Academic literature on the topic 'Scl25015'

Abstract The NLRP3 protein is a key initiator of inflammation in humans. NLRP3 becomes activated by a multitude of danger signals, including pathogens, markers of metabolic dysfunction, and internalized particulates. Upon activation, NLRP3 nucleates formation of a multiprotein complex called the inflammasome, in which caspase-1 activity mediates processing of the pro-inflammatory cytokines IL-1β and IL-18 and induces pyroptosis, a pro-inflammatory form of cell death that serves to release the mature cytokines along with other inflammatory mediators, thus initiating the inflammatory response. While multiple regulators of the NLRP3 inflammasome have been described, specific ligand(s) of NLRP3 and its mechanism of activation remain largely unknown. We performed a proteomics screen using co-immunoprecipitation (IP) and mass spectrometry to identify cellular proteins that bind NLRP3. Using this screen, we identified multiple NLRP3 interactors, including a family of solute carrier (SLC) proteins that localize to the inner mitochondrial membrane. Co-IP experiments verified that four of these proteins, namely SLC25A1, a citrate transporter, SLC25A6, an ADP/ATP translocase, SLC25A11, a 2-oxoglutarate/malate carrier, and SLC25A13, a calcium-dependent glutamate transporter, specifically interact with NLRP3. Because of its linkage with known metabolic disease, we further assessed the role of SCL25A13 in inflammasome function. When ectopically expressed, SLC25A13 enhanced IL-1β release from cells, while CRISPR/Cas9 knockout of SLC25A13 from THP-1 macrophages partially abrogated NLRP3-dependent pyroptosis. Our studies reveal a novel role for mitochondrial SLC25 proteins in regulation of the NLRP3 inflammasome.

Abstract Emerging evidence indicates that chemoresistance is closely related to altered metabolism in cancer. Here, we hypothesized that distinct metabolic gene expression profiling (GEP) signatures might be correlated with outcome and with specific fluorodeoxyglucose positron emission tomography (FDG-PET) radiomic profiles in diffuse large B-cell lymphoma (DLBCL). We retrospectively analyzed a discovery cohort of 48 consecutive patients with DLBCL treated at our center with standard first-line chemoimmunotherapy by performing targeted GEP (T-GEP)– and FDG-PET radiomic analyses on the same target lesions at baseline. T-GEP–based metabolic profiling identified a 6-gene signature independently associated with outcomes in univariate and multivariate analyses. This signature included genes regulating mitochondrial oxidative metabolism (SCL25A1, PDK4, PDPR) that were upregulated and was inversely associated with genes involved in hypoxia and glycolysis (MAP2K1, HIF1A, GBE1) that were downregulated. These data were validated in 2 large publicly available cohorts. By integrating FDG-PET radiomics and T-GEP, we identified a radiometabolic signature (RadSig) including 4 radiomic features (histo kurtosis, histo energy, shape sphericity, and neighboring gray level dependence matrix contrast), significantly associated with the metabolic GEP–based signature (r = 0.43, P = .0027) and with progression-free survival (P = .028). These results were confirmed using different target lesions, an alternative segmentation method, and were validated in an independent cohort of 64 patients. RadSig retained independent prognostic value in relation to the International Prognostic Index score and metabolic tumor volume (MTV). Integration of RadSig and MTV further refined prognostic stratification. This study provides the proof of principle for the use of FDG-PET radiomics as a tool for noninvasive assessment of cancer metabolism and prognostic stratification in DLBCL.

Purpose. Skin cutaneous melanoma (SKCM) is one of the most malignant and aggressive cancers with poor prognosis due to its rapid progression towards metastasis. Thus, finding clinically relevant biomarkers for early diagnosis, prognosis, and therapy prediction is essential. This study focused on the identification of SLC25A13 as a novel biomarker for SKCM and is aimed at investigating the biological functions of solute carrier family 25 member 13 (SLC25A13) in the development of SKCM. Methods. GEPIA was used to analyze the diagnostic and prognostic values of SLC25A13 in SKCM using the TCGA dataset. PrognoScan was used to validate the prognostic value of SLC25A13 and its coexpressed genes in SKCM. TISIDB was established to reveal the relationship between the expression of SLC25A13 and immune infiltration in SKCM. The protein expression of SLC25A13 in SKCM was evaluated by the Human Protein Atlas. The signaling pathways and biological functions of SLC25A13 in SKCM were analyzed by LinkOmics. Metascape was applied to analyze the functional enrichment analysis of SLC25A13. Protein-protein interaction analysis of SLC25A13 was performed by GeneMANIA. Results. The mRNA and protein levels of SLC25A13 in the SKCM were much higher than those in the normal tissue. Furthermore, the overexpression of SLC25A13 predicts worse outcomes of SKCM patients. Moreover, the SLC25A13 expression was negatively correlated with the immune infiltration level of SKCM. The overexpression of SLC25A13 coexpressed genes, such as ACLY and AFG3L2, and SCL25A13 interacting genes also predicted the unfavorable prognosis of SKCM patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of SLC25A13 coexpressed genes showed that these genes are enriched in ATPase activity, cell cycle, mTOR, and VEGFA-VEGFR2 signaling pathways, which were relevant to tumor development and angiogenesis. Gene set enrichment analysis (GSEA) demonstrated that the SLC25A13 expression was related to infiltrating immune cells in SKCM. Conclusion. Our findings revealed that SLC25A13 might be a potential prognostic and therapeutic biomarker for SKCM.

