Clear cell renal cell carcinoma (CC-RCC) is a devastating disease with limited therapeutic options available for advanced stages. The objective of this study was to investigate HMG-CoA reductase inhibitors, also known as statins, as potential therapeutics for CC-RCC. Importantly, treatment with statins was found to be synthetically lethal with the loss of the von Hippel-Lindau () tumor suppressor gene, which occurs in 90% of CC-RCC driving the disease. This effect has been confirmed in three different CC-RCC cell lines with three different lipophilic statins. Inhibition of mevalonate synthesis by statins causes a profound cytostatic effect at nanomolar concentrations and becomes cytotoxic at low micromolar concentrations in -deficient CC-RCC. The synthetic lethal effect can be fully rescued by both mevalonate and geranylgeranylpyrophosphate, but not by squalene, indicating that the effect is due to disruption of small GTPase isoprenylation and not the inhibition of cholesterol synthesis. Inhibition of Rho and Rho kinase (ROCK) signaling contributes to the synthetic lethality effect, and overactivation of hypoxia-inducible factor signaling resulting from loss is required. Finally, statin treatment is able to inhibit both tumor initiation and progression of subcutaneous 786-OT1-based CC-RCC tumors in mice. Thus, statins represent potential therapeutics for the treatment of -deficient CC-RCC..
Profiling RNA expression in a cell-specific manner continues to be a grand challenge in biochemical research. Bioorthogonal nucleosides can be utilized to track RNA expression; however, these methods currently have limitations due to background and incorporation of analogs into undesired cells. Herein, we design and demonstrate that uracil phosphoribosyltransferase can be engineered to match 5-vinyluracil for cell-specific metabolic labeling of RNA with exceptional specificity and stringency.
We report a strategy for the orthogonal conjugation of the vinyl nucleosides, 5-vinyluridine (5-VU) and 2-vinyladenosine (2-VA), via selective reactivity with maleimide and tris(2-carboxyethyl)phosphine (TCEP), respectively. The orthogonality was investigated using density functional theory (DFT) and confirmed by reactions with vinyl nucleosides. Further, these chemistries were used to modify RNA for fluorescent cell imaging. These reactions allow for the expanded use of RNA metabolic labeling to study nascent RNA expression within different RNA populations.
Tracking the expression of RNA in a cell-specific manner is a major challenge in basic and disease research. Herein we outline the current state of employing chemical approaches for cell-specific RNA expression studies. We define the utility of metabolic labels for tracking RNA synthesis, the approaches for characterizing metabolic incorporation and enrichment of labeled RNAs, and finally outline how these approaches have been used to study biological systems by providing mechanistic insights into transcriptional dynamics. Further efforts on this front will be the continued development of novel chemical handles for RNA enrichment and profiling as well as innovative approaches to control cell-specific incorporation of chemically modified metabolic probes. These advancements in RNA metabolic labeling techniques permit sensitive detection of RNA expression dynamics within relatively small subsets of cells in living tissues and organisms that are critical to performing complex developmental and pathological processes. This article is categorized under: RNA Methods > RNA Analyses in Cells RNA Evolution and Genomics > Ribonomics RNA Structure and Dynamics > RNA Structure, Dynamics and Chemistry
Tissues and organs are composed of many diverse cell types, making cell-specific gene expression profiling a major challenge. Herein we report that endogenous enzymes, unique to a cell of interest, can be utilized to enable cell-specific metabolic labeling of RNA. We demonstrate that appropriately designed "caged" nucleosides can be rendered active by serving as a substrate for cancer-cell specific enzymes to enable RNA metabolic labeling, only in cancer cells. We envision that the ease and high stringency of our approach will enable expression analysis of tumor cells in complex environments.
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