Application of Multiplexed Kinase Inhibitor Beads to Study Kinome Adaptations in Drug-Resistant Leukemia

Protein kinases play key roles in oncogenic signaling and are a major focus in the development of targeted cancer therapies. Imatinib, a BCR-Abl tyrosine kinase inhibitor, is a successful front-line treatment for chronic myelogenous leukemia (CML). However, resistance to imatinib may be acquired by BCR-Abl mutations or hyperactivation of Src family kinases such as Lyn. We have used multiplexed kinase inhibitor beads (MIBs) and quantitative mass spectrometry (MS) to compare kinase expression and activity in an imatinib-resistant (MYL-R) and -sensitive (MYL) cell model of CML. Using MIB/MS, expression and activity changes of over 150 kinases were quantitatively measured from various protein kinase families. Statistical analysis of experimental replicates assigned significance to 35 of these kinases, referred to as the MYL-R kinome profile. MIB/MS and immunoblotting confirmed the over-expression and activation of Lyn in MYL-R cells and identified additional kinases with increased (MEK, ERK, IKKα, PKCβ, NEK9) or decreased (Abl, Kit, JNK, ATM, Yes) abundance or activity. Inhibiting Lyn with dasatinib or by shRNA-mediated knockdown reduced the phosphorylation of MEK and IKKα. Because MYL-R cells showed elevated NF-κB signaling relative to MYL cells, as demonstrated by increased IκBα and IL-6 mRNA expression, we tested the effects of an IKK inhibitor (BAY 65-1942). MIB/MS and immunoblotting revealed that BAY 65-1942 increased MEK/ERK signaling and that this increase was prevented by co-treatment with a MEK inhibitor (AZD6244). Furthermore, the combined inhibition of MEK and IKKα resulted in reduced IL-6 mRNA expression, synergistic loss of cell viability and increased apoptosis. Thus, MIB/MS analysis identified MEK and IKKα as important downstream targets of Lyn, suggesting that co-targeting these kinases may provide a unique strategy to inhibit Lyn-dependent imatinib-resistant CML. These results demonstrate the utility of MIB/MS as a tool to identify dysregulated kinases and to interrogate kinome dynamics as cells respond to targeted kinase inhibition.     

Two Distinct Functional Patterns of Hepatitis C Virus (HCV)-Specific T Cell Responses in Seronegative,Aviremic Patients

In hepatitis C Virus (HCV) high-risk groups, HCV-specific T cell responses have been detected in seronegative, aviremic persons who have no evidence of HCV infection. Herein, we investigated functional profiles of HCV-specific T-cell responses in seronegative, aviremic patients of a HCV high-risk group. Seventy seven hemodialysis patients with chronic renal disease were analyzed by IFN-γ ELISpot assays, and eight of 71 (11.3%) seronegative, aviremic patients displayed HCV-specific T-cell responses. Their HCV-specific memory T cells were characterized by assessing cytokine polyfunctionality, known to provide antiviral protection. By intracellular staining of IFN-γ, TNF-α, IL-2 and MIP-1β, we identified two distinct populations in the seronegative, aviremic patients: polyfunctional responders and TNF-α-predominant responders. In further analysis, occult HCV infection was excluded as a cause of the HCV-specific T cell response via secondary nested RT-PCR of HCV RNA in peripheral blood mononuclear cell samples. HCV-specific T cells targeted multiple epitopes including non-structural proteins in a single patient, implying that their T cells might have been primed by HCV proteins synthesized within the host. We conclude that HCV-specific memory T cells of seronegative, aviremic patients arise from authentic HCV replication in the host, but not from current occult HCV infection. By functional pattern of HCV-specific T cells, there are two distinct populations in these patients: polyfunctional responders and TNF-α-predominant responders.     

Generating In Vivo Cloning Vectors for Parallel Cloning of Large Gene Clusters by Homologous Recombination

A robust method for the in vivo cloning of large gene clusters was developed based on homologous recombination (HR), requiring only the transformation of PCR products into Escherichia coli cells harboring a receiver plasmid. Positive clones were selected by an acquired antibiotic resistance, which was activated by the recruitment of a short ribosome-binding site plus start codon sequence from the PCR products to the upstream position of a silent antibiotic resistance gene in receiver plasmids. This selection was highly stringent and thus the cloning efficiency of the GFPuv gene (size: 0.7 kb) was comparable to that of the conventional restriction-ligation method, reaching up to 4.3 × 10⁴ positive clones per μg of DNA. When we attempted parallel cloning of GFPuv fusion genes (size: 2.0 kb) and carotenoid biosynthesis pathway clusters (sizes: 4 kb, 6 kb, and 10 kb), the cloning efficiency was similarly high regardless of the DNA size, demonstrating that this would be useful for the cloning of large DNA sequences carrying multiple open reading frames. However, restriction analyses of the obtained plasmids showed that the selected cells may contain significant amounts of receiver plasmids without the inserts. To minimize the amount of empty plasmid in the positive selections, the sacB gene encoding a levansucrase was introduced as a counter selection marker in receiver plasmid as it converts sucrose to a toxic levan in the E. coli cells. Consequently, this method yielded completely homogeneous plasmids containing the inserts via the direct transformation of PCR products into E. coli cells.     

