Total Synthesis of Paracaseolide A

The total synthesis of paracaseolide A, a valuable cell-cycle progression inhibitor, was accomplished in 8 steps from known compounds, with 6.6% overall yield. The synthetic strategy creates strong potential for diversification

Toxicology evaluation of radiotracer doses of 3'-deoxy-3'-[18F]fluorothymidine (18F-FLT) for human PET imaging: Laboratory analysis of serial blood samples and comparison to previously investigated therapeutic FLT doses

Background: 18F-FLT is a novel PET radiotracer which has demonstrated a strong potential utility for imaging cellular proliferation in human tumors in vivo. To facilitate future regulatory approval of 18F-FLT for clinical use, we wished to demonstrate the safety of radiotracer doses of 18F-FLT administered to human subjects, by: 1) performing an evaluation of the toxicity of 18F-FLT administered in radiotracer amounts for PET imaging, 2) comparing a radiotracer dose of FLT to clinical trial doses of FLT. Methods: Twenty patients gave consent to a 18F-FLT injection, subsequent PET imaging, and blood draws. For each patient, blood samples were collected at multiple times before and after 18F-FLT PET. These samples were assayed for a comprehensive metabolic panel, total bilirubin, complete blood and platelet counts. 18F-FLT doses of 2.59 MBq/Kg with a maximal dose of 185 MBq (5 mCi) were used. Blood time-activity curves were generated for each patient from dynamic PET data, providing a measure of the area under the FLT concentration curve for 12 hours (AUC12). Results: No side effects were reported. Only albumin, red blood cell count, hematocrit and hemoglobin showed a statistically significant decrease over time. These changes are attributed to IV hydration during PET imaging and to subsequent blood loss at surgery. The AUC12 values estimated from imaging data are not significantly different from those found from serial measures of FLT blood concentrations (p = 0.66). The blood samples-derived AUC12 values range from 0.232 ng*h/mL to 1.339 ng*h/mL with a mean of 0.802 � 0.303 ng*h/mL. This corresponds to 0.46% to 2.68% of the lowest and least toxic clinical trial AUC12 of 50 ng*h/mL reported by Flexner et al (1994). This single injection also corresponds to a nearly 3,000-fold lower cumulative dose than in Flexner's twice daily trial. Conclusion: This study shows no evidence of toxicity or complications attributable to 18F-FLT injected intravenously.This study was supported by NIH grant R01 CA115559, 1R01 CA107264, and 1R01 CA80907

Lipoglycans Contribute to Innate Immune Detection of Mycobacteria

Innate immune recognition is based on the detection, by pattern recognition receptors (PRRs), of molecular structures that are unique to microorganisms. Lipoglycans are macromolecules specific to the cell envelope of mycobacteria and related genera. They have been described to be ligands, as purified molecules, of several PRRs, including the C-type lectins Mannose Receptor and DC-SIGN, as well as TLR2. However, whether they are really sensed by these receptors in the context of a bacterium infection remains unclear. To address this question, we used the model organism Mycobacterium smegmatis to generate mutants altered for the production of lipoglycans. Since their biosynthesis cannot be fully abrogated, we manipulated the biosynthesis pathway of GDP-Mannose to obtain some strains with either augmented (∼1.7 fold) or reduced (∼2 fold) production of lipoglycans. Interestingly, infection experiments demonstrated a direct correlation between the amount of lipoglycans in the bacterial cell envelope on one hand and the magnitude of innate immune signaling in TLR2 reporter cells, monocyte/macrophage THP-1 cell line and human dendritic cells, as revealed by NF-κB activation and IL-8 production, on the other hand. These data establish that lipoglycans are bona fide Microbe-Associated Molecular Patterns contributing to innate immune detection of mycobacteria, via TLR2 among other PRRs

SRH and HrQOL: does social position impact differently on their link with health status?

