Identification of DISE-inducing shRNAs by monitoring cellular responses.
Monal PatelMarcus E PeterPublished in: Cell cycle (Georgetown, Tex.) (2018)
Off-target effects (OTE) are an undesired side effect of RNA interference (RNAi) caused by partial complementarity between the targeting siRNA and mRNAs other than the gene to be silenced. The death receptor CD95 and its ligand CD95L contain multiple sequences that when expressed as either si- or shRNAs kill cancer cells through a defined OTE that targets critical survival genes. Death induced by survival gene elimination (DISE) is characterized by specific morphological changes such as elongated cell shapes, senescence-like enlarged cells, appearance of large intracellular vesicles, release of mitochondrial ROS followed by activation of caspase-2, and induction of a necrotic form of mitotic catastrophe. Using genome-wide shRNA lethality screens with eight different cancer cell lines, we recently identified 651 genes as critical for the survival of cancer cells. To determine whether the toxic shRNAs targeting these 651 genes contained shRNAs that kill cancer cell through DISE rather than by silencing their respective target genes, we tested all shRNAs in the TRC library derived from a subset of these genes targeting tumor suppressors (TS). We now report that only by monitoring the responses of cancer cells following expression of shRNAs derived from these putative TS it was possible to identify DISE-inducing shRNAs in five of the genes. These data indicate that DISE in general is not an undefined toxic response of cells caused by a random OTE but rather a specific cellular response with shared features that points at a specific biological function involving multiple genes in the genome.
Keyphrases
- genome wide
- dna methylation
- genome wide identification
- bioinformatics analysis
- copy number
- genome wide analysis
- induced apoptosis
- poor prognosis
- dna damage
- cancer therapy
- oxidative stress
- gene expression
- endothelial cells
- cell cycle arrest
- transcription factor
- young adults
- signaling pathway
- free survival
- electronic health record
- machine learning
- high throughput
- neural network
- deep learning
- lymph node metastasis