Epoxy-amino groups: a new tool for improved immobilization of proteins by the epoxy method

The properties of a new commercially available amino-epoxy support (amino-epoxy-Sepabeads) for immobilizing enzymes have been compared to those of conventional epoxy supports. The new support has a layer of epoxy groups over a layer of ethylenediamine that is covalently bound to the support. Thus, this support has a great anionic exchanger power and a high number of epoxy groups. We have found a number of advantages to this new heterofunctional support. Immobilization proceeds at low ionic strength using amino epoxy Sepabeads while requiring high ionic strength using conventional monofunctional epoxy supports. Immobilization is much more rapid using amino-epoxy supports than employing conventional epoxy supports. The possibility of achieving immobilized preparations in which the enzyme orientation may be different to that obtained using the traditional hydrophobic supports (with likely effects in terms of activity or stability). Stability of the immobilized enzyme has been found to be much higher using the new support than in preparations using the conventional ones in many cases. Here we show some examples of these advantages using different enzymes (beta-galactosidases, lipase, glutaryl acylase, invertase, and glucoamylase).     

Synthesis and antiviral activity of novel 2',4'-doubly branched carbocyclic nucleosides

A series of 2' and 4'-doubly branched carbocyclic nucleosides 15, 16, 17 and 18 were synthesized starting from simple acyclic ketone derivatives. The required 4'-quatemary carbon was constructed using Claisen rearrangement. In addition, the installation of a methyl group in the 2'-position was accomplished using a Grignard carbonyl addition of isopropenylmagnesium bromide. Bis-vinyl was successfully cyclized using a Grubbs' catalyst II. Natural bases (adenine, cytosine) were efficiently coupled by using Pd(0) catalyst.     

Further analysis of familial transmission of congenital glaucoma.

A re-analysis of familial transmission of congenital glaucoma is performed here using the unified model and considering, respectively, joint and conditional likelihoods. Whereas the familial aggregation of this malformation is consistent with the segregation of a recessive major gene and a high proportion of sporadic cases using conditional likelihood, this is rejected using joint likelihood. The difference in the results obtained using each of these two strategies is discussed.     

Photocontrolled nanoparticles for on-demand release of proteins

We describe here light-regulated swelling and degradation features of polymeric nanoparticles that are produced using an inverse microemulsion polymerization method. We demonstrate the phototriggered release characteristics of the nanoparticles by sequestering protein molecules and releasing them using light as a trigger. Furthermore, the intracellular translocation of the nanoparticles, along with its fluorescent protein payload, was achieved using a cell-penetrating peptide-based surface modification. We expect that the noncovalent encapsulation of proteins using nanoparticles and their photo triggered release using an external light would provide opportunities for achieving intracellular release of molecular therapeutics for on-demand requirements.     

Microsatellite loci from the house wren (Troglodytes aedon)

We cloned seven microsatellite loci from house wrens (Troglodytes aedon) using a biotin enrichment protocol. Starting with fragments generated using DOP–PCR, fragments containing microsatellite motifs AC and AAC were captured using biotinylated probes and streptavidin coated magnetic particles. Captured fragments were cloned into plasmids; prior to sequencing, the plasmids were screened for microsatellites using a simple PCR approach. Five of the loci showed variation in a sample of nine individuals.     

Monitoring of hormonal drug effect in a single breast cancer cell using an estrogen responsive GFP reporter vector delivered by a nanoneedle

In this study, we have evaluated a sensor system for a hormonal drug effect in a single cell level using a novel low invasive single cell DNA delivery technology using a nanoneedle. An estrogen responsive GFP reporter vector (pEREGFP9) was constructed and its estrogenic response activity was confirmed in breast cancer cells (MCF-7) using lipofection as the means of transferring the vector to the cells. The pEREGFP9 vector was delivered to a single MCF-7 using a nanoneedle and the effect of ICI 182,780, which is an antagonist of estrogen, was observed using the GFP expression level. By ICI 182,780 treatment, the fluorescence intensity of the GFP was decreased by 30-50% within 24h. This technology is the very first trial of single cell diagnosis and we are looking forward to applying it to precious single cell diagnosis in medical fields.     

