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Crystal structures of hGPR40, a target for treatment of type 2 diabetes, bound to a partial and an allosteric agonist explain the binding cooperativity between these ligands and present new opportunities for structure-guided drug design. Clinical studies indicate that partial agonists of the G-protein-coupled, free fatty acid receptor 1 GPR40 enhance glucose-dependent insulin secretion and represent a potential mechanism for the treatment of type 2 diabetes mellitus. Full allosteric agonists (AgoPAMs) of GPR40 bind to a site distinct from partial agonists and can provide additional efficacy. We report the 3.2-Å crystal structure of human GPR40 (hGPR40) in complex with both the partial agonist MK-8666 and an AgoPAM, which exposes a novel lipid-facing AgoPAM-binding pocket outside the transmembrane helical bundle. Comparison with an additional 2.2-Å structure of the hGPR40–MK-8666 binary complex reveals an induced-fit conformational coupling between the partial agonist and AgoPAM binding sites, involving rearrangements of the transmembrane helices 4 and 5 (TM4 and TM5) and transition of the intracellular loop 2 (ICL2) into a short helix. These conformational changes likely prime GPR40 to a more active-like state and explain the binding cooperativity between these ligands.

High throughput mRNA expression profiling can be used to characterize the response of cell culture models to perturbations such as pharmacologic modulators and genetic perturbations. As profiling campaigns expand in scope, it is important to homogenize, summarize, and analyze the resulting data in a manner that captures significant biological signals in spite of various noise sources such as batch effects and stochastic variation. We used the L1000 platform for large-scale profiling of 978 representative genes across thousands of compound treatments. Here, a method is described that uses deep learning techniques to convert the expression changes of the landmark genes into a perturbation barcode that reveals important features of the underlying data, performing better than the raw data in revealing important biological insights. The barcode captures compound structure and target information, and predicts a compound's high throughput screening promiscuity, to a higher degree than the original data measurements, indicating that the approach uncovers underlying factors of the expression data that are otherwise entangled or masked by noise. Furthermore, we demonstrate that visualizations derived from the perturbation barcode can be used to more sensitively assign functions to unknown compounds through a guilt-by-association approach, which we use to predict and experimentally validate the activity of compounds on the MAPK pathway. The demonstrated application of deep metric learning to large-scale chemical genetics projects highlights the utility of this and related approaches to the extraction of insights and testable hypotheses from big, sometimes noisy data.

A fluorescence resonance energy transfer assay has been developed for monitoring Bacillus anthracis lethal factor (LF) protease activity. A fluorogenic 16-mer peptide based on the known LF protease substrate MEK1 was synthesized and found to be cleaved by the enzyme at the anticipated site. Extension of this work to a fluorogenic 19-mer peptide, derived, in part, from a consensus sequence of known LF protease targets, produced a much better substrate, cleaving approximately 100 times more efficiently. This peptide sequence was modified further on resin to incorporate donor/quencher pairs to generate substrates for use in fluorescence resonance energy transfer-based appearance assays. All peptides cleaved at similar rates with signal/background ranging from 9-16 at 100% turnover. One of these substrates, denoted (Cou)Consensus(K(QSY-35)GG)-NH2, was selected for additional assay optimization. A plate-based assay requiring only low nanomolar levels of enzyme was developed for screening and inhibitor characterization.

How easy is it to improve the catalytic power of an enzyme? To address this question, the gene encoding a sluggish mutant triose-phosphate isomerase (D-glyceraldehyde-3-phosphate ketol-isomerase, EC 5.3.1.1) has been subjected to random mutagenesis over its whole length by using \"spiked\" oligonucleotide primers. Transformation of an isomerase-minus strain of Escherichia coli was followed by selection of those colonies harboring an enzyme of higher catalytic potency. Six amino acid changes in the Glu-165 → Asp mutant of triosephosphate isomerase improve the specific catalytic activity of this enzyme (from 1.3-fold to 19-fold). The suppressor sites are scattered across the sequence (at positions 10, 96, 97, 167, and 233), but each of them is very close to the active site. These experiments show both that there are relatively few single amino acid changes that increase the catalytic potency of this enzyme and that all of these improvements derive from alterations that are in, or very close to, the active site.

BackgroundRevascularization after endovascular therapy for acute ischemic stroke is measured by the Thrombolysis In Cerebral Infarction (TICI) scale, yet variability exists in scale definitions. We examined the degree of reperfusion with the expanded TICI (eTICI) scale and association with outcomes in the HERMES collaboration of recent endovascular trials.MethodsThe HERMES Imaging Core, blind to all other data, evaluated angiography after endovascular therapy in HERMES. A battery of TICI scores (mTICI, TICI, TICI2C) was used to define reperfusion of the initial target occlusion defined by non-invasive imaging and conventional angiography.ResultsAngiography of 801 subjects was available, including 797 defined by non-invasive imaging (154 internal carotid artery (ICA), 583 M1, 60 M2) and 748 by conventional angiography (195 ICA, 459 M1, 94 M2). Among 729 subjects in whom the reperfusion grade could be established, using eTICI (3=100%, 2C=90–99%, 2b67=67–89%, 2b50=50–66%) of the conventional angiography target occlusion, there were 63 eTICI 3 (9%), 166 eTICI 2c (23%), 218 eTICI 2b67 (30%), 103 eTICI 2b50 (14%), 100 eTICI 2a (14%), 19 eTICI 1 (3%), and 60 eTICI 0 (8%). Modified Rankin Scale shift analyses from baseline to 90 days showed that increasing TICI grades were linked with better outcomes, with significant distinctions between TICI 0/1 versus 2a (p=0.028), 2a versus 2b50 (p=0.017), and 2b50 versus 2b67 (p=0.014).ConclusionsThe benefit of endovascular therapy in HERMES was strongly associated with increasing degrees of reperfusion defined by eTICI. The eTICI metric identified meaningful distinctions in clinical outcomes and may be used in future studies and routine practice.

