Genetic interactions reveal the evolutionary trajectories of duplicate genes

Duplicate genes show significantly fewer interactions than singleton genes, and functionally similar duplicates can exhibit dissimilar profiles because common interactions are ‘hidden' due to buffering.Genetic interaction profiles provide insights into evolutionary mechanisms of duplicate retention by distinguishing duplicates under dosage selection from those retained because of some divergence in function.The genetic interactions of duplicate genes evolve in an extremely asymmetric way and the directionality of this asymmetry correlates well with other evolutionary properties of duplicate genes.Genetic interaction profiles can be used to elucidate the divergent function of specific duplicate pairs

Systematic exploration of essential yeast gene function with temperature-sensitive mutants

Conditional temperature-sensitive (ts) mutations are valuable reagents for studying essential genes in the yeast Saccharomyces cerevisiae. We constructed 787 ts strains, covering 497 (~45%) of the 1,101 essential yeast genes, with ~30% of the genes represented by multiple alleles. All of the alleles are integrated into their native genomic locus in the S288C common reference strain and are linked to a kanMX selectable marker, allowing further genetic manipulation by synthetic genetic array (SGA)–based, high-throughput methods. We show two such manipulations: barcoding of 440 strains, which enables chemical-genetic suppression analysis, and the construction of arrays of strains carrying different fluorescent markers of subcellular structure, which enables quantitative analysis of phenotypes using high-content screening. Quantitative analysis of a GFP-tubulin marker identified roles for cohesin and condensin genes in spindle disassembly. This mutant collection should facilitate a wide range of systematic studies aimed at understanding the functions of essential genes

Similarity measures evaluated in this study.

<p>Similarity measures evaluated in this study.</p

Role of simulated batch effects in genetic interaction data on similarity measure performance.

<p>(A) shows the performance of similarity measures on the query side of the <i>S. cerevisiae</i> genetic interaction network when simulated intermediate batch effects were added to the data. The batch effects were added by creating random batches of size 5 and for each batch, Gaussian noise (μ = 0 and σ = 0.02) was added. Furthermore, Gaussian noise (μ = 0 and σ = 0.02) was added to entire dataset. (B) A stronger batch effect signature and noise was added (μ = 0, σ = 0.04 for both batch effect and noise) (C), (D) are similar plots for the query side of the <i>S.pombe</i> genetic interaction data (μ = 0, σ = 1 for (C), and μ = 0, σ = 2 for (D)). The bar plot on the upper right corner in each section shows the area under the precision-recall curve (AUPRC) above the background for each similarity measure. The area was calculated by summation of the areas of trapezoids at in increments of 2ⁿ (log₂ units). The bars are sorted by their respective areas above background.</p

Role of thresholding genetic interaction data in the performance of similarity measures.

<p>The precision-recall plots were compared on the query side of the <i>S. cerevisiae</i> genetic interaction data at several thresholds (A) ε<−0.08 - only negative genetic interactions at intermediate threshold, (B) ε<−0.2 - only negative genetic interactions at a stringent threshold, (C) ε >0.08 - only positive genetic interactions at an intermediate threshold, (D) ε >0.2 - only positive genetic interactions at a stringent threshold, (E) |ε| >0.08, negative and positive interaction at an intermediate threshold, and (F) |ε| >0.2, negative and positive interaction at a stringent threshold. The bar plot on the upper right corner in each section shows the area under the precision-recall curve (AUPRC) above the background for each similarity measure. The area was calculated by summation of the areas of trapezoids at in increments of 2ⁿ (log₂ units). The bars are sorted by their respective areas above background.</p

Comparison of similarity measures applied to genetic interaction datasets.

<p>Gene pair correlations derived from each similarity measure were benchmarked against a Gene Ontology-based standard using precision-recall statistics. The comparison was conducted on (A) <i>S. cerevisiae</i> genetic interaction data (Costanzo <i>et al.</i> 2010) - query genes’ similarities, (B) <i>S. cerevisiae</i> genetic interaction data - array genes’ similarities, (C) <i>S. pombe</i> genetic interaction data (Ryan <i>et al.</i> 2012) - query genes’ similarities, and (D) <i>S. pombe</i> genetic interaction data – array genes’ similarities. The horizontal dotted line shows the background precision expected from randomized ranking of gene pairs. The bar plot on the upper right corner in each section shows the area under the precision-recall curve (AUPRC) above the background for each similarity measure. The area was calculated by summation of the areas of trapezoids at increments of 2ⁿ (log₂ units). The bars are sorted by their respective areas above background.</p

Role of noise in the genetic interaction data on similarity measure performance.

