bioRxivImmunologyPreprint
CRY-NLRP3 complexes define a circadian checkpoint controlling inflammasome activation
Léa BardouletiD, Delphine Burlet, Hubert B. LeloupiD, François VirardiD, Sabine Hacot, Elsa Gratianne GuillotiD, Nouhaila El Kasmi, Camille CossoniD, Lyvia Moudombi, Marie‐Cécile MichalletiD, Arnaud Bonnafoux, Melina Gautier, Agnès TissieriD, Katja A LamiaiD, Bénédicte F. PyiD, Virginie PétrilliiD, Anne‐Laure HuberiD
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Abstract
Innate immune sensors such as the NLRP3 inflammasome can trigger inflammatory responses within minutes, raising the question of how circadian clocks influence such rapid decisions. Here, we identify a protein-level circadian checkpoint that links core clock components to NLRP3 inflammasome activation. We show that NLRP3 associates with the circadian repressors CRY1 and CRY2, forming oscillatory complexes that restrain inflammasome activation and rapidly dissociate upon stimulation. Pharmacological stabilization of CRY proteins preserves CRY-NLRP3 association and attenuates inflammasome assembly, IL-1{beta} secretion and pyroptotic cell death in primary human macrophages. In synchronized macrophages, both NLRP3 inflammasome activation and its inhibition by the NLRP3 inhibitor MCC950 vary with circadian time. Finally, a subset of NLRP3 variants reported in cohorts of patients with Cryopyrin-Associated Periodic Syndromes (CAPS), a group of hereditary fever syndromes caused by mutations in NLRP3, weaken CRY binding and are associated with altered time-of-day patterns of inflammasome activation and MCC950 responsiveness. Together, these findings define CRY-NLRP3 complexes as a circadian checkpoint that modulates inflammasome activity and drug response, revealing time of day as a critical dimension of NLRP3-driven inflammation.
- organism: human
- assay: proteomics
- assay: metagenomics
- research focus: host-pathogen & immune response
- research focus: cell type annotation
- research focus: variant effect prediction
- approach: experimental assay
- approach: genetic perturbation / CRISPR
- approach: statistical model
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