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A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
Published on: May 27, 2021
Modeling synthetic lethality
Nolwenn Le Meur1, Robert Gentleman
1Division of Public Health Sciences, Fred Hutchinson Cancer Center Research, Program in Computational Biology, Seattle, WA 98109, USA. nlemeur@fhcrc.org
Background:
Synthetic lethality defines a genetic interaction where the combination of mutations in two or more genes leads to cell death. The implications of synthetic lethal screens have been discussed in the context of drug development as synthetic lethal pairs could be used to selectively kill cancer cells, but leave normal cells relatively unharmed. A challenge is to assess genome-wide experimental data and integrate the results to better understand the underlying biological processes. We propose statistical and computational tools that can be used to find relationships between synthetic lethality and cellular organizational units.
Results:
In Saccharomyces cerevisiae, we identified multi-protein complexes and pairs of multi-protein complexes that share an unusually high number of synthetic genetic interactions. As previously predicted, we found that synthetic lethality can arise from subunits of an essential multi-protein complex or between pairs of multi-protein complexes. Finally, using multi-protein complexes allowed us to take into account the pleiotropic nature of the gene products.
Conclusions:
Modeling synthetic lethality using current estimates of the yeast interactome is an efficient approach to disentangle some of the complex molecular interactions that drive a cell. Our model in conjunction with applied statistical methods and computational methods provides new tools to better characterize synthetic genetic interactions.
Insights
Synthetic lethality, a genetic interaction causing cell death, can be modeled using protein complexes. This approach helps understand complex molecular interactions and aids in developing targeted cancer therapies.
Area of Science:
- Genetics
- Systems Biology
- Computational Biology
Background:
- Synthetic lethality describes how mutations in multiple genes cause cell death.
- Synthetic lethal interactions offer potential for targeted cancer therapy by sparing normal cells.
- Analyzing genome-wide data to understand biological processes is challenging.
Purpose of the Study:
- To develop statistical and computational tools for analyzing synthetic lethality.
- To find relationships between synthetic lethality and cellular organizational units.
- To model synthetic lethality using protein complexes.
Main Methods:
- Utilized the yeast interactome (Saccharomyces cerevisiae).
- Identified multi-protein complexes and pairs of complexes with high synthetic genetic interactions.
- Incorporated pleiotropic effects of gene products.
Main Results:
- Identified multi-protein complexes and pairs exhibiting significant synthetic genetic interactions.
- Confirmed synthetic lethality arises within essential multi-protein complexes and between complex pairs.
- Demonstrated that protein complexes account for gene product pleiotropy.
Conclusions:
- Modeling synthetic lethality with the yeast interactome efficiently disentangles complex molecular interactions.
- The proposed model, statistical, and computational methods offer new tools for characterizing synthetic genetic interactions.
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