Sensitization of cancer cells to ferroptosis coincident with cell cycle arrest.

Ferroptosis is a non-apoptotic form of cell death that can be triggered by inhibiting the system xc- cystine/glutamate antiporter or the phospholipid hydroperoxidase glutathione peroxidase 4 (GPX4). We have investigated how cell cycle arrest caused by stabilization of p53 or inhibition of cyclin-dependent kinase 4/6 (CDK4/6) impacts ferroptosis sensitivity. Here, we show that cell cycle arrest can enhance sensitivity to ferroptosis induced by covalent GPX4 inhibitors (GPX4i) but not system xc- inhibitors. Greater sensitivity to GPX4i is associated with increased levels of oxidizable polyunsaturated fatty acid-containing phospholipids (PUFA-PLs). Higher PUFA-PL abundance upon cell cycle arrest involves reduced expression of membrane-bound O-acyltransferase domain-containing 1 (MBOAT1) and epithelial membrane protein 2 (EMP2). A candidate orally bioavailable GPX4 inhibitor increases lipid peroxidation and shrinks tumor volumes when combined with a CDK4/6 inhibitor. Thus, cell cycle arrest may make certain cancer cells more susceptible to ferroptosis in vivo.

A Cell Cycle-Dependent Ferroptosis Sensitivity Switch Governed by EMP2.

Ferroptosis is a non-apoptotic form of cell death characterized by iron-dependent lipid peroxidation. Ferroptosis can be induced by system xc- cystine/glutamate antiporter inhibition or by direct inhibition of the phospholipid hydroperoxidase glutathione peroxidase 4 (GPX4). The regulation of ferroptosis in response to system xc- inhibition versus direct GPX4 inhibition may be distinct. Here, we show that cell cycle arrest enhances sensitivity to ferroptosis triggered by GPX4 inhibition but not system xc- inhibition. Arrested cells have increased levels of oxidizable polyunsaturated fatty acid-containing phospholipids, which drives sensitivity to GPX4 inhibition. Epithelial membrane protein 2 (EMP2) expression is reduced upon cell cycle arrest and is sufficient to enhance ferroptosis in response to direct GPX4 inhibition. An orally bioavailable GPX4 inhibitor increased markers of ferroptotic lipid peroxidation in vivo in combination with a cell cycle arresting agent. Thus, responses to different ferroptosis-inducing stimuli can be regulated by cell cycle state.

SNPAAMapper-Python: A highly efficient genome-wide SNP variant analysis pipeline for Next-Generation Sequencing data.

Currently, there are many publicly available Next Generation Sequencing tools developed for variant annotation and classification. However, as modern sequencing technology produces more and more sequencing data, a more efficient analysis program is desired, especially for variant analysis. In this study, we updated SNPAAMapper, a variant annotation pipeline by converting perl codes to python for generating annotation output with an improved computational efficiency and updated information for broader applicability. The new pipeline written in Python can classify variants by region (Coding Sequence, Untranslated Regions, upstream, downstream, intron), predict amino acid change type (missense, nonsense, etc.), and prioritize mutation effects (e.g., synonymous > non-synonymous) while being faster and more efficient. Our new pipeline works in five steps. First, exon annotation files are generated. Next, the exon annotation files are processed, and gene mapping and feature information files are produced. Afterward, the python scrips classify the variants based on genomic regions and predict the amino acid change category. Lastly, another python script prioritizes and ranks the mutation effects of variants to output the result file. The Python version of SNPAAMapper accomplished the overall speed by running most annotation steps in a substantially shorter time. The Python script can classify variants by region in 53 s compared to 166 s for the Perl script in a test sample run on a Latitude 7480 Desktop computer with 8GB RAM and an Intel Core i5-6300 CPU @ 2.4Ghz. Steps of predicting amino acid change type and prioritizing mutation effects of variants were executed within 1 s for both pipelines. SNPAAMapper-Python was developed and tested on the ClinVar database, a NCBI database of information on genomic variation and its relationship to human health. We believe our developed Python version of SNPAAMapper variant annotation pipeline will benefit the community by elucidating the variant consequence and speed up the discovery of causative genetic variants through whole genome/exome sequencing. Source codes, test data files, instructions, and further explanations are available on the web at https://github.com/BaiLab/SNPAAMapper-Python.

The MEK5-ERK5 Kinase Axis Controls Lipid Metabolism in Small-Cell Lung Cancer.

Small-cell lung cancer (SCLC) is an aggressive form of lung cancer with dismal survival rates. While kinases often play key roles driving tumorigenesis, there are strikingly few kinases known to promote the development of SCLC. Here, we investigated the contribution of the MAPK module MEK5-ERK5 to SCLC growth. MEK5 and ERK5 were required for optimal survival and expansion of SCLC cell lines in vitro and in vivo. Transcriptomics analyses identified a role for the MEK5-ERK5 axis in the metabolism of SCLC cells, including lipid metabolism. In-depth lipidomics analyses showed that loss of MEK5/ERK5 perturbs several lipid metabolism pathways, including the mevalonate pathway that controls cholesterol synthesis. Notably, depletion of MEK5/ERK5 sensitized SCLC cells to pharmacologic inhibition of the mevalonate pathway by statins. These data identify a new MEK5-ERK5-lipid metabolism axis that promotes the growth of SCLC. SIGNIFICANCE: This study is the first to investigate MEK5 and ERK5 in SCLC, linking the activity of these two kinases to the control of cell survival and lipid metabolism.