Alpha-selenoconotoxins, a new class of potent alpha7 neuronal nicotinic receptor antagonists

Disulfide bonds are important structural motifs that play an essential role in maintaining the conformational stability of many bioactive peptides. Of particular importance are the conotoxins, which selectively target a wide range of ion channels that are implicated in numerous disease states. Despite the enormous potential of conotoxins as therapeutics, their multiple disulfide bond frameworks are inherently unstable under reducing conditions. Reduction or scrambling by thiol-containing molecules such as glutathione or serum albumin in intracellular or extracellular environments such as blood plasma can decrease their effectiveness as drugs. To address this issue, we describe a new class of selenoconotoxins where cysteine residues are replaced by selenocysteine to form isosteric and nonreducible diselenide bonds. Three isoforms of alpha-conotoxin ImI were synthesized by t-butoxycarbonyl chemistry with systematic replacement of one ([Sec(2,8)]ImI or [Sec(3,12)]ImI), or both ([Sec(2,3,8,12)]ImI) disulfide bonds with a diselenide bond. Each analogue demonstrated remarkable stability to reduction or scrambling under a range of chemical and biological reducing conditions. Three-dimensional structural characterization by NMR and CD spectroscopy indicates conformational preferences that are very similar to those of native ImI, suggesting fully isomorphic structures. Additionally, full bioactivity was retained at the alpha7 nicotinic acetylcholine receptor, with each selenoanalogue exhibiting a dose-response curve that overlaps with wild-type ImI, thus further supporting an isomorphic structure. These results demonstrate that selenoconotoxins can be used as highly stable scaffolds for the design of new drugs.     

The small regulatory RNA molecule MicA is involved in <Emphasis Type="Italic">Salmonella enterica</Emphasis> serovar Typhimurium biofilm formation

Background  

LuxS is the synthase enzyme of the quorum sensing signal AI-2. In Salmonella Typhimurium, it was previously shown that a luxS deletion mutant is impaired in biofilm formation. However, this phenotype could not be complemented by extracellular addition of quorum sensing signal molecules.     

Functional characterisation of the regulation of CAAT enhancer binding protein alpha by GSK-3 phosphorylation of Threonines 222/226

Background  

Glycogen Synthase Kinase-3 (GSK3) activity is repressed following insulin treatment of cells. Pharmacological inhibition of GSK3 mimics the effect of insulin on Phosphoenolpyruvate Carboxykinase (PEPCK), Glucose-6 Phosphatase (G6Pase) and IGF binding protein-1 (IGFBP1) gene expression. CAAT/enhancer binding protein alpha (C/EBPα) regulates these gene promoters in liver and is phosphorylated on two residues (T222/T226) by GSK3, although the functional outcome of the phosphorylation has not been established. We aimed to establish whether CEBPα is a link between GSK3 and these gene promoters.     

Multiple duplications of yeast hexose transport genes in response to selection in a glucose-limited environment

When microbes evolve in a continuous, nutrient-limited environment, natural selection can be predicted to favor genetic changes that give cells greater access to limiting substrate. We analyzed a population of baker's yeast that underwent 450 generations of glucose-limited growth. Relative to the strain used as the inoculum, the predominant cell type at the end of this experiment sustains growth at significantly lower steady-state glucose concentrations and demonstrates markedly enhanced cell yield per mole glucose, significantly enhanced high-affinity glucose transport, and greater relative fitness in pairwise competition. These changes are correlated with increased levels of mRNA hybridizing to probe generated from the hexose transport locus HXT6. Further analysis of the evolved strain reveals the existence of multiple tandem duplications involving two highly similar, high- affinity hexose transport loci, HXT6 and HXT7. Selection appears to have favored changes that result in the formation of more than three chimeric genes derived from the upstream promoter of the HXT7 gene and the coding sequence of HXT6. We propose a genetic mechanism to account for these changes and speculate as to their adaptive significance in the context of gene duplication as a common response of microorganisms to nutrient limitation.      

