Identification of therapeutic targets from genetic association studies using hierarchical component analysis.

BACKGROUND: Mapping disease-associated genetic variants to complex disease pathophysiology is a major challenge in translating findings from genome-wide association studies into novel therapeutic opportunities. The difficulty lies in our limited understanding of how phenotypic traits arise from non-coding genetic variants in highly organized biological systems with heterogeneous gene expression across cells and tissues. RESULTS: We present a novel strategy, called GWAS component analysis, for transferring disease associations from single-nucleotide polymorphisms to co-expression modules by stacking models trained using reference genome and tissue-specific gene expression data. Application of this method to genome-wide association studies of blood cell counts confirmed that it could detect gene sets enriched in expected cell types. In addition, coupling of our method with Bayesian networks enables GWAS components to be used to discover drug targets. CONCLUSIONS: We tested genome-wide associations of four disease phenotypes, including age-related macular degeneration, Crohn's disease, ulcerative colitis and rheumatoid arthritis, and demonstrated the proposed method could select more functional genes than S-PrediXcan, the previous single-step model for predicting gene-level associations from SNP-level associations.

Lyme Disease Patient Trajectories Learned from Electronic Medical Data for Stratification of Disease Risk and Therapeutic Response.

Lyme disease (LD) is the most common tick-borne illness in the United States. Although appropriate antibiotic treatment is effective for most cases, up to 20% of patients develop post-treatment Lyme disease syndrome (PTLDS). There is an urgent need to improve clinical management of LD using precise understanding of disease and patient stratification. We applied machine-learning to electronic medical records to better characterize the heterogeneity of LD and developed predictive models for identifying medications that are associated with risks of subsequent comorbidities. For broad disease categories, we identified 3, 16, and 17 comorbidities within 2, 5, and 10 years of diagnosis, respectively. At a higher resolution of ICD-9 codes, we identified known associations with LD including chronic pain and cognitive disorders, as well as particular comorbidities on a timescale that matched PTLDS symptomology. We identified 7, 30, and 35 medications associated with risks of these comorbidities within 2, 5, and 10 years, respectively. For instance, the first-line antibiotic doxycycline exhibited a consistently protective association for typical symptoms of LD, including backache. Our approach and findings may suggest new hypotheses for more personalized treatments regimens for LD patients.

The Rapid Prediction of Carbapenem Resistance in Patients With Klebsiella pneumoniae Bacteremia Using Electronic Medical Record Data.

BACKGROUND: The administration of active antibiotics is often delayed in cases of carbapenem-resistant gram-negative bacteremia. Using electronic medical record (EMR) data to rapidly predict carbapenem resistance in patients with Klebsiella pneumoniae bacteremia could help reduce the time to active therapy. METHODS: All cases of Klebsiella pneumoniae bacteremia at Mount Sinai Hospital from September 2012 through September 2016 were included. Cases were randomly divided into a "training set" and a "testing set." EMR data from the training set cases were reviewed, and significant risk factors for carbapenem resistance were entered into a multiple logistic regression model. Performance was assessed by repeated K-fold cross-validation and by applying the training set model to the testing set. All cases were also reviewed to determine the time to effective antibiotic therapy. RESULTS: A total of 613 cases of Klebsiella pneumoniae bacteremia were included, 61 (10%) of which were carbapenem-resistant. The training and testing sets consisted of 460 and 153 cases, respectively. The regression model derived from the training set correctly predicted 73% of carbapenem-resistant cases and 59% of carbapenem-susceptible cases in the testing set (sensitivity, 73%; specificity, 59%; positive predictive value, 16%; negative predictive value, 95%). The mean area under the receiver operator characteristic curve of the K-fold cross-validation repeats was 0.731. Patients with carbapenem-resistant infections received active antibiotics significantly later than those with susceptible infections (40.4 hours vs 9.6 hours, P < .0001). CONCLUSIONS: A multiple logistic regression model using EMR data can generate rapid, sensitive predictions of carbapenem resistance in patients with Klebsiella pneumoniae bacteremia, which could help shorten the time to effective therapy in these cases.

G-Protein/ß-Arrestin-Linked Fluctuating Network of G-Protein-Coupled Receptors for Predicting Drug Efficacy and Bias Using Short-Term Molecular Dynamics Simulation.

The efficacy and bias of signal transduction induced by a drug at a target protein are closely associated with the benefits and side effects of the drug. In particular, partial agonist activity and G-protein/ß-arrestin-biased agonist activity for the G-protein-coupled receptor (GPCR) family, the family with the most target proteins of launched drugs, are key issues in drug discovery. However, designing GPCR drugs with appropriate efficacy and bias is challenging because the dynamic mechanism of signal transduction induced by ligand-receptor interactions is complicated. Here, we identified the G-protein/ß-arrestin-linked fluctuating network, which initiates large-scale conformational changes, using sub-microsecond molecular dynamics (MD) simulations of the ß2-adrenergic receptor (ß2AR) with a diverse collection of ligands and correlation analysis of their G protein/ß-arrestin efficacy. The G-protein-linked fluctuating network extends from the ligand-binding site to the G-protein-binding site through the connector region, and the ß-arrestin-linked fluctuating network consists of the NPxxY motif and adjacent regions. We confirmed that the averaged values of fluctuation in the fluctuating network detected are good quantitative indexes for explaining G protein/ß-arrestin efficacy. These results indicate that short-term MD simulation is a practical method to predict the efficacy and bias of any compound for GPCRs.