Purification and identification of O-GlcNAc-modified peptides using phosphate-based alkyne CLICK chemistry in combination with titanium dioxide chromatography and mass spectrometry

A selective method for the enrichment of O-GlcNAcylated peptides using a novel CLICK chemistry reagent is described. Peptides modified by O-GlcNAc were enzymatically labeled with N-azidoacetylgalactosamine. The azide was then reacted with a phospho-alkyne using CLICK chemistry and O-GlcNAcGalNAzPO(4)-containing peptides were enriched using titanium dioxide chromatography. Modified peptides were analyzed using a combination of higher energy collision dissociation for identification and electron transfer dissociation to localize the site of O-GlcNAc attachment. The enrichment method was developed and optimized using an alpha-crystallin standard protein and then applied to a soluble protein preparation of mouse brain tissue and a nuclear preparation generated from HeLa cells. A total of 42 unique O-GlcNAcylated peptides were identified, including 7 novel O-GlcNAc sites.     

Herbivory and plant resource competition: a review of two interacting interactions

This review discusses the prevalence and potential for interactive effects between herbivory and competition on plant growth and biomass, and it is apparent that such effects typically arise when there is a mismatch between the spatial scale of herbivore behaviour (food or patch choice) and the spatial heterogeneity of the plant community. Historically, such interactive effects have been examined using two approaches. Studies using the first approach have excluded plant neighbors and herbivores in a factorial experiment, and scored effects on plant biomass. Studies using the second approach have observed herbivore abundance or herbivory on plants with or without plant neighbors, and have identified a large number of mechanisms underlying such interactive effects. The two types of studies have produced somewhat conflicting results, where interactive effects have been commonly observed in studies using the second approach and only rarely in studies using the first approach. This is most likely a consequence of a biased choice of study systems, where studies using the first approach have primarily studied mammalian herbivory while studies using the second approach have been more focussed on insect herbivory. Moreover, studies using the first approach have typically been very small-scale manipulations and this probably precludes most possible interactive effects in systems with mammalian herbivory. This points to the fact that studies examining interactive effects of herbivory and plant competition should more carefully consider the behaviour and life history of herbivores included in the study prior to the design of removal experiments.     

Determination of partial amino acid composition from tandem mass spectra for use in peptide identification strategies

We demonstrate a new approach to the determination of amino acid composition from tandem mass spectrometrically fragmented peptides using both experimental and simulated data. The approach has been developed to be used as a search-space filter in a protein identification pipeline with the aim of increased performance above that which could be attained by using immonium ion information. Three automated methods have been developed and tested: one based upon a simple peak traversal, in which all intense ion peaks are treated as being either a b- or y-ion using a wide mass tolerance; a second which uses a much narrower tolerance and does not perform transformations of ion peaks to the complementary type; and the unique fragments method which allows for b- or y-ion type to be inferred and corroborated using a scan of the other ions present in each peptide spectrum. The combination of these methods is shown to provide a high-accuracy set of amino acid predictions using both experimental and simulated data sets. These high quality predictions, with an accuracy of over 85%, may be used to identify peptide fragments that are hard to identify using other methods. The data simulation algorithm is also shown post priori to be a good model of noiseless tandem mass spectrometric peptide data.     

Modeling, simulation, and kinetic analysis of a heterogeneous reaction system for the enzymatic conversion of poorly soluble substrate.

We developed a kinetic model that describes a heterogeneous reaction system consisting of a solid substrate suspension for the production of D-amino acid using D-hydantoinase. As a biocatalyst, mass-produced free and whole cell enzymes were used. The heterogeneous reaction system involves dissolution of a solid substrate, enzymatic conversion of the dissolved D-form substrate, spontaneous racemization of an L-form substrate to D-form, and deactivation of the enzyme. In the case of using whole cell enzymes, transfer of the dissolved substrate and product through the cell membrane was considered. The kinetic parameters were determined from experiments, literature data, and by using Marquardt's method of nonlinear regression analysis. The model was simulated using the kinetic parameters and compared with experimental data, and a good agreement was observed between the experimental results and the simulation ones. Factors affecting the kinetics of the heterogeneous reaction system were analyzed on the basis of the kinetic model, and the efficiency of the reaction systems using free and whole cell enzymes was also compared.     

Magnetic separation of CD14+ cells using antibody binding with protein A expressed on bacterial magnetic particles for generating dendritic cells

Herein the potential of a highly efficient cell separation system using bacterial magnetic particles expressing protein A (protein A-BacMPs) was demonstrated. Protein A was expressed on BacMPs using the transmembrane proteins Mms13 and MagA as anchor molecules. The evaluations of the numbers of bound antibody molecules and binding capabilities of the protein A-BacMPs using Mms13 indicated that the antibodies were efficiently introduced into protein A-BacMPs using Mms13 in comparison to MagA. In addition, the recovery ratio of the target cells on the magnetic cell separation system was enhanced by using protein A-BacMPs with Mms13. Using positive selection against peripheral blood mononuclear cells, the CD14(+) cells were separated at a purity of more than 99% by protein A-BacMPs using Mms13. Furthermore, in the evaluation of the influence of protein A-BacMPs on the separated cells, the CD14(+) cells separated using protein A-BacMPs and were successfully differentiated into dendritic cells.     

