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Vettor, R.

Publications and source records attributed to Vettor, R..

2 recordsLinked to original sources

CD300e Controls Macrophage Lipid Handling and Safeguards Adipose Tissue Function in Obesity

Previous studies in monozygotic twins discordant for body mass index (BMI) revealed that obese individuals exhibit elevated expression of the immune receptor CD300e in white adipose tissue (WAT). Notably, CD300e levels decreased following weight loss, implicating its involvement in adipose tissue remodeling and metabolic regulation. To elucidate the functional role of CD300e, we employed a Cd300e knockout (Cd300e-/-) mouse model subjected to a high-fat diet (HFD). Our findings demonstrate that CD300e deficiency exacerbates obesity-associated metabolic dysfunction, including increased weight gain, adipocyte hypertrophy, hepatic steatosis, and impaired glucose and insulin sensitivity. Adipose tissue macrophages (ATMs) lacking CD300e displayed reduced lipid and glucose uptake, alongside diminished mitochondrial respiration--a phenotype consistent with a broader metabolic impairment, as evidenced by proteomic profiling. These metabolic deficits were genotype-dependent and persisted after 16 weeks of HFD. Concurrently, adipocytes from Cd300e-/- mice exhibited enhanced lipogenesis and attenuated lipolysis. Remarkably, the impaired metabolic fitness exhibited by Cd300e-/- mouse macrophages was recapitulated in human cells upon gene silencing. Collectively, these results establish CD300e as essential for ATM metabolic activation, positioning it as a key regulator of adipose tissue homeostasis and a critical mediator of obesity-induced metabolic dysfunction. Given its pivotal role, CD300e emerges as a promising therapeutic target for modulating adipose tissue function and improving metabolic health in obesity.

immunology↗

In silico prediction of blood cholesterol levels from genotype data

In this work we present a framework for blood cholesterol levels prediction from genotype data. The predictor is based on an algorithm for cholesterol metabolism simulation available in literature, implemented and optimized by our group in R language. Main weakness of the former simulation algorithm was the need of experimental data to simulate mutations in genes altering the cholesterol metabolism. This caveat strongly limited the application of the model in the clinical practice. In this work we present how this limitation could be bypassed thanks to an optimization of model parameters based on patients cholesterol levels retrieved from literature. Prediction performance has been assessed taking in consideration several scoring indices currently used for performance evaluation of machine learning methods. Our assessment shows how the optimization phase improved model performance, compared to the original version available in literature.

systems biology↗