Chemical modifications of imidazole-containing alkoxyamines increase C–ON bond homolysis rate: Effects on their cytotoxic properties in glioblastoma cells

Previously, we described alkoxyamines bearing a pyridine ring as new pro-drugs with low molecular weights and theranostic activity. Upon chemical stimulus, alkoxyamines undergo homolysis and release free radicals, which can, reportedly, enhance magnetic resonance imaging and trigger cancer cell death. In the present study, we describe the synthesis and the anti-cancer activity of sixteen novel alkoxyamines that contain an imidazole ring. Activation of the homolysis was conducted by protonation and/or methylation. These new molecules displayed cytotoxic activities towards human glioblastoma cell lines, including the U251-MG cells that are highly resistant to the conventional chemotherapeutic agent Temozolomide. We further showed that the biological activities of the alkoxyamines were not only related to their half-life times of homolysis. We lastly identified the alkoxyamine (RS/SR)-4a, with both a high antitumour activity and favourable logD_7.4 and pK_a values, which make it a robust candidate for blood-brain barrier penetrating therapeutics against brain neoplasia.     

Empirical Bayes inference of pairwise F(ST) and its distribution in the genome

Populations often have very complex hierarchical structure. Therefore, it is crucial in genetic monitoring and conservation biology to have a reliable estimate of the pattern of population subdivision. F(ST)'s for pairs of sampled localities or subpopulations are crucial statistics for the exploratory analysis of population structures, such as cluster analysis and multidimensional scaling. However, the estimation of F(ST) is not precise enough to reliably estimate the population structure and the extent of heterogeneity. This article proposes an empirical Bayes procedure to estimate locus-specific pairwise F(ST)'s. The posterior mean of the pairwise F(ST) can be interpreted as a shrinkage estimator, which reduces the variance of conventional estimators largely at the expense of a small bias. The global F(ST) of a population generally varies among loci in the genome. Our maximum-likelihood estimates of global F(ST)'s can be used as sufficient statistics to estimate the distribution of F(ST) in the genome. We demonstrate the efficacy and robustness of our model by simulation and by an analysis of the microsatellite allele frequencies of the Pacific herring. The heterogeneity of the global F(ST) in the genome is discussed on the basis of the estimated distribution of the global F(ST) for the herring and examples of human single nucleotide polymorphisms (SNPs).     

Personalized medicine and proteomics: lessons from non-small cell lung cancer

Personalized medicine allows the selection of treatments best suited to an individual patient and disease phenotype. To implement personalized medicine, effective tests predictive of response to treatment or susceptibility to adverse events are needed, and to develop a personalized medicine test, both high quality samples and reliable data are required. We review key features of state-of-the-art proteomic profiling and introduce further analytic developments to build a proteomic toolkit for use in personalized medicine approaches. The combination of novel analytical approaches in proteomic data generation, alignment and comparison permit translation of identified biomarkers into practical assays. We further propose an expanded statistical analysis to understand the sources of variability between individuals in terms of both protein expression and clinical variables and utilize this understanding in a predictive test.     

Prediction of GPCR-G protein coupling specificity using features of sequences and biological functions

Understanding the coupling specificity between G protein-coupled receptors （GPCRs） and specific classes of G proteins is important for further elucidation of receptor functions within a cell. Increasing information on GPCR sequences and the G protein family would facilitate prediction of the coupling properties of GPCRs. In this study, we describe a novel approach for predicting the coupling specificity between GPCRs and G proteins. This method uses not only GPCR sequences but also the functional knowledge generated by natural language processing, and can achieve 92.2% prediction accuracy by using the C4.5 algorithm. Furthermore, rules related to GPCR-G protein coupling are generated. The combination of sequence analysis and text mining improves the prediction accuracy for GPCR-G protein coupling specificity, and also provides clues for understanding GPCR signaling.     

