Final Report Summary - 2B-ROBUST (The evolution of genetic robustness)
Most genes are not required for viability and are not sensitive to changes in dosage. We have aimed to understand the mechanisms and evolution of robustness to mutation.
The specific objectives of the project have been:
- to investigate the evolutionary conservation of functional redundancy between duplicated genes;
- to develop a method to predict modifiers of genetic phenotypes / diseases;
- to investigate the mechanisms underlying robustness to changes in gene-dosage.
Below is a description of the work performed towards these objectives, including the wider implications of the project.
Robustness to mutation can arise from genetic redundancy. The simplest mechanism that can generate genetic redundancy is gene duplication. Genetic redundancy is considered an evolutionary unstable state. By performing phylogenetic analysis of genetically redundant pairs of duplicated genes we have shown that in > 90 % of cases, duplicated genes have maintained an overlapping function for at least 100 million years of evolution. Multiple pairs of genes that have maintained an overlapping function for ~1 billion years.
We have shown that genetic redundancy between duplicated genes can be an evolutionarily stable state that can be maintained for hundreds of million years of evolution (Vavouri et al., TIGS, 2008). The finding that genetic redundancy can be maintained for hundreds of million years has implications in predicting pairs of mutations that can have a synergistic effect in human using experimental data from model organisms. It means that genetic interactions between duplicated genes identified in model organisms are meaningful for human biology.
Unlike genetic interactions between duplicated genes, genetic interactions between unrelated genes are not more conserved than expected by chance. These genetic interactions are harder to predict because they are caused by different, poorly understood mechanisms and they are difficult to identify because the number of potential interactions is very large. To address this problem and in collaboration with Dr Insuk Lee (Yonsei University, Korea) we have tested whether probabilistic functional networks can be used to predict genetic interactions in C. elegans. A high quality functional network was generated by integrating a diverse set of functional datasets. We assessed the performance of this approach using an RNAi screen in C. elegans, which confirmed that a high quality functional network can be used to predict interacting pairs of mutations.
The implication of this work is that it demonstrates a way to discover modifier loci in human. By building a high quality functional network it would be possible to predict likely modifier loci of a disease and therefore test the association of these candidate loci with the disease rather than the entire genome (Lee et al., Genome Res, in press).
Why are genes harmful when they are overexpressed? We tested many different possible causes of overexpression phenotypes in yeast and found that intrinsic protein disorder is an important determinant of dosage sensitivity (Vavouri et al., Cell, 2009). Disordered regions are prone to make promiscuous molecular interactions when their concentration is increased. We demonstrated that this is the likely cause of pathology when genes are overexpressed.
We validated our findings in two animals, Drosophila melanogaster and C. elegans. In mice and humans the same properties are strongly associated with dosage sensitive oncogenes, such that mass-action-driven molecular interactions may be a frequent cause of cancer. Dosage sensitive genes are tightly regulated at the transcriptional, RNA and protein levels, which may serve to prevent harmful increases in protein concentration under physiological conditions. Mass-action-driven interaction promiscuity is a single theoretical framework that can be used to understand, predict, and possibly treat the effects of increased gene expression in evolution and disease.
The specific objectives of the project have been:
- to investigate the evolutionary conservation of functional redundancy between duplicated genes;
- to develop a method to predict modifiers of genetic phenotypes / diseases;
- to investigate the mechanisms underlying robustness to changes in gene-dosage.
Below is a description of the work performed towards these objectives, including the wider implications of the project.
Robustness to mutation can arise from genetic redundancy. The simplest mechanism that can generate genetic redundancy is gene duplication. Genetic redundancy is considered an evolutionary unstable state. By performing phylogenetic analysis of genetically redundant pairs of duplicated genes we have shown that in > 90 % of cases, duplicated genes have maintained an overlapping function for at least 100 million years of evolution. Multiple pairs of genes that have maintained an overlapping function for ~1 billion years.
We have shown that genetic redundancy between duplicated genes can be an evolutionarily stable state that can be maintained for hundreds of million years of evolution (Vavouri et al., TIGS, 2008). The finding that genetic redundancy can be maintained for hundreds of million years has implications in predicting pairs of mutations that can have a synergistic effect in human using experimental data from model organisms. It means that genetic interactions between duplicated genes identified in model organisms are meaningful for human biology.
Unlike genetic interactions between duplicated genes, genetic interactions between unrelated genes are not more conserved than expected by chance. These genetic interactions are harder to predict because they are caused by different, poorly understood mechanisms and they are difficult to identify because the number of potential interactions is very large. To address this problem and in collaboration with Dr Insuk Lee (Yonsei University, Korea) we have tested whether probabilistic functional networks can be used to predict genetic interactions in C. elegans. A high quality functional network was generated by integrating a diverse set of functional datasets. We assessed the performance of this approach using an RNAi screen in C. elegans, which confirmed that a high quality functional network can be used to predict interacting pairs of mutations.
The implication of this work is that it demonstrates a way to discover modifier loci in human. By building a high quality functional network it would be possible to predict likely modifier loci of a disease and therefore test the association of these candidate loci with the disease rather than the entire genome (Lee et al., Genome Res, in press).
Why are genes harmful when they are overexpressed? We tested many different possible causes of overexpression phenotypes in yeast and found that intrinsic protein disorder is an important determinant of dosage sensitivity (Vavouri et al., Cell, 2009). Disordered regions are prone to make promiscuous molecular interactions when their concentration is increased. We demonstrated that this is the likely cause of pathology when genes are overexpressed.
We validated our findings in two animals, Drosophila melanogaster and C. elegans. In mice and humans the same properties are strongly associated with dosage sensitive oncogenes, such that mass-action-driven molecular interactions may be a frequent cause of cancer. Dosage sensitive genes are tightly regulated at the transcriptional, RNA and protein levels, which may serve to prevent harmful increases in protein concentration under physiological conditions. Mass-action-driven interaction promiscuity is a single theoretical framework that can be used to understand, predict, and possibly treat the effects of increased gene expression in evolution and disease.