Signalling and Regulation of the Glucagon-like Peptide-1 receptor

Following nutrient ingestion, glucagon-like peptide 1 (GLP-1) secreted from intestinal L-cells mediates anti-diabetic effects, most notably stimulating glucose-dependent insulin release from pancreatic β-cells but also inhibiting glucagon release, promoting satiety and weight reduction and potentially enhancing or preserving β-cell mass. These effects are through the GLP-1 receptor (GLP-1R) which is a therapeutic target in type 2 diabetes. The present study focused on desensitisation and re-sensitisation of GLP-1R-mediated signalling and interactions of orthosteric and allosteric ligands. Data demonstrate GLP-1R desensitisation and subsequent re-sensitisation following removal of extracellular ligand with ligand-specific features. Following GLP-1-mediated desensitisation, re-sensitisation is dependent on receptor internalisation, endosomal acidification and receptor recycling. Re-sensitisation is also dependent on endothelin converting enzyme-1 (ECE-1) activity, possibly through proteolysis of GLP-1 in endosomes, facilitating disassociation of receptor-β-arrestin complexes leading to GLP-1R recycling and re-sensitisation. ECE-1 activity also regulates GLP-1-induced activation of extracellular signal regulated kinase (ERK) and generation of cAMP possibly through a G protein independent/β-arrestin dependent mechanism. By contrast, following GLP-1R activation by the orthosteric agonist, exendin-4, or allosteric agonist, compound 2, re-sensitisation was slow and independent of ECE-1 activity. Thus, different ligands depend on different events during GLP-1R trafficking which could be important for re-sensitisation and signalling, particularly that mediated by scaffolding around β-arrestin. As the GLP-1R is targeted therapeutically at orthosteric and allosteric sites, this study examined activation of the GLP-1R by orthosteric and allosteric agonists and in particular interactions between ligands of these sites. Challenging the GLP-1R with the allosteric ligand, compound 2, along with GLP-1 9-36 amide, a low affinity, low efficacy metabolite of GLP-1 7-36 amide, results in synergistic receptor activation. This may be important for therapeutic approaches with allosteric ligands, as metabolites of GLP-1 may be present in vivo at concentrations higher than the classic endogenous ligand. Indeed this could present a novel therapeutic approach

Table3_A metabolism-associated gene signature for prognosis prediction of hepatocellular carcinoma.XLSX

Hepatocellular carcinoma (HCC), the most frequently occurring type of cancer, is strongly associated with metabolic disorders. In this study, we aimed to characterize the metabolic features of HCC and normal tissue adjacent to the tumor (NAT). By using samples from The Cancer Genome Atlas (TCGA) liver cancer cohort and comparing 85 well-defined metabolic pathways obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG), 70 and 7 pathways were found to be significantly downregulated and upregulated, respectively, in HCC, revealing that tumor tissue lacks the ability to maintain normal metabolic levels. Through unsupervised hierarchical clustering of metabolic pathways, we found that metabolic heterogeneity correlated with prognosis in HCC samples. Thus, using the least absolute shrinkage and selection operator (LASSO) and filtering independent prognostic genes by the Cox proportional hazards model, a six-gene-based metabolic score model was constructed to enable HCC classification. This model showed that high expression of LDHA and CHAC2 was associated with an unfavorable prognosis but that high ADPGK, GOT2, MTHFS, and FTCD expression was associated with a favorable prognosis. Patients with higher metabolic scores had poor prognoses (p value = 2.19e-11, hazard ratio = 3.767, 95% CI = 2.555–5.555). By associating the score level with clinical features and genomic alterations, it was found that NAT had the lowest metabolic score and HCC with tumor stage III/IV the highest. qRT‒PCR results for HCC patients also revealed that tumor samples had higher score levels than NAT. Regarding genetic alterations, patients with higher metabolic scores had more TP53 gene mutations than those with lower metabolic scores (p value = 8.383e-05). Validation of this metabolic score model was performed using another two independent HCC cohorts from the Gene Expression Omnibus (GEO) repository and other TCGA datasets and achieved good performance, suggesting that this model may be used as a reliable tool for predicting the prognosis of HCC patients.</p

Theoretical Insight into Core–Shell Preference for Bimetallic Pt‑M (M = Ru, Rh, Os, and Ir) Cluster and Its Electronic Structure

