Type 2 Diabetes Biomarkers of Human Gut Microbiota Selected via Iterative Sure Independent Screening Method

Type 2 diabetes, which is a complex metabolic disease influenced by genetic and environment, has become a worldwide problem. Previous published results focused on genetic components through genome-wide association studies that just interpret this disease to some extent. Recently, two research groups published metagenome-wide association studies (MGWAS) result that found meta-biomarkers related with type 2 diabetes. However, One key problem of analyzing genomic data is that how to deal with the ultra-high dimensionality of features. From a statistical viewpoint it is challenging to filter true factors in high dimensional data. Various methods and techniques have been proposed on this issue, which can only achieve limited prediction performance and poor interpretability. New statistical procedure with higher performance and clear interpretability is appealing in analyzing high dimensional data. To address this problem, we apply an excellent statistical variable selection procedure called iterative sure independence screening to gene profiles that obtained from metagenome sequencing, and 48/24 meta-markers were selected in Chinese/European cohorts as predictors with 0.97/0.99 accuracy in AUC (area under the curve), which showed a better performance than other model selection methods, respectively. These results demonstrate the power and utility of data mining technologies within the large-scale and ultra-high dimensional genomic-related dataset for diagnostic and predictive markers identifying.     

Genetic Prediction of Antidepressant Drug Response and Nonresponse in Korean Patients

Genetic polymorphism contributes to variation in response to drug treatment of depression. We conducted three independent 6-week treatment studies in outpatients with major depressive disorder (MDD) to develop a pharmacogenomic model predicting response and nonresponse. We screened candidate genomic markers for association with response to selective serotonin reuptake inhibitors (SSRIs). No patients had received any antidepressant drug treatment in the current episode of depression. Outcome evaluation was blinded to drug and genotype data. The prediction model derived from a development sample of 239 completer cases treated with SSRIs comprised haplotypes and polymorphisms related to serotonin synthesis, serotonin transport, glutamate receptors, and GABA synthesis. The model was evaluated prospectively for prediction of outcome in a validation sample of 176 new SSRI-treated completer cases. The model gave a prediction in 60% of these cases. Predictive values were 85% for predicted responders and 86% for predicted nonresponders, compared to prior probabilities of 66% for observed response and 34% for observed nonresponse in those cases (both P<0.001). Convergent cross-validation was obtained through failure of the model to predict outcomes in a third independent sample of 189 completer cases who received non-SSRI antidepressants. We suggest proof of principle for genetic guidance to use or avoid SSRIs in a majority of Korean depressed patients.     

Phosphotyrosine-based Phosphoproteomics for Target Identification and Drug Response Prediction in AML Cell Lines

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Highlights

• •pY phosphoproteomes and dedicated ranking analyses for 16 AML cell lines.
• •RTK drivers, 6 mutant cell lines confirmed, identification for 4 more cell lines.
• •MAPK1/3 phosphorylation for cell lines without TK driver, indicating RAS mutation.
• •Drug target space phosphorylation correlates with drug IC50s in specific cell lines.

     

Combining Evolutionary Information and an Iterative Sampling Strategy for Accurate Protein Structure Prediction

Recent work has shown that the accuracy of ab initio structure prediction can be significantly improved by integrating evolutionary information in form of intra-protein residue-residue contacts. Following this seminal result, much effort is put into the improvement of contact predictions. However, there is also a substantial need to develop structure prediction protocols tailored to the type of restraints gained by contact predictions. Here, we present a structure prediction protocol that combines evolutionary information with the resolution-adapted structural recombination approach of Rosetta, called RASREC. Compared to the classic Rosetta ab initio protocol, RASREC achieves improved sampling, better convergence and higher robustness against incorrect distance restraints, making it the ideal sampling strategy for the stated problem. To demonstrate the accuracy of our protocol, we tested the approach on a diverse set of 28 globular proteins. Our method is able to converge for 26 out of the 28 targets and improves the average TM-score of the entire benchmark set from 0.55 to 0.72 when compared to the top ranked models obtained by the EVFold web server using identical contact predictions. Using a smaller benchmark, we furthermore show that the prediction accuracy of our method is only slightly reduced when the contact prediction accuracy is comparatively low. This observation is of special interest for protein sequences that only have a limited number of homologs.     

