Determining Protein Interactions Involving Essential Genes of Unknown Function
EMSL Project ID
51037
Abstract
The DOE's goal of advancing biological and environmental research for the development of renewable and sustainable sources of biofuels and bio-based chemicals from biomass relies on our ability to understand and engineer microbial hosts for chemical biosynthesis. Ever increasing amounts of sequencing data have allowed conserved sequences to be identified across numerous microorganisms using automated pipelines for genome annotation; however, the functional annotation of significant percentages these genes remains elusive even for model microbes. The percentage of conserved genes of unknown function in emerging model and non-model systems Is even higher. Our recent work with Yarrowia lipolytica highlights the problem. The natural and engineered ability of Yarrowia lipolytica to metabolize a range of carbon sources including glucose, glycerol, and xylose into high concentrations of intracellular lipids makes it an attractive microbial host for the bio-production of diesel- and jet-fuel replacements and lipid-based chemicals. Through a prior DNA synthesis award from the JGI, we developed a CRISPR-Cas9 system to generate a genome-scale knockout library using redundant sgRNAs. These data enabled a quality-controlled annotation of essential genes for growth on glucose containing media. Unfortunately, over 50% of the Y. lipolytica genome is either an uncharacterized protein or is weakly homologous to other proteins of (un)known function. Understanding the true function of the genes at a throughput greater than ingle gene remains the critical bottleneck to understanding the Y. lipolytica genome, and microbial genomes in general. Here, we propose collection of two high-throughput functional data sets that are needed to understand the true function of these genes. Previously obtained essentiality screens provide one piece of evidence that we plan to complement with subcellular localization studies and identifying interaction partners using fast-reaction proximity labeling. As a proof of principle, we will generate 50 open reading frames for essential and conserved genes of unknown function in Y. lipolytica based on our CRISPR-Cas9 library data and elucidate cellular localization and protein interaction networks leading to improved annotation for genes of undetermined function.
Project Details
Project type
Exploratory Research
Start Date
2019-11-26
End Date
2021-03-31
Status
Closed
Released Data Link
Team
Principal Investigator