Pt_<i>m</i>M_<i>n</i> (M = Ru, Rh, Os, and Ir; <i>m</i>+<i>n</i> = 38 and 55) clusters are systematically investigated using the DFT method. In an octahedral 38-atom cluster, core–shell structure M₆@- Pt₃₂ with M₆ core and Pt₃₂ shell is stable for Pt–Rh and Pt–Ir combinations but is not for Pt–Ru and Pt–Os combinations. In a 55-atom cluster, icosahedral M₁₃@Pt₄₂ structure is stable for all Pt-M combinations, indicating that a large cluster is more preferable to stabilizing the core–shell structure than a small cluster. The difference in cohesive energy (<i>E</i>_coh) between M₁₃ and Pt₁₃ and the distortion energy {<i>E</i>_dis(M₁₃)} of M₁₃ are parallel to the segregation energy (<i>E</i>_seg), indicating that these are important factors for stabilizing M₁₃@Pt₄₂. One more crucially important factor is the interaction energy (<i>E</i>_int) between M₁₃ core and Pt₄₂ shell, because <i>E</i>_int is parallel to <i>E</i>_seg and its absolute value is much larger than those of <i>E</i>_dis(M₁₃) and <i>E</i>_dis(Pt₄₂). The <i>E</i>_int depends on the energy gap between LUMO of M₁₃ core and HOMO of Pt₄₂ shell, indicating that LUMO energy of M₁₃ and HOMO energy of Pt₄₂ are good properties for understanding and predicting stability of core–shell structure. Pt atom is more positively charged in M₁₃@Pt₄₂ than in Pt₅₅ and the HOMO energy of M₁₃@Pt₄₂ is higher than that of Pt₅₅. The presence of these two contrary factors for O₂ binding suggests that M₁₃@Pt₄₂ is not bad for O₂ binding

Theoretical Insight into Core–Shell Preference for Bimetallic Pt‑M (M = Ru, Rh, Os, and Ir) Cluster and Its Electronic Structure

Pt_<i>m</i>M_<i>n</i> (M = Ru, Rh, Os, and Ir; <i>m</i>+<i>n</i> = 38 and 55) clusters are systematically investigated using the DFT method. In an octahedral 38-atom cluster, core–shell structure M₆@- Pt₃₂ with M₆ core and Pt₃₂ shell is stable for Pt–Rh and Pt–Ir combinations but is not for Pt–Ru and Pt–Os combinations. In a 55-atom cluster, icosahedral M₁₃@Pt₄₂ structure is stable for all Pt-M combinations, indicating that a large cluster is more preferable to stabilizing the core–shell structure than a small cluster. The difference in cohesive energy (<i>E</i>_coh) between M₁₃ and Pt₁₃ and the distortion energy {<i>E</i>_dis(M₁₃)} of M₁₃ are parallel to the segregation energy (<i>E</i>_seg), indicating that these are important factors for stabilizing M₁₃@Pt₄₂. One more crucially important factor is the interaction energy (<i>E</i>_int) between M₁₃ core and Pt₄₂ shell, because <i>E</i>_int is parallel to <i>E</i>_seg and its absolute value is much larger than those of <i>E</i>_dis(M₁₃) and <i>E</i>_dis(Pt₄₂). The <i>E</i>_int depends on the energy gap between LUMO of M₁₃ core and HOMO of Pt₄₂ shell, indicating that LUMO energy of M₁₃ and HOMO energy of Pt₄₂ are good properties for understanding and predicting stability of core–shell structure. Pt atom is more positively charged in M₁₃@Pt₄₂ than in Pt₅₅ and the HOMO energy of M₁₃@Pt₄₂ is higher than that of Pt₅₅. The presence of these two contrary factors for O₂ binding suggests that M₁₃@Pt₄₂ is not bad for O₂ binding

Zeolite- and MgO-Supported Molecular Iridium Complexes: Support and Ligand Effects in Catalysis of Ethene Hydrogenation and H–D Exchange in the Conversion of H₂ + D₂

Zeolite- and MgO-supported mononuclear iridium diethene complexes were formed by the reaction of Ir(C₂H₄)₂(acac) (acac = acetylacetonate, C₅H₇O₂^–) with each support. Changes in the ligand environment of the supported iridium complexes were characterized by infrared, X-ray absorption near edge structure, and extended X-ray absorption fine structure spectroscopies as various mixtures of H₂, C₂H₄, and CO flowed over the samples. In contrast to the nonuniform metal complexes anchored to metal oxides, our zeolite-supported metal complexes were highly uniform, allowing precise determinations of the chemistry, including the role of the support as a macroligand. Zeolite- and MgO-supported Ir(C₂H₄)₂ complexes are each rapidly converted to Ir(CO)₂ upon contact with a pulse of CO, and the ν_CO frequencies indicate that the iridium is more electron-deficient when the support is the zeolite. The Ir(CO)₂ complex supported on MgO was highly stable in the presence of various combinations of CO, C₂H₄, and helium. In contrast, the zeolite-supported Ir(CO)₂ complex was found to be highly reactive, forming Ir(CO)₃, Ir(CO)(C₂H₄), Ir(CO)₂(C₂H₄), and Ir(CO)(C₂H₄)₂. The π-bonded ethene ligands of the zeolite-supported Ir(C₂H₄)₂ in H₂ were facilely converted to σ-bonded ethyl when treated. However, the stability of the ethene ligands was markedly increased when the support was changed to MgO or when a CO ligand was simultaneously bonded to the iridium. The rates of catalytic ethene hydrogenation and H₂/D₂ exchange in the presence of a catalyst initially consisting of Ir(C₂H₄)₂ on the zeolite were found to be more than an order of magnitude higher than when MgO was the support. The iridium complexes containing one or more CO ligands were found to be inactive for H₂/D₂ exchange reactions when the support was MgO, but they were moderately active when it was the zeolite. The effects of the MgO and zeolite supports on reactivity and catalytic activity are attributed to their differences as ligands donating or withdrawing electrons, respectively

