A Bioinformatics Approach to Characterize Mammalian Selenoprotein T

The exact function and trafficking of selenoprotein T (SelT) are still unclear. This study was focused on using bioinformatics analysis as an approach to understanding the structure-function relationship of SelT and the trafficking of SelT between cellular compartments. Blast analysis revealed that SelT is present in mammals, birds, frogs, zebra fish, and green algae. Structural analyses revealed that SelT contains a CxxU motif in a thioredoxin-like fold, suggesting a redox function of SelT. Cysteine (Cys) residues were found in the place of selenocysteine in SelT Cys homologs in insects, roundworms, and plants. The SignalP program recognized signal peptides in both SelT and SelT Cys homologs. Mammalian SelT was predicted to contain an N-terminal signal peptide of 19 amino acid residues, which may be involved in targeting SelT to the endoplasmic reticulum. Finally, SelT may be localized in the plasma membrane in addition to its presence in the Golgi apparatus and the endoplasmic reticulum.     

A DNA Barcoding Approach to Characterize Pollen Collected by Honeybees

In the present study, we investigated DNA barcoding effectiveness to characterize honeybee pollen pellets, a food supplement largely used for human nutrition due to its therapeutic properties. We collected pollen pellets using modified beehives placed in three zones within an alpine protected area (Grigna Settentrionale Regional Park, Italy). A DNA barcoding reference database, including rbcL and trnH-psbA sequences from 693 plant species (104 sequenced in this study) was assembled. The database was used to identify pollen collected from the hives. Fifty-two plant species were identified at the molecular level. Results suggested rbcL alone could not distinguish among congeneric plants; however, psbA-trnH identified most of the pollen samples at the species level. Substantial variability in pollen composition was observed between the highest elevation locality (Alpe Moconodeno), characterized by arid grasslands and a rocky substrate, and the other two sites (Cornisella and Ortanella) at lower altitudes. Pollen from Ortanella and Cornisella showed the presence of typical deciduous forest species; however in samples collected at Ortanella, pollen of the invasive Lonicera japonica, and the ornamental Pelargonium x hortorum were observed. Our results indicated pollen composition was largely influenced by floristic local biodiversity, plant phenology, and the presence of alien flowering species. Therefore, pollen molecular characterization based on DNA barcoding might serve useful to beekeepers in obtaining honeybee products with specific nutritional or therapeutic characteristics desired by food market demands.     

A Liquid Chromatography-Mass Spectrometry-based Approach to Characterize the Substrate Specificity of Mammalian Heparanase

Extracellular heparanase activity releases growth factors and angiogenic factors from heparan sulfate (HS) storage sites and alters the integrity of the extracellular matrix. These activities lead to a loss of normal cell matrix adherent junctions and correlate with invasive cellular phenotypes. Elevated expression of heparanase is associated with several human cancers and with vascular remodeling. Heparanase cleaves only a limited fraction of glucuronidic linkages in HS. There have been few investigations of the functional consequences of heparanase activity, largely due to the heterogeneity and complexity of HS. Here, we report a liquid chromatography-mass spectrometry (LC-MS)-based approach to profile the terminal structures created by heparanase digestion and reconstruct the heparanase cleavage sites from the products. Using this method, we demonstrate that heparanase cleaves at the non-reducing side of highly sulfated HS domains, exposing cryptic growth factor binding sites. This cleavage pattern is observed in HS from several tissue sources, regardless of overall sulfation degree, indicating a common recognition pattern. We further demonstrate that heparanase cleavage of HS chains leads to increased ability to support FGF2-dependent cell proliferation. These results suggest a new mechanism to explain how heparanase might potentiate the uncontrolled cell proliferation associated with cancer through its ability to activate nascent growth factor-promoting domains within HS.     

A Kinetic Approach to Characterize the Electrostatic Environments of Thiol Groups in Proteins

In this study, we synthesized a zwitterionic DTNB derivative, 5-(2-aminoethyl)-dithio-2-nitrobenzoate (ADNB), and characterized its reactions with several cationic, anionic, and neutral thiols. Reactions with ADNB, unlike those with DTNB, are relatively insensitive to electrostatic environments and ionic strengths. At relatively low ionic strength, rate ratios,k_ADNB/k_DTNB, varied from 0.22 for reactions with low-molecular-weight cationic thiols to 3.0 for those with low-molecular-weight anionic thiols. Ak_ADNB/k_DTNBratio of 200 for Cys-34 of BSA appears to reflect a very anionic environment.k_ADNB/k_DTNBratios of 6 and 1, respectively, for canine and equine serum albumins, which have Glu-82 → Asp and Glu-82 → Ala substitutions suggest Glu-82 is the most important anionic residues affecting the reactivity of Cys-34 in BSA.k_ADNB/k_DTNBratios appear to be useful for characterizing electrostatic environments of thiol groups in proteins.     