以布氏锥虫亮氨酰tRNA合成酶为靶标的抗锥虫药物筛选系统的建立

锥虫是人的血液寄生虫,对热带乃至拉丁美洲贫困地区影响极大,但目前的传统治疗药物存在副作用大、有效性不断降低的问题。根据亮氨酰tRNA合成酶在微生物中可作为药物靶点的事实,在新型抗锥虫药物筛选中,通过对锥虫的亮氨酰tRNA合成酶的克隆、表达和纯化,以及该酶活性测定的优化,建立了该酶抑制物的筛选系统。筛选结果表明,这一以锥虫亮氨酰tRNA合成酶为靶标的抗锥虫药物筛选系统可以有效筛选抗锥虫化合物,选出的化合物有一定的抗锥虫特异性,并可以用于化合物的进一步优化和测定其半抑制浓度。使用这一系统筛选到了对锥虫有较好抑制作用,但对人类细胞毒性较小的一系列新型化合物,因而锥虫亮氨酰tRNA合成酶很可能成为开发有效抗锥虫药物的新靶标。     

Atomic-Accuracy Prediction of Protein Loop Structures through an RNA-Inspired Ansatz

Consistently predicting biopolymer structure at atomic resolution from sequence alone remains a difficult problem, even for small sub-segments of large proteins. Such loop prediction challenges, which arise frequently in comparative modeling and protein design, can become intractable as loop lengths exceed 10 residues and if surrounding side-chain conformations are erased. Current approaches, such as the protein local optimization protocol or kinematic inversion closure (KIC) Monte Carlo, involve stages that coarse-grain proteins, simplifying modeling but precluding a systematic search of all-atom configurations. This article introduces an alternative modeling strategy based on a ‘stepwise ansatz’, recently developed for RNA modeling, which posits that any realistic all-atom molecular conformation can be built up by residue-by-residue stepwise enumeration. When harnessed to a dynamic-programming-like recursion in the Rosetta framework, the resulting stepwise assembly (SWA) protocol enables enumerative sampling of a 12 residue loop at a significant but achievable cost of thousands of CPU-hours. In a previously established benchmark, SWA recovers crystallographic conformations with sub-Angstrom accuracy for 19 of 20 loops, compared to 14 of 20 by KIC modeling with a comparable expenditure of computational power. Furthermore, SWA gives high accuracy results on an additional set of 15 loops highlighted in the biological literature for their irregularity or unusual length. Successes include cis-Pro touch turns, loops that pass through tunnels of other side-chains, and loops of lengths up to 24 residues. Remaining problem cases are traced to inaccuracies in the Rosetta all-atom energy function. In five additional blind tests, SWA achieves sub-Angstrom accuracy models, including the first such success in a protein/RNA binding interface, the YbxF/kink-turn interaction in the fourth ‘RNA-puzzle’ competition. These results establish all-atom enumeration as an unusually systematic approach to ab initio protein structure modeling that can leverage high performance computing and physically realistic energy functions to more consistently achieve atomic accuracy.     

Improved Behavioral Response as a Valid Biomarker for Drug Screening Program in Transgenic Rodent Models of Tauopathies

Neurodegenerative tauopathies are defined as a group of dementia and movement disorders characterized by prominent filamentous tau inclusions and degeneration located within certain brain regions. Their common sign is a presence of proteinaceous aggregates composed of hyperphosphorylated and truncated tau proteins. The molecular mechanisms of the disease still remain unresolved, therefore transgenic organisms displaying tau-related neurodegenerative cascade have been created to allow decoding of individual pathways involved in human pathological conditions. Moreover, use of transgenic model organisms enables the application of potential therapeutic approaches. The expression of mutated or misfolded tau as a transgene in vivo leads to significant alteration of neurobehavioral features of experimental animal, therefore detailed classification of behavioral phenotype become one of the first crucial analyses, while it functionally correlates with central nervous system impairment. Currently, two major types of behavioral impairment have been described in transgenic rodent models of tauopathies, (1) progressive motor impairment associated with muscular weakness and premature death and (2) age-related impairment of cognitive functions attended with unaffected motor status. Up to the present, only transgenic models displaying motor impairment were successfully applied into the drug trials targeting misfolded tau protein, despite their behavioral inconsistence with clinical profile of progressive human tauopathy. The aim of this study was, therefore, to summarize the pros and cons of used transgenic rodent models mimicking human tauopathies in connection with development of therapeutic strategies.     

