DNA Arrays and Membrane Hybridization Methods for Screening of Six Lactobacillus Species Common in Food Products

The Implications of Fragmented Genomic DNA Size Range on the Hybridization Efficiency in NanoGene Assay

DNA hybridization-based assays are well known for their ability to detect and quantify specific bacteria. Assays that employ DNA hybridization include a NanoGene assay, fluorescence in situ hybridization, and microarrays. Involved in DNA hybridization, fragmentation of genomic DNA (gDNA) is necessary to increase the accessibility of the probe DNA to the target gDNA. However, there has been no thorough and systematic characterization of different fragmented gDNA sizes and their effects on hybridization efficiency. An optimum fragmented size range of gDNA for the NanoGene assay is hypothesized in this study. Bacterial gDNA is fragmented via sonication into different size ranges prior to the NanoGene assay. The optimum size range of gDNA is determined via the comparison of respective hybridization efficiencies (in the form of quantification capabilities). Different incubation durations are also investigated. Finally, the quantification capability of the fragmented (at optimum size range) and unfragmented gDNA is compared.

Characterization of denaturation and renaturation of DNA for DNA hybridization

OBJECTIVES

The present study was designed to systematically characterize the denaturation and the renaturation of double stranded DNA (dsDNA), which is suitable for DNA hybridization.

METHODS

A series of physical and chemical denaturation methods were implemented on well-defined 86-bp dsDNA fragment. The degree of each denaturation was measured and the most suitable denaturation method was determined. DNA renaturation tendency was also investigated for the suggested denaturation method.

RESULTS

Heating, beads mill, and sonication bath did not show any denaturation for 30 minutes. However probe sonication fully denatured DNA in 5 minutes. 1 mol/L sodium hydroxide (alkaline treatment) and 60% dimethyl sulfoxide (DMSO) treatment fully denatured DNA in 2-5 minutes.

CONCLUSIONS

Among all the physical methods applied, the direct probe sonication was the most effective way to denature the DNA fragments. Among chemical methods, 60% DMSO was the most adequate denaturation method since it does not cause full renaturation during DNA hybridization.

Effects of pretreatment on the denaturation and fragmentation of genomic DNA for DNA hybridization

DNA hybridization is an important step for a number of bioassays such as fluorescence in situ hybridization, microarrays, as well as the NanoGene assay. Denaturation and fragmentation of genomic DNA are two critical pretreatments for DNA hybridization. However, no thorough and systematic characterization on denaturation and fragmentation has been carried out for the NanoGene assay so far. In this study, we investigated the denaturation and fragmentation of the bacterial gDNA with physical treatments (i.e., heating and sonication) and chemical treatments (i.e., dimethyl sulfoxide). First of all, a simple approach for indicating the denaturation fraction was developed based on the absorbance difference (i.e., hyperchromic effect) between the double-stranded DNA and single-stranded DNA fragments. Then the denaturation capabilities of the treatments to the gDNA were elucidated, followed by the examination of the possible renaturation over time. The fragmentation of the gDNA by each treatment was also investigated. Based on denaturation efficiency, minimum renaturation tendency, and fragmentation, the sonication method was found to be the best among the six methods. We further demonstrated that the sonication method produced the best result among the treatments examined for the DNA hybridization in the NanoGene assay.

A GPU-based calculation using the three-dimensional FDTD method for electromagnetic field analysis

Numerical simulations with the numerical human model using the finite-difference time domain (FDTD) method have recently been performed frequently in a number of fields in biomedical engineering. However, the FDTD calculation runs too slowly. We focus, therefore, on general purpose programming on the graphics processing unit (GPGPU). The three-dimensional FDTD method was implemented on the GPU using Compute Unified Device Architecture (CUDA). In this study, we used the NVIDIA Tesla C1060 as a GPGPU board. The performance of the GPU is evaluated in comparison with the performance of a conventional CPU and a vector supercomputer. The results indicate that three-dimensional FDTD calculations using a GPU can significantly reduce run time in comparison with that using a conventional CPU, even a native GPU implementation of the three-dimensional FDTD method, while the GPU/CPU speed ratio varies with the calculation domain and thread block size.

Automatic analysis of macroarrays images

The analysis of dot blot (macroarray) images is currently based on the human identification of positive/negative dots, which is a subjective and time consuming process. This paper presents a system for the automatic analysis of dot blot images, using a pre-defined grid of markers, including a number of ON and OFF controls. The geometric deformations of the input image are corrected, and the individual markers detected, both tasks fully automatically. Based on a previous training stage, the probability for each marker to be ON is established. This information is provided together with quality parameters for training, noise and classification, allowing for a fully automatic evaluation of a dot blot image.

Automatic detection of molecular markers in digital images

Labeled molecular markers are an important tool in molecular biology. This work presents a method for the automatic identification of molecular markers in dot blot images. The method detects the location of markers in the image and their size. An experiment was made with 6 test images, which were used to produce an additional set of 222 images with various rotation, translation, contrast and noise levels. Over 7500 markers were identified automatically and compared with reference values obtained manually. The RMS error for the marker positioning in the original test images were between 1.1 and 3.8 pixels, which is about 1/10 of the typical radius (26 pixels). The method proposed was found to be almost insensitive to grid rotation and translation, and reasonably robust to image contrast changes and presence of noise.