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Synthetic Lethality (SLAM) Project Data

ABSTRACT: Synthetic lethality is a type of genetic interaction in which two non-lethal mutations acting together result in a loss of viability. Such interactions are important for the insights they may offer into how gene functions are organized into distinct cellular processes. The datasets in this Series represent an effort to identify synthetic lethal genetic interactions in the yeast Saccharomyces cerevisiae on a genome-wide scale.

ORGANISM(S): Saccharomyces cerevisiae

PROVIDER: GSE14217 | GEO | 2013/02/28

SECONDARY ACCESSION(S): PRJNA111345

REPOSITORIES: GEO

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Synthetic Lethality (SLAM) Project Data

Project description:Synthetic lethality is a type of genetic interaction in which two non-lethal mutations acting together result in a loss of viability. Such interactions are important for the insights they may offer into how gene functions are organized into distinct cellular processes. The datasets in this Series represent an effort to identify synthetic lethal genetic interactions in the yeast Saccharomyces cerevisiae on a genome-wide scale. Please see the Pubmed IDs in the individual Sample annotations.

2013-02-28 | E-GEOD-14217 | biostudies-arrayexpress

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SML1 synthetic lethality

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Project description:Assessment of synthetic lethality between a cin8 deletion mutation and Yeast Deletion mutations. Keywords = CIN8 Keywords = yeast Keywords = deletion Keywords = synthetic lethality Keywords = tag Keywords = barcode Keywords: repeat sample

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LCD1-overexpression synthetic lethality

Project description:Assessment of synthetic lethality between an overexpressed LCD1 gene and Yeast Deletion mutations Keywords = LCD1 Keywords = overexpression Keywords = yeast Keywords = deletion Keywords = synthetic lethality Keywords = tag Keywords = barcode Keywords: repeat sample

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CRISPR genome-wide synthetic lethality screens of KRAS-driven mouse pancreatic ductal adenocarcinoma cells

Project description:We performed CRISPR genome-wide synthetic lethality screens to identify genetic vulnerabilities and chemogenetic interactions in KRAS-driven pancreatic cancer. We identify ERAD as a genetic target that synergizes with FTS treatment and we show that FTS treatment induces ER stress, which was further enhanced by inhibition of ERAD, leading to synergistic induction of apoptosis in pancreatic tumor cells. Altogether, our screens enable us to identify combination therapies for cancer treatment.

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Project description:Hereditary leiomyomatosis and renal cell carcinoma (HLRCC) is a cancer predisposition syndrome driven by mutation of the tumor suppressor fumarate hydratase (FH). Inactivation of FH causes accumulation of the electrophilic oncometabolite fumarate. In the absence of methods for reactivation, tumor suppressors can be targeted via identification of synthetic lethal interactions using genetic screens. Inspired by recent advances in chemoproteomic target identification, here we test the hypothesis that the electrophilicity of the HLRCC metabolome may produce unique susceptibilities to covalent small molecules, a phenomenon we term conditional covalent lethality. Screening a panel of chemically diverse electrophiles we identified a covalent ligand, MP-1, that exhibits FH-dependent cytotoxicity. Synthesis and structure-activity profiling identified key molecular determinants underlying the molecule’s effects. Chemoproteomic profiling of cysteine reactivity together with clickable probes validated the ability of MP-1 to engage an array of functional cysteines, including one lying in the Zn-finger domain of the tRNA methyltransferase enzyme TRMT1. TRMT1 overexpression rescues tRNA methylation from inhibition by MP-1 and partially attenuates the covalent ligand’s cytotoxicity. Our studies highlight the potential for covalent metabolites and small molecules to synergistically produce novel synthetic lethal interactions and raise the possibility of applying phenotypic screening with chemoproteomic target identification to identify new functional oncometabolite targets.

2024-01-26 | PXD033546 | Pride

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