Biology subjects

Brown, G. W.

Publications and source records attributed to Brown, G. W..

4 recordsLinked to original sources

bioRxiv2017

Mapping DNA damage-dependent genetic interactions in yeast via orgy mating and barcode fusion genetics

Condition-dependent genetic interactions can reveal functional relationships between genes that are not evident under standard culture conditions. State-of-the-art yeast genetic interaction mapping, which relies on robotic manipulation of arrays of double mutant strains, does not scale readily to multi-condition studies. Here we describe Barcode Fusion Genetics to map Genetic Interactions (BFG-GI), by which double mutant strains generated via en masse party mating can also be monitored en masse for growth and genetic interactions. By using site-specific recombination to fuse two DNA barcodes, each representing a specific gene deletion, BFG-GI enables multiplexed quantitative tracking of double mutants via next-generation sequencing. We applied BFG-GI to a matrix of DNA repair genes under nine different conditions, including methyl methanesulfonate (MMS), 4-nitroquinoline 1-oxide (4NQO), bleomycin, zeocin, and three other DNA-damaging environments. BFG-GI recapitulated known genetic interactions and yielded new condition-dependent genetic interactions. We validated and further explored a subnetwork of condition-dependent genetic interactions involving MAG1, SLX4, and genes encoding the Shu complex, and inferred that loss of the Shu complex leads to a decrease in the activation or activity of the checkpoint protein kinase Rad53.

systems biology↗

bioRxiv2017

Functional Annotation of Chemical Libraries across Diverse Biological Processes

Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells to a compound, revealing chemical-genetic interactions that can elucidate a compounds mode of action. We developed a highly parallel and unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized, diagnostic mutant collection that is predictive all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode sequencing protocol, enabling assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened 7 different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

systems biology↗

bioRxiv2017

Genome-scale genetic interactions position the Mitotic Exit Network as a major antagonist of transient Topoisomerase II deficiency.

Topoisomerase II (Top2) is the essential protein that resolves DNA catenations. When the Top2 is inactivated, mitotic catastrophe results from massive entanglement of chromosomes. Top2 is also the target of many first-line anticancer drugs, the so-called Top2 poisons. Often, tumours become resistant to these drugs by downregulating Top2. Here, we have compared two isogenic yeast strains carrying top2 thermosensitive alleles that differ in their resistance to Top2 poisons, the broadly-used poison-sensitive top2-4 and the poison-resistant top2-5. We found that top2-5 transits through anaphase faster than top2-4. In order to define the biological importance of this difference, we performed genome-scale Synthetic Gene Array (SGA) analyses during chronic sublethal Top2 downregulation and acute, yet transient, Top2 inactivation. We find that downregulation of cell cycle progression, especially the Mitotic Exit Network (MEN), protects against Top2 deficiency. In all conditions, genetic protection was stronger in top2-5, and this correlated with destabilization of anaphase bridges by execution of MEN. We suggest that mitotic exit may be a therapeutic target to hypersensitize cancer cells carrying downregulating mutations in TOP2.

genetics↗

bioRxiv2017

Comparative analysis of protein abundance studies to quantify the Saccharomyces cerevisiae proteome

Global gene expression and proteomics tools have allowed large-scale analyses of the transcriptome and proteome in eukaryotic cells. These tools have enabled studies of protein abundance changes that occur in cells under stress conditions, providing insight into regulatory programs required for cellular adaptation. While the proteome of yeast has been subjected to the most comprehensive analysis of any eukary-ote, each of the existing datasets is separate and reported in different units. A comparison of all the available protein abundance data sets is key towards developing a complete understanding of the yeast proteome. We evaluated 19 quantitative proteomic analyses performed under normal and stress conditions and normalized and converted all measurements of protein abundance into absolute molecules per cell. Our analysis yields an estimate of the cellular abundance of 97% of the proteins in the yeast proteome, as well as an assessment of the variation in each abundance measurement. We evaluate the variance and sensitivity associated with different measurement methods. We find that C-terminal tagging of proteins, and the accompanying alterations to the 3 untranslated regions of the tagged genes, has little effect on protein abundance. Finally, our normalization of diverse datasets facilitates comparisons of protein abundance remodeling of the proteome during cellular stresses.

bioinformatics↗