Serum concentration of hepatic enzymes are linked to liver dysfunction, metabolic and cardiovascular diseases. We perform genetic analysis on serum levels of alanine transaminase (ALT), alkaline phosphatase (ALP) and gamma-glutamyl transferase (GGT) using data on 437,438 UK Biobank participants. Replication in 315,572 individuals from European descent from the Million Veteran Program, Rotterdam Study and Lifeline study confirms 517 liver enzyme SNPs. Genetic risk score analysis using the identified SNPs is strongly associated with serum activity of liver enzymes in two independent European descent studies (The Airwave Health Monitoring study and the Northern Finland Birth Cohort 1966). Gene-set enrichment analysis using the identified SNPs highlights involvement in liver development and function, lipid metabolism, insulin resistance, and vascular formation. Mendelian randomization analysis shows association of liver enzyme variants with coronary heart disease and ischemic stroke. Genetic risk score for elevated serum activity of liver enzymes is associated with higher fat percentage of body, trunk, and liver and body mass index. Our study highlights the role of molecular pathways regulated by the liver in metabolic disorders and cardiovascular disease. Plasma levels of liver enzymes provide insights into hepatic function and related diseases. Here, the authors perform a genome-wide association study on three liver enzymes, identifying genetic variants associated with their plasma concentration as well as links to metabolic and cardiovascular diseases.

Background Identification of genetic risk factors that are shared between Alzheimer’s disease (AD) and other traits, i.e., pleiotropy, can help improve our understanding of the etiology of AD and potentially detect new therapeutic targets. Previous epidemiological correlations observed between cardiometabolic traits and AD led us to assess the pleiotropy between these traits. Methods We performed a set of bivariate genome-wide association studies coupled with colocalization analysis to identify loci that are shared between AD and eleven cardiometabolic traits. For each of these loci, we performed colocalization with Genotype-Tissue Expression (GTEx) project expression quantitative trait loci (eQTL) to identify candidate causal genes. Results We identified three previously unreported pleiotropic trait associations at known AD loci as well as four novel pleiotropic loci. One associated locus was tagged by a low-frequency coding variant in the gene DOCK4 and is potentially implicated in its alternative splicing. Colocalization with GTEx eQTL data identified additional candidate genes for the loci we detected, including ACE , the target of the hypertensive drug class of ACE inhibitors. We found that the allele associated with decreased ACE expression in brain tissue was also associated with increased risk of AD, providing human genetic evidence of a potential increase in AD risk from use of an established anti-hypertensive therapeutic. Conclusion Our results support a complex genetic relationship between AD and these cardiometabolic traits, and the candidate causal genes identified suggest that blood pressure and immune response play a role in the pleiotropy between these traits.

Infarct volume (FIV) on follow-up diffusion-weighted imaging (FU-DWI) is only moderately associated with functional outcome in acute ischemic stroke patients. However, FU-DWI may contain other imaging biomarkers that could aid in improving outcome prediction models for acute ischemic stroke. We included FU-DWI data from the HERMES, ISLES, and MR CLEAN-NO IV databases. Lesions were segmented using a deep learning model trained on the HERMES and ISLES datasets. We assessed the performance of three classifiers in predicting functional independence for the MR CLEAN-NO IV trial cohort based on: (1) FIV alone, (2) the most important features obtained from a trained convolutional autoencoder (CAE), and (3) radiomics. Furthermore, we investigated feature importance in the radiomic-feature-based model. For outcome prediction, we included 206 patients: 144 scans were included in the training set, 21 in the validation set, and 41 in the test set. The classifiers that included the CAE and the radiomic features showed AUC values of 0.88 and 0.81, respectively, while the model based on FIV had an AUC of 0.79. This difference was not found to be statistically significant. Feature importance results showed that lesion intensity heterogeneity received more weight than lesion volume in outcome prediction. This study suggests that predictions of functional outcome should not be based on FIV alone and that FU-DWI images capture additional prognostic information.

Pretreatment of wastewater has been strictly enforced by the Metropolitan Waste Control Commission in St. Paul, MN. Most industries cooperate fully with local regulations, and noncompliant companies receive harsh penalties. In 1989, 257 firms were cited in violation, and 74 industrial users were listed as significant violators. Fortunately, the use of stipulation agreements has preempted the need for legal action in all of these cases. The stipulation agreement specifies the actions required for compliance and mandates a compliance schedule. This legal contract also specifies penalties if compliance is not achieved.