Early-onset torsion dystonia: a novel high-throughput yeast genetic screen for factors modifying protein levels of torsinAΔE

Dystonia is the third most common movement disorder, but its diagnosis and treatment remain challenging. One of the most severe types of dystonia is early-onset torsion dystonia (EOTD). The best studied and validated EOTD-associated mutation, torsinAΔE, is a deletion of a C-terminal glutamate residue in the AAA+ ATPase torsinA. TorsinA appears to be an endoplasmic reticulum (ER)/nuclear envelope chaperone with multiple roles in the secretory pathway and in determining subcellular architecture. Many functions are disabled in the torsinAΔE variant, and torsinAΔE is also less stable than wild-type torsinA and is a substrate for ER-associated degradation. Nevertheless, the molecular factors involved in the biogenesis and degradation of torsinA and torsinAΔE have not been fully explored. To identify conserved cellular factors that can alter torsinAΔE protein levels, we designed a new high-throughput, automated, genome-wide screen utilizing our validated Saccharomyces cerevisiae torsinA expression system. By analyzing the yeast non-essential gene deletion collection, we identified 365 deletion strains with altered torsinAΔE steady-state levels. One notable hit was EUG1, which encodes a member of the protein disulfide isomerase family (PDIs). PDIs reside in the ER and catalyze the formation of disulfide bonds, mediate protein quality control and aid in nascent protein folding. We validated the role of select human PDIs in torsinA biogenesis in mammalian cells and found that overexpression of PDIs reduced the levels of torsinA and torsinAΔE. Together, our data report the first genome-wide screen to identify cellular factors that alter expression levels of the EOTD-associated protein torsinAΔE. More generally, the identified hits help in dissecting the cellular machinery involved in folding and degrading a torsinA variant, and constitute potential therapeutic factors for EOTD. This screen can also be readily adapted to identify factors impacting the levels of any protein of interest, considerably expanding the applicability of yeast in both basic and applied research.

ScreenTroll: a searchable database to compare genome-wide yeast screens

Systematic biological screens typically identify many genes or proteins that are implicated in a specific phenotype. However, deriving mechanistic insight from these screens typically involves focusing upon one or a few genes within the set in order to elucidate their precise role in producing the phenotype. To find these critical genes, researchers use a variety of tools to query the set of genes to uncover underlying common genetic or physical interactions or common functional annotations (e.g. gene ontology terms). Not only it is necessary to find previous screens containing genes in common with the new set, but also useful to easily access the individual manuscript or study that classified those genes. Unfortunately, no tool currently exists to facilitate this task. We have developed a web-based tool (ScreenTroll) that queries one or more genes against a database of systematic yeast screens. The software determines which genome-wide yeast screens also identified the queried gene(s) and the resulting screens are listed in an order based on the extent of the overlap between the queried gene(s) and the open reading frames (ORFs) characterized in each individual yeast screen. In a separate list, the corresponding ORFs that are found in both the queried set of genes and each individual genome-wide screen are displayed along with links to the relevant manuscript via NIH's PubMed database. ScreenTroll is useful for comparing a list of ORFs with genes identified in a wide array of published genome-wide screens. This comparison informs users whether any of their queried ORFs overlaps a previous study in the ScreenTroll database. By listing the manuscript of the published screen, users can read more about the phenotype associated with that study. Together, this information provides insight into the function of the queried genes and helps the user focus on a subset of them.

Sec24p and Sec16p cooperate to regulate the GTP cycle of the COPII coat

Vesicle budding from the endoplasmic reticulum (ER) employs a cycle of GTP binding and hydrolysis to regulate assembly of the COPII coat. We have identified a novel mutation (sec24-m11) in the cargo-binding subunit, Sec24p, that specifically impacts the GTP-dependent generation of vesicles in vitro. Using a high-throughput approach, we defined genetic interactions between sec24-m11 and a variety of trafficking components of the early secretory pathway, including the candidate COPII regulators, Sed4p and Sec16p. We defined a fragment of Sec16p that markedly inhibits the Sec23p- and Sec31p-stimulated GTPase activity of Sar1p, and demonstrated that the Sec24p-m11 mutation diminished this inhibitory activity, likely by perturbing the interaction of Sec24p with Sec16p. The consequence of the heightened GTPase activity when Sec24p-m11 is present is the generation of smaller vesicles, leading to accumulation of ER membranes and more stable ER exit sites. We propose that association of Sec24p with Sec16p creates a novel regulatory complex that retards the GTPase activity of the COPII coat to prevent premature vesicle scission, pointing to a fundamental role for GTP hydrolysis in vesicle release rather than in coat assembly/disassembly.

Srs2 overexpression reveals a helicase-independent role at replication forks that requires diverse cell functions

Srs2 is a 3'-5' DNA helicase that regulates many aspects of DNA metabolism in Saccharomyces cerevisiae. It is best known for its ability to counteract homologous recombination by dismantling Rad51 filaments, but is also involved in checkpoint activation, adaptation and recovery, and in resolution of late recombination intermediates. To further address its biological roles and uncover new genetic interactions, we examined the consequences of overexpressing SRS2 as well as two helicase-dead mutants, srs2-K41A and srs2-K41R, in the collection of 4827 yeast haploid deletion mutants. We identified 274 genes affecting a large variety of cellular functions that are required for cell growth when SRS2 or its mutants are overexpressed. Further analysis of these interactions reveals that Srs2 acts independently of its helicase function at replication forks likely through its recruitment by the sumoylated PCNA replication clamp. This helicase-independent function is responsible for the negative interactions with DNA metabolism genes and for the toxicity of SRS2 overexpression in many of the diverse cellular pathways revealed in our screens.

Long telomeres are preferentially extended during recombination-mediated telomere maintenance

Most human somatic cells do not express telomerase. Consequently, with each cell division their telomeres progressively shorten until replicative senescence is induced. Approximately 15% of human cancers maintain their telomeres using telomerase-independent, recombination-based mechanisms collectively termed Alternative Lengthening of Telomeres (ALT). In the yeast Saccharomyces cerevisiae, ALT cells are referred to as “survivors”. One type of survivor (type II) resembles human ALT cells in that both are defined by the amplification of telomeric repeats. We analyzed recombination-mediated telomere extension events at individual telomeres in telomerase-negative yeast during type II survivor formation and find that long telomeres are preferentially extended. Furthermore, we find that senescent cells with long telomeres are more efficient at bypassing senescence via the type II pathway. We speculate that telomere length may be important in determining whether cancer cells utilize telomerase or ALT to bypass replicative senescence.

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

Background

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 ScreenMill, a new software suite that automates image analysis and simplifies data review and analysis for high-throughput biological experiments.

Results

The ScreenMill, software suite includes three software tools or "engines": an open source Colony Measurement Engine (CM Engine) to quantitate colony growth data from plate images, a web-based Data Review Engine (DR Engine) to validate and analyze quantitative screen data, and a web-based Statistics Visualization Engine (SV Engine) 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 DR Engine and SV Engine can be used to visualize and analyze other types of quantitative high-throughput data.

Conclusions

ScreenMill automates quantification, analysis and visualization of high-throughput screen data. The algorithms implemented in ScreenMill are transparent allowing users to be confident about the results ScreenMill produces. Taken together, the tools of ScreenMill offer biologists a simple and flexible way of analyzing their data, without requiring programming skills.