Development of reverse phase protein microarrays for the validation of clusterin, a mid-abundant blood biomarker

Background  

Many putative disease blood biomarkers discovered in genomic and proteomic studies await validation in large clinically annotated cohorts of patient samples. ELISA assays require large quantities of precious blood samples and are not high-throughput. The reverse phase protein microarray platform has been developed for the high-throughput quantification of protein levels in small amounts of clinical samples.     

Evaluation of DNA extraction methods for Bacillus anthracis spores isolated from spiked food samples

Aims

Nine commercial DNA extraction kits were evaluated for the isolation of DNA from 10‐fold serial dilutions of Bacillus anthracis spores using quantitative real‐time PCR (qPCR). The three kits determined by qPCR to yield the most sensitive and consistent detection (Epicenter MasterPure Gram Positive; MoBio PowerFood; ABI PrepSeq) were subsequently tested for their ability to isolate DNA from trace amounts of B. anthracis spores (approx. 6·5 × 10¹ and 1·3 × 10² CFU in 25 ml or 50 g of food sample) spiked into complex food samples including apple juice, ham, whole milk and bagged salad and recovered with immunomagnetic separation (IMS).

Methods and Results

The MasterPure kit effectively and consistently isolated DNA from low amounts of B. anthracis spores captured from food samples. Detection was achieved from apple juice, ham, whole milk and bagged salad from as few as 65 ± 14, 68 ± 8, 66 ± 4 and 52 ± 16 CFU, respectively, and IMS samples were demonstrated to be free of PCR inhibitors.

Conclusions

Detection of B. anthracis spores isolated from food by IMS differs substantially between commercial DNA extraction kits; however, sensitive results can be obtained with the MasterPure Gram Positive kit.

Significance and Impact of the Study

The extraction protocol identified herein combined with IMS is novel for B. anthracis and allows detection of low levels of B. anthracis spores from contaminated food samples.     

VennMaster: Area-proportional Euler diagrams for functional GO analysis of microarrays

Background  

Microarray experiments generate vast amounts of data. The functional context of differentially expressed genes can be assessed by querying the Gene Ontology (GO) database via GoMiner. Directed acyclic graph representations, which are used to depict GO categories enriched with differentially expressed genes, are difficult to interpret and, depending on the particular analysis, may not be well suited for formulating new hypotheses. Additional graphical methods are therefore needed to augment the GO graphical representation.     

Telomere length determined by the fluorescence in situ hybridisation distinguishes malignant and benign cells in cytological specimens

Background

Telomeres are tandem repeats of TTAGGG at the end of eukaryotic chromosomes that play a key role in preventing chromosomal instability. The aim of the present study is to determine telomere length using fluorescence in situ hybridisation (FISH) on cytological specimens.

Methods

Aspiration samples (n = 41) were smeared on glass slides and used for FISH.

Results

Telomere signal intensity was significantly lower in positive cases (cases with malignancy, n = 25) as compared to negative cases (cases without malignancy, n = 16), and the same was observed for centromere intensity. The difference in DAPI intensity was not statistically significant. The ratio of telomere to centromere intensity did not show a significant difference between positive and negative cases. There was no statistical difference in the signal intensities of aspiration samples from ascites or pleural effusion (n = 23) and endoscopic ultrasound‐guided FNA samples from the pancreas (n = 18).

Conclusions

The present study revealed that telomere length can be used as an indicator to distinguish malignant and benign cells in cytological specimens. This novel approach may help improve diagnosis for cancer patients.     

Simplifying gene trees for easier comprehension

Background  

In the genomic age, gene trees may contain large amounts of data making them hard to read and understand. Therefore, an automated simplification is important.     

Iterative class discovery and feature selection using Minimal Spanning Trees

Background  

Clustering is one of the most commonly used methods for discovering hidden structure in microarray gene expression data. Most current methods for clustering samples are based on distance metrics utilizing all genes. This has the effect of obscuring clustering in samples that may be evident only when looking at a subset of genes, because noise from irrelevant genes dominates the signal from the relevant genes in the distance calculation.     

WebCARMA: a web application for the functional and taxonomic classification of unassembled metagenomic reads

Background  

Metagenomics is a new field of research on natural microbial communities. High-throughput sequencing techniques like 454 or Solexa-Illumina promise new possibilities as they are able to produce huge amounts of data in much shorter time and with less efforts and costs than the traditional Sanger technique. But the data produced comes in even shorter reads (35-100 basepairs with Illumina, 100-500 basepairs with 454-sequencing). CARMA is a new software pipeline for the characterisation of species composition and the genetic potential of microbial samples using short, unassembled reads.     

