Reduced adipocyte glutaminase activity promotes energy expenditure and metabolic health.

Glutamine and glutamate are interconverted by several enzymes and alterations in this metabolic cycle are linked to cardiometabolic traits. Herein, we show that obesity-associated insulin resistance is characterized by decreased plasma and white adipose tissue glutamine-to-glutamate ratios. We couple these stoichiometric changes to perturbed fat cell glutaminase and glutamine synthase messenger RNA and protein abundance, which together promote glutaminolysis. In human white adipocytes, reductions in glutaminase activity promote aerobic glycolysis and mitochondrial oxidative capacity via increases in hypoxia-inducible factor 1α abundance, lactate levels and p38 mitogen-activated protein kinase signalling. Systemic glutaminase inhibition in male and female mice, or genetically in adipocytes of male mice, triggers the activation of thermogenic gene programs in inguinal adipocytes. Consequently, the knockout mice display higher energy expenditure and improved glucose tolerance compared to control littermates, even under high-fat diet conditions. Altogether, our findings highlight white adipocyte glutamine turnover as an important determinant of energy expenditure and metabolic health.

Transcriptional determinants of lipid mobilization in human adipocytes.

Defects in adipocyte lipolysis drive multiple aspects of cardiometabolic disease, but the transcriptional framework controlling this process has not been established. To address this, we performed a targeted perturbation screen in primary human adipocytes. Our analyses identified 37 transcriptional regulators of lipid mobilization, which we classified as (i) transcription factors, (ii) histone chaperones, and (iii) mRNA processing proteins. On the basis of its strong relationship with multiple readouts of lipolysis in patient samples, we performed mechanistic studies on one hit, ZNF189, which encodes the zinc finger protein 189. Using mass spectrometry and chromatin profiling techniques, we show that ZNF189 interacts with the tripartite motif family member TRIM28 and represses the transcription of an adipocyte-specific isoform of phosphodiesterase 1B (PDE1B2). The regulation of lipid mobilization by ZNF189 requires PDE1B2, and the overexpression of PDE1B2 is sufficient to attenuate hormone-stimulated lipolysis. Thus, our work identifies the ZNF189-PDE1B2 axis as a determinant of human adipocyte lipolysis and highlights a link between chromatin architecture and lipid mobilization.

Α Humanized RANKL Transgenic Mouse Model of Progestin-Induced Mammary Carcinogenesis for Evaluation of Novel Therapeutics.

Receptor activator of nuclear factor-κB ligand (RANKL) is critically involved in mammary gland pathophysiology, while its pharmaceutical inhibition is being currently investigated in breast cancer. Herein, we investigated whether the overexpression of human RANKL in transgenic mice affects hormone-induced mammary carcinogenesis, and evaluated the efficacy of anti-RANKL treatments, such as OPG-Fc targeting both human and mouse RANKL or Denosumab against human RANKL. We established novel MPA/DMBA-driven mammary carcinogenesis models in TgRANKL mice that express both human and mouse RANKL, as well as in humanized humTgRANKL mice expressing only human RANKL, and compared them to MPA/DMBA-treated wild-type (WT) mice. Our results show that TgRANKL and WT mice have similar levels of susceptibility to mammary carcinogenesis, while OPG-Fc treatment restored mammary ductal density, and prevented ductal branching and the formation of neoplastic foci in both genotypes. humTgRANKL mice also developed MPA/DMBA-induced tumors with similar incidence and burden to those of WT and TgRANKL mice. The prophylactic treatment of humTgRANKL mice with Denosumab significantly prevented the rate of appearance of mammary tumors from 86.7% to 15.4% and the early stages of carcinogenesis, whereas therapeutic treatment did not lead to any significant attenuation of tumor incidence or tumor burden compared to control mice, suggesting the importance of RANKL primarily in the initial stages of tumorigenesis. Overall, we provide unique genetic tools for investigating the involvement of RANKL in breast carcinogenesis, and allow the preclinical evaluation of novel therapeutics that target hormone-related breast cancers.

A network-based computational and experimental framework for repurposing compounds toward the treatment of non-alcoholic fatty liver disease.

Non-alcoholic fatty liver disease (NAFLD) is among the most common liver pathologies, however, none approved condition-specific therapy yet exists. The present study introduces a drug repositioning (DR) approach that combines in vitro steatosis models with a network-based computational platform, constructed upon genomic data from diseased liver biopsies and compound-treated cell lines, to propose effectively repositioned therapeutic compounds. The introduced in silico approach screened 20'000 compounds, while complementary in vitro and proteomic assays were developed to test the efficacy of the 46 in silico predictions. This approach successfully identified six compounds, including the known anti-steatogenic drugs resveratrol and sirolimus. In short, gallamine triethiotide, diflorasone, fenoterol, and pralidoxime ameliorate steatosis similarly to resveratrol/sirolimus. The implementation holds great potential in reducing screening time in the early drug discovery stages and in delivering promising compounds for in vivo testing.

A facile consensus ranking approach enhances virtual screening robustness and identifies a cell-active DYRK1α inhibitor.

BACKGROUND: Virtual screening is vital for contemporary drug discovery but striking performance fluctuations are commonly encountered, thus hampering error-free use. Results and Methodology: A conceptual framework is suggested for combining screening algorithms characterized by orthogonality (docking-scoring calculations, 3D shape similarity, 2D fingerprint similarity) into a simple, efficient and expansible python-based consensus ranking scheme. An original experimental dataset is created for comparing individual screening methods versus the novel approach. Its utilization leads to identification and phosphoproteomic evaluation of a cell-active DYRK1α inhibitor. CONCLUSION: Consensus ranking considerably stabilizes screening performance at reasonable computational cost, whereas individual screens are heavily dependent on calculation settings. Results indicate that the novel approach, currently available as a free online tool, is highly suitable for prospective screening by nonexperts.

Combined Virtual and Experimental Screening for CK1 Inhibitors Identifies a Modulator of p53 and Reveals Important Aspects of in Silico Screening Performance.

A compound collection of pronounced structural diversity was comprehensively screened for inhibitors of the DNA damage-related kinase CK1. The collection was evaluated in vitro. A potent and selective CK1 inhibitor was discovered and its capacity to modulate the endogenous levels of the CK1-regulated tumor suppressor p53 was demonstrated in cancer cell lines. Administration of 10 µM of the compound resulted in significant increase of p53 levels, reaching almost 2-fold in hepatocellular carcinoma cells. In parallel to experimental screening, two representative and orthogonal in silico screening methodologies were implemented for enabling the retrospective assessment of virtual screening performance on a case-specific basis. Results showed that both techniques performed at an acceptable and fairly comparable level, with a slight advantage of the structure-based over the ligand-based approach. However, both approaches demonstrated notable sensitivity upon parameters such as screening template choice and treatment of redundancy in the enumerated compound collection. An effort to combine insight derived by sequential implementation of the two methods afforded poor further improvement of screening performance. Overall, the presented assessment highlights the relation between improper use of enrichment metrics and misleading results, and demonstrates the inherent delicacy of in silico methods, emphasizing the challenging character of virtual screening protocol optimization.