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Jeon, M.

Publications and source records attributed to Jeon, M..

2 recordsLinked to original sources

Modulation of actin polymerization affects nucleocytoplasmic transport in multiple forms of Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease of unknown etiology. Although defects in nucleocytoplasmic transport (NCT) may be central to the pathogenesis of ALS and other neurodegenerative diseases, the molecular mechanisms modulating the nuclear pore function are still largely unknown. Here we show that genetic and pharmacological modulation of actin polymerization disrupts nuclear pore integrity, nuclear import, and downstream pathways such as mRNA post-transcriptional regulation. Importantly, we demonstrate that modulation of actin homeostasis can rescue nuclear pore instability and dysfunction caused by mutant PFN1 as well as by C9ORF72 repeat expansions, the most common mutations in ALS patients. Collectively, our data link NCT defects to ALS-associated pathology and propose the regulation of actin homeostasis as a novel therapeutic strategy for ALS and other neurodegenerative diseases.

neuroscience

Community assessment of cancer drug combination screens identifies strategies for synergy prediction

The effectiveness of most cancer targeted therapies is short lived since tumors evolve and develop resistance. Combinations of drugs offer the potential to overcome resistance, however the number of possible combinations is vast necessitating data-driven approaches to find optimal treatments tailored to a patients tumor. AstraZeneca carried out 11,576 experiments on 910 drug combinations across 85 cancer cell lines, recapitulating in vivo response profiles. These data, the largest openly available screen, were hosted by DREAM alongside deep molecular characterization from the Sanger Institute for a Challenge to computationally predict synergistic drug pairs and associated biomarkers. 160 teams participated to provide the most comprehensive methodological development and subsequent benchmarking to date. Winning methods incorporated prior knowledge of putative drug target interactions. For >60% of drug combinations synergy was reproducibly predicted with an accuracy matching biological replicate experiments, however 20% of drug combinations were poorly predicted by all methods. Genomic rationale for synergy predictions were identified, including antagonism unique to combined PIK3CB/D inhibition with the ADAM17 inhibitor where synergy is seen with other PI3K pathway inhibitors. All data, methods and code are freely available as a resource to the community.

bioinformatics