Stabilizing Au2+ in a mixed-valence 3D halide perovskite.

Although Cu2+ is ubiquitous, the relativistic destabilization of the 5d orbitals makes the isoelectronic Au2+ exceedingly rare, typically stabilized only through Au-Au bonding or by using redox non-innocent ligands. Here we report the perovskite Cs4AuIIAuIII2Cl12, an extended solid with mononuclear Au2+ sites, which is stable to ambient conditions and characterized by single-crystal X-ray diffraction. The 2+ oxidation state of Au was assigned using 197Au Mössbauer spectroscopy, electron paramagnetic resonance, and magnetic susceptibility measurements, with comparison to paramagnetic and diamagnetic analogues with Cu2+ and Pd2+, respectively, as well as to density functional theory calculations. This gold perovskite offers an opportunity to study the optical and electronic transport of the uncommon Au2+/3+ mixed-valence state and the characteristics of the elusive Au2+ ion coordinated to simple ligands. Compared with the perovskite Cs2AuIAuIIICl6, which has been studied since the 1920s, Cs4AuIIAuIII2Cl12 exhibits a 0.7 eV reduction in optical absorption onset and a 103-fold increase in electronic conductivity.

Metabolomic Profiling of Cholesterol Efflux Capacity in a Multiethnic Population: Insights From MESA.

BACKGROUND: Impaired cholesterol efflux capacity (CEC) is a novel lipid metabolism trait associated with atherosclerotic cardiovascular disease. Mechanisms underlying CEC variation are unknown. We evaluated associations of circulating metabolites with CEC to advance understanding of metabolic pathways involved in cholesterol efflux regulation. METHODS: Participants enrolled in the MESA (Multi-Ethnic Study of Atherosclerosis) who underwent nuclear magnetic resonance metabolome profiling and CEC measurement (N=3543) at baseline were included. Metabolite associations with CEC were evaluated using standard linear regression analyses. Repeated ElasticNet and multilayer perceptron regression were used to assess metabolite profile predictive performance for CEC. Features important for CEC prediction were identified using Shapley Additive Explanations values. RESULTS: Greater CEC was significantly associated with metabolite clusters composed of the largest-sized particle subclasses of VLDL (very-low-density lipoprotein) and HDL (high-density lipoprotein), as well as their constituent apo A1, apo A2, phospholipid, and cholesterol components (ß=0.072-0.081; P<0.001). Metabolite profiles had poor accuracy for predicting in vitro CEC in linear and nonlinear analyses (R2<0.02; Spearman ρ<0.18). The most important feature for CEC prediction was race, with Black participants having significantly lower CEC compared with other races. CONCLUSIONS: We identified independent associations among CEC, the largest-sized particle subclasses of VLDL and HDL, and their constituent apolipoproteins and lipids. A large proportion of variation in CEC remained unexplained by metabolites and traditional clinical risk factors, supporting further investigation into genomic, proteomic, and phospholipidomic determinants of CEC.

Binding Domain Characterization of Growth Hormone Secretagogue Receptor.

Background and Objectives: Activation of ghrelin receptor growth hormone secretagogue receptor (GHS-R) by endogenous or synthetic ligands amplifies pulsatile release of growth hormone (GH) and enhances food intake, very relevant to development and growth. GHS-R is a G-protein coupled receptor that has great druggable potential. Understanding the precise ligand and receptor interactions is crucial to advance the application of GHS-R. Materials and Methods: We used radiolabeled ligand-binding assay and growth hormone release assay to assess the binding and functional characteristics of GHS-R to synthetic agonists MK-0677 and GHS-25, as well as to endogenous peptide ligand ghrelin. We analyzed the ligand-dependent activity of GHS-R by measuring aequorin-based [Ca++]i responses. To define a ligand-binding pocket of GHS-R, we generated a series of human/puffer fish GHS-R chimeras by domain swapping, as well as a series of mutants by site-directed mutagenesis. Results: We found that the synthetic ligands have high binding affinity to GHS-R in the in vitro competitive binding assay. Remarkably, the in vivo GH secretagogue activity is higher with the synthetic agonists MK-0677 and GHS-25 than that of ghrelin. Importantly, the activity was completely abolished in GHS-R knockout mice. In GHS-R chimera analysis, we identified the C-terminal region, particularly the transmembrane domain 6 (TM6), to be critical for the ligand-dependent activity. Our site-directed mutagenesis study further revealed that amino acid residues D99 and W276 in GHS-R are essential for ligand binding. Interestingly, critical residues distinctively interact with different ligands, MK-0677 activation depends on E124, while ghrelin and GHS-25 preferentially interact with F279. Conclusion: The ligand-binding pocket of human GHS-R is mainly defined by interactive residues in TM6 and the adjacent region of the receptor. This novel finding in GHS-R binding domains advances the structural/ functional understanding of GHS-R, which will help to select/design better GHS-R agonists/ antagonists for future therapeutic applications.

Metabolic flexibility of mitochondrial respiratory chain disorders predicted by computer modelling.

Mitochondrial respiratory chain dysfunction causes a variety of life-threatening diseases affecting about 1 in 4300 adults. These diseases are genetically heterogeneous, but have the same outcome; reduced activity of mitochondrial respiratory chain complexes causing decreased ATP production and potentially toxic accumulation of metabolites. Severity and tissue specificity of these effects varies between patients by unknown mechanisms and treatment options are limited. So far most research has focused on the complexes themselves, and the impact on overall cellular metabolism is largely unclear. To illustrate how computer modelling can be used to better understand the potential impact of these disorders and inspire new research directions and treatments, we simulated them using a computer model of human cardiomyocyte mitochondrial metabolism containing over 300 characterised reactions and transport steps with experimental parameters taken from the literature. Overall, simulations were consistent with patient symptoms, supporting their biological and medical significance. These simulations predicted: complex I deficiencies could be compensated using multiple pathways; complex II deficiencies had less metabolic flexibility due to impacting both the TCA cycle and the respiratory chain; and complex III and IV deficiencies caused greatest decreases in ATP production with metabolic consequences that parallel hypoxia. Our study demonstrates how results from computer models can be compared to a clinical phenotype and used as a tool for hypothesis generation for subsequent experimental testing. These simulations can enhance understanding of dysfunctional mitochondrial metabolism and suggest new avenues for research into treatment of mitochondrial disease and other areas of mitochondrial dysfunction.