Acceptor specificity of the human leukocyte alpha3 fucosyltransferase: role of FucT-VII in the generation of selectin ligands

The alpha3 fucosyltransferase, FucT-VII, is one of the key glycosyltransferases involved in the biosynthesis of the sialyl Lewis X (sLex) antigen on human leukocytes. The sialyl Lewis X antigen (NeuAcalpha(2-3)Galbeta(1-4)[Fucalpha(1-3)]GlcNAc-R) is an essential component of the recruitment of leukocytes to sites of inflammation, mediating the primary interaction between circulating leukocytes and activated endothelium. In order to characterize the enzymatic properties of the leukocyte alpha3 fucosyltransferase FucT-VII, the enzyme has been expressed in Trichoplusia ni insect cells. The enzyme is capable of synthesizing both sLexand sialyl-dimeric-Lexstructures in vitro , from 3'-sialyl-lacNAc and VIM-2 structures, respectively, with only low levels of fucose transfer observed to neutral or 3'-sulfated acceptors. Studies using fucosylated NeuAcalpha(2-3)-(Galbeta(1- 4)GlcNAc)3-Me acceptors demonstrate that FucT-VII is able to synthesize both di-fucosylated and tri-fucosylated structures from mono- fucosylated precursors, but preferentially fucosylates the distal GlcNAc within a polylactosamine chain. Furthermore, the rate of fucosylation of the internal GlcNAc residues is reduced once fucose has been added to the distal GlcNAc. These results indicate that FucT-VII is capable of generating complex selectin ligands, in vitro , however the order of fucose addition to the lactosamine chain affects the rate of selectin ligand synthesis.      

High definition bronchoscopy: a randomized exploratory study of diagnostic value compared to standard white light bronchoscopy and autofluorescence bronchoscopy

Background

Videobronchoscopy is an essential diagnostic procedure for evaluation of the central airways and pivotal for the diagnosis and staging of lung cancer. Technological improvements have resulted in high definition (HD) images with advanced real time image enhancement techniques (i-scan).

Objectives

In this study we aimed to explore the sensitivity of HD+ i-scan bronchoscopy for detection of epithelial changes like vascular abnormalities and suspicious preinvasive lesions, and tumors.

Methods

In patients scheduled for a therapeutic or diagnostic procedure under general anesthesia videos of the bronchial tree were made using 5 videobronchoscopy modes in random order: normal white light videobronchoscopy (WLB), HD-bronchoscopy (HD), HD bronchoscopy with surface enhancement technique (i-scan1), HD with surface- and tone enhancement technique (i-scan2) and dual mode autofluorescence videobronchoscopy (AFB). The videos were scored in random order by two independent and blinded expert bronchoscopists.

Results

In 29 patients all videos were available for analysis. Vascular abnormalities were scored most frequently in HD + i-scan2 bronchoscopy (1.33 ± 0.29 abnormal or suspicious sites per patient) as compared to 0.12 ± 0.05 site for AFB (P = 0.003). Sites suspicious for preinvasive lesions were most frequently reported using AFB (0.74 ± 0.12 sites per patient) as compared to 0.17 ± 0.06 for both WLB and HD bronchoscopy (P = 0.003). Tumors were detected equally by all modalities. The preferred modality was HD bronchoscopy with i-scan (tone- plus surface and surface enhancement in respectively 38% and 35% of cases P = 0.006).

Conclusions

This study shows that high definition bronchoscopy with image enhancement technique may result in better detection of subtle vascular abnormalities in the airways. Since these abnormalities may be related to preneoplastic lesions and tumors this is of clinical relevance. Further investigations using this technique relating imaging to histology are warranted.     

Oxidative folding and preparation of α‐conotoxins for use in high‐throughput structure–activity relationship studies

α‐Conotoxins are peptide neurotoxins that selectively inhibit various subtypes of nicotinic acetylcholine receptors. They are important research tools for studying numerous pharmacological disorders, with profound potential for developing drug leads for treating pain, tobacco addiction, and other conditions. They are characterized by the presence of two disulfide bonds connected in a globular arrangement, which stabilizes a bioactive helical conformation. Despite extensive structure–activity relationship studies that have produced α‐conotoxin analogs with increased potency and selectivity towards specific nicotinic acetylcholine receptor subtypes, the efficient production of diversity‐oriented α‐conotoxin combinatorial libraries has been limited by inefficient folding and purification procedures. We have investigated the optimized conditions for the reliable folding of α‐conotoxins using simplified oxidation procedures for use in the accelerated production of synthetic combinatorial libraries of α‐conotoxins. To this end, the effect of co‐solvent, redox reagents, pH, and temperature on the proportion of disulfide bond isomers was determined for α‐conotoxins exhibiting commonly known Cys loop spacing frameworks. In addition, we have developed high‐throughput ‘semi‐purification’ methods for the quick and efficient parallel preparation of α‐conotoxin libraries for use in accelerated structure–activity relationship studies. Our simplified procedures represent an effective strategy for the preparation of large arrays of correctly folded α‐conotoxin analogs and permit the rapid identification of active hits directly from high‐throughput pharmacological screening assays. Copyright © 2012 European Peptide Society and John Wiley & Sons, Ltd.     

