Microcalorimetric study on expression of foreign genes in Bacillus thuringiensis

The thermogenic curves of the aerobic metabolism of the three strains of Bacillus thuringiensis B.t. A, B.t. B and B.t. C have been determined by using an LKB-2277 BioActivity Monitor. B.t. A was the host bacterium without foreign gene. B.t. B and B.t. C were constructed by transforming different foreign genes into the host B.t. A, respectively. B.t. B expressed erythromycin resistant gene, while B.t. C expressed both erythromycin resistant gene and tyrosinase gene. The heat flow rate of these strains is B.t. A> B.t. B >B.t. C. These results indicated that there is obvious interrelation between expression of foreign genes and heat flow rate of B.t. strains. This revised version was published online in August 2006 with corrections to the Cover Date.     

Maedamide,a novel chymotrypsin inhibitor from a marine cyanobacterial assemblage of Lyngbya sp.

Maedamide, a novel chymotrypsin-inhibiting depsipeptide, was isolated from a cyanobacterial assemblage that mostly consisted of Lyngbya sp. Its structure was elucidated by spectroscopic analyses and chiral HPLC analyses of hydrolysis products. Maedamide selectively inhibited chymotrypsin but not elastase and trypsin. In addition, Maedamide strongly inhibited the growth of HeLa cells and HL60 cells.     

Cloning and expression of the gene for xylose isomerase fromThermus flavus AT62 inEscherichia coli

The gene encoding xylose isomerase (xylA) was cloned fromThermus flavus AT62 and the DNA sequence was determined. ThexylA gene encodes the enzyme xylose isomerase (XI orxylA) consisting of 387 amino acids (calculated Mr of 44,941). Also, there was a partial xylulose kinase gene that was 4 bp overlapped in the end of XI gene. The XI gene was stably expressed inE. coli under the control oftac promoter. XI produced inE. coli was simply purified by heat treatment at 90°C for 10 min and column chromatography of DEAE-Sephacel. The Mr of the purified enzyme was estimated to be 45 kDa on SDS-polyacrylamide gel electrophoresis. However, Mr of the cloned XI was 185 kDa on native condition, indicating that the XI consists of homomeric tetramer. The enzyme has an optimum temperature at 90°C. Thermostability tests revealed that half life at 85°C was 2 mo and 2 h at 95°C. The optimum pH is around 7.0, close to where by-product formation is minimal. The isomerization yield of the cloned XI was about 55% from glucose, indicating that the yield is higher than those of reported enzymes. The Km values for various sugar substrates were calculated as 106 mM for glucose. Divalent cations such as Mn²⁺, Co²⁺, and Mg²⁺ are required for the enzyme activity and 100 mM EDTA completely inhibited the enzyme activity.     

Floating cultivation of marine cyanobacteria using coal fly ash

The aim of this study was to develop improved methodologies for bulk culturing of biotechnologically useful marine cyanobacteria in the open ocean. We have investigated the viability of using coal fly ash (CFA) blocks as the support medium in a novel floating culture system for marine microalgae. The marine cyanobacterium Synechococcus sp. NKBG 040607 was found to adhere to floating CFA blocks in liquid culture medium. Maximum density of attached cells of 2.0×10⁸ cells/cm² was achieved using sea water. The marine cyanobacterium Synechococcus sp. NKBG 042902 weaklyadhered to floating CFA blocks in BG-11 medium. Increasing the concentration of calcium ion in the culture medium enhanced adherence to CFA blocks.     

Cloning and expression of L-asparaginase gene in Escherichia coli

The L-asparaginase (ASN) from Escherichia coli AS1.357 was cloned as a DNA fragment generated using polymerase chain reaction technology and primers derived from conserved regions of published ASN gene sequences. Recombinant plasmid pASN containing ASN gene and expression vector pBV220 was transformed in different E. coli host strains. The activity and expression level of ASN in the engineering strains could reach 228 IU/mL of culture fluid and about 50% of the total soluble cell protein respectively, more than 40-fold the enzyme activity of the wild strain. The recombinant plasmid in E. coli AS1.357 remained stable after 72h of cultivation and 5h of heat induction without selective pressure. The ASN gene of E. coli AS1.357 was sequenced and had high homology compared to the reported data.     

