Colony size measurement of the yeast gene deletion strains for functional genomics

BACKGROUND: Numerous functional genomics approaches have been developed to study the model organism yeast, Saccharomyces cerevisiae, with the aim of systematically understanding the biology of the cell. Some of these techniques are based on yeast growth differences under different conditions, such as those generated by gene mutations, chemicals or both. Manual inspection of the yeast colonies that are grown under different conditions is often used as a method to detect such growth differences. RESULTS: Here, we developed a computerized image analysis system called Growth Detector (GD), to automatically acquire quantitative and comparative information for yeast colony growth. GD offers great convenience and accuracy over the currently used manual growth measurement method. It distinguishes true yeast colonies in a digital image and provides an accurate coordinate oriented map of the colony areas. Some post-processing calculations are also conducted. Using GD, we successfully detected a genetic linkage between the molecular activity of the plant-derived antifungal compound berberine and gene expression components, among other cellular processes. A novel association for the yeast mek1 gene with DNA damage repair was also identified by GD and confirmed by a plasmid repair assay. The results demonstrate the usefulness of GD for yeast functional genomics research. CONCLUSION: GD offers significant improvement over the manual inspection method to detect relative yeast colony size differences. The speed and accuracy associated with GD makes it an ideal choice for large-scale functional genomics investigations

Global investigation of protein–protein interactions in yeast Saccharomyces cerevisiae using re-occurring short polypeptide sequences

Protein–protein interaction (PPI) maps provide insight into cellular biology and have received considerable attention in the post-genomic era. While large-scale experimental approaches have generated large collections of experimentally determined PPIs, technical limitations preclude certain PPIs from detection. Recently, we demonstrated that yeast PPIs can be computationally predicted using re-occurring short polypeptide sequences between known interacting protein pairs. However, the computational requirements and low specificity made this method unsuitable for large-scale investigations. Here, we report an improved approach, which exhibits a specificity of ∼99.95% and executes 16 000 times faster. Importantly, we report the first all-to-all sequence-based computational screen of PPIs in yeast, Saccharomyces cerevisiae in which we identify 29 589 high confidence interactions of ∼2 × 107 possible pairs. Of these, 14 438 PPIs have not been previously reported and may represent novel interactions. In particular, these results reveal a richer set of membrane protein interactions, not readily amenable to experimental investigations. From the novel PPIs, a novel putative protein complex comprised largely of membrane proteins was revealed. In addition, two novel gene functions were predicted and experimentally confirmed to affect the efficiency of non-homologous end-joining, providing further support for the usefulness of the identified PPIs in biological investigations

Chemical-genetic profile analysis in yeast suggests that a previously uncharacterized open reading frame, YBR261C, affects protein synthesis

<p>Abstract</p> <p>Background</p> <p>Functional genomics has received considerable attention in the post-genomic era, as it aims to identify function(s) for different genes. One way to study gene function is to investigate the alterations in the responses of deletion mutants to different stimuli. Here we investigate the genetic profile of yeast non-essential gene deletion array (yGDA, ~4700 strains) for increased sensitivity to paromomycin, which targets the process of protein synthesis.</p> <p>Results</p> <p>As expected, our analysis indicated that the majority of deletion strains (134) with increased sensitivity to paromomycin, are involved in protein biosynthesis. The remaining strains can be divided into smaller functional categories: metabolism (45), cellular component biogenesis and organization (28), DNA maintenance (21), transport (20), others (38) and unknown (39). These may represent minor cellular target sites (side-effects) for paromomycin. They may also represent novel links to protein synthesis. One of these strains carries a deletion for a previously uncharacterized ORF, <it>YBR261C</it>, that we term <it>TAE1 </it>for Translation Associated Element 1. Our focused follow-up experiments indicated that deletion of <it>TAE1 </it>alters the ribosomal profile of the mutant cells. Also, gene deletion strain for <it>TAE1 </it>has defects in both translation efficiency and fidelity. Miniaturized synthetic genetic array analysis further indicates that <it>TAE1 </it>genetically interacts with 16 ribosomal protein genes. Phenotypic suppression analysis using <it>TAE1 </it>overexpression also links <it>TAE1 </it>to protein synthesis.</p> <p>Conclusion</p> <p>We show that a previously uncharacterized ORF, <it>YBR261C</it>, affects the process of protein synthesis and reaffirm that large-scale genetic profile analysis can be a useful tool to study novel gene function(s).</p

