Fasting glucose and insulin are intermediate traits for type 2 diabetes. Here we explore the role of coding variation on these traits by analysis of variants on the HumanExome BeadChip in 60,564 non-diabetic individuals and in 16,491 T2D cases and 81,877 controls. We identify a novel association of a low-frequency nonsynonymous SNV in GLP1R (A316T; rs10305492; MAF=1.4%) with lower FG (β=-0.09±0.01 mmol L−1, p=3.4×10−12), T2D risk (OR[95%CI]=0.86[0.76-0.96], p=0.010), early insulin secretion (β=-0.07±0.035 pmolinsulin mmolglucose−1, p=0.048), but higher 2-h glucose (β=0.16±0.05 mmol L−1, p=4.3×10−4). We identify a gene-based association with FG at G6PC2 (pSKAT=6.8×10−6) driven by four rare protein-coding SNVs (H177Y, Y207S, R283X and S324P). We identify rs651007 (MAF=20%) in the first intron of ABO at the putative promoter of an antisense lncRNA, associating with higher FG (β=0.02±0.004 mmol L−1, p=1.3×10−8). Our approach identifies novel coding variant associations and extends the allelic spectrum of variation underlying diabetes-related quantitative traits and T2D susceptibility.

Force spectroscopy measurements of the rupture of the molecular bond between biotin and streptavidin often results in a wide distribution of rupture forces. We attribute the long tail of high rupture forces to the nearly simultaneous rupture of more than one molecular bond. To decrease the number of possible bonds, we employed hydrophilic polymeric tethers to attach biotin molecules to the atomic force microscope probe. It is shown that the measured distributions of rupture forces still contain high forces that cannot be described by the forced dissociation from a deep potential well. We employed a recently developed analytical model of simultaneous rupture of two bonds connected by polymer tethers with uneven length to fit the measured distributions. The resulting kinetic parameters agree with the energy landscape predicted by molecular dynamics simulations. It is demonstrated that when more than one molecular bond might rupture during the pulling measurements there is a noise-limited range of probe velocities where the kinetic parameters measured by force spectroscopy correspond to the true energy landscape. Outside this range of velocities, the kinetic parameters extracted by using the standard most probable force approach might be interpreted as artificial energy barriers that are not present in the actual energy landscape. Factors that affect the range of useful velocities are discussed.

Interactions between fullerene C60 molecules in water were measured by force spectroscopy. Fullerene molecules were covalently connected to bifunctional water-soluble poly(ethylene glycol) (PEG) linkers and subsequently tethered to the substrate and to the tip of the atomic force microscope to facilitate single molecule detection and avoid spurious surface effects. The distributions of rupture forces for substrates prepared with different incubation times of C60-PEG-NH2 exhibit high rupture forces that cannot be explained by the theoretical distribution of single molecule binding. Moreover, the relative amplitude of the high force peak in the histogram increases with incubation time. These observations are explained by attributing the measured high forces to the rupture of multiple bonds between fullerene molecules. Force spectroscopy data analysis based on the most probable forces gives significantly different dissociation rates for samples that exhibit different amplitudes of the high force peak. An approximate analytical model that considers ruptures of two bonds that are simultaneously loaded by tethers with different lengths is proposed. This model successfully fits the distributions of the rupture forces using the same set of kinetic parameters for samples prepared with different grafting densities. It is proposed that this model can be used as a common tool to analyze the probability distributions of rupture forces that contain peaks or shoulders on the high force side of the distribution.

The glucagon-like peptide 1 receptor (GLP1R) is a G protein-coupled receptor (GPCR) involved in insulin synthesis and regulation; therefore, it is an important drug target for treatment of diabetes. However, GLP1R is a member of the class B1 family of GPCRs for which there are no experimental structures. To provide a structural basis for drug design and to probe class B GPCR activation, we predicted the transmembrane (TM) bundle structure of GLP1R bound to the peptide Exendin-4 (Exe4; a GLP1R agonist on the market for treating diabetes) using the MembStruk method for scanning TM bundle conformations. We used protein-protein docking methods to combine the TM bundle with the X-ray crystal structure of the 143-aa N terminus coupled to the Exe4 peptide. This complex was subjected to 28 ns of full-solvent, full-lipid molecular dynamics. We find 14 strong polar interactions of Exe4 with GLP1R, of which 8 interactions are in the TM bundle (2 interactions confirmed by mutation studies) and 6 interactions involve the N terminus (3 interactions found in the crystal structure). We also find 10 important hydrophobic interactions, of which 4 interactions are in the TM bundle (2 interactions confirmed by mutation studies) and 6 interactions are in the N terminus (6 interactions present in the crystal structure). Thus, our predicted structure agrees with available mutagenesis studies. We suggest a number of mutation experiments to further validate our predicted structure. The structure should be useful for guiding drug design and can provide a structural basis for understanding ligand binding and receptor activation of GLP1R and other class B1 GPCRs.protein structure prediction | incretin receptors | peptide hormones G protein-coupled receptors (GPCRs) are the largest family of integral membrane proteins within the human genome, and they are all characterized by seven transmembrane (TM) helices, with the N terminus on the extracellular (EC) side and the C terminus on the intracellular (IC). This family of proteins senses molecules outside of the cell and activates signal transduction pathways to cause cellular responses. Because of this vital role in cellular signaling networks, GPCRs are involved in many diseases, and they are the target of ∼40% of all prescription pharmaceuticals on the market (1). Because of the importance of GPCRs as drug targets, it is vital to gain structural information for aiding in drug design. Unfortunately, GPCRs, like other membrane proteins, are difficult to crystallize. There are now X-ray crystal structures for more than 12 distinct receptors of the (at least) 800 human GPCRs (2-16). All of the crystallized receptors belong to the class A (rhodopsin-like) family of GPCRs. However, a phylogenetic analysis of GPCRs classifies them into different subfamilies: class A (rhodopsin-like), class B1 (secretin-like), class B2 (adhesionlike), class C (glutamate-like), and Frizzled/Taste2 (12).Class B1 (secretin-like) GPCRs are activated by peptide hormones. The large ectodomain of these receptors interacts...

This Supporting Information provides a detailed description of the data manipulation and includes all of the histograms and fits referred to in the main text. Data Collection and the Extended Freely Jointed Chain Fit:Upon collection, force curves are saved as waves in Igor Pro 5 (Wavemetrics Inc., Portland, OR). These waves are processed with a custom program written in Matlab (MathWorks, Inc., Natick, MA). The majority of the collected force curves do not exhibit characteristic separation events. Data processing is automated to remove user bias and speed up the data analysis. The program converts the force curves from deflection versus displacement into force versus separation. The deflection noise value is calculated as a standard deviation from the off-surface part of the approach line, and the mean noise value based threshold is used to detect rupture events. A threshold of five standard deviations is applied to the retract curve to detect the abrupt † Current address: Division