Clustering of time-course gene expression data using functional data analysis

Clustering of gene expression data collected across time is receiving growing attention in the biological literature since time-course experiments allow one to understand dynamic biological processes and identify genes governed by the same processes. It is believed that genes demonstrating similar expression profiles over time might give an informative insight into how underlying biological mechanisms work. In this paper, we propose a method based on functional data analysis (FNDA) to cluster time-dependent gene expression profiles. Consideration of clustering problems using the FNDA setting provides ways to take time dependency into account by using basis function expansion to describe the partially observed curves. We also discuss how to choose the number of bases in the basis function expansion in FNDA. A synthetic cycle data and a real data are used to demonstrate the proposed method and some comparisons between the proposed and existing approaches using the adjusted Rand indices are made.     

A class imbalance-aware Relief algorithm for the classification of tumors using microarray gene expression data

DNA microarray data has been widely used in cancer research due to the significant advantage helped to successfully distinguish between tumor classes. However, typical gene expression data usually presents a high-dimensional imbalanced characteristic, which poses severe challenge for traditional machine learning methods to construct a robust classifier performing well on both the minority and majority classes. As one of the most successful feature weighting techniques, Relief is considered to particularly suit to handle high-dimensional problems. Unfortunately, almost all relief-based methods have not taken the class imbalance distribution into account. This study identifies that existing Relief-based algorithms may underestimate the features with the discernibility ability of minority classes, and ignore the distribution characteristic of minority class samples. As a result, an additional bias towards being classified into the majority classes can be introduced. To this end, a new method, named imRelief, is proposed for efficiently handling high-dimensional imbalanced gene expression data. imRelief can correct the bias towards to the majority classes, and consider the scattered distributional characteristic of minority class samples in the process of estimating feature weights. This way, imRelief has the ability to reward the features which perform well at separating the minority classes from other classes. Experiments on four microarray gene expression data sets demonstrate the effectiveness of imRelief in both feature weighting and feature subset selection applications.     

Cancer classification based on microarray gene expression data using a principal component accumulation method

The classification of cancer is a major research topic in bioinformatics. The nature of high dimensionality and small size associated with gene expression data,however,makes the classification quite challenging. Although principal component analysis (PCA) is of particular interest for the high-dimensional data,it may overemphasize some aspects and ignore some other important information contained in the richly complex data,because it displays only the difference in the first twoor three-dimensional PC subsp...     

Determining PPARγ-ligand binding affinity using fluorescent assay with cis-parinaric acid as a probe

Upon the study of small-molecules binding to proteins, the traditional methods for calculating dissociation constants (Kd and Ki) have shortcomings in dealing with the single binding site models. In this paper, two equations have been derived to solve this problem. These two equations are independent of the total concentration or initial degree of saturation of receptor and the activity of the competitive molecule. Through nonlinear fitting against these two equations, Kd value of a probe can be obtained by binding assay, and Ki value of a ligand can be obtained by competitive assay. Moreover, only the total concentrations of receptor([R]t), ligand([L]t) and probe([P]t) are required for the data fitting. In this work, Ki values of some typical ligands of PPARγ were successfully determined by use of our equations, among which the Ki value of PPARγ-LY171883 was reported for the first time.     

