OBJECTIVE: Type 3 Familial Partial Lipodystrophy (FPLD3) is a rare metabolic disease related to pathogenic PPARG gene variants. FPLD3 is characterized by a loss of fatty tissue in the upper and lower limbs, hips, and face. FPLD3 pathophysiology is usually associated with metabolic comorbidities such as type 2 diabetes, insulin resistance, hypertriglyceridemia, and liver dysfunction. Here, we clinically and molecularly characterized FPLD3 patients harboring novel PPARG pathogenic variants.
METHODS: Lipodystrophy-suspected patients were recruited by clinicians from an Endocrinology Reference Center. Clinical evaluation was performed, biological samples were collected for biochemical analysis, and DNA sequencing was performed to define the pathogenic variants associated with the lipodystrophic phenotype found in our clinically diagnosed FPLD subjects. Bioinformatics predictions were conducted to characterize the novel mutated PPARγ proteins.
RESULTS: We clinically described FPLD patients harboring two novel heterozygous PPARG variants in Brazil. Case 1 had the c.533T > C variant, which promotes the substitution of leucine to proline in position 178 (p.Leu178Pro), and cases 2 and 3 had the c.641 C > T variant, which results in the substitution of proline to leucine in the position 214 (p.Pro214Leu) at the PPARγ2 protein. These variants result in substantial conformational changes in the PPARγ2 protein.
CONCLUSIONS: Two novel PPARG pathogenic variants related to FPLD3 were identified in a Brazilian FPLD cohort. These data will provide new epidemiologic data concerning FPLD3 and help understand the genotype-phenotype relationships related to the PPARG gene.

Molecular Dynamics (MD) is a computational technique widely used to evaluate a molecular system\'s thermodynamic properties and conformational behavior over time. In particular, the energy analysis of a protein conformation ensemble produced though MD simulations plays a crucial role in explaining the relationship between protein dynamics and its mechanism of action. In this research work, the HINT (Hydropathic INTeractions) LogP-based scoring function was first used to handle MD trajectories and investigate the molecular basis behind the intricate PPARγ mechanism of activation. The Peroxisome Proliferator-Activated Receptor γ (PPARγ) is an emblematic example of a highly flexible protein due to the extended ω-loop delimiting the active site, and it is responsible for the receptor\'s ability to bind chemically different compounds. In this work, we focused on the PPARγ complex with Rosiglitazone, a common anti-diabetic compound and analyzed the molecular basis of the flexible ω-loop stabilization effect produced by the Oleic Acid co-binding. The HINT-based analysis of the produced MD trajectories allowed us to account for all of the energetic contributions involved in interconverting between conformational states and describe the intramolecular interactions between the flexible ω-loop and the helix H3 triggered by the allosteric binding mechanism.

Our studies in primary human adipocytes show that naringenin, a citrus flavonoid, increases oxygen consumption rate and gene expression of uncoupling protein 1 (UCP1), glucose transporter type 4, and carnitine palmitoyltransferase 1β (CPT1β). We investigated the safety of naringenin, its effects on metabolic rate, and blood glucose and insulin responses in a single female subject with diabetes. The subject ingested 150 mg naringenin from an extract of whole oranges standardized to 28% naringenin three times/day for 8 weeks, and maintained her usual food intake. Body weight, resting metabolic rate, respiratory quotient, and blood chemistry panel including glucose, insulin, and safety markers were measured at baseline and after 8 weeks. Adverse events were evaluated every 2 weeks. We also examined the involvement of peroxisome proliferator-activated receptor α (PPARα), peroxisome proliferator-activated receptor γ (PPARγ), protein kinase A (PKA), and protein kinase G (PKG) in the response of human adipocytes to naringenin treatment. Compared to baseline, the body weight decreased by 2.3 kg. The metabolic rate peaked at 3.5% above baseline at 1 h, but there was no change in the respiratory quotient. Compared to baseline, insulin decreased by 18%, but the change in glucose was not clinically significant. Other blood safety markers were within their reference ranges, and there were no adverse events. UCP1 and CPT1β mRNA expression was reduced by inhibitors of PPARα and PPARγ, but there was no effect of PKA or PKG inhibition. We conclude that naringenin supplementation is safe in humans, reduces body weight and insulin resistance, and increases metabolic rate by PPARα and PPARγ activation. The effects of naringenin on energy expenditure and insulin sensitivity warrant investigation in a randomized controlled clinical trial.

The structural and dynamical properties of PPARγ receptor in a complex with either partial or full agonists have been intensively studied but little is known about the receptor antagonistic conformation. A composition of microsecond accelerated molecular dynamics (aMD) simulation show that like partial agonists a non-covalent PPARγ full antagonist can bind in different modes of similar population size and free energies of binding. Four different and periodically exchanging ligand conformations are detected and described. The studied antagonist interacts with different receptor substructures and affects both the co-activator and the Cdk5 phosphorylation sites and, presumably, the natural complex with the DNA. However, no significant changes in the conformational states of the activation helix 12, and in particular an antagonist orientation, have been recorded. Finally, our results show also that the aMD approach can be successfully used in recovering the possible binding modes, considering fully the receptor flexibility, and is not dependent on the starting conformation.

The aim of this paper was to provide a proof of concept demonstrating that molecular modelling methodologies can be employed as a part of an integrated strategy to support toxicity prediction consistent with the mode of action/adverse outcome pathway (MoA/AOP) framework. To illustrate the role of molecular modelling in predictive toxicology, a case study was undertaken in which molecular modelling methodologies were employed to predict the activation of the peroxisome proliferator-activated nuclear receptor γ (PPARγ) as a potential molecular initiating event (MIE) for liver steatosis. A stepwise procedure combining different in silico approaches (virtual screening based on docking and pharmacophore filtering, and molecular field analysis) was developed to screen for PPARγ full agonists and to predict their transactivation activity (EC50). The performance metrics of the classification model to predict PPARγ full agonists were balanced accuracy=81%, sensitivity=85% and specificity=76%. The 3D QSAR model developed to predict EC50 of PPARγ full agonists had the following statistical parameters: q2cv=0.610, Nopt=7, SEPcv=0.505, r2pr=0.552. To support the linkage of PPARγ agonism predictions to prosteatotic potential, molecular modelling was combined with independently performed mechanistic mining of available in vivo toxicity data followed by ToxPrint chemotypes analysis. The approaches investigated demonstrated a potential to predict the MIE, to facilitate the process of MoA/AOP elaboration, to increase the scientific confidence in AOP, and to become a basis for 3D chemotype development.