Reconstitution of human pyroptotic cell death in Saccharomyces cerevisiae

Abstract

Pyroptosis is a lytic form of programmed cell death induced by the activation of gasdermins. The precise mechanism of gasdermin activation by upstream proteases remains incompletely understood. Here, we reconstituted human pyroptotic cell death in yeast by inducible expression of caspases and gasdermins. Functional interactions were reflected by the detection of cleaved gasdermin-D (GSDMD) and gasdermin-E (GSDME), plasma membrane permeabilization, and reduced growth and proliferative potential. Following overexpression of human caspases-1, -4, -5, and -8, GSDMD was cleaved. Similarly, active caspase-3 induced proteolytic cleavage of co-expressed GSDME. Caspase-mediated cleavage of GSDMD or GSDME liberated the ~ 30 kDa cytotoxic N-terminal fragments of these proteins, permeabilized the plasma membrane and compromised yeast growth and proliferation potential. Interestingly, the observation of yeast lethality mediated by co-expression of caspases-1 or -2 with GSDME signified functional cooperation between these proteins in yeast. The small molecule pan-caspase inhibitor Q-VD-OPh reduced caspase-mediated yeast toxicity, allowing us to expand the utility of this yeast model to investigate the activation of gasdermins by caspases that would otherwise be highly lethal to yeast. These yeast biological models provide handy platforms to study pyroptotic cell death and to screen for and characterize potential necroptotic inhibitors.

A molecular view of single-atom catalysis toward carbon dioxide conversion

“…425 On the other hand, the innovative exploitation of computational science and big data has promisingly ignited the revolutionary development of characterization techniques by not only predicting the most probable structure of SACs referring to the existing experimental data 426,427 but also assisting the collection and analysis of data and accordingly increasing the efficiency and accuracy. 428,429 Martini et al provided an example of tracking the evolution of SACs for the CO 2 electrocatalytic reduction using operando XAS and machine learning, while addressing the nature, stability, and evolution of the Ni active sites during the reaction. 430 The combination of unsupervised and supervised machine learning (SML) approaches is shown to be able to decipher the X-ray absorption near edge structure (XANES) of the transition-metal based single-atom sites, decoupling the roles of diversied metal sites coexisting in the working catalyst with quantitative structural information about the local environment of active species and the interplay between metal species and adsorbates, such as CO (Fig.…”