Safety evaluation of excessive ingestion of mozuku fucoidan in human.

Fucoidan is a sulfated polysaccharide in the brown sea algae. This study was conducted in 20 subjects taking excessive fucoidan up to 4.05 g daily for 2 wk. They recorded questionnaire sheets about their health. Blood and urine were collected before and after 2 wk of ingestion. We found that no disorder was apparent in the test period. Although total cholesterol (P value 0.017) and low-density lipoprotein cholesterol (P value 0.017) showed statistically significant reduction and Cl (P value 0.002) showed significant increase, nothing deviated from the range of normal values. In conclusion, this study showed no abnormalities in the abdominal, fecal states, blood and urine at all.

Low-driving-current InGaAsP photonic-wire optical switches using III-V CMOS photonics platform.

Electrically-driven Mach-Zehnder interferometer type InGaAsP photonic-wire optical switches have been demonstrated using a III-V-on-insulator structure bonded on a thermally oxidized Si with an Al(2)O(3)/InP bonding interfacial layer which enables strong wafer bonding and low propagation loss. Lateral p-i-n junctions in the InGaAsP photonic-wire waveguides were formed by using ion implantation for changing refractive index in the InGaAsP waveguide through carrier injection. Optical switching with 10 dB extinction ratio was achieved with driving current of 200 µA which is approximately 10 times smaller than that of Si photonic-wire optical switch owing to larger free-carrier effect in InGaAsP than that in Si.

Structural insight into receptor-selectivity for lurasidone.

Lurasidone is a novel antipsychotic agent with high affinity for dopamine D(2), 5-hydroxyltryptamine 5-HT(2A), and 5-HT(7) receptors. Lurasidone has negligible affinity for histamine H(1) and muscarinic M(1) receptors, which are thought to contribute to side effects such as weight gain, sedation, and worsening of cognitive deficits. Our interests focus on why lurasidone has such high selectivity for only a part of these aminergic G-protein coupled receptors (GPCRs) and the different binding profile from ziprasidone, which has the same benzisothiazolylpiperazine moiety as lurasidone. In order to address these issues, we constructed structural models of lurasidone-GPCR complexes by homology modeling of receptors, exhaustive docking of ligand, and molecular dynamics simulation-based refinement of complexes. This computational study gave reliable structural models for D(2), 5-HT(2A), and 5-HT(7), which had overall structural complementarities with a salt bridge anchor at the center of the lurasidone molecule, but not for H(1) and M(1) owing to steric hindrance between the norbornane-2,3-dicarboximide and/or cyclohexane part of lurasidone and both receptors. By comparison with the structural models of olanzapine-GPCRs and ziprasidone-GPCRs constructed using the same computational protocols, it was suggested that the bulkiness of the norbornane-2,3-dicarboximide part and the rigidity and the bulkiness of the cyclohexyl linker gave lurasidone high selectivity for the desired aminergic GPCRs. Finally, this structural insight was validated by a binding experiment of the novel benzisothiazolylpiperazine derivatives. This knowledge on the structural mechanism behind the receptor selectivity should help to design new antipsychotic agents with preferable binding profiles, and the established computational protocols realize virtual screening and structure-based drug design for other central nervous system drugs with desired selectivity for multiple targets.

Isotopic labeling of heterologous proteins in the yeast Pichia pastoris and Kluyveromyces lactis.

Several protein expression systems are available for the preparation of stable isotope-labeled recombinant proteins for NMR studies. Yeast expression systems have several advantages over prokaryotic systems, such as the widely used Escherichia coli expression system. Protein expression using the methylotrophic yeast Pichia pastoris is commonly employed for the preparation of isotope-labeled proteins. Recently, the hemiascomycete yeast Kluyveromyces lactis expression system was reported as being useful for preparing proteins for NMR studies. Since each yeast expression system has different features, their applications have increased in number. In this chapter, we describe procedures for the efficient production of uniformly isotope-labeled proteins using the P. pastoris and the K. lactis yeast expression systems.

Structural basis of the collagen-binding mode of discoidin domain receptor 2.

Discoidin domain receptor (DDR) is a cell-surface receptor tyrosine kinase activated by the binding of its discoidin (DS) domain to fibrillar collagen. Here, we have determined the NMR structure of the DS domain in DDR2 (DDR2-DS domain), and identified the binding site to fibrillar collagen by transferred cross-saturation experiments. The DDR2-DS domain structure adopts a distorted jellyroll fold, consisting of eight beta-strands. The collagen-binding site is formed at the interloop trench, consisting of charged residues surrounded by hydrophobic residues. The surface profile of the collagen-binding site suggests that the DDR2-DS domain recognizes specific sites on fibrillar collagen. This study provides a molecular basis for the collagen-binding mode of the DDR2-DS domain.