | MEDLINE | ID: mdl-28035989

RESUMO

Pediatric cancers rarely exhibit recurrent mutational events when compared to most adult cancers. This poses a challenge in understanding how cancers initiate, progress, and metastasize in early childhood. Also, due to limited detected driver mutations, it is difficult to benchmark key genes for drug development. In this review, we use neuroblastoma, a pediatric solid tumor of neural crest origin, as a paradigm for exploring "big data" applications in pediatric oncology. Computational strategies derived from big data science-network- and machine learning-based modeling and drug repositioning-hold the promise of shedding new light on the molecular mechanisms driving neuroblastoma pathogenesis and identifying potential therapeutics to combat this devastating disease. These strategies integrate robust data input, from genomic and transcriptomic studies, clinical data, and in vivo and in vitro experimental models specific to neuroblastoma and other types of cancers that closely mimic its biological characteristics. We discuss contexts in which "big data" and computational approaches, especially network-based modeling, may advance neuroblastoma research, describe currently available data and resources, and propose future models of strategic data collection and analyses for neuroblastoma and other related diseases.


Assuntos
Simulação por Computador , Modelos Biológicos , Neuroblastoma/patologia , Criança , Humanos , Neuroblastoma/epidemiologia , Neuroblastoma/genética , Neuroblastoma/terapia , Análise de Sobrevida
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