Ribosomal S6 kinase 1 regulates inflammaging via the senescence secretome.

Inhibition of S6 kinase 1 (S6K1) extends lifespan and improves healthspan in mice, but the underlying mechanisms are unclear. Cellular senescence is a stable growth arrest accompanied by an inflammatory senescence-associated secretory phenotype (SASP). Cellular senescence and SASP-mediated chronic inflammation contribute to age-related pathology, but the specific role of S6K1 has not been determined. Here we show that S6K1 deletion does not reduce senescence but ameliorates inflammation in aged mouse livers. Using human and mouse models of senescence, we demonstrate that reduced inflammation is a liver-intrinsic effect associated with S6K deletion. Specifically, we show that S6K1 deletion results in reduced IRF3 activation; impaired production of cytokines, such as IL1ß; and reduced immune infiltration. Using either liver-specific or myeloid-specific S6K knockout mice, we also demonstrate that reduced immune infiltration and clearance of senescent cells is a hepatocyte-intrinsic phenomenon. Overall, deletion of S6K reduces inflammation in the liver, suggesting that suppression of the inflammatory SASP by loss of S6K could underlie the beneficial effects of inhibiting this pathway on healthspan and lifespan.

Detection of senescence using machine learning algorithms based on nuclear features.

Cellular senescence is a stress response with broad pathophysiological implications. Senotherapies can induce senescence to treat cancer or eliminate senescent cells to ameliorate ageing and age-related pathologies. However, the success of senotherapies is limited by the lack of reliable ways to identify senescence. Here, we use nuclear morphology features of senescent cells to devise machine-learning classifiers that accurately predict senescence induced by diverse stressors in different cell types and tissues. As a proof-of-principle, we use these senescence classifiers to characterise senolytics and to screen for drugs that selectively induce senescence in cancer cells but not normal cells. Moreover, a tissue senescence score served to assess the efficacy of senolytic drugs and identified senescence in mouse models of liver cancer initiation, ageing, and fibrosis, and in patients with fatty liver disease. Thus, senescence classifiers can help to detect pathophysiological senescence and to discover and validate potential senotherapies.

COPI vesicle formation and N-myristoylation are targetable vulnerabilities of senescent cells.

Drugs that selectively kill senescent cells (senolytics) improve the outcomes of cancer, fibrosis and age-related diseases. Despite their potential, our knowledge of the molecular pathways that affect the survival of senescent cells is limited. To discover senolytic targets, we performed RNAi screens and identified coatomer complex I (COPI) vesicle formation as a liability of senescent cells. Genetic or pharmacological inhibition of COPI results in Golgi dispersal, dysfunctional autophagy, and unfolded protein response-dependent apoptosis of senescent cells, and knockdown of COPI subunits improves the outcomes of cancer and fibrosis in mouse models. Drugs targeting COPI have poor pharmacological properties, but we find that N-myristoyltransferase inhibitors (NMTi) phenocopy COPI inhibition and are potent senolytics. NMTi selectively eliminated senescent cells and improved outcomes in models of cancer and non-alcoholic steatohepatitis. Our results suggest that senescent cells rely on a hyperactive secretory apparatus and that inhibiting trafficking kills senescent cells with the potential to treat various senescence-associated diseases.