ScreenMill: A freely available software suite for growth measurement, analysis and visualization of high-throughput screen data

<p>Abstract</p> <p>Background</p> <p>Many high-throughput genomic experiments, such as Synthetic Genetic Array and yeast two-hybrid, use colony growth on solid media as a screen metric. These experiments routinely generate over 100,000 data points, making data analysis a time consuming and painstaking process. Here we describe <it>ScreenMill</it>, a new software suite that automates image analysis and simplifies data review and analysis for high-throughput biological experiments.</p> <p>Results</p> <p>The <it>ScreenMill</it>, software suite includes three software tools or "engines": an open source <it>Colony Measurement Engine </it>(<it>CM Engine</it>) to quantitate colony growth data from plate images, a web-based <it>Data Review Engine </it>(<it>DR Engine</it>) to validate and analyze quantitative screen data, and a web-based <it>Statistics Visualization Engine </it>(<it>SV Engine</it>) to visualize screen data with statistical information overlaid. The methods and software described here can be applied to any screen in which growth is measured by colony size. In addition, the <it>DR Engine </it>and <it>SV Engine </it>can be used to visualize and analyze other types of quantitative high-throughput data.</p> <p>Conclusions</p> <p><it>ScreenMill </it>automates quantification, analysis and visualization of high-throughput screen data. The algorithms implemented in S<it>creenMill </it>are transparent allowing users to be confident about the results <it>ScreenMill </it>produces. Taken together, the tools of <it>ScreenMill </it>offer biologists a simple and flexible way of analyzing their data, without requiring programming skills.</p

Global Transcriptome and Deletome Profiles of Yeast Exposed to Transition Metals

A variety of pathologies are associated with exposure to supraphysiological concentrations of essential metals and to non-essential metals and metalloids. The molecular mechanisms linking metal exposure to human pathologies have not been clearly defined. To address these gaps in our understanding of the molecular biology of transition metals, the genomic effects of exposure to Group IB (copper, silver), IIB (zinc, cadmium, mercury), VIA (chromium), and VB (arsenic) elements on the yeast Saccharomyces cerevisiae were examined. Two comprehensive sets of metal-responsive genomic profiles were generated following exposure to equi-toxic concentrations of metal: one that provides information on the transcriptional changes associated with metal exposure (transcriptome), and a second that provides information on the relationship between the expression of ∼4,700 non-essential genes and sensitivity to metal exposure (deletome). Approximately 22% of the genome was affected by exposure to at least one metal. Principal component and cluster analyses suggest that the chemical properties of the metal are major determinants in defining the expression profile. Furthermore, cells may have developed common or convergent regulatory mechanisms to accommodate metal exposure. The transcriptome and deletome had 22 genes in common, however, comparison between Gene Ontology biological processes for the two gene sets revealed that metal stress adaptation and detoxification categories were commonly enriched. Analysis of the transcriptome and deletome identified several evolutionarily conserved, signal transduction pathways that may be involved in regulating the responses to metal exposure. In this study, we identified genes and cognate signaling pathways that respond to exposure to essential and non-essential metals. In addition, genes that are essential for survival in the presence of these metals were identified. This information will contribute to our understanding of the molecular mechanism by which organisms respond to metal stress, and could lead to an understanding of the connection between environmental stress and signal transduction pathways

Srf1 Is a Novel Regulator of Phospholipase D Activity and Is Essential to Buffer the Toxic Effects of C16:0 Platelet Activating Factor