Drug Development-targeted Screening of Leptin Agonist Glycopeptides

A glycosylated dodecapeptide fragment corresponding to the hypothalamus-active cytokine leptin exhibits agonistic properties to the leptin receptor (ObR) in vitro and penetrates into the brain in vivo. In order to characterize the drug development potential of the lead peptide and to optimize it for pharmacological applicability, a series of biochemical screening assays were custom-tailored to the leptin/ObR system. To identify peptides that bind the extracellular domain of ObR, we characterized the optimal conditions for an ELISA-type assay where the leptin fragments were immobilized to the plates. With this technology we could identify low-dose binder peptidomimetics which, according to a comparison of the conventional cell proliferation assay and a measure of metabolically active cells, revealed that agonists identified by these cellular assays may not necessarily induce the expected growth characteristics in ObR expressing cells. The original glycopeptide lead displayed a 2 h half life in 25% diluted mouse serum but poor stability in mouse brain extract. Fifteen percent of the glycopeptide crossed a dual endothelial/astrocyte cell layer (representing an in vitro model of blood-brain-barrier) in 30 min, and the coexistence of the two cell types appeared necessary to quantify the level of brain accessibility. Finally, in an in vivo mouse model, a Cy5.5 labeled glycopeptide was more evenly distributed all over the body, including the brain, than a similarly labeled full-sized leptin protein.     

CDA: Combinatorial Drug Discovery Using Transcriptional Response Modules

Background

Anticancer therapies that target single signal transduction pathways often fail to prevent proliferation of cancer cells because of overlapping functions and cross-talk between different signaling pathways. Recent research has identified that balanced multi-component therapies might be more efficacious than highly specific single component therapies in certain cases. Ideally, synergistic combinations can provide 1) increased efficacy of the therapeutic effect 2) reduced toxicity as a result of decreased dosage providing equivalent or increased efficacy 3) the avoidance or delayed onset of drug resistance. Therefore, the interest in combinatorial drug discovery based on systems-oriented approaches has been increasing steadily in recent years.

Methodology

Here we describe the development of Combinatorial Drug Assembler (CDA), a genomics and bioinformatics system, whereby using gene expression profiling, multiple signaling pathways are targeted for combinatorial drug discovery. CDA performs expression pattern matching of signaling pathway components to compare genes expressed in an input cell line (or patient sample data), with expression patterns in cell lines treated with different small molecules. Then it detects best pattern matching combinatorial drug pairs across the input gene set-related signaling pathways to detect where gene expression patterns overlap and those predicted drug pairs could likely be applied as combination therapy. We carried out in vitro validations on non-small cell lung cancer cells and triple-negative breast cancer (TNBC) cells. We found two combinatorial drug pairs that showed synergistic effect on lung cancer cells. Furthermore, we also observed that halofantrine and vinblastine were synergistic on TNBC cells.

Conclusions

CDA provides a new way for rational drug combination. Together with phExplorer, CDA also provides functional insights into combinatorial drugs. CDA is freely available at http://cda.i-pharm.org.     

Stability of Mixed-Strategy-Based Iterative Logit Quantal Response Dynamics in Game Theory

Using the Logit quantal response form as the response function in each step, the original definition of static quantal response equilibrium (QRE) is extended into an iterative evolution process. QREs remain as the fixed points of the dynamic process. However, depending on whether such fixed points are the long-term solutions of the dynamic process, they can be classified into stable (SQREs) and unstable (USQREs) equilibriums. This extension resembles the extension from static Nash equilibriums (NEs) to evolutionary stable solutions in the framework of evolutionary game theory. The relation between SQREs and other solution concepts of games, including NEs and QREs, is discussed. Using experimental data from other published papers, we perform a preliminary comparison between SQREs, NEs, QREs and the observed behavioral outcomes of those experiments. For certain games, we determine that SQREs have better predictive power than QREs and NEs.     