Antiproton stopping power data for radiation therapy simulations

Stopping powers of H, He, H₂, and H₂O targets for antiprotons have been calculated using a convergent close-coupling method. For He and H₂ targets electron–electron correlations are fully accounted for using a multiconfiguration approximation. Two-electron processes are included using an independent-event model. The water molecule is described using a neon-like structure model with a pseudo-spherical potential. Results are tabulated for the purpose of Monte Carlo simulations to model antiproton transport through matter for radiation therapy.     

Cell analysis using a multiple internal reflection photonic lab-on-a-chip

Here we present a protocol for analyzing cell cultures using a photonic lab-on-a-chip (PhLoC). By using a broadband light source and a spectrometer, the spectrum of a given cell culture with an arbitrary population is acquired. The PhLoC can work in three different regimes: light scattering (using label-free cells), light scattering plus absorption (using stained cells) and, by subtraction of the two former regimes, absorption (without the scattering band). The acquisition time of the PhLoC is ～30 ms. Hence, it can be used for rapid cell counting, dead/live ratio estimation or multiparametric measurements through the use of different dyes. The PhLoC, including microlenses, micromirrors and microfluidics, is simply fabricated in a single-mask process (by soft lithographic methods) using low-cost materials. Because of its low cost it can easily be implemented for point-of-care applications. From raw substrates to final results, this protocol can be completed in 29 h.     

Insertion of microscopic objects through plant cell walls using laser microsurgery

A detailed protocol is presented for precisely inserting microscopic objects into the periplasmic region of plant callus cells using laser microsurgery. Ginkgo biloba and Agrobacterium rhizogenes were used as the model system for developing the optical tweezers and scalpel techniques using a single laser. We achieved better than 95% survival after plasmolyzing G. biloba cells, ablating a 2-4-μm hole through the cell wall using a pulsed UV laser beam, trapping and translating bacteria into the periplasmic region using a pulsed infrared laser beam, and then deplasmolyzing the cells. Insertion of bacteria is also described. A thermal model for temperature changes of trapped bacteria is included. Comparisons with other methods, such as a reverse-pressure gradient technique, are discussed and additional experiments on plants using laser microsurgery are suggested. Copyright 1998 John Wiley & Sons, Inc.     

Shortest path analysis using partial correlations for classifying gene functions from gene expression data

MOTIVATION: Gaussian graphical models (GGMs) are a popular tool for representing gene association structures. We propose using estimated partial correlations from these models to attach lengths to the edges of the GGM, where the length of an edge is inversely related to the partial correlation between the gene pair. Graphical lasso is used to fit the GGMs and obtain partial correlations. The shortest paths between pairs of genes are found. Where terminal genes have the same biological function intermediate genes on the path are classified as having the same function. We validate the method using genes of known function using the Rosetta Compendium of yeast (Saccharomyces Cerevisiae) gene expression profiles. We also compare our results with those obtained using a graph constructed using correlations. RESULTS: Using a partial correlation graph, we are able to classify approximately twice as many genes to the same level of accuracy as when using a correlation graph. More importantly when both methods are tuned to classify a similar number of genes, the partial correlation approach can increase the accuracy of the classifications.     

Double-layer fluorescent zymography for processing protease detection

We have developed a novel double-layer zymographic method for the detection of specific processing proteases of a target proprotease using a specific fluorescent substrate. The target processing proteases were separated using sodium dodecyl sulfate-polyacrylamide gel electrophoresis, and the gel was subsequently incubated with the target proenzyme used as the substrate. A cellulose acetate membrane was immersed in 10% glycerol and then soaked in the fluorescent substrate solution. The slab gel of the processing protease was covered with the fluorescent substrate membrane, making a double layer. The double layer was incubated at 37 degrees C, and the released fluorescent band, in which the processing protease was located, was detected using UV light. The advantages of the double-layer fluorescent zymographic method are as follows: (i) the specific detection of target proprotease using a specific substrate, (ii) a relatively rapid and sensitive method, (iii) effective detection using small amounts of crude material, and (iv) wide applications that include the detection of processing proteases and activators for target proteases. Typical examples used for the detection of the processing proteases, such as plasminogen activator, chymotrypsinogen activator, procaspase-3 processing protease and caspase-3 activators, using this new method are described in this article.     

