Biology subjects

Edward M Marcotte

Publications and source records attributed to Edward M Marcotte.

3 recordsLinked to original sources

bioRxiv2016

Predicting Drug Synergy and Antagonism from Genetic Interaction Neighborhoods

Although drug combinations have proven efficacious in a variety of diseases, the design of such regimens often involves extensive experimental screening due to the myriad choice of drugs and doses. To address these challenges, we utilize the budding yeast Saccharomyces cerevisiae as a model organism to evaluate whether drug synergy or antagonism is mediated through genetic interactions between their target genes. Specifically, we hypothesize that if the inhibition targets of one chemical compound are in close proximity to those of a second compound in a genetic interaction network, then the compound pair will exhibit synergy or antagonism. Graph metrics are employed to make precise the notion of proximity in a network. Knowledge of genetic interactions and small-molecule targets are compiled through literature sources and curated databases, with predictions validated according to experimentally determined gold standards. Finally, we test whether genetic interactions propagate through networks according to a \"guilt-by-association\" framework. Our results suggest that close proximity between the target genes of one drug and those of another drug does not strongly predict synergy or antagonism. In addition, we find that the extent to which the growth of a double gene mutant deviates from expectation is moderately anti-correlated with their distance in a genetic interaction network.

Systems Biology↗

bioRxiv2016

Predictability of Genetic Interactions from Functional Gene Modules

Characterizing genetic interactions is crucial to understanding cellular and organismal response to gene-level perturbations. Such knowledge can inform the selection of candidate disease therapy targets. Yet experimentally determining whether genes interact is technically non-trivial and time-consuming. High-fidelity prediction of different classes of genetic interactions in multiple organisms would substantially alleviate this experimental burden. Under the hypothesis that functionally-related genes tend to share common genetic interaction partners, we evaluate a computational approach to predict genetic interactions in Homo sapiens, Drosophila melanogaster, and Saccharomyces cerevisiae. By leveraging knowledge of functional relationships between genes, we cross-validate predictions on known genetic interactions and observe high-predictive power of multiple classes of genetic interactions in all three organisms. Additionally, our method suggests high-confidence candidate interaction pairs that can be directly experimentally tested. A web application is provided for users to query genes for predicted novel genetic interaction partners. Finally, by subsampling the known yeast genetic interaction network, we found that novel genetic interactions are predictable even when knowledge of currently known interactions is minimal.

Genetics↗

bioRxiv2015

Interrogating conserved elements of diseases using Boolean combinations of orthologous phenotypes

Conserved genetic programs often predate the homologous structures and phenotypes to which they give rise; eyes, for example, have evolved several dozen times, but their development seems to involve a common set of conserved genes. Recently, the concept of orthologous phenotypes (or phenologs) offered a quantitative way to describe this property. Phenologs are phenotypes or diseases from separate species who share an unexpectedly large set of their associated gene orthologs. It has been shown that the phenotype pairs which make up a phenolog are mutually predictive in terms of the genes involved. Recently, we demonstrated the ranking of gene-phenotype association predictions using multiple phenologs from an array of species. In this work, we demonstrate a computational method which provides a more targeted view of the conserved pathways which give rise to diseases. Our approach involves the generation of synthetic pseudophenotypes made up of Boolean combinations (union, intersection, and difference) of the gene sets for phenotypes from our database. We search for diseases that overlap significantly with these Boolean phenotypes, and find a number of highly predictive combinations. While set unions produce less specific predictions (as expected), intersection and difference-based combinations appear to offer insights into extremely specific aspects of target diseases. For example, breast cancer is predicted by zebrafish methylmercury response minus metal ion response, with predictions MT-COI, JUN, SOD2, GADD45B, and BAX all involved in the pro-apoptotic response to reactive oxygen species, thought to be a key player in cancer. We also demonstrate predictions from Arabidopsis Boolean phenotypes for increased brown adipose tissue in mouse (salt stress responses intersection with sucrose stimulus response); and for human myopathy (red light response minus water deprivation response). We demonstrate the ranking of predictions for human holoprosencephaly from the set intersections between each pair of a variety of closely-related zebrafish phenotypes. Our results suggest that Boolean phenolog combinations may provide a more informed insight into the conserved pathways underlying diseases than either regular phenologs or the naive Bayes approach.

Bioinformatics↗