hESC Expansion and Stemness Are Independent of Connexin Forty-Three-Mediated Intercellular Communication between hESCs and hASC Feeder Cells

Background

Human embryonic stem cells (hESCs) are a promising and powerful source of cells for applications in regenerative medicine, tissue engineering, cell-based therapies, and drug discovery. Many researchers have employed conventional culture techniques using feeder cells to expand hESCs in significant numbers, although feeder-free culture techniques have recently been developed. In regard to stem cell expansion, gap junctional intercellular communication (GJIC) is thought to play an important role in hESC survival and differentiation. Indeed, it has been reported that hESC-hESC communication through connexin 43 (Cx43, one of the major gap junctional proteins) is crucial for the maintenance of hESC stemness during expansion. However, the role of GJIC between hESCs and feeder cells is unclear and has not yet been reported.

Methodology/Principal Findings

This study therefore examined whether a direct Cx43-mediated interaction between hESCs and human adipose-derived stem cells (hASCs) influences the maintenance of hESC stemness. Over 10 passages, hESCs cultured on a layer of Cx43-downregulated hASC feeder cells showed normal morphology, proliferation (colony growth), and stemness, as assessed by alkaline phosphatase (AP), OCT4 (POU5F1-Human gene Nomenclature Database), SOX2, and NANOG expression.

Conclusions/Significance

These results demonstrate that Cx43-mediated GJIC between hESCs and hASC feeder cells is not an important factor for the conservation of hESC stemness and expansion.     

Predicting Drug-Target Interactions Using Drug-Drug Interactions

Computational methods for predicting drug-target interactions have become important in drug research because they can help to reduce the time, cost, and failure rates for developing new drugs. Recently, with the accumulation of drug-related data sets related to drug side effects and pharmacological data, it has became possible to predict potential drug-target interactions. In this study, we focus on drug-drug interactions (DDI), their adverse effects () and pharmacological information (), and investigate the relationship among chemical structures, side effects, and DDIs from several data sources. In this study, data from the STITCH database, from drugs.com, and drug-target pairs from ChEMBL and SIDER were first collected. Then, by applying two machine learning approaches, a support vector machine (SVM) and a kernel-based L1-norm regularized logistic regression (KL1LR), we showed that DDI is a promising feature in predicting drug-target interactions. Next, the accuracies of predicting drug-target interactions using DDI were compared to those obtained using the chemical structure and side effects based on the SVM and KL1LR approaches, showing that DDI was the data source contributing the most for predicting drug-target interactions.     

Antifatigue Effects of Panax ginseng C.A. Meyer: A Randomised,Double-Blind,Placebo-Controlled Trial

The present study investigated the antifatigue effects of Panax ginseng C.A. Meyer in 90 subjects (21 men and 69 women) with idiopathic chronic fatigue (ICF) in a randomised, double-blind, placebo-controlled and parallel designed trial. A bespoke 20% ethanol extract of P. ginseng (1 g or 2 g day^–1) or a placebo was administered to each group for 4 weeks, and then fatigue severity was monitored using a self-rating numeric scale (NRS) and a visual analogue scale (VAS) as a primary endpoint. Serum levels of reactive oxygen species (ROS), malondialdehyde (MDA), total glutathione (GSH) contents and glutathione reductase (GSH-Rd) activity were determined. After 4-week, P. ginseng administration decreased the total NRS score, but they were not statistically significant compared with placebo (P>0.05). Mental NRS score was significantly improved by P. ginseng administrations as 20.4±5.0 to 15.1±6.5 [95% CI 2.3∼8.2] for 1 g and 20.7±6.3 to 13.8±6.2 [95% CI −0.1∼4.2] for 2 g compared with placebo 20.9±4.5 to 18.8±2.9 [95% CI 4.1∼9.9, P<0.01]. Only 2 g P. ginseng significantly reduced the VAS score from 7.3±1.3 to 4.4±1.8 [95% CI 0.7∼1.8] compared with the placebo 7.1±1.0 to 5.8±1.3 [95% CI 2.2 ∼3.7, P<0.01]. ROS and MDA levels were lowered by P. ginseng compared to placebo. P. ginseng 1 g increased GSH concentration and GSH-Rd activity. Our results provide the first evidence of the antifatigue effects of P. ginseng in patients with ICF, and we submit that these changes in antioxidant properties contribute in part to its mechanism.

Trial Registration

Clinical Research Information Service (CRIS) KCT0000048     

Use of Hangeul Twitter to Track and Predict Human Influenza Infection

Influenza epidemics arise through the accumulation of viral genetic changes. The emergence of new virus strains coincides with a higher level of influenza-like illness (ILI), which is seen as a peak of a normal season. Monitoring the spread of an epidemic influenza in populations is a difficult and important task. Twitter is a free social networking service whose messages can improve the accuracy of forecasting models by providing early warnings of influenza outbreaks. In this study, we have examined the use of information embedded in the Hangeul Twitter stream to detect rapidly evolving public awareness or concern with respect to influenza transmission and developed regression models that can track levels of actual disease activity and predict influenza epidemics in the real world. Our prediction model using a delay mode provides not only a real-time assessment of the current influenza epidemic activity but also a significant improvement in prediction performance at the initial phase of ILI peak when prediction is of most importance.