PROPHECY—a database for high-resolution phenomics

The rapid recent evolution of the field phenomics—the genome-wide study of gene dispensability by quantitative analysis of phenotypes—has resulted in an increasing demand for new data analysis and visualization tools. Following the introduction of a novel approach for precise, genome-wide quantification of gene dispensability in Saccharomyces cerevisiae we here announce a public resource for mining, filtering and visualizing phenotypic data—the PROPHECY database. PROPHECY is designed to allow easy and flexible access to physiologically relevant quantitative data for the growth behaviour of mutant strains in the yeast deletion collection during conditions of environmental challenges. PROPHECY is publicly accessible at http://prophecy.lundberg.gu.se

PROPHECY—a yeast phenome database, update 2006

Connecting genotype to phenotype is fundamental in biomedical research and in our understanding of disease. Phenomics—the large-scale quantitative phenotypic analysis of genotypes on a genome-wide scale—connects automated data generation with the development of novel tools for phenotype data integration, mining and visualization. Our yeast phenomics database PROPHECY is available at . Via phenotyping of 984 heterozygous diploids for all essential genes the genotypes analysed and presented in PROPHECY have been extended and now include all genes in the yeast genome. Further, phenotypic data from gene overexpression of 574 membrane spanning proteins has recently been included. To facilitate the interpretation of quantitative phenotypic data we have developed a new phenotype display option, the Comparative Growth Curve Display, where growth curve differences for a large number of mutants compared with the wild type are easily revealed. In addition, PROPHECY now offers a more informative and intuitive first-sight display of its phenotypic data via its new summary page. We have also extended the arsenal of data analysis tools to include dynamic visualization of phenotypes along individual chromosomes. PROPHECY is an initiative to enhance the growing field of phenome bioinformatics

The HOG Pathway Dictates the Short-Term Translational Response after Hyperosmotic Shock

In the global osmoshock translational response in yeast, some gene products were translationally mobilized without transcriptional up-regulation. Conversely, other transcriptionally up-regulated mRNAs were translationally inhibited. Analogous changes occurred on the protein level. These translational responses were strongly dependent on Hog1 and Rck2

The use of high-throughput microscopy in the characterisation of phenotypes associated with the Unfolded Protein Response in Saccharomyces cerevisiae

Proteins traversing the secretory pathway begin their passage in the endoplasmic reticulum (ER) where they must be correctly folded and processed to pass quality control measures. Complications with this process can result in the accumulation of misfolded proteins, commonly referred to as ER-stress, which has been associated with a number of diseases. The unfolded protein response (UPR) is the cell’s mechanism of dealing with ER-stress and is activated via the IRE1-HAC1 pathway in yeast. Ire1p is the ER-stress sensor and upon recognising misfolded proteins Ire1 oligomerises and forms local clusters. Activated Ire1p then splices out an inhibitory intron from the UPR specific transcription factor Hac1p which goes on to initiate downstream responses to alleviate ER-stress. Here we utilise high-throughput microscopy and UPR-specific GFP reporter systems to characterise the UPR in the yeast Saccharomyces cerevisiae. High-throughput microscopy and automated image analysis is increasingly being used as a screening tool for investigating genome-wide collections of yeast strains, including the yeast deletion mutant array and the yeast GFP collection. We describe the use of GFP labelled Ire1p to visualise cluster formation as a reporter for early UPR recognition of misfolded proteins, as well as a GFP controlled by a Hac1p responsive promoter to measure downstream UPR activation. These UPR-specific GFP reporter systems were used to screen a collection of non-essential gene deletion strains, identifying gene deletions that induce UPR activation and thus are likely to function in the early secretory pathway. This included well known components such as the ALG members of the glycosylation pathway and various ER chaperones such as LHS1 and SCJ1. Additionally this analysis revealed 44 previously uncharacterised genes, suggesting there are still processes related to the secretory pathway that are yet to be described. Moreover, by inducing ER-stress in this screening system we revealed genes required for the normal activation of the UPR including ribosomal/translation and chromatin/transcriptionally related genes, as well as various genes from throughout the secretory pathway. Furthermore, we screened a collection of ~4000 strains, each expressing a different GFP fusion protein, under ER-stress conditions to identify protein expression and localisation changes induced by the UPR. Comparison to UPR deficient Δhac1 cells uncovered a set of UPR specific targets including 26 novel UPR targets that had not been identified in previous studies measuring changes at the transcript level. As part of this work, we developed a dual red fluorescent protein system to label cells for automated image segmentation to enable single cell phenotype measurements. Here we describe the use of texture analysis as a means of increasing automation in the identification of phenotypic changes across the proteome. These novel techniques may be more widely applied to screening GFP collections to increase automation of image analysis, particularly as manual annotation of phenotypic changes is a major bottleneck in high-throughput screening. The results presented here from microscopy based screening compare well with other techniques in the literature, but also provide new information highlighting the synergistic effects of integrating high-throughput imaging into traditional screening methodologies

Functional classification filtering allows dissection of biochemical pathways: visualizing the growth behaviour of the components of a signalling pathway—the HOG pathway—during saline stress

<p><b>Copyright information:</b></p><p>Taken from "PROPHECY—a database for high-resolution phenomics"</p><p>Nucleic Acids Research 2004 ;33(Database Issue):D369-D373.</p><p>Published online 17 Dec 2004 </p><p>PMCID:PMC540080.</p><p>Copyright © 2005 Oxford University Press</p> Deletion strains are represented by red growth curves, representative reference strain by black curves. Gene names in red indicate significant (LSC < 0; < 0.001) phenotypes. Yellow circles indicate strains not present in the yeast deletion strain collection