Biology subjects

Deol, K.

Publications and source records attributed to Deol, K..

2 recordsLinked to original sources

bioRxiv2022

Identification of structurally diverse FSP1 inhibitors that sensitize cancer cells to ferroptosis

Ferroptosis is a regulated form of cell death associated with the iron-dependent accumulation of lipid peroxides. Inducing ferroptosis is a promising approach to treat therapy resistant cancer. Ferroptosis suppressor protein 1 (FSP1) promotes ferroptosis resistance in cancer by generating the antioxidant form of coenzyme Q10 (CoQ). Despite the important role of FSP1, few molecular tools exist that target the CoQ-FSP1 pathway. Exploiting a series of chemical screens, we identify several structurally diverse FSP1 inhibitors. The most potent of these compounds, ferroptosis sensitizer 1 (FSEN1), is an uncompetitive inhibitor that acts selectively through on target inhibition of FSP1 to sensitize cancer cells to ferroptosis. Furthermore, a synthetic lethality screen reveals that FSEN1 synergizes with endoperoxide-containing ferroptosis inducers, including dihydroartemisinin, to trigger ferroptosis. These results provide new tools that catalyze the exploration of FSP1 as a therapeutic target and highlight the value of combinatorial therapeutic regimes targeting FSP1 and additional ferroptosis inducers.

cancer biology↗

bioRxiv2022

SlowMoMan: A web app for discovery of important features along user-drawn trajectories in 2D embeddings

Nonlinear low-dimensional embeddings allow humans to visualize high-dimensional data, as is often seen in bioinformatics, where data sets may have tens of thousands of dimensions. However, relating the axes of a nonlinear embedding to the original dimensions is a nontrivial problem. In particular, humans may identify patterns or interesting subsections in the embedding, but cannot easily identify what those patterns correspond to in the original data. Thus, we present SlowMoMan (SLOW Motions on MANifolds), a web application which allows the user to draw a 1-dimensional path onto a 2-dimensional embedding. Then, by back-projecting the manifold to the original, high-dimensional space, we sort the original features such that those most discriminative along the manifold are ranked highly. We show a number of pertinent use cases for our tool, including trajectory inference, spatial transcriptomics, and automatic cell classification. Software availabilityhttps://yunwilliamyu.github.io/SlowMoMan/ Code availabilityhttps://github.com/yunwilliamyu/SlowMoMan

bioinformatics↗