Abstract Metabolic rewiring is a hallmark of cancer and a predominant feature of aggressive lymphoproliferative disorders such as diffuse large B-cell lymphomas (DLBCL), which need a reshaped metabolism in order to meet the increased demands related to rapid cell proliferation. Emerging evidence indicates that chemoresistance is closely related to altered metabolism in cancer. However, the relationship between metabolic rewiring and chemoresistance in lymphoma is yet to be elucidated. Radiomic analysis applied to functional imaging with fluoroedoxyglucose positron emission tomography (FDG-PET) provides a unique opportunity to explore DLBCL metabolism. In this study we hypothesized that distinct gene expression (GEP) signatures might be correlated with specific FDG-PET radiomics signatures, which in turn could be associated with resistance to standard chemoimmunotherapy and DLBCL outcome. First, we retrospectively analyzed a discovery cohort of 48 consecutive DLBCL patients (pts) treated at our center with standard first line R-CHOP/R-CHOP-like chemoimmunotherapy from 2010 to 2018, with available formalin-fixed paraffin embedded (FFPE) tissue from the initial diagnostic biopsy and FDG-PET radiomics data extracted from the same target lesion. Median follow-up was 55 months (range 18-110). We profiled this cohort with targeted-GEP (T-GEP) (NanoString platform), using a custom panel to define the cell of origin (COO) and MYC/BCL-2 levels, and a dedicated panel comprising 180 genes encompassing the most relevant cancer metabolism pathways. By applying the maxstat package we found that a 6-gene metabolic signature was strongly associated with outcome and outperformed the COO, the MYC/BCL-2 status and the International Prognostic Index (IPI) score for progression free survival (PFS) and overall survival (OS) in multivariate analysis. The 6-gene metabolic signature included genes regulating oxidative metabolism and fatty acid oxidation (SCL25A1, PDK4, PDPR) which were upregulated, and was inversely associated with genes involved in glycolytic pathways (MAP2K1, HIF1A, GBE1) which were downregulated. Notably 5-year PFS and OS were 100% and 95% in metabolic signature (met-Sig) low pts vs 24% and 45% in met-Sig high pts respectively (p<0.0001 for PFS and OS). There was no significant association between the COO, MYC/BCL-2 levels, standardized uptake value (SUV), and the 6-gene signature. The prognostic value of the 6-gene signature for OS was validated in 2 large publicly available cohorts of 469 (Sha et al. J Clin Oncol 2019) and 233 (Lenz et al. N Eng J Med 2005) pts. Next, we integrated PET radiomics and T-GEP data. Radiomics analysis (LifeX package) was performed by applying regions of interest semi-automatically, using a 25% SUV max threshold for segmentation. Fifty-five radiomic features (RFs) were extracted and 10 RFs significantly correlated either positively or negatively with the T-GEP metabolic signature (Spearman). After stability evaluation, applying a stepwise feature selection procedure, 4 RFs (Histo Curtosis, Histo Energy, Shape Sphericity, NGLDM Contrast) were used to generate a radiomic signature (hereafter called radiometabolic signature) characterized by the most significant correlation with both the metabolic T-GEP signature (r=0.43, p=0.0027) and PFS (p=0.004). These results (obtained analyzing the lesion of the initial diagnostic biopsy), were confirmed using different target lesions (i.e. the most FDG-avid and the largest lesion), and were validated in a second independent cohort of 64 patients (validation cohort) treated at our center in the same period of time (with no FFPE tissue available). A multivariate analysis performed in the whole cohort of 112 pts (discovery + validation) indicated that the radiometabolic signature retained independent prognostic value in relation to the IPI score and metabolic tumor volume. The robustness of the radiometabolic signature was further confirmed by using a second segmentation method (fixed 2.5 SUV max threshold). These data indicate that oxidative metabolic rewiring could be a powerful adverse prognostic predictor, suggesting the possibility of targeting oxidative metabolism to overcome chemorefractoriness in DLBCL. This study provides the proof of principle for the use of FDG-PET radiomics as a tool for non-invasive assessment of cancer metabolism, and for predicting metabolic vulnerabilities in DLBCL. Figure 1 Figure 1. Disclosures Tarella: ADC-THERAPEUTICS: Other: ADVISORY BOARD; Abbvie: Other: ADVISORY BOARD. Pileri: CELGENE: Other: ADVISORY BOARD; ROCHE: Other: ADVISORY-BOARD; NANOSTRING: Other: ADVISORY BOARD. Derenzini: TAKEDA: Research Funding; BEIGENE: Other: ADVISORY BOARD; ASTRA-ZENECA: Consultancy, Other: ADVISORY-BOARD; TG-THERAPEUTICS: Research Funding; ADC-THERAPEUTICS: Research Funding.