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Individual Robustness in Development and Cancer

Mid-Term Report Summary - IR-DC (Individual Robustness in Development and Cancer)

Biological systems are robust to perturbations, with many genetic, stochastic and environmental challenges having no or little phenotypic consequence. However, the extent of this robustness varies across individuals, for example the same mutation or treatment may only affect a subset of individuals. The overall objective of this project is to understand the cellular and molecular mechanisms that confer this robustness to perturbation and why this robustness varies across individuals.
To systematically understand how genetic variation results in phenotypic variation, we are using deep-mutation scanning to construct complete local genotype-phenotype maps for various processes. Using this approach we constructed the first comprehensive genotype-phenotype map for alternative splicing, revealing that exon inclusion is very sensitive to mutation and how interactions between mutations can substantially alter their effects. To understand the causes of dosage sensitivity we analyzed the properties of dosage-sensitive proteins in yeast, finding that they share properties with proteins that undergo physiological liquid phase separation. We showed that for a model protein the cause of toxicity when its concentration is increased is indeed a liquid-liquid demixing.
To understand robustness to mechanical perturbations we have used single cell tracking in the early C. elegans embryo. We found that specific regulative cell movements compensate for the effects of compression in this embryo, conferring robustness to a major physiological perturbation.
Somatic mutations are a striking example of stochastic processes in biology with major phenotypic consequences. We have shown that differential DNA repair is the main cause of regional mutation rate variation in the human genome and have used somatic mutations in tumors and machine learning to systematically determine the rules of nonsense mediated mRNA, a process that results in the degradation of mRNAs containing premature stop codons. We also developed a network-based method to prioritize cancer driver genes from somatic mutation data and a method to determine how driver mutations interact, finding that this changes substantially in different cancer types.