The nuclear hormone receptor peroxisome proliferator-activated receptor ␥ (PPAR␥) plays central roles in adipogenesis and glucose homeostasis and is the molecular target for the thiazolidinedione (TZD) class of antidiabetic drugs. Activation of PPAR␥ by TZDs improves insulin sensitivity; however, this is accompanied by the induction of several undesirable side effects. We have identified a novel synthetic PPAR␥ ligand, T2384, to explore the biological activities associated with occupying different regions of the receptor ligand-binding pocket. X-ray crystallography studies revealed that T2384 can adopt two distinct binding modes, which we have termed "U" and "S", interacting with the ligand-binding pocket of PPAR␥ primarily via hydrophobic contacts that are distinct from full agonists. The different binding modes occupied by T2384 induced distinct patterns of coregulatory protein interaction with PPAR␥ in vitro and displayed unique receptor function in cell-based activity assays. We speculate that these unique biochemical and cellular activities may be responsible for the novel in vivo profile observed in animals treated systemically with T2384. When administered to diabetic KKAy mice, T2384 rapidly improved insulin sensitivity in the absence of weight gain, hemodilution, and anemia characteristics of treatment with rosiglitazone (a TZD). Moreover, upon coadministration with rosiglitazone, T2384 was able to antagonize the side effects induced by rosiglitazone treatment alone while retaining robust effects on glucose disposal. These results are consistent with the hypothesis that interactions between ligands and specific regions of the receptor ligand-binding pocket might selectively trigger a subset of receptor-mediated biological responses leading to the improvement of insulin sensitivity, without eliciting less desirable responses associated with full activation of the receptor. We suggest that T2384 may represent a prototype for a novel class of PPAR␥ ligand and, furthermore, that molecules sharing some of these properties would be useful for treatment of type 2 diabetes.

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In recent years, visual analytics (VA) has shown promise in alleviating the challenges of interpreting black-box deep learning (DL) models. While the focus of VA for explainable DL has been mainly on classification problems, DL is gaining popularity in high-dimensional-to-high-dimensional (H-H) problems such as image-to-image translation. In contrast to classification, H-H problems have no explicit instance groups or classes to study. Each output is continuous, high-dimensional, and changes in an unknown non-linear manner with changes in the input. These unknown relations between the input, model and output necessitate the user to analyze them in conjunction, leveraging symmetries between them. Since classification tasks do not exhibit some of these challenges, most existing VA systems and frameworks allow limited control of the components required to analyze models beyond classification. Hence, we identify the need for and present a unified conceptual framework, the Transform-and-Perform framework (T&P), to facilitate the design of VA systems for DL model analysis focusing on H-H problems. T&P provides guidelines to structure and identify workflows and analysis strategies to design new VA systems, and understand existing ones to uncover potential gaps for improvements. The goal is to aid the creation of effective VA systems that support the structuring of model understanding and identifying actionable insights for model improvements. We highlight the growing need for new frameworks like T&P with a real-world image-to-image translation application. We also illustrate how T&P effectively supports the understanding and identifying potential gaps in existing VA systems.