<p>Abstract</p> <p>Background</p> <p>Self-rated Health (SRH) and health-related quality of life (HRQoL) are used to evaluate health disparities. Like all subjective measures of health, they are dependent on health expectations that are associated with socioeconomic characteristics. It is thus needed to analyse the influence played by socioeconomic position (SEP) on the relationship between these two indicators and health conditions if we aim to use them to study health disparities. Our objective is to assess the influence of SEP on the relationship between physical health status and subjective health status, measured by SRH and HRQoL using the SF-36 scale.</p> <p>Methods</p> <p>We used data from the French National Health Survey. SEP was assessed by years of education and household annual income. Physical health status was measured by functional limitations and chronic low back pain.</p> <p>Results</p> <p>Regardless of their health status, people with lower SEP were more likely than their more socially advantaged counterparts to report poor SRH and poorer HRQoL, using any of the indicators of SEP. The negative impact of chronic low back pain on SRH was relatively greater in people with a high SEP than in those with a low SEP. In contrast, chronic low back pain and functional limitations had less impact on physical and mental component scores of quality of life for socially advantaged men and women.</p> <p>Conclusions</p> <p>Both SRH and HRQoL were lower among those reporting functional limitations or chronic low back pain. However, the change varied according SEP and the measure. In relative term, the negative impact of a given health condition seems to be greater on SRH and lower on HRQoL for people with higher SEP in comparison with people with low SEP. Using SRH could thus decrease socioeconomic differences. In contrast using HRQoL could increase these differences, suggesting being cautious when using these indicators for analyzing health disparities.</p

DNA Barcode Sequence Identification Incorporating Taxonomic Hierarchy and within Taxon Variability

For DNA barcoding to succeed as a scientific endeavor an accurate and expeditious query sequence identification method is needed. Although a global multiple–sequence alignment can be generated for some barcoding markers (e.g. COI, rbcL), not all barcoding markers are as structurally conserved (e.g. matK). Thus, algorithms that depend on global multiple–sequence alignments are not universally applicable. Some sequence identification methods that use local pairwise alignments (e.g. BLAST) are unable to accurately differentiate between highly similar sequences and are not designed to cope with hierarchic phylogenetic relationships or within taxon variability. Here, I present a novel alignment–free sequence identification algorithm–BRONX–that accounts for observed within taxon variability and hierarchic relationships among taxa. BRONX identifies short variable segments and corresponding invariant flanking regions in reference sequences. These flanking regions are used to score variable regions in the query sequence without the production of a global multiple–sequence alignment. By incorporating observed within taxon variability into the scoring procedure, misidentifications arising from shared alleles/haplotypes are minimized. An explicit treatment of more inclusive terminals allows for separate identifications to be made for each taxonomic level and/or for user–defined terminals. BRONX performs better than all other methods when there is imperfect overlap between query and reference sequences (e.g. mini–barcode queries against a full–length barcode database). BRONX consistently produced better identifications at the genus–level for all query types

Triad pattern algorithm for predicting strong promoter candidates in bacterial genomes

Abstract Background Bacterial promoters, which increase the efficiency of gene expression, differ from other promoters by several characteristics. This difference, not yet widely exploited in bioinformatics, looks promising for the development of relevant computational tools to search for strong promoters in bacterial genomes. Results We describe a new triad pattern algorithm that predicts strong promoter candidates in annotated bacterial genomes by matching specific patterns for the group I σ70 factors of Escherichia coli RNA polymerase. It detects promoter-specific motifs by consecutively matching three patterns, consisting of an UP-element, required for interaction with the α subunit, and then optimally-separated patterns of -35 and -10 boxes, required for interaction with the σ70 subunit of RNA polymerase. Analysis of 43 bacterial genomes revealed that the frequency of candidate sequences depends on the A+T content of the DNA under examination. The accuracy of in silico prediction was experimentally validated for the genome of a hyperthermophilic bacterium, Thermotoga maritima, by applying a cell-free expression assay using the predicted strong promoters. In this organism, the strong promoters govern genes for translation, energy metabolism, transport, cell movement, and other as-yet unidentified functions. Conclusion The triad pattern algorithm developed for predicting strong bacterial promoters is well suited for analyzing bacterial genomes with an A+T content of less than 62%. This computational tool opens new prospects for investigating global gene expression, and individual strong promoters in bacteria of medical and/or economic significance.</p