Quantitation of phosphotyrosine signals in human prostate cell adhesion sites

A simple method is described for the quantitation of phosphotyrosine signaling in human prostate cell cultures. The phosphotyrosine signals are observed by standard immunohistochemistry techniques, and the resulting digital images are analyzed using the Scion image software program. The signals within the cell adhesion sites are quantitated using the density slice and particle analysis features of the software. The immunohistochemistry results are compared with detection of phosphotyrosine signals using a standard Western blotting procedure with whole cell lysates. The resulting data is converted into graphs using the Sigma Plot Program. This method is illustrated using damage-induced signaling within cell adhesion sites after a low dose of ionizing radiation.     

Estimation of carbon storage based on individual tree detection in <Emphasis Type="Italic">Pinus densiflora</Emphasis> stands using a fusion of aerial photography and LiDAR data

The objective of this study was to estimate the carbon storage capacity of Pinus densiflora stands using remotely sensed data by combining digital aerial photography with light detection and ranging (LiDAR) data. A digital canopy model (DCM), generated from the LiDAR data, was combined with aerial photography for segmenting crowns of individual trees. To eliminate errors in over and under-segmentation, the combined image was smoothed using a Gaussian filtering method. The processed image was then segmented into individual trees using a marker-controlled watershed segmentation method. After measuring the crown area from the segmented individual trees, the individual tree diameter at breast height (DBH) was estimated using a regression function developed from the relationship observed between the field-measured DBH and crown area. The above ground biomass of individual trees could be calculated by an image-derived DBH using a regression function developed by the Korea Forest Research Institute. The carbon storage, based on individual trees, was estimated by simple multiplication using the carbon conversion index (0.5), as suggested in guidelines from the Intergovernmental Panel on Climate Change. The mean carbon storage per individual tree was estimated and then compared with the field-measured value. This study suggested that the biomass and carbon storage in a large forest area can be effectively estimated using aerial photographs and LiDAR data.     

An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo

This paper examined if an electromyography (EMG) driven musculoskeletal model of the human knee could be used to predict knee moments, calculated using inverse dynamics, across a varied range of dynamic contractile conditions. Muscle-tendon lengths and moment arms of 13 muscles crossing the knee joint were determined from joint kinematics using a three-dimensional anatomical model of the lower limb. Muscle activation was determined using a second-order discrete non-linear model using rectified and low-pass filtered EMG as input. A modified Hill-type muscle model was used to calculate individual muscle forces using activation and muscle tendon lengths as inputs. The model was calibrated to six individuals by altering a set of physiologically based parameters using mathematical optimisation to match the net flexion/extension (FE) muscle moment with those measured by inverse dynamics. The model was calibrated for each subject using 5 different tasks, including passive and active FE in an isokinetic dynamometer, running, and cutting manoeuvres recorded using three-dimensional motion analysis. Once calibrated, the model was used to predict the FE moments, estimated via inverse dynamics, from over 200 isokinetic dynamometer, running and sidestepping tasks. The inverse dynamics joint moments were predicted with an average R(2) of 0.91 and mean residual error of approximately 12 Nm. A re-calibration of only the EMG-to-activation parameters revealed FE moments prediction across weeks of similar accuracy. Changing the muscle model to one that is more physiologically correct produced better predictions. The modelling method presented represents a good way to estimate in vivo muscle forces during movement tasks.     

Semiparametric regression of multidimensional genetic pathway data: least-squares kernel machines and linear mixed models

We consider a semiparametric regression model that relates a normal outcome to covariates and a genetic pathway, where the covariate effects are modeled parametrically and the pathway effect of multiple gene expressions is modeled parametrically or nonparametrically using least-squares kernel machines (LSKMs). This unified framework allows a flexible function for the joint effect of multiple genes within a pathway by specifying a kernel function and allows for the possibility that each gene expression effect might be nonlinear and the genes within the same pathway are likely to interact with each other in a complicated way. This semiparametric model also makes it possible to test for the overall genetic pathway effect. We show that the LSKM semiparametric regression can be formulated using a linear mixed model. Estimation and inference hence can proceed within the linear mixed model framework using standard mixed model software. Both the regression coefficients of the covariate effects and the LSKM estimator of the genetic pathway effect can be obtained using the best linear unbiased predictor in the corresponding linear mixed model formulation. The smoothing parameter and the kernel parameter can be estimated as variance components using restricted maximum likelihood. A score test is developed to test for the genetic pathway effect. Model/variable selection within the LSKM framework is discussed. The methods are illustrated using a prostate cancer data set and evaluated using simulations.