RNA Interference In Yeast Research Articles

The role of co-opted ESCRT proteins and lipid factors in protection of tombusviral double-stranded RNA replication intermediate against reconstituted RNAi in yeast.

Reconstituted antiviral defense pathway in surrogate host yeast is used as an intracellular probe to further our understanding of virus-host interactions and the role of co-opted host factors in formation of membrane-bound viral replicase complexes in protection of the viral RNA against ribonucleases. The inhibitory effect of the RNA interference (RNAi) machinery of S. castellii, which only consists of the two-component DCR1 and AGO1 genes, was measured against tomato bushy stunt virus (TBSV) in wild type and mutant yeasts. We show that deletion of the co-opted ESCRT-I (endosomal sorting complexes required for transport I) or ESCRT-III factors makes TBSV replication more sensitive to the RNAi machinery in yeast. Moreover, the lack of these pro-viral cellular factors in cell-free extracts (CFEs) used for in vitro assembly of the TBSV replicase results in destruction of dsRNA replication intermediate by a ribonuclease at the 60 min time point when the CFE from wt yeast has provided protection for dsRNA. In addition, we demonstrate that co-opted oxysterol-binding proteins and membrane contact sites, which are involved in enrichment of sterols within the tombusvirus replication compartment, are required for protection of viral dsRNA. We also show that phosphatidylethanolamine level influences the formation of RNAi-resistant replication compartment. In the absence of peroxisomes in pex3Δ yeast, TBSV subverts the ER membranes, which provide as good protection for TBSV dsRNA against RNAi or ribonucleases as the peroxisomal membranes in wt yeast. Altogether, these results demonstrate that co-opted protein factors and usurped lipids are exploited by tombusviruses to build protective subcellular environment against the RNAi machinery and possibly other cellular ribonucleases.

Identification of gene knockdown targets conferring enhanced isobutanol and 1-butanol tolerance to Saccharomyces cerevisiae using a tunable RNAi screening approach.

Improving yeast tolerance to 1-butanol and isobutanol is a step toward enabling high-titer production. To identify previously unknown genetic targets leading to increased tolerance, we establish a tunable RNA interference (RNAi) screening approach. Specifically, we optimized the efficiency and tunability of RNA interference library screening in yeast, ultimately enabling downregulation efficiencies from 0 to 94%. Using this system, we identified the Hsp70 family as a key regulator of isobutanol tolerance in a single round of screening, with downregulation of these genes conferring up to 64% increased growth in 12g/L isobutanol. For 1-butanol, we find through two rounds of iterative screening that the combined downregulation of alcohol dehydrogenase and enolase improves growth up to 3100% in 10g/L 1-butanol. Collectively, this work improves the tunability of RNAi in yeast as demonstrated by the discovery of novel effectors for these complex phenotypes.

Genetic interaction networks: better understand to better predict

A genetic interaction (GI) between two genes generally indicates that the phenotype of a double mutant differs from what is expected from each individual mutant. In the last decade, genome scale studies of quantitative GIs were completed using mainly synthetic genetic array technology and RNA interference in yeast and Caenorhabditis elegans. These studies raised questions regarding the functional interpretation of GIs, the relationship of genetic and molecular interaction networks, the usefulness of GI networks to infer gene function and co-functionality, the evolutionary conservation of GI, etc. While GIs have been used for decades to dissect signaling pathways in genetic models, their functional interpretations are still not trivial. The existence of a GI between two genes does not necessarily imply that these two genes code for interacting proteins or that the two genes are even expressed in the same cell. In fact, a GI only implies that the two genes share a functional relationship. These two genes may be involved in the same biological process or pathway; or they may also be involved in compensatory pathways with unrelated apparent function. Considering the powerful opportunity to better understand gene function, genetic relationship, robustness and evolution, provided by a genome-wide mapping of GIs, several in silico approaches have been employed to predict GIs in unicellular and multicellular organisms. Most of these methods used weighted data integration. In this article, we will review the later knowledge acquired on GI networks in metazoans by looking more closely into their relationship with pathways, biological processes and molecular complexes but also into their modularity and organization. We will also review the different in silico methods developed to predict GIs and will discuss how the knowledge acquired on GI networks can be used to design predictive tools with higher performances.