The BioLexicon: a large-scale terminological resource for biomedical text mining

Background

Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or events) involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events.

Results

This article provides an overview of the design, construction and evaluation of a large-scale lexical and conceptual resource for the biomedical domain, the BioLexicon. The resource can be exploited by text mining tools at several levels, e.g., part-of-speech tagging, recognition of biomedical entities, and the extraction of events in which they are involved. As such, the BioLexicon must account for real usage of words in biomedical texts. In particular, the BioLexicon gathers together different types of terms from several existing data resources into a single, unified repository, and augments them with new term variants automatically extracted from biomedical literature. Extraction of events is facilitated through the inclusion of biologically pertinent verbs (around which events are typically organized) together with information about typical patterns of grammatical and semantic behaviour, which are acquired from domain-specific texts. In order to foster interoperability, the BioLexicon is modelled using the Lexical Markup Framework, an ISO standard.

Conclusions

The BioLexicon contains over 2.2 M lexical entries and over 1.8 M terminological variants, as well as over 3.3 M semantic relations, including over 2 M synonymy relations. Its exploitation can benefit both application developers and users. We demonstrate some such benefits by describing integration of the resource into a number of different tools, and evaluating improvements in performance that this can bring.     

Application of Artificial Intelligence/Machine Vision & Learning for the Development of a Live Single-cell Phenotypic Biomarker Test to Predict Prostate Cancer Tumor Aggressiveness

To assess the usefulness and applications of machine vision (MV) and machine learning (ML) techniques that have been used to develop a single cell-based phenotypic (live and fixed biomarkers) platform that correlates with tumor biological aggressiveness and risk stratification, 100 fresh prostate samples were acquired, and areas of prostate cancer were determined by post-surgery pathology reports logged by an independent pathologist. The prostate samples were dissociated into single-cell suspensions in the presence of an extracellular matrix formulation. These samples were analyzed via live-cell microscopy. Dynamic and fixed phenotypic biomarkers per cell were quantified using objective MV software and ML algorithms. The predictive nature of the ML algorithms was developed in two stages. First, random forest (RF) algorithms were developed using 70% of the samples. The developed algorithms were then tested for their predictive performance using the blinded test dataset that contained 30% of the samples in the second stage. Based on the ROC (receiver operating characteristic) curve analysis, thresholds were set to maximize both sensitivity and specificity. We determined the sensitivity and specificity of the assay by comparing the algorithm-generated predictions with adverse pathologic features in the radical prostatectomy (RP) specimens. Using MV and ML algorithms, the biomarkers predictive of adverse pathology at RP were ranked and a prostate cancer patient risk stratification test was developed that distinguishes patients based on surgical adverse pathology features. The ability to identify and track large numbers of individual cells over the length of the microscopy experimental monitoring cycles, in an automated way, created a large biomarker dataset of primary biomarkers. This biomarker dataset was then interrogated with ML algorithms used to correlate with post-surgical adverse pathology findings. Algorithms were generated that predicted adverse pathology with >0.85 sensitivity and specificity and an AUC (area under the curve) of >0.85. Phenotypic biomarkers provide cellular and molecular details that are informative for predicting post-surgical adverse pathologies when considering tumor biopsy samples. Artificial intelligence ML-based approaches for cancer risk stratification are emerging as important and powerful tools to compliment current measures of risk stratification. These techniques have capabilities to address tumor heterogeneity and the molecular complexity of prostate cancer. Specifically, the phenotypic test is a novel example of leveraging biomarkers and advances in MV and ML for developing a powerful prognostic and risk-stratification tool for prostate cancer patients.