A Multiresolution Approach to Discrete Tomography Using DART

In discrete tomography, a scanned object is assumed to consist of only a few different materials. This prior knowledge can be effectively exploited by a specialized discrete reconstruction algorithm such as the Discrete Algebraic Reconstruction Technique (DART), which is capable of providing more accurate reconstructions from limited data compared to conventional reconstruction algorithms. However, like most iterative reconstruction algorithms, DART suffers from long computation times. To increase the computational efficiency as well as the reconstruction quality of DART, a multiresolution version of DART (MDART) is proposed, in which the reconstruction starts on a coarse grid with big pixel (voxel) size. The resulting reconstruction is then resampled on a finer grid and used as an initial point for a subsequent DART reconstruction. This process continues until the target pixel size is reached. Experiments show that MDART can provide a significant speed-up, reduce missing wedge artefacts and improve feature reconstruction in the object compared with DART within the same time, making its use with large datasets more feasible.     

Exposing Diversity: A Methodological Approach to the Use of Video Narratives in Interdisciplinary Practices

This article explores the potential of ethnographic video narratives for collaborative learning in an interdisciplinary context. Previous research describes the area between tacit knowing and explicit knowledge as an area of “muddy water” that creates a space for constructing new understandings and knowledge. In this regard, video narratives can be seen as a pathway to feelings and other sensations, and therefore to tacit commonsense notions about the nature of practices. The question is how the methodical use of video narratives contributes to new understandings of practice. The article outlines a study in which two video narratives were presented in interdisciplinary focus groups in a nursing-home setting. The multilayered complexity of the video narratives, combined with different professional approaches to elderly care, opens up for diverse, opposing and detailed understandings of practice in the dialogue about the narratives. This diversity raises new questions about the nature of practice and seems to fuel a collaborative learning process. In conclusion, the article suggests that future interventions using video narratives would benefit from firmer facilitation and categorization of this diversity in order to enhance the potential for collaborative learning.     

发现微生物药物的新途径:从基因到新化合物

本文介绍一种基于基因组扫描(鸟枪法)和生物信息学相结合的寻找微生物新化合物的平台.基因组扫描发现新的合成基因,通过对新的次生代谢产物合成途径基因的分析,预测化合物的结构、物理化学性质参数,指导该化合物的分离与纯化.     

A New Modeling Approach to the Effect of Antimicrobial Agents on Heterogeneous Microbial Populations

We develop a mathematical framework to model the dynamics of the effect of antimicrobial agents on heterogeneous microbial populations of distributed antimicrobial resistance. Our framework uses the concept of cumulants of a distribution. Simplifications that result in easily usable approximation tools are presented. A case study on experimental data exemplifies shortcomings of standard methods and the usefulness of the proposed approach. Suggestions for future development are made. Author to whom all correspondence should be addressed     

Learning Rates and States from Biophysical Time Series: A Bayesian Approach to Model Selection and Single-Molecule FRET Data

Time series data provided by single-molecule Förster resonance energy transfer (smFRET) experiments offer the opportunity to infer not only model parameters describing molecular complexes, e.g., rate constants, but also information about the model itself, e.g., the number of conformational states. Resolving whether such states exist or how many of them exist requires a careful approach to the problem of model selection, here meaning discrimination among models with differing numbers of states. The most straightforward approach to model selection generalizes the common idea of maximum likelihood—selecting the most likely parameter values—to maximum evidence: selecting the most likely model. In either case, such an inference presents a tremendous computational challenge, which we here address by exploiting an approximation technique termed variational Bayesian expectation maximization. We demonstrate how this technique can be applied to temporal data such as smFRET time series; show superior statistical consistency relative to the maximum likelihood approach; compare its performance on smFRET data generated from experiments on the ribosome; and illustrate how model selection in such probabilistic or generative modeling can facilitate analysis of closely related temporal data currently prevalent in biophysics. Source code used in this analysis, including a graphical user interface, is available open source via http://vbFRET.sourceforge.net.     

Determination of the Relative Configuration of Organic Compounds Using NMR and DG: A Systematic Approach for a Model System

The assignment of the configuration of natural products or organic compounds in general is often carried out by X-ray crystallography or chemical synthesis. NMR spectroscopy is often used only in a qualitative way in the context of configuration assignment. This paper will illuminate a systematic investigation of the combined use of NMR spectroscopy and distance geometry (DG) calculations for the determination of the relative configuration of an organic model compound. The NMR/DG approach allows to quantify configurational assignments.     