Functional interaction of compound 2 and GLP-1 9–36 amide at the GLP-1R in HEK-GLP-1R cells.

<p>The pEC₅₀ values of GLP-1 9–36 amide-mediated cAMP generation in the presence of increasing concentrations of compound 2. The pEC₅₀ values have been determined from the data presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047936#pone-0047936-g003" target="_blank">Figure 3</a>. Data are mean±s.e.m., n = 4, ** P<0.01 and *** P<0.001 versus 0 µM compound 2 by Dunnett's multiple range test following oneway ANOVA.</p

Are there physicochemical differences between allosteric and competitive ligands?

<div><p>Previous studies have compared the physicochemical properties of allosteric compounds to non-allosteric compounds. Those studies have found that allosteric compounds tend to be smaller, more rigid, more hydrophobic, and more drug-like than non-allosteric compounds. However, previous studies have not properly corrected for the fact that some protein targets have much more data than other systems. This generates concern regarding the possible skew that can be introduced by the inherent bias in the available data. Hence, this study aims to determine how robust the previous findings are to the addition of newer data. This study utilizes the Allosteric Database (ASD v3.0) and ChEMBL v20 to systematically obtain large datasets of both allosteric and competitive ligands. This dataset contains 70,219 and 9,511 unique ligands for the allosteric and competitive sets, respectively. Physically relevant compound descriptors were computed to examine the differences in their chemical properties. Particular attention was given to removing redundancy in the data and normalizing across ligand diversity and varied protein targets. The resulting distributions only show that allosteric ligands tend to be more aromatic and rigid and do not confirm the increase in hydrophobicity or difference in drug-likeness. These results are robust across different normalization schemes.</p></div

Functional interaction between ligands on GLP-1R-mediated cAMP generation in HEK-GLP-1R cells.

<p>HEK-GLP-1R cells were pretreated (Pre-) for 10 min with 1 µM GLP-1 9–36 amide in the presence of IBMX before challenge for 15 min with the indicated concentrations of agonists. Where no pre-treatment is indicated, an equivalent volume of buffer (KHB) was added for 10 min in the presence of IBMX prior to ligand addition for 15 min. Levels of intracellular cAMP were then determined relative to the cellular protein content. The final concentration of DMSO (vehicle) for the 15 min treatment period was 5% v/v in all cases. Data are mean±s.e.m., n = 3.</p

ERK signaling through the GLP-1R.

<p>HEK-GLP-1R cells (<b>A and B</b>) or INS-1E cells (<b>C and D</b>) were challenged for 5 min with either vehicle (1% v/v DMSO), GLP-1 9–36 amide (1 or 10 µM as indicated) or compound 2 alone or GLP-1 9–36 amide (1 µM) and compound 2 together as indicated. Cells were also challenged with GLP-1 7–36 amide (10 nM). Levels of phospho-ERK were then determined by Western blotting. The intensity of the bands representing phospho-ERK was determined using ImageJ and the mean data are shown in the panels below the immunoblot with basal (0) levels subtracted. Data are either representative of 3 experiments or mean+s.e.m., n = 3. *, P<0.05 and ** P<0.01 by Student's test versus the numerical sum of both GLP-1 9–36 amide (1 µM) and compound 2 at the concentration indicated when used alone.</p

Compound 2 potentiates GLP-1 9–36 amide-mediated cAMP generation by membranes from HEK-GLP-1R cells.

<p>Cell membranes prepared from HEK-GLP-1R cells were incubated with ligands as indicated for 5 min at 30°C in the presence of IBMX before determination of cAMP. A) Concentration-dependent cAMP generation in response to GLP-1 7-36 amide or to GLP-1 9–36 amide either alone or in combination with 3 µM compound 2. <b>B</b>) Responses to GLP-1 9–36 amide or compound 2 alone or the two in combination. The numerical sums of cAMP generation in response to GLP-1 9–36 amide and compound 2 are shown. Data are mean±/+s.e.m., n = 3. For * P<0.05 by Bonferroni's multiple range. For clarity only differences between ‘numerical’ and ‘co-addition’ conditions are shown.</p