<p>In each panel, simulated noise was added to the <i>S. cerevisiae</i> genetic interaction data, and query correlations were used for comparing the similarity measures. The simulated noise conditions are (A) false negatives –95% of the significant interactions whose absolute value of interaction is greater than 0.08 were randomly set to 0, (B) false positives – values were randomly sampled from the set of genetic interactions whose absolute interaction value were greater than 0.08 and were randomly substituted in place of randomly selected non-interactions. This random sampling was repeated until 10 times the number of significant interactions were added as false positives in the original data, and (C) Gaussian noise - random values from a Gaussian distribution of mean 0 and standard deviation 0.08 were added to all values (interactions and non-interactions) in the dataset. The bar plot on the upper right corner in each section shows the area under the precision-recall curve (AUPRC) above the background for each similarity measure. The area was calculated by summation of the areas of trapezoids at in increments of 2ⁿ (log₂ units). The bars are sorted by their respective areas above background.</p

TheCellMap.org: A Web-Accessible Database for Visualizing and Mining the Global Yeast Genetic Interaction Network

Providing access to quantitative genomic data is key to ensure large-scale data validation and promote new discoveries. TheCellMap.org serves as a central repository for storing and analyzing quantitative genetic interaction data produced by genome-scale Synthetic Genetic Array (SGA) experiments with the budding yeast Saccharomyces cerevisiae. In particular, TheCellMap.org allows users to easily access, visualize, explore, and functionally annotate genetic interactions, or to extract and reorganize subnetworks, using data-driven network layouts in an intuitive and interactive manner

Unraveling the Biology of a Fungal Meningitis Pathogen Using Chemical Genetics

The fungal meningitis pathogen Cryptococcus neoformans is a central driver of mortality in HIV/AIDS. We report a genome-scale chemical genetic data map for this pathogen that quantifies the impact of 439 small-molecule challenges on 1,448 gene knockouts. We identified chemical phenotypes for 83% of mutants screened and at least one genetic response for each compound. C. neoformans chemical-genetic responses are largely distinct from orthologous published profiles of Saccharomyces cerevisiae, demonstrating the importance of pathogen-centered studies. We used the chemical-genetic matrix to predict novel pathogenicity genes, infer compound mode of action, and to develop an algorithm, O2M, that predicts antifungal synergies. These predictions were experimentally validated, thereby identifying virulence genes, a molecule that triggers G2/M arrest and inhibits the Cdc25 phosphatase, and many compounds that synergize with the antifungal drug fluconazole. Our work establishes a chemical-genetic foundation for approaching an infection responsible for greater than one-third of AIDS-related deaths

Unraveling the Biology of a Fungal Meningitis Pathogen Using Chemical Genetics

The fungal meningitis pathogen Cryptococcus neoformans is a central driver of mortality in HIV/AIDS. We report a genome-scale chemical genetic data map for this pathogen that quantifies the impact of 439 small-molecule challenges on 1,448 gene knockouts. We identified chemical phenotypes for 83% of mutants screened and at least one genetic response for each compound. C. neoformans chemical-genetic responses are largely distinct from orthologous published profiles of Saccharomyces cerevisiae, demonstrating the importance of pathogen-centered studies. We used the chemical-genetic matrix to predict novel pathogenicity genes, infer compound mode of action, and to develop an algorithm, O2M, that predicts antifungal synergies. These predictions were experimentally validated, thereby identifying virulence genes, a molecule that triggers G2/M arrest and inhibits the Cdc25 phosphatase, and many compounds that synergize with the antifungal drug fluconazole. Our work establishes a chemical-genetic foundation for approaching an infection responsible for greater than one-third of AIDS-related deaths