Functional Module Connectivity Map (FMCM): A Framework for Searching Repurposed Drug Compounds for Systems Treatment of Cancer and an Application to Colorectal Adenocarcinoma

Drug repurposing has become an increasingly attractive approach to drug development owing to the ever-growing cost of new drug discovery and frequent withdrawal of successful drugs caused by side effect issues. Here, we devised Functional Module Connectivity Map (FMCM) for the discovery of repurposed drug compounds for systems treatment of complex diseases, and applied it to colorectal adenocarcinoma. FMCM used multiple functional gene modules to query the Connectivity Map (CMap). The functional modules were built around hub genes identified, through a gene selection by trend-of-disease-progression (GSToP) procedure, from condition-specific gene-gene interaction networks constructed from sets of cohort gene expression microarrays. The candidate drug compounds were restricted to drugs exhibiting predicted minimal intracellular harmful side effects. We tested FMCM against the common practice of selecting drugs using a genomic signature represented by a single set of individual genes to query CMap (IGCM), and found FMCM to have higher robustness, accuracy, specificity, and reproducibility in identifying known anti-cancer agents. Among the 46 drug candidates selected by FMCM for colorectal adenocarcinoma treatment, 65% had literature support for association with anti-cancer activities, and 60% of the drugs predicted to have harmful effects on cancer had been reported to be associated with carcinogens/immune suppressors. Compounds were formed from the selected drug candidates where in each compound the component drugs collectively were beneficial to all the functional modules while no single component drug was harmful to any of the modules. In cell viability tests, we identified four candidate drugs: GW-8510, etacrynic acid, ginkgolide A, and 6-azathymine, as having high inhibitory activities against cancer cells. Through microarray experiments we confirmed the novel functional links predicted for three candidate drugs: phenoxybenzamine (broad effects), GW-8510 (cell cycle), and imipenem (immune system). We believe FMCM can be usefully applied to repurposed drug discovery for systems treatment of other types of cancer and other complex diseases.     

A Method to Predict Blood-Brain Barrier Permeability of Drug-Like Compounds Using Molecular Dynamics Simulations

The blood-brain barrier (BBB) is formed by specialized tight junctions between endothelial cells that line brain capillaries to create a highly selective barrier between the brain and the rest of the body. A major problem to overcome in drug design is the ability of the compound in question to cross the BBB. Neuroactive drugs are required to cross the BBB to function. Conversely, drugs that target other parts of the body ideally should not cross the BBB to avoid possible psychotropic side effects. Thus, the task of predicting the BBB permeability of new compounds is of great importance. Two gold-standard experimental measures of BBB permeability are logBB (the concentration of drug in the brain divided by concentration in the blood) and logPS (permeability surface-area product). Both methods are time-consuming and expensive, and although logPS is considered the more informative measure, it is lower throughput and more resource intensive. With continual increases in computer power and improvements in molecular simulations, in silico methods may provide viable alternatives. Computational predictions of these two parameters for a sample of 12 small molecule compounds were performed. The potential of mean force for each compound through a 1,2-dioleoyl-sn-glycero-3-phosphocholine bilayer is determined by molecular dynamics simulations. This system setup is often used as a simple BBB mimetic. Additionally, one-dimensional position-dependent diffusion coefficients are calculated from the molecular dynamics trajectories. The diffusion coefficient is combined with the free energy landscape to calculate the effective permeability (P_eff) for each sample compound. The relative values of these permeabilities are compared to experimentally determined logBB and logPS values. Our computational predictions correlate remarkably well with both logBB (R² = 0.94) and logPS (R² = 0.90). Thus, we have demonstrated that this approach may have the potential to provide reliable, quantitatively predictive BBB permeability, using a relatively quick, inexpensive method.     

A Method to Predict Blood-Brain Barrier Permeability of Drug-Like Compounds Using Molecular Dynamics Simulations

The blood-brain barrier (BBB) is formed by specialized tight junctions between endothelial cells that line brain capillaries to create a highly selective barrier between the brain and the rest of the body. A major problem to overcome in drug design is the ability of the compound in question to cross the BBB. Neuroactive drugs are required to cross the BBB to function. Conversely, drugs that target other parts of the body ideally should not cross the BBB to avoid possible psychotropic side effects. Thus, the task of predicting the BBB permeability of new compounds is of great importance. Two gold-standard experimental measures of BBB permeability are logBB (the concentration of drug in the brain divided by concentration in the blood) and logPS (permeability surface-area product). Both methods are time-consuming and expensive, and although logPS is considered the more informative measure, it is lower throughput and more resource intensive. With continual increases in computer power and improvements in molecular simulations, in silico methods may provide viable alternatives. Computational predictions of these two parameters for a sample of 12 small molecule compounds were performed. The potential of mean force for each compound through a 1,2-dioleoyl-sn-glycero-3-phosphocholine bilayer is determined by molecular dynamics simulations. This system setup is often used as a simple BBB mimetic. Additionally, one-dimensional position-dependent diffusion coefficients are calculated from the molecular dynamics trajectories. The diffusion coefficient is combined with the free energy landscape to calculate the effective permeability (P_eff) for each sample compound. The relative values of these permeabilities are compared to experimentally determined logBB and logPS values. Our computational predictions correlate remarkably well with both logBB (R² = 0.94) and logPS (R² = 0.90). Thus, we have demonstrated that this approach may have the potential to provide reliable, quantitatively predictive BBB permeability, using a relatively quick, inexpensive method.