Trauma Activation Patients: Evidence for Routine Alcohol and Illicit Drug Screening

Background

Statistics from the National Trauma Data Bank imply that discretionary blood alcohol and urine drug testing is common. However, there is little evidence to determine which patients are appropriate for routine testing, based on information available at trauma center arrival. In 2002, Langdorf reported alcohol and illicit drug rates in Trauma Activation Patients.

Methodology/Principal Findings

This is a retrospective investigation of alcohol and illicit drug rates in consecutive St. Elizabeth Health Center (SEHC) trauma patients. SEHC Trauma Activation Patients are compared with the Langdorf Activation Patients and with the SEHC Trauma Nonactivation Patients. Minimum Rates are positive tests divided by total patients (tested and not tested). Activation patients: The minimum alcohol rates were: SEHC 23.1%, Langdorf 28.2%, combined 24.8%. The minimum illicit drug rates were: SEHC 15.7%, Langdorf 23.5, combined 18.3%. The minimum alcohol and/or illicit drug rates were: SEHC 33.4%, Langdorf 41.8%, combined 36.2%. Nonactivation patients: The SEHC minimum alcohol rate was 4.7% and the minimum illicit drug rate was 6.0%.

Conclusions

Alcohol and illicit drug rates were significantly greater for Trauma Activation Patients, when compared to Nonactivation Patients. At minimum, Trauma Activation Patients are likely to have a 1-in-3 positive test for alcohol and/or an illicit drug. This substantial rate suggests that Trauma Activation Patients, a readily discernible group at trauma center arrival, are appropriate for routine alcohol and illicit drug testing. However, discretionary testing is more reasonable for Trauma Nonactivation Patients, because minimum rates are low.     

Response of renal calcium-binding protein. Independence of kidney vitamin D hydroxylation.

Dietary calcium and dietary phosphorus restriction were studied in chicks fed either cholecalciferol or 1alpha-hydroxycholecalciferol. Intestinal calcium absorption and calcium-binding protein of 1alpha-hydroxycholecalciferol-treated chicks remained unchanged under dietary calcium restriction, but increased under dietary phosphorus restriction. Kidney calcium-binding protein was not altered by dietary caclium restriction in chidks treated with either cholecalciferol or 1alpha-hydroxycholecalciferol, but increased under dietary phosphorus restriction independent of the vitamin D source. In contrast to the intestine, calcium-binding activity of the kidney was found to be poorly related to the calcium-binding protein concentration. It is suggested that kidney calcium-binding protein is regulated by a mechanism different from that of intestinal calcium-binding protein, and that its concentration in renal tissue is related to renal caclium excretion or plasma calcium level.     

SynergyFinder Plus: Toward Better Interpretation and Annotation of Drug Combination Screening Datasets

Combinatorial therapies have been recently proposed to improve the efficacy of anticancer treatment. The Synergy Finder R package is a software used to analyze pre-clinical drug combination datasets. Here, we report the major updates to the Synergy Finder R package for improved interpretation and annotation of drug combination screening results. Unlike the existing implementations, the updated Synergy Finder R package includes five main innovations. 1) We extend the mathematical models to higher...     

DeepCPI:A Deep Learning-based Framework for Large-scale in silico Drug Screening

Accurate identification of compound–protein interactions(CPIs) in silico may deepen our understanding of the underlying mechanisms of drug action and thus remarkably facilitate drug discovery and development.Conventional similarity-or docking-based computational methods for predicting CPIs rarely exploit latent features from currently available large-scale unlabeled compound and protein data and often limit their usage to relatively small-scale datasets.In the present study,we propose Deep CPI,a novel general and scalable computational framework that combines effective feature embedding(a technique of representation learning) with powerful deep learning methods to accurately predict CPIs at a large scale.Deep CPI automatically learns the implicit yet expressive low-dimensional features of compounds and proteins from a massive amount of unlabeled data.Evaluations of the measured CPIs in large-scale databases,such as Ch EMBL and Binding DB,as well as of the known drug–target interactions from Drug Bank,demonstrated the superior predictive performance of Deep CPI.Furthermore,several interactions among smallmolecule compounds and three G protein-coupled receptor targets(glucagon-like peptide-1 receptor,glucagon receptor,and vasoactive intestinal peptide receptor) predicted using Deep CPI were experimentally validated.The present study suggests that Deep CPI is a useful and powerful tool for drug discovery and repositioning.The source code of Deep CPI can be downloaded from https://github.com/Fangping Wan/Deep CPI.     

