Back

Multi-species genome-wide CRISPR screens identify GPX4 as a conserved suppressor of cold-induced cell death

Lam, B.; Kajderowicz, K. M.; Keys, H. R.; Roessler, J. M.; Frenkel, E. M.; Kirkland, A.; Bisht, P.; El-Brolosy, M. A.; Jaenisch, R.; Bell, G. W.; Weissman, J. S.; Griffith, E. C.; Hrvatin, S.

2024-07-26 cell biology
10.1101/2024.07.25.605098 bioRxiv
Show abstract

Cells must adapt to environmental changes to maintain homeostasis. One of the most striking environmental adaptations is entry into hibernation during which core body temperature can decrease from 37{degrees}C to as low at 4{degrees}C. How mammalian cells, which evolved to optimally function within a narrow range of temperatures, adapt to this profound decrease in temperature remains poorly understood. In this study, we conducted the first genome-scale CRISPR-Cas9 screen in cells derived from Syrian hamster, a facultative hibernator, as well as human cells to investigate the genetic basis of cold tolerance in a hibernator and a non-hibernator in an unbiased manner. Both screens independently revealed glutathione peroxidase 4 (GPX4), a selenium-containing enzyme, and associated proteins as critical for cold tolerance. We utilized genetic and pharmacological approaches to demonstrate that GPX4 is active in the cold and its catalytic activity is required for cold tolerance. Furthermore, we show that the role of GPX4 as a suppressor of cold-induced cell death extends across hibernating species, including 13-lined ground squirrels and greater horseshoe bats, highlighting the evolutionary conservation of this mechanism of cold tolerance. This study identifies GPX4 as a central modulator of mammalian cold tolerance and advances our understanding of the evolved mechanisms by which cells mitigate cold-associated damage--one of the most common challenges faced by cells and organisms in nature.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.