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Fantuzzi, F.

Publications and source records attributed to Fantuzzi, F..

2 recordsLinked to original sources

A novel cell indirect calorimetry method unveils the metabolic fluxomic signatures of human monocyte derived M(LPS+INF-γ) and M(IL-4) macrophages

Macrophages (M{Phi}) display distinct immunometabolic phenotypes upon polarization. While transcriptomic analyses have suggested divergent metabolic programs in human M(LPS+INF-{gamma}) and M(IL-4) M{Phi}, a comprehensive assessment of their metabolic fluxes is lacking. Aim of this study is to 1. develop and validate a novel indirect microcalorimetry method for quantifying cellular metabolic fluxes, and 2. exploit it to characterize fluxomic signatures of polarized human monocyte-derived macrophages. MethodsM{Phi} from healthy donors were differentiated into M0, M(LPS+INF-{gamma}), and M(IL-4) phenotypes and studied in four defined media: substrate-free, glucose, glycyl-glutamine, and glucose + glycyl-glutamine. A steady-state fluxomic model was constructed by integrating four independent measures - oxygen consumption and proton production (Seahorse XFp), lactate and ammonia release (microfluorimetry) - into stoichiometric equations of metabolism (SAAM II software). ResultsFluxes revealed that macrophages rely on glucose to sustain glycolysis, contributing [~]30% of citrate synthase flux, and predominantly on lipids for net citrate synthesis (first step of Krebs cycle). Upon polarization, M(LPS+INF-{gamma}) macrophages showed increased anaerobic glycolysis versus M0 and M(IL-4), with similar TCA fluxes to M0. In contrast, M(IL-4) macrophages exhibited higher TCA and malic enzyme fluxes, especially with glucose and glycyl-glutamine, and a trend toward enhanced lipid oxidation. ConclusionsThis novel method enables precise quantification of bioenergetic fluxes. In human M{Phi}, it reveals that M(LPS+INF-{gamma}) and M(IL-4) subsets exhibit distinct metabolic phenotypes, consistent with their immunological roles. These results resolve transcriptomic-metabolic discrepancies and provide a robust framework for assessing immunometabolism in primary human cells.

physiology↗

A functional genomic approach to identify reference genes for human pancreatic beta cell real-time quantitative RT-PCR analysis

Exposure of human pancreatic beta cells to pro-inflammatory cytokines or metabolic stressors is used to model events related to type 1 and type 2 diabetes, respectively. Quantitative real-time PCR is commonly used to quantify changes in gene expression. The selection of the most adequate reference gene(s) for gene expression normalization is an important pre-requisite to obtain accurate and reliable results. There are no universally applicable reference genes, and the human beta cell expression of commonly used reference genes can be altered by different stressors. Here we aimed to identify the most stably expressed genes in human beta cells to normalize quantitative real-time PCR gene expression. We used comprehensive RNA-sequencing data from the human pancreatic beta cell line EndoC-{beta}H1, human islets exposed to cytokines or the free fatty acid palmitate in order to identify the most stably expressed genes. Genes were filtered based on their level of significance (adjusted P-value >0.05), fold-change (|fold-change| <1.5) and a coefficient of variation <10%. Candidate reference genes were validated by quantitative real-time PCR in independent samples. We identified a total of 264 genes stably expressed in EndoC-{beta}H1 cells and human islets following cytokine- or palmitate-induced stress, displaying a low coefficient of variation. Validation by quantitative real-time PCR of the top five genes ARF1, CWC15, RAB7A, SIAH1 and VAPA corroborated their expression stability under most of the tested conditions. Further validation in independent samples indicated that the geometric mean of ACTB and VAPA expression can be used as a reliable normalizing factor in human beta cells.

systems biology↗