Synthesis and antiproliferative activity of benzocyclobutacarbazol derivatives. A new class of potential antitumor agents

Several benzocyclobutacarbazol derivatives were synthesized and evaluated for their potential cytotoxic properties. A number of these compounds exhibited significant antiproliferative activity with concomitant interaction with the cell cycle and represent a new class of potential anticancer agents.     

Soluble and filamentous proteins in Arabidopsis sieve elements

Phloem sieve elements are highly differentiated cells involved in the long-distance transport of photoassimilates. These cells contain both aggregated phloem-proteins (P-proteins) and soluble proteins, which are also translocated by mass flow. We used liquid chromatography-tandem mass spectrometry (LC-MS/MS) to carry out a proteomic survey of the phloem exudate of Arabidopsis thaliana, collected by the ethylenediaminetetraacetic acid (EDTA)-facilitated method. We identified 287 proteins, a large proportion of which were enzymes involved in the metabolic precursor generation and amino acid synthesis, suggesting that sieve tubes display high levels of metabolic activity. RNA-binding proteins, defence proteins and lectins were also found. No putative P-proteins were detected in the EDTA-exudate fraction, indicating a lack of long-distance translocation of such proteins in Arabidopsis. In parallel, we investigated the organization of P-proteins, by high-resolution transmission electron microscopy, and the localization of the phloem lectin PP2, a putative P-protein component, by immunolocalization with antibodies against PP2-A1. Transmission electron microscopy observations of P-proteins revealed bundles of filaments resembling strings of beads. PP2-A1 was found weakly associated with these structures in the sieve elements and bound to plastids. These observations suggest that PP2-A1 is anchored to P-proteins and organelles rather than being a structural component of P-proteins.     

Efficient algorithms for optimal designs with correlated observations in pharmacokinetics and dose-finding studies

Random effects models are widely used in population pharmacokinetics and dose-finding studies. However, when more than one observation is taken per patient, the presence of correlated observations (due to shared random effects and possibly residual serial correlation) usually makes the explicit determination of optimal designs difficult. In this article, we introduce a class of multiplicative algorithms to be able to handle correlated data and thus allow numerical calculation of optimal experimental designs in such situations. In particular, we demonstrate its application in a concrete example of a crossover dose-finding trial, as well as in a typical population pharmacokinetics example. Additionally, we derive a lower bound for the efficiency of any given design in this context, which allows us on the one hand to monitor the progress of the algorithm, and on the other hand to investigate the efficiency of a given design without knowing the optimal one. Finally, we extend the methodology such that it can be used to determine optimal designs if there exist some requirements regarding the minimal number of treatments for several (in some cases all) experimental conditions.     

Life cycle assessment (LCA) applied to the process industry: a review

Purpose

Life cycle assessment (LCA) methodology is a well-established analytical method to quantify environmental impacts, which has been mainly applied to products. However, recent literature would suggest that it has also the potential as an analysis and design tool for processes, and stresses that one of the biggest challenges of this decade in the field of process systems engineering (PSE) is the development of tools for environmental considerations.

Method

This article attempts to give an overview of the integration of LCA methodology in the context of industrial ecology, and focuses on the use of this methodology for environmental considerations concerning process design and optimization.

Results

The review identifies that LCA is often used as a multi-objective optimization of processes: practitioners use LCA to obtain the inventory and inject the results into the optimization model. It also shows that most of the LCA studies undertaken on process analysis consider the unit processes as black boxes and build the inventory analysis on fixed operating conditions.

Conclusions

The article highlights the interest to better assimilate PSE tools with LCA methodology, in order to produce a more detailed analysis. This will allow optimizing the influence of process operating conditions on environmental impacts and including detailed environmental results into process industry.     

iPiG: Integrating Peptide Spectrum Matches into Genome Browser Visualizations

Proteogenomic approaches have gained increasing popularity, however it is still difficult to integrate mass spectrometry identifications with genomic data due to differing data formats. To address this difficulty, we introduce iPiG as a tool for the integration of peptide identifications from mass spectrometry experiments into existing genome browser visualizations. Thereby, the concurrent analysis of proteomic and genomic data is simplified and proteomic results can directly be compared to genomic data. iPiG is freely available from https://sourceforge.net/projects/ipig/. It is implemented in Java and can be run as a stand-alone tool with a graphical user-interface or integrated into existing workflows. Supplementary data are available at PLOS ONE online.     

A genetic validation study reveals a role of vitamin D metabolism in the response to interferon-alfa-based therapy of chronic hepatitis C

Background

To perform a comprehensive study on the relationship between vitamin D metabolism and the response to interferon-α-based therapy of chronic hepatitis C.

Methodology/Principal Findings

Associations between a functionally relevant polymorphism in the gene encoding the vitamin D 1α-hydroxylase (CYP27B1-1260 rs10877012) and the response to treatment with pegylated interferon-α (PEG-IFN-α) and ribavirin were determined in 701 patients with chronic hepatitis C. In addition, associations between serum concentrations of 25-hydroxyvitamin D₃ (25[OH]D₃) and treatment outcome were analysed. CYP27B1-1260 rs10877012 was found to be an independent predictor of sustained virologic response (SVR) in patients with poor-response IL28B genotypes (15% difference in SVR for rs10877012 genotype AA vs. CC, p = 0.02, OR = 1.52, 95% CI = 1.061–2.188), but not in patients with favourable IL28B genotype. Patients with chronic hepatitis C showed a high prevalence of vitamin D insufficiency (25[OH]D₃<20 ng/mL) during all seasons, but 25(OH)D₃ serum levels were not associated with treatment outcome.

Conclusions/Significance

Our study suggests a role of bioactive vitamin D (1,25[OH]₂D₃, calcitriol) in the response to treatment of chronic hepatitis C. However, serum concentration of the calcitriol precursor 25(OH)D₃ is not a suitable predictor of treatment outcome.     

Confidence-based Somatic Mutation Evaluation and Prioritization

Next generation sequencing (NGS) has enabled high throughput discovery of somatic mutations. Detection depends on experimental design, lab platforms, parameters and analysis algorithms. However, NGS-based somatic mutation detection is prone to erroneous calls, with reported validation rates near 54% and congruence between algorithms less than 50%. Here, we developed an algorithm to assign a single statistic, a false discovery rate (FDR), to each somatic mutation identified by NGS. This FDR confidence value accurately discriminates true mutations from erroneous calls. Using sequencing data generated from triplicate exome profiling of C57BL/6 mice and B16-F10 melanoma cells, we used the existing algorithms GATK, SAMtools and SomaticSNiPer to identify somatic mutations. For each identified mutation, our algorithm assigned an FDR. We selected 139 mutations for validation, including 50 somatic mutations assigned a low FDR (high confidence) and 44 mutations assigned a high FDR (low confidence). All of the high confidence somatic mutations validated (50 of 50), none of the 44 low confidence somatic mutations validated, and 15 of 45 mutations with an intermediate FDR validated. Furthermore, the assignment of a single FDR to individual mutations enables statistical comparisons of lab and computation methodologies, including ROC curves and AUC metrics. Using the HiSeq 2000, single end 50 nt reads from replicates generate the highest confidence somatic mutation call set.