Biology subjects

Gilardi, F.

Publications and source records attributed to Gilardi, F..

4 recordsLinked to original sources

bioRxiv2026

Metabolomics identifies endocannabinoid system remodeling during obesity associated inflammation in adipose tissue

Visceral white adipose tissue (vAT) is more susceptible than subcutaneous white adipose tissue (scAT) to obesity-associated metaflammation, but the metabolic pathways accompanying this depot specific transition remain incompletely defined. We profiled vAT and scAT from mice fed a control diet or a 60% kcal high-fat diet (HFD) for 1 or 8 weeks. Untargeted metabolomics was integrated with targeted endocannabinoid quantification, gene expression and promoter associated chromatin analyses, adipocyte and stromal vascular fraction (SVF) measurements, cross-omics correlation analysis, and cell-culture perturbations. After 1 week, HFD induced relatively few metabolic changes, although vAT showed early alterations associated with sphingomyelin metabolism. By week 8, both depots exhibited extensive but distinct metabolic remodeling. The KEGG retrograde endocannabinoid-signaling pathway was the highest-ranked enriched pathway in vAT, with arachidonoylglycerol (2AG) among the most strongly altered metabolites. Targeted analysis confirmed selective 2AG accumulation in vAT, accompanied by coordinated changes in genes and promoter-associated chromatin marks related to endocannabinoid synthesis, degradation and receptor expression. Cellular fractionation localized excess 2AG predominantly to vAT adipocytes, whereas Cnr2 was enriched in the SVF and induced by HFD specifically in vAT. Cross omics integration further revealed a denser and predominantly inverse transcript metabolite association network in vAT than in scAT. Using macrophage adipocyte co-culture and secretome profiling, we demonstrated that cannabinoid stimulation shifts activated macrophage outputs away from pro-inflammatory cytokines (Il1b, Ccl2) toward extracellular matrix (ECM) proteoglycan remodeling and intercellular signaling.

molecular biology↗

bioRxiv2024

Activity based proteome profiling of serum serinehydrolases: application in pediatric abusive head trauma

PurposeTraumatic brain injury (TBI), including pediatric abusive head trauma (AHT), is the leading cause of death and disability in children and young adults worldwide. The current understanding of trauma-induced molecular changes in the brain of human subjects with intracranial haemorrhage (ICH) remains inadequate and requires further investigation to improve the outcome and management of TBI in the clinic. Calcium-mediated damage at the site of brain injury has been shown to activate several catalytic enzymes. Experimental designSerine hydrolases (SHs) are major catalytic enzymes involved in the biochemical pathways of blood coagulation, systemic inflammation and neuronal signaling. Here we investigated activity-based protein profiling (ABPP) by measuring the activity status of SH enzymes in the serum of infants with severe ICH as a consequence of AHT or atraumatic infants who died of sudden infant death syndrome (SIDS). ResultsOur proof-of-principle study revealed significantly reduced physiological activity of dozens of metabolic SHs in the serum of infants with severe AHT compared to the SIDS group, with some of the enzymes being related to neurodevelopment and basic brain metabolism. Conclusions and clinical relevanceTo our knowledge, this is the first study to investigate the ABPP of the SHs enzyme family to detect changes in their physiological activity in blood serum in severe TBI. We used antemortem (AM) serum from infants under the age of 2 years who were victims of AHT with a severe form of ICH. The analytical approach used in the proof-of-principle study shows reduced activities of serum serine lipases in AHT cases and could be further investigated in mild forms of AHT, which currently show 30% of misdiagnosed cases in clinics.

neuroscience↗

bioRxiv2020

Adipose-tissue derived signals control bone remodelling.

Long bones from mammals host blood cell formation and contain multiple cell types, including adipocytes. Physiological functions of bone marrow adipocytes are poorly documented. Herein, we used adipocyte-deficient PPAR{gamma}-whole body null mice to investigate the consequence of total adipocyte deficiency on bone homeostasis in mice. We first highlight the dual bone phenotype of PPAR{gamma} null mice: on the one hand the increase bone formation and subsequent trabecularization extending in the long bone diaphysis, due to the well-known impact of PPAR{gamma} deficiency on osteoblasts formation and activity; on the other hand, an increased osteoclastogenesis in the cortical bone. We then further explore the cause of this unexpected increased osteoclastogenesis using two independent models of lipoatrophy, which recapitulated this phenotype. This demonstrates that hyperosteoclastogenesis is not intrinsically linked to PPAR{gamma} deficiency, but is a consequence of the total lipodystrophy. We further showed that adiponectin, a cytokine produced by adipocytes and mesenchymal stromal cells is a potent inhibitor of osteoclastogenesis in vitro and in vivo. Moreover, pharmacological activation of adiponectin receptors by the synthetic agonist AdipoRon inhibits mature osteoclast activity both in mouse and human cells by blocking podosome formation through AMPK activation. Finally, we demonstrated that AdipoRon treatment blocks bone erosion in vivo in a murine model of inflammatory bone loss, providing potential new approaches to treat osteoporosis.

cell biology↗

bioRxiv2020

Visualization and normalization of drift effect across batches in metabolome-wide association studies

As a powerful phenotyping technology, metabolomics provides new opportunities in biomarker discovery through metabolome-wide association studies (MWAS) and identification of metabolites having regulatory effect in various biological processes. While MS-based metabolomics assays are endowed with high-throughput and sensitivity, large-scale MWAS are doomed to long-term data acquisition generating an overtime-analytical signal drift that can hinder the uncovering of true biologically relevant changes. We developed "dbnorm", a package in R environment, which allows visualization and removal of signal heterogeneity from large metabolomics datasets. "dbnorm" integrates advanced statistical tools to inspect dataset structure, at both macroscopic (sample batch) and microscopic (metabolic features) scales. To compare model performance on data correction, "dbnorm" assigns a score, which allows the straightforward identification of the best fitting model for each dataset. Herein, we show how "dbnorm" efficiently removes signal drift among batches to capture the true biological heterogeneity of data in two large-scale metabolomics studies.

bioinformatics↗