During Alzheimer's Disease, sustained exposure to amyloid-β42 oligomers perturbs metabolism of ether-linked glycerophospholipids defined by a saturated 16 carbon chain at the sn-1 position. The intraneuronal accumulation of 1-O-hexadecyl-2-acetyl-sn-glycerophosphocholine (C16:0 PAF), but not its immediate precursor 1-O-hexadecyl-sn-glycerophosphocholine (C16:0 lyso-PAF), participates in signaling tau hyperphosphorylation and compromises neuronal viability. As C16:0 PAF is a naturally occurring lipid involved in cellular signaling, it is likely that mechanisms exist to protect cells against its toxic effects. Here, we utilized a chemical genomic approach to identify key processes specific for regulating the sensitivity of Saccharomyces cerevisiae to alkyacylglycerophosphocholines elevated in Alzheimer's Disease. We identified ten deletion mutants that were hypersensitive to C16:0 PAF and five deletion mutants that were hypersensitive to C16:0 lyso-PAF. Deletion of YDL133w, a previously uncharacterized gene which we have renamed SRF1 (Spo14 Regulatory Factor 1), resulted in the greatest differential sensitivity to C16:0 PAF over C16:0 lyso-PAF. We demonstrate that Srf1 physically interacts with Spo14, yeast phospholipase D (PLD), and is essential for PLD catalytic activity in mitotic cells. Though C16:0 PAF treatment does not impact hydrolysis of phosphatidylcholine in yeast, C16:0 PAF does promote delocalization of GFP-Spo14 and phosphatidic acid from the cell periphery. Furthermore, we demonstrate that, similar to yeast cells, PLD activity is required to protect mammalian neural cells from C16:0 PAF. Together, these findings implicate PLD as a potential neuroprotective target capable of ameliorating disruptions in lipid metabolism in response to accumulating oligomeric amyloid-β42

Chemical-genetic profile analysis of five inhibitory compounds in yeast

<p>Abstract</p> <p>Background</p> <p>Chemical-genetic profiling of inhibitory compounds can lead to identification of their modes of action. These profiles can help elucidate the complex interactions between small bioactive compounds and the cell machinery, and explain putative gene function(s).</p> <p>Results</p> <p>Colony size reduction was used to investigate the chemical-genetic profile of cycloheximide, 3-amino-1,2,4-triazole, paromomycin, streptomycin and neomycin in the yeast <it>Saccharomyces cerevisiae</it>. These compounds target the process of protein biosynthesis. More than 70,000 strains were analyzed from the array of gene deletion mutant yeast strains. As expected, the overall profiles of the tested compounds were similar, with deletions for genes involved in protein biosynthesis being the major category followed by metabolism. This implies that novel genes involved in protein biosynthesis could be identified from these profiles. Further investigations were carried out to assess the activity of three profiled genes in the process of protein biosynthesis using relative fitness of double mutants and other genetic assays.</p> <p>Conclusion</p> <p>Chemical-genetic profiles provide insight into the molecular mechanism(s) of the examined compounds by elucidating their potential primary and secondary cellular target sites. Our follow-up investigations into the activity of three profiled genes in the process of protein biosynthesis provided further evidence concerning the usefulness of chemical-genetic analyses for annotating gene functions. We termed these genes <it>TAE2</it>, <it>TAE3 </it>and <it>TAE4 </it>for translation associated elements 2-4.</p

Oxidative stress in saccharomyces cerevisiae.

Master of Science in Biochemistry. University of KwaZulu-Natal, Durban 2017.Abstract available in PDF file

Manganese-induced cellular disturbance in the baker’s yeast, Saccharomyces cerevisiae with putative implications in neuronal dysfunction

Manganese (Mn) is an essential element, but in humans, chronic and/or acute exposure to this metal can lead to neurotoxicity and neurodegenerative disorders including Parkinsonism and Parkinson’s Disease by unclear mechanisms. To better understand the effects that exposure to Mn 2+ exert on eukaryotic cell biology, we exposed a non-essential deletion library of the yeast Saccharomyces cerevisiae to a sub-inhibitory concentration of M