A Protein Deep Sequencing Evaluation of Metastatic Melanoma Tissues

Malignant melanoma has the highest increase of incidence of malignancies in the western world. In early stages, front line therapy is surgical excision of the primary tumor. Metastatic disease has very limited possibilities for cure. Recently, several protein kinase inhibitors and immune modifiers have shown promising clinical results but drug resistance in metastasized melanoma remains a major problem. The need for routine clinical biomarkers to follow disease progression and treatment efficacy is high. The aim of the present study was to build a protein sequence database in metastatic melanoma, searching for novel, relevant biomarkers. Ten lymph node metastases (South-Swedish Malignant Melanoma Biobank) were subjected to global protein expression analysis using two proteomics approaches (with/without orthogonal fractionation). Fractionation produced higher numbers of protein identifications (4284). Combining both methods, 5326 unique proteins were identified (2641 proteins overlapping). Deep mining proteomics may contribute to the discovery of novel biomarkers for metastatic melanoma, for example dividing the samples into two metastatic melanoma “genomic subtypes”, (“pigmentation” and “high immune”) revealed several proteins showing differential levels of expression. In conclusion, the present study provides an initial version of a metastatic melanoma protein sequence database producing a total of more than 5000 unique protein identifications. The raw data have been deposited to the ProteomeXchange with identifiers PXD001724 and PXD001725.     

Efficacy and Safety of Sitagliptin Added to Insulin in Japanese Patients with Type 2 Diabetes: The EDIT Randomized Trial

Aims

To clarify the efficacy and safety of adding sitagliptin to insulin therapy in Japanese patients with suboptimally controlled type 2 diabetes (T2DM).

Study Design and Methods

This was a 24-week, prospective, randomized, open-labeled, controlled trial. Patients with T2DM who were suboptimally controlled despite receiving at least twice daily injection of insulin were enrolled in the study. The patients were randomized to continuation of insulin treatment (Insulin group) or addition of sitagliptin 50 to 100 mg daily to insulin treatment (Ins+Sita group). The primary outcome was change in HbA1c at week 24.

Results

Adding sitagliptin to insulin significantly reduced HbA1c from 7.9 ± 1.0% at baseline to 7.0 ± 0.8% at week 24 (P <0.0001), while there was no significant change in HbA1c in the Insulin group (7.8 ± 0.7% vs. 7.8 ± 1.1%, P = 0.32). The difference in HbA1c reduction between the groups was 0.9% (95% confidence interval, 0.4 to 1.5, P = 0.01). There was no significant weight gain in either group. Incidence of hypoglycemia was significantly reduced in the Ins+Sita group compared with the Insulin group. Treatment satisfaction was improved in the Ins+Sita group. Baseline HbA1c level and beta cell function were associated with the magnitude of reduction in HbA1c in the Ins+Sita group.

Conclusion

Adding sitagliptin to insulin reduced HbA1c without weight gain or increase in hypoglycemia, and improved treatment satisfaction in Japanese patients with T2DM who were suboptimally controlled despite at least twice daily injection of insulin.

Trial Registration

The University Hospital Medical Information Network (UMIN) Clinical Trials Registry UMIN000004678     

The reaction to c-banding of c-banded constituents during mitotic cycle ofCrepis capillaris

The reaction to C-banding was investigated throughout the mitotic cycle ofCrepis capillaris (2n=6): (1) 18–22 C-bodies or C-bands were found during mid telophase and interphase to prophase and metaphase, and also 12–14 at late anaphase to early telophase in the mitotic cycle. Fewer C-bands in late anaphase to early telophase were due to the absence of minute bands; (2) large and medium sized C-bands were strongly stained by Giemsa, while small and minute bands stained palely. It is suggested that inCrepis capillaris the difference of color in C-banded segments following Giemsa staining is referable to the amount of constitutive heterochromatin rather than to the difference in the condensation and decondensation; (3) the size of C-bodies changed during telophase to interphase and prophase. It is inferred that the extent of C-bodies is regulated by both the length of DNA sequences of constitutive heterochromatin and the amount of proteins combined with C-banded DNA. It was shown that the reaction to C-banding is neither due to the differential condensation of chromatin nor to a higher concentration of DNA in the C-banded regions, in the C-banding mechanism as has been suggested so far at least.