Why are genes harmful when they are overexpressed? By testing possible causes of overexpression phenotypes in yeast, we identify intrinsic protein disorder as an important determinant of dosage sensitivity. Disordered regions are prone to make promiscuous molecular interactions when their concentration is increased, and we demonstrate that this is the likely cause of pathology when genes are overexpressed. We validate our findings in two animals, Drosophila melanogaster and Caenorhabditis 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.

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Caenorhabditis elegans is an important model organism in biology, but until now no antibiotic selection markers have been successfully demonstrated for this species. We have developed a selection system using puromycin that allows the rapid and easy isolation of large populations of transgenic worms. This approach is sufficiently powerful to select single-copy transgenes, does not require any particular genetic background and also works in C. briggsae.

A central challenge in genetics is to predict phenotypic variation from individual genome sequences. Here we construct and evaluate phenotypic predictions for 19 strains of Saccharomyces cerevisiae. We use conservation-based methods to predict the impact of protein-coding variation within genes on protein function. We then rank strains using a prediction score that measures the total sum of function-altering changes in different sets of genes reported to influence over 100 phenotypes in genome-wide loss-of-function screens. We evaluate our predictions by comparing them with the observed growth rate and efficiency of 15 strains tested across 20 conditions in quantitative experiments. The median predictive performance, as measured by ROC AUC, was 0.76, and predictions were more accurate when the genes reported to influence a trait were highly connected in a functional gene network.