Differential Effects of Cancer-Associated Mutations Enriched in Helix H3 of PPARγ

Enhanced detection of ligand-PPARγ binding based on surface plasmon resonance through complexation with SRC1- or NCOR2-related polypeptide

A previous study reported the use of a biosensing technique based on surface plasmon resonance (SPR) for the ligand binding detection of peroxisome proliferator activator receptor gamma (PPARγ). This detection was designed based on the structural properties of PPARγ. Because of cross-linked protein inactivation and the low molecular weight of conventional ligands, direct ligand binding detection based on SPR has low stability and repeatability. In this study, we report an indirect response methodology based on SPR technology in which anti-His CM5 chip binds fresh PPARγ every cycle, resulting in more stable detection. We developed a remarkable improvement in ligand-protein binding detectability in vitro by introducing two coregulator-related polypeptides into this system. In parallel, a systematic indirect response methodology can reflect the interaction relationship between ligands and proteins to some extent by detecting the changes in SA-SRC1 and GST-NCOR2 binding to PPARγ. Rosiglitazone, a PPARγ agonist with strong affinity, is a potent insulin-sensitizing agent. Some ligands may be competitively exerted at the same sites of PPARγ (binding rosiglitazone). We demonstrated using indirect response methodology that selective PPARγ modulator (SPPARM) candidates of PPARγ can be found by competing for the binding of the rosiglitazone site on PPARγ, although they may have no effect on polypeptides and PPARγ binding.

PPARγ Targets-Derived Diagnostic and Prognostic Index for Papillary Thyroid Cancer

Simple Summary

Through targeted next-generation sequencing of thyroid cancer-related genes in monozygotic twins with papillary thyroid cancer (PTC), we identified common variants of the gene encoding peroxisome proliferator activated receptor gamma (PPARG). Notably, the expression levels of PPARγ target genes were frequently deregulated in PTC compared to benign tissues and were closely associated with disease-specific survival (DSS) outcomes in a TCGA-PTC cohort. Machine learning-powered personalized scoring index comprising 10 PPARγ targets, termed as PPARGi, achieved a near-perfect accuracy in distinguishing cancers from benign tissues, and further identified a small subpopulation of patients at high-risk across different profiling platforms.

Abstract

In most cases, papillary thyroid cancer (PTC) is highly curable and associated with an excellent prognosis. Yet, there are several clinicopathological features that lead to a poor prognosis, underscoring the need for a better genomic strategy to refine prognostication and patient management. We hypothesized that PPARγ targets could be potential markers for better diagnosis and prognosis due to the variants found in PPARG in three pairs of monozygotic twins with PTC. Here, we developed a 10-gene personalized prognostic index, designated PPARGi, based on gene expression of 10 PPARγ targets. Through scRNA-seq data analysis of PTC tissues derived from patients, we found that PPARGi genes were predominantly expressed in macrophages and epithelial cells. Machine learning algorithms showed a near-perfect performance of PPARGi in deciding the presence of the disease and in selecting a small subset of patients with poor disease-specific survival in TCGA-THCA and newly developed merged microarray data (MMD) consisting exclusively of thyroid cancers and normal tissues.

Structural Insights into the Loss-of-Function R288H Mutant of Human PPARγ

Peroxisome proliferator-activated receptor gamma (PPARγ) is a nuclear receptor and the molecular target of thiazolidinedione-class antidiabetic drugs. It has been reported that the loss of function R288H mutation in the human PPARγ ligand-binding domain (LBD) may be associated with the onset of colon cancer. A previous in vitro study showed that this mutation dampens 15-deoxy-Δ^12,14-prostaglandin J2 (15d-PGJ2, a natural PPARγ agonist)-dependent transcriptional activation; however, it is poorly understood why the function of the R288H mutant is impaired and what role this arginine (Arg) residue plays. In this study, we found that the apo-form of R288H PPARγ mutant displays several altered conformational arrangements of the amino acid side chains in LBD: 1) the loss of a salt bridge between Arg288 and Glu295 leads to increased helix 3 movement; 2) closer proximity of Gln286 and His449 via a hydrogen bond, and closer proximity of Cys285 and Phe363 via hydrophobic interaction, stabilize the helix 3-helix 11 interaction; and 3) there is steric hindrance between Cys285/Gln286/Ser289/His449 and the flexible ligands 15d-PGJ2, 6-oxotetracosahexaenoic acid (6-oxoTHA), and 17-oxodocosahexaenoic acid (17-oxoDHA). These results suggest why Arg288 plays an important role in ligand binding and why the R288H mutation is disadvantageous for flexible ligand binding.

PPARs as Metabolic Sensors and Therapeutic Targets in Liver Diseases

Carbohydrates and lipids are two components of the diet that provide the necessary energy to carry out various physiological processes to help maintain homeostasis in the body. However, when the metabolism of both biomolecules is altered, development of various liver diseases takes place; such as metabolic-associated fatty liver diseases (MAFLD), hepatitis B and C virus infections, alcoholic liver disease (ALD), and in more severe cases, hepatocelular carcinoma (HCC). On the other hand, PPARs are a family of ligand-dependent transcription factors with an important role in the regulation of metabolic processes to hepatic level as well as in other organs. After interaction with specific ligands, PPARs are translocated to the nucleus, undergoing structural changes to regulate gene transcription involved in lipid metabolism, adipogenesis, inflammation and metabolic homeostasis. This review aims to provide updated data about PPARs’ critical role in liver metabolic regulation, and their involvement triggering the genesis of several liver diseases. Information is provided about their molecular characteristics, cell signal pathways, and the main pharmacological therapies that modulate their function, currently engaged in the clinic scenario, or in pharmacological development.