Molecular Characterisation of Small Molecule Agonists Effect on the Human Glucagon Like Peptide-1 Receptor Internalisation

The glucagon-like peptide receptor (GLP-1R), which is a G-protein coupled receptor (GPCR), signals through both Gαs and Gαq coupled pathways and ERK phosphorylation to stimulate insulin secretion. The aim of this study was to determine molecular details of the effect of small molecule agonists, compounds 2 and B, on GLP-1R mediated cAMP production, intracellular Ca2+ accumulation, ERK phosphorylation and its internalisation. In human GLP-1R (hGLP-1R) expressing cells, compounds 2 and B induced cAMP production but caused no intracellular Ca2+ accumulation, ERK phosphorylation or hGLP-1R internalisation. GLP-1 antagonists Ex(9-39) and JANT-4 and the orthosteric binding site mutation (V36A) in hGLP-1R failed to inhibit compounds 2 and B induced cAMP production, confirming that their binding site distinct from the GLP-1 binding site on GLP-1R. However, K334A mutation of hGLP-1R, which affects Gαs coupling, inhibited GLP-1 as well as compounds 2 and B induced cAMP production, indicating that GLP-1, compounds 2 and B binding induce similar conformational changes in the GLP-1R for Gαs coupling. Additionally, compound 2 or B binding to the hGLP-1R had significantly reduced GLP-1 induced intracellular Ca2+ accumulation, ERK phosphorylation and hGLP-1R internalisation. This study illustrates pharmacology of differential activation of GLP-1R by GLP-1 and compounds 2 and B

An integrated Shannon Entropy and reference ideal method for the selection of enhanced oil recovery pilot areas based on an unsupervised machine learning algorithm

Pilot-scale enhanced oil recovery in hydrocarbon field development is often implemented to reduce investment risk due to geological uncertainties. Selection of the pilot area is important, since the result will be extended to the full field. The main challenge in choosing a pilot region is the absence of a systematic and quantitative method. In this paper, we present a novel quantitative and systematic method composed of reservoir-geology and operational-economic criteria where a cluster analysis is utilized as an unsupervised machine learning method. A field of study will be subdivided into pilot candidate areas, and the optimized pilot size is calculated using the economic objective function. Subsequently, the corresponding Covariance (COV) matrix is computed for the simulated 3-D reservoir quality maps in the areas. The areas are optimally clustered to select the dominant cluster. The operational-economic criteria could be applied for decision making as well as the proximity of each area to the center of dominant cluster as a geological-reservoir criterion. Ultimately, the Shannon entropy weighting and the reference ideal method are applied to compute the pilot opportunity index in each area. The proposed method was employed for a pilot study on an oil field in south west Iran