Model-driven analysis of experimentally determined growth phenotypes for 465 yeast gene deletion mutants under 16 different conditions

An iterative approach that integrates high-throughput measurements of yeast deletion mutants and flux balance model predictions improves understanding of both experimental and computational results

Evaluation of methods for detection of fluorescence labeled subcellular objects in microscope images

<p>Abstract</p> <p>Background</p> <p>Several algorithms have been proposed for detecting fluorescently labeled subcellular objects in microscope images. Many of these algorithms have been designed for specific tasks and validated with limited image data. But despite the potential of using extensive comparisons between algorithms to provide useful information to guide method selection and thus more accurate results, relatively few studies have been performed.</p> <p>Results</p> <p>To better understand algorithm performance under different conditions, we have carried out a comparative study including eleven spot detection or segmentation algorithms from various application fields. We used microscope images from well plate experiments with a human osteosarcoma cell line and frames from image stacks of yeast cells in different focal planes. These experimentally derived images permit a comparison of method performance in realistic situations where the number of objects varies within image set. We also used simulated microscope images in order to compare the methods and validate them against a ground truth reference result. Our study finds major differences in the performance of different algorithms, in terms of both object counts and segmentation accuracies.</p> <p>Conclusions</p> <p>These results suggest that the selection of detection algorithms for image based screens should be done carefully and take into account different conditions, such as the possibility of acquiring empty images or images with very few spots. Our inclusion of methods that have not been used before in this context broadens the set of available detection methods and compares them against the current state-of-the-art methods for subcellular particle detection.</p

Learning a Prior on Regulatory Potential from eQTL Data

Genome-wide RNA expression data provide a detailed view of an organism's biological state; hence, a dataset measuring expression variation between genetically diverse individuals (eQTL data) may provide important insights into the genetics of complex traits. However, with data from a relatively small number of individuals, it is difficult to distinguish true causal polymorphisms from the large number of possibilities. The problem is particularly challenging in populations with significant linkage disequilibrium, where traits are often linked to large chromosomal regions containing many genes. Here, we present a novel method, Lirnet, that automatically learns a regulatory potential for each sequence polymorphism, estimating how likely it is to have a significant effect on gene expression. This regulatory potential is defined in terms of “regulatory features”—including the function of the gene and the conservation, type, and position of genetic polymorphisms—that are available for any organism. The extent to which the different features influence the regulatory potential is learned automatically, making Lirnet readily applicable to different datasets, organisms, and feature sets. We apply Lirnet both to the human HapMap eQTL dataset and to a yeast eQTL dataset and provide statistical and biological results demonstrating that Lirnet produces significantly better regulatory programs than other recent approaches. We demonstrate in the yeast data that Lirnet can correctly suggest a specific causal sequence variation within a large, linked chromosomal region. In one example, Lirnet uncovered a novel, experimentally validated connection between Puf3—a sequence-specific RNA binding protein—and P-bodies—cytoplasmic structures that regulate translation and RNA stability—as well as the particular causative polymorphism, a SNP in Mkt1, that induces the variation in the pathway

Meeting Report on Experimental Approaches to Evolution and Ecology Using Yeast and Other Model Systems

The fourth EMBO-sponsored conference on Experimental Approaches to Evolution and Ecology Using Yeast and Other Model Systems (https://www.embl.de/training/events/2016/EAE16-01/), was held at the EMBL in Heidelberg, Germany, October 19–23, 2016. The conference was organized by Judith Berman (Tel Aviv University), Maitreya Dunham (University of Washington), Jun-Yi Leu (Academia Sinica), and Lars Steinmetz (EMBL Heidelberg and Stanford University). The meeting attracted ∼120 researchers from 28 countries and covered a wide range of topics in the fields of genetics, evolutionary biology, and ecology, with a unifying focus on yeast as a model system. Attendees enjoyed the Keith Haring-inspired yeast florescence microscopy artwork (Figure 1), a unique feature of the meeting since its inception, and the 1 min flash talks that catalyzed discussions at two vibrant poster sessions. The meeting coincided with the 20th anniversary of the publication describing the sequence of the first eukaryotic genome, Saccharomyces cerevisiae. Many of the conference talks focused on important questions about what is contained in the genome, how genomes evolve, and the architecture and behavior of communities of phenotypically and genotypically diverse microorganisms. Here, we summarize highlights of the research talks around these themes. Nearly all presentations focused on novel findings, and we refer the reader to relevant manuscripts that have subsequently been published

Computational Inference Software for Tetrad Assembly from Randomly Arrayed Yeast Colonies

We describe an information-theory-based method and associated software for computationally identifying sister spores derived from the same meiotic tetrad. The method exploits specific DNA sequence features of tetrads that result from meiotic centromere and allele segregation patterns. Because the method uses only the genomic sequence, it alleviates the need for tetrad-specific barcodes or other genetic modifications to the strains. Using this method, strains derived from randomly arrayed spores can be efficiently grouped back into tetrads

Proteomic analysis of Dhh1 complexes reveals a role for Hsp40 chaperone Ydj1 in yeast P-body assembly

P-bodies (PB) are ribonucleoprotein (RNP) complexes that aggregate into cytoplasmic foci when cells are exposed to stress. While the conserved mRNA decay and translational repression machineries are known components of PB, how and why cells assemble RNP complexes into large foci remain unclear. Using mass spectrometry to analyze proteins immunoisolated with the core PB protein Dhh1, we show that a considerable number of proteins contain low-complexity (LC) sequences, similar to proteins highly represented in mammalian RNP granules. We also show that the Hsp40 chaperone Ydj1, which contains an LC domain and controls prion protein aggregation, is required for the formation of Dhh1-GFP foci upon glucose depletion. New classes of proteins that reproducibly coenrich with Dhh1-GFP during PB induction include proteins involved in nucleotide or amino acid metabolism, glycolysis, tRNA aminoacylation, and protein folding. Many of these proteins have been shown to form foci in response to other stresses. Finally, analysis of RNA associated with Dhh1-GFP shows enrichment of mRNA encoding the PB protein Pat1 and catalytic RNAs along with their associated mitochondrial RNA-binding proteins. Thus, global characterization of PB composition has uncovered proteins important for PB assembly and evidence suggesting an active role for RNA in PB function

Transcriptional Profiling of Biofilm Regulators Identified by an Overexpression Screen in Saccharomyces cerevisiae

Biofilm formation by microorganisms is a major cause of recurring infections and removal of biofilms has proven to be extremely difficult given their inherent drug resistance . Understanding the biological processes that underlie biofilm formation is thus extremely important and could lead to the development of more effective drug therapies, resulting in better infection outcomes. Using the yeast Saccharomyces cerevisiae as a biofilm model, overexpression screens identified DIG1, SFL1, HEK2, TOS8, SAN1, and ROF1/YHR177W as regulators of biofilm formation. Subsequent RNA-seq analysis of biofilm and nonbiofilm-forming strains revealed that all of the overexpression strains, other than DIG1 and TOS8, were adopting a single differential expression profile, although induced to varying degrees. TOS8 adopted a separate profile, while the expression profile of DIG1 reflected the common pattern seen in most of the strains, plus substantial DIG1-specific expression changes. We interpret the existence of the common transcriptional pattern seen across multiple, unrelated overexpression strains as reflecting a transcriptional state, that the yeast cell can access through regulatory signaling mechanisms, allowing an adaptive morphological change between biofilm-forming and nonbiofilm states