High Throughput Screening for Drug Discovery: Continually Transitioning into New Technology

Those working in HTS laboratories, pressured to find increasing numbers of drug leads while containing costs, are seeking larger compound sets, more automated systems to screen them faster, and an integrated set of equipment and consumables. Enabling technologies are continually being developed and suppliers are teaming up to supply integrated equipment and consumable sets. Miniaturization, microfluidic chips, subnanoliter dispensing, fluorescence, homogeneous assays for HTS, and virtual screening are just some of the evolving tools that HTS experts are continually evaluating and incorporating into drug discovery operations.     

用于筛选抑制丙型肝炎病毒内部核糖体进入位点活性药物的整合型细胞药物筛选模型的构建和评价

目的：构建一个能用于筛选抑制丙型肝炎病毒内部核糖体进入位点（HCV—IRES）活性药物的整合型细胞药筛模型。方法：构建携带HCV 5＇-UTR调控报告基因分泌型碱性磷酸酶（SEAP）基因的质粒pHCV5’-SEAP和用作筛选标记的pNeo^R,线性化后共转染于Huh-7细胞中,经G418筛选后得到抗性细胞克隆,进行SEAP定量检测筛选后,得到能用于筛选抑制HCV-IRES活性药物的整合型细胞药筛模型;连续培养24代,用MTT法测细胞相对活力评价该药筛模型的生长稳定性,用SEAP定量检测法评价该药筛模型的SEAP表达稳定性,用反义寡聚核苷酸抑制试验评价模型的药筛灵敏度。结果：G418筛选后得到26个抗性细胞克隆;对其中5个抗性细胞克隆进行SEAP定量检测筛选后,得到3个能表达SEAP的阳性细胞克隆;连续培养24代过程中,该药筛模型的生长稳定性大干80％,SEAP表达稳定性达95％。结论：构建的细胞模型具有良好的稳定性和药筛灵敏度,符合作为药物筛选模型的要求。     

Gemcitabine and Arabinosylcytosin Pharmacogenomics: Genome-Wide Association and Drug Response Biomarkers

Cancer patients show large individual variation in their response to chemotherapeutic agents. Gemcitabine (dFdC) and AraC, two cytidine analogues, have shown significant activity against a variety of tumors. We previously used expression data from a lymphoblastoid cell line-based model system to identify genes that might be important for the two drug cytotoxicity. In the present study, we used that same model system to perform a genome-wide association (GWA) study to test the hypothesis that common genetic variation might influence both gene expression and response to the two drugs. Specifically, genome-wide single nucleotide polymorphisms (SNPs) and mRNA expression data were obtained using the Illumina 550K® HumanHap550 SNP Chip and Affymetrix U133 Plus 2.0 GeneChip, respectively, for 174 ethnically-defined “Human Variation Panel” lymphoblastoid cell lines. Gemcitabine and AraC cytotoxicity assays were performed to obtain IC₅₀ values for the cell lines. We then performed GWA studies with SNPs, gene expression and IC₅₀ of these two drugs. This approach identified SNPs that were associated with gemcitabine or AraC IC₅₀ values and with the expression regulation for 29 genes or 30 genes, respectively. One SNP in IQGAP2 (rs3797418) was significantly associated with variation in both the expression of multiple genes and gemcitabine and AraC IC₅₀. A second SNP in TGM3 (rs6082527) was also significantly associated with multiple gene expression and gemcitabine IC50. To confirm the association results, we performed siRNA knock down of selected genes with expression that was associated with rs3797418 and rs6082527 in tumor cell and the knock down altered gemcitabine or AraC sensitivity, confirming our association study results. These results suggest that the application of GWA approaches using cell-based model systems, when combined with complementary functional validation, can provide insights into mechanisms responsible for variation in cytidine analogue response.