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Mattei, M.

Publications and source records attributed to Mattei, M..

2 recordsLinked to original sources

Frataxin deficiency induces lipid accumulation and affects thermogenesis in brown adipose tissue

Decreased expression of the mitochondrial protein frataxin (FXN) causes Friedreichs ataxia (FRDA). FRDA is a neurodegenerative disease also characterized by systemic metabolic alterations that increase the risk of developing type 2 diabetes thus aggravating FRDA prognosis. Brown adipose tissue (BAT) is a mitochondria-enriched and anti-diabetic tissue that, in addition to its thermoregulatory role, turns excess energy into heat to maintain energy balance. Here we report that the FXN knock-in/knock-out (KIKO) mouse shows reduced energy expenditure and VO2, hyperlipidemia, decreased insulin sensitivity and enhanced circulating levels of leptin, recapitulating diabetes-like signatures. FXN deficiency leads to alteration of mitochondrial structure and oxygen consumption, decreased lipolysis and lipid accumulation in BAT. Transcriptomic data highlighted a blunted thermogenesis response, as several biological processes related to thermogenesis (e.g. response to temperature stimuli, mitochondrial gene transcription, triglyceride metabolism, adipogenesis) resulted affected in BAT of KIKO mice upon cold exposure. Decreased adaptation to cool temperature in association with limited PKA-mediated lipolysis and downregulation of the expression of the genes controlling mitochondrial metabolism and lipid catabolism were observed in KIKO mice. T37i brown adipocytes and primary adipocytes with FXN deficiency showed reduced thermogenesis and adipogenesis markers respectively recapitulating the molecular signatures detected in KIKO mice.\n\nCollectively our data point to BAT dysfunction in FRDA and suggest BAT as a promising target to overcome metabolic complications in FRDA.

pathology

Automated analysis of large-scale NMR data generates metabolomic signatures and links them to candidate metabolites

Identification of metabolites in large-scale 1H NMR data from human biofluids remains challenging due to the complexity of the spectra and their sensitivity to pH and ionic concentrations. In this work, we test the capacity of three analysis tools to extract metabolite signatures from 968 NMR profiles of human urine samples. Specifically, we studied sets of co-varying features derived from Principal Component Analysis (PCA), the Iterative Signature Algorithm (ISA) and Averaged Correlation Profiles (ACP), a new method we devised inspired by the STOCSY approach. We used our previously developed metabomatching method to match the sets generated by these algorithms to NMR spectra of individual metabolites available in public databases. Based on the number and quality of the matches we concluded that both ISA and ACP can robustly identify about a dozen metabolites, half of which were shared, while PCA did not produce any signatures with robust matches.

bioinformatics