Data-driven identification of plasma metabolite clusters and metabolites of interest for potential detection of early-stage non-small cell lung cancer cases versus cancer-free controls

NLRP1 inflammasome promotes senescence and senescence-associated secretory phenotype

BACKGROUND

Senescence is a cellular aging-related process triggered by different stresses and characterized by the secretion of various inflammatory factors referred to as senescence-associated secretory phenotype (SASP), some of which are produced by the NLRP3 inflammasome. Here, we present evidence that the NLRP1 inflammasome is a DNA damage sensor and a key mediator of senescence.

METHODS

Senescence was induced in fibroblasts in vitro and in mice. Cellular senescence was assessed by Western blot analysis of several proteins, including p16, p21, p53, and SASP factors, released in the culture media or serum. Inflammasome components, including NLRP1, NLRP3 and GSDMD were knocked out or silenced using siRNAs.

RESULTS

In vitro and in vivo results suggest that the NLRP1 inflammasome promotes senescence by regulating the expression of p16, p21, p53, and SASP factors in a Gasdermin D (GSDMD)-dependent manner. Mechanistically, the NLRP1 inflammasome is activated in response to genomic damage detected by the cytosolic DNA sensor cGMP-AMP (cGAMP) synthase (cGAS).

CONCLUSION

Our findings show that NLRP1 is a cGAS-dependent DNA damage sensor during senescence and a mediator of SASP release through GSDMD. This study advances the knowledge on the biology of the NLRP1 inflammasome and highlights this pathway as a potential pharmcological target to modulate senescence.

Lung cancer progression alters lung and gut microbiomes and lipid metabolism

Despite advances in medical technology, lung cancer still has one of the highest mortality rates among all malignancies. Therefore, efforts must be made to understand the precise mechanisms underlying lung cancer development. In this study, we conducted lung and gut microbiome analyses and a comprehensive lipid metabolome analysis of host tissues to assess their correlation. Alternations in the lung microbiome due to lung cancer, such as a significantly decreased abundance of Firmicutes and Deferribacterota, were observed compared to a mock group. However, mice with lung cancer had significantly lower relative abundances of Actinobacteria and Proteobacteria and higher relative abundances of Cyanobacteria and Patescibacteria in the gut microbiome. The activations of retinol, fatty acid metabolism, and linoleic acid metabolism metabolic pathways in the lung and gut microbiomes was inversely correlated. Additionally, changes occurred in lipid metabolites not only in the lungs but also in the blood, small intestine, and colon. Compared to the mock group, mice with lung cancer showed that the levels of adrenic, palmitic, stearic, and oleic (a ω-9 polyunsaturated fatty acid) acids increased in the lungs. Conversely, these metabolites consistently decreased in the blood (serum) and colon. Leukotriene B4 and prostaglandin E2 exacerbate lung cancer, and were upregulated in the lungs of the mice with lung cancer. However, isohumulone, a peroxisome proliferator-activated receptor gamma activator, and resolvin (an ω-3 polyunsaturated fatty acid) both have anti-cancer effects, and were upregulated in the small intestine and colon. Our multi-omics data revealed that shifts in the microbiome and metabolome occur during the development of lung cancer and are of possible clinical importance. These results reveal one of the gut-lung axis mechanisms related to lung cancer and provide insights into potential new targets for lung cancer treatment and prophylaxis.

Targeted metabolomic profiling as a tool for diagnostics of patients with non-small-cell lung cancer

Lung cancer is referred to as the second most common cancer worldwide and is mainly associated with complex diagnostics and the absence of personalized therapy. Metabolomics may provide significant insights into the improvement of lung cancer diagnostics through identification of the specific biomarkers or biomarker panels that characterize the pathological state of the patient. We performed targeted metabolomic profiling of plasma samples from individuals with non-small cell lung cancer (NSLC, n = 100) and individuals without any cancer or chronic pathologies (n = 100) to identify the relationship between plasma endogenous metabolites and NSLC by means of modern comprehensive bioinformatics tools, including univariate analysis, multivariate analysis, partial correlation network analysis and machine learning. Through the comparison of metabolomic profiles of patients with NSCLC and noncancer individuals, we identified significant alterations in the concentration levels of metabolites mainly related to tryptophan metabolism, the TCA cycle, the urea cycle and lipid metabolism. Additionally, partial correlation network analysis revealed new ratios of the metabolites that significantly distinguished the considered groups of participants. Using the identified significantly altered metabolites and their ratios, we developed a machine learning classification model with an ROC AUC value equal to 0.96. The developed machine learning lung cancer model may serve as a prototype of the approach for the in-time diagnostics of lung cancer that in the future may be introduced in routine clinical use. Overall, we have demonstrated that the combination of metabolomics and up-to-date bioinformatics can be used as a potential tool for proper diagnostics of patients with NSCLC.

NLRP1 inflammasome modulates senescence and senescence-associated secretory phenotype

Senescence is a cellular aging-related process triggered by different stresses and characterized by the secretion of various inflammatory factors referred to as the senescence-associated secretory phenotype (SASP). Here, we present evidence that the inflammasome sensor, NLRP1, is a key mediator of senescence induced by irradiation both in vitro and in vivo. The NLRP1 inflammasome promotes senescence by regulating the expression of p16, p21, p53, and SASP in Gasdermin D (GSDMD)-dependent manner as these responses are reduced in conditions of NLRP1 insufficiency or GSDMD inhibition. Mechanistically, the NLRP1 inflammasome is activated downstream of the cytosolic DNA sensor cGMP-AMP (cGAMP) synthase (cGAS) in response to genomic damage. These findings provide a rationale for inhibiting the NLRP1 inflammasome-GSDMD axis to treat senescence-driven disorders.