Consensus analysis of multiple classifiers using non-repetitive variables: diagnostic application to microarray gene expression data

Class prediction based on DNA microarray data has been emerged as one of the most important application of bioinformatics for diagnostics/prognostics. Robust classifiers are needed that use most biologically relevant genes embedded in the data. A consensus approach that combines multiple classifiers has attributes that mitigate this difficulty compared to a single classifier. A new classification method named as consensus analysis of multiple classifiers using non-repetitive variables (CAMCUN) was proposed for the analysis of hyper-dimensional gene expression data. The CAMCUN method combined multiple classifiers, each of which was built from distinct, non-repeated genes that were selected for effectiveness in class differentiation. Thus, the CAMCUN utilized most biologically relevant genes in the final classifier. The CAMCUN algorithm was demonstrated to give consistently more accurate predictions for two well-known datasets for prostate cancer and leukemia. Importantly, the CAMCUN algorithm employed an integrated 10-fold cross-validation and randomization test to assess the degree of confidence of the predictions for unknown samples.     

Microtiter plate assay for phosphate using a europium-tetracycline complex as a sensitive luminescent probe

A new luminescent europium probe is presented for the determination of phosphate (P) in microtiter plate format. The assay is based on the quenching of the luminescence of the europium-tetracycline (EuTc) 1:1 complex by phosphate using a reagent concentration of 20.8 μmol/L. The probe is excited at 400 nm and displays a large Stokes’ shift of 210 nm. The emission maximum is located at 616 nm. The system works best at neutral pH 7 and is therefore suitable for phosphate determination in biological and biochemical systems. The linear range of the calibration plot is from 5 × 10⁻⁶ mol/L to 7.5 × 10⁻⁴ mol/L of phosphate, and the limit of detection is 3 μmol/L.     

Electrochemical immunoassay using quantum dot/antibody probe for identification of cyanobacterial hepatotoxin microcystin-LR

The presence of cyanobacterial hepatotoxins such as microcystin-LR poses health threats to humans due to their potential for causing severe physiological effects when contaminated drinking water is ingested. Here, the electrochemical detection of microcystin-LR is explored using a quantum dot/antibody (QD/Ab) probe for nanoparticle-based amplification and direct electrochemical transduction. The immunological recognition of microcystin-LR using the QD/Ab probe was amplified and converted to an electrochemical signal by measuring the cadmium ions released from QD based on square wave stripping voltammetry under optimized electrochemical factors. Whereas a qualitative analysis for microcystin-LR was achieved using the specific peak potential of the anodic voltammogram at −0.6 ± 0.05 V, concentration of the toxin was quantified based on the charge density of the anodic peak; a dynamic range of 0.227 to 50 μg/L and limit of detection of 0.099 μg/L were obtained with high sensitivity. The extracted microcystin-LR from Microcystis aeruginosa was estimated as 1,944 μg/g of dried weight of the microorganism.     

Improved binary PSO for feature selection using gene expression data

Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a medical diagnosis tool. Compared to the number of genes involved, available training data sets generally have a fairly small sample size in cancer type classification. These training data limitations constitute a challenge to certain classification methodologies. A reliable selection method for genes relevant for sample classification is needed in order to speed up the processing rate, decrease the predictive error rate, and to avoid incomprehensibility due to the large number of genes investigated. Improved binary particle swarm optimization (IBPSO) is used in this study to implement feature selection, and the K-nearest neighbor (K-NN) method serves as an evaluator of the IBPSO for gene expression data classification problems. Experimental results show that this method effectively simplifies feature selection and reduces the total number of features needed. The classification accuracy obtained by the proposed method has the highest classification accuracy in nine of the 11 gene expression data test problems, and is comparative to the classification accuracy of the two other test problems, as compared to the best results previously published.     

Nuclear factor of activated T cells negatively regulates expression of the tumor necrosis factor receptor-related 2 gene in T cells

Tumor necrosis factor receptor-related 2 (TR2, HVEM or TNFRSF-14) plays an important role in immune responses, however, the mechanisms regulating its expression are unclear. To understand the control of TR2 gene expression, we studied the upstream region of the gene. Gel supershift assays revealed inducible binding of nuclear factor of activated T cells (NFAT) to a putative NFAT site within the TR2 promoter. Furthermore, cotransfection of a dominant negative NFAT construct, or siRNA for NFAT, resulted in increased expression of a TR2 reporter gene. Our findings demonstrate that NFAT negatively regulates TR2 expression in activated T cells.