FORG3D: Force-directed 3D graph editor for visualization of integrated genome scale data

Background  

Genomics research produces vast amounts of experimental data that needs to be integrated in order to understand, model, and interpret the underlying biological phenomena. Interpreting these large and complex data sets is challenging and different visualization methods are needed to help produce knowledge from the data.     

A stepwise framework for the normalization of array CGH data

Background  

In two-channel competitive genomic hybridization microarray experiments, the ratio of the two fluorescent signal intensities at each spot on the microarray is commonly used to infer the relative amounts of the test and reference sample DNA levels. This ratio may be influenced by systematic measurement effects from non-biological sources that can introduce biases in the estimated ratios. These biases should be removed before drawing conclusions about the relative levels of DNA. The performance of existing gene expression microarray normalization strategies has not been evaluated for removing systematic biases encountered in array-based comparative genomic hybridization (CGH), which aims to detect single copy gains and losses typically in samples with heterogeneous cell populations resulting in only slight shifts in signal ratios. The purpose of this work is to establish a framework for correcting the systematic sources of variation in high density CGH array images, while maintaining the true biological variations.     

Critical points of DNA quantification by real-time PCR – effects of DNA extraction method and sample matrix on quantification of genetically modified organisms

Background  

Real-time PCR is the technique of choice for nucleic acid quantification. In the field of detection of genetically modified organisms (GMOs) quantification of biotech products may be required to fulfil legislative requirements. However, successful quantification depends crucially on the quality of the sample DNA analyzed. Methods for GMO detection are generally validated on certified reference materials that are in the form of powdered grain material, while detection in routine laboratories must be performed on a wide variety of sample matrixes. Due to food processing, the DNA in sample matrixes can be present in low amounts and also degraded. In addition, molecules of plant origin or from other sources that affect PCR amplification of samples will influence the reliability of the quantification. Further, the wide variety of sample matrixes presents a challenge for detection laboratories. The extraction method must ensure high yield and quality of the DNA obtained and must be carefully selected, since even components of DNA extraction solutions can influence PCR reactions. GMO quantification is based on a standard curve, therefore similarity of PCR efficiency for the sample and standard reference material is a prerequisite for exact quantification. Little information on the performance of real-time PCR on samples of different matrixes is available.     

<Emphasis Type="Italic">In silico</Emphasis> microdissection of microarray data from heterogeneous cell populations

Background  

Very few analytical approaches have been reported to resolve the variability in microarray measurements stemming from sample heterogeneity. For example, tissue samples used in cancer studies are usually contaminated with the surrounding or infiltrating cell types. This heterogeneity in the sample preparation hinders further statistical analysis, significantly so if different samples contain different proportions of these cell types. Thus, sample heterogeneity can result in the identification of differentially expressed genes that may be unrelated to the biological question being studied. Similarly, irrelevant gene combinations can be discovered in the case of gene expression based classification.     

A summarization approach for Affymetrix GeneChip data using a reference training set from a large, biologically diverse database

Background  

Many of the most popular pre-processing methods for Affymetrix expression arrays, such as RMA, gcRMA, and PLIER, simultaneously analyze data across a set of predetermined arrays to improve precision of the final measures of expression. One problem associated with these algorithms is that expression measurements for a particular sample are highly dependent on the set of samples used for normalization and results obtained by normalization with a different set may not be comparable. A related problem is that an organization producing and/or storing large amounts of data in a sequential fashion will need to either re-run the pre-processing algorithm every time an array is added or store them in batches that are pre-processed together. Furthermore, pre-processing of large numbers of arrays requires loading all the feature-level data into memory which is a difficult task even with modern computers. We utilize a scheme that produces all the information necessary for pre-processing using a very large training set that can be used for summarization of samples outside of the training set. All subsequent pre-processing tasks can be done on an individual array basis. We demonstrate the utility of this approach by defining a new version of the Robust Multi-chip Averaging (RMA) algorithm which we refer to as refRMA.     

Normalization of Illumina Infinium whole-genome SNP data improves copy number estimates and allelic intensity ratios

Background  

Illumina Infinium whole genome genotyping (WGG) arrays are increasingly being applied in cancer genomics to study gene copy number alterations and allele-specific aberrations such as loss-of-heterozygosity (LOH). Methods developed for normalization of WGG arrays have mostly focused on diploid, normal samples. However, for cancer samples genomic aberrations may confound normalization and data interpretation. Therefore, we examined the effects of the conventionally used normalization method for Illumina Infinium arrays when applied to cancer samples.     

Gene function prediction using labeled and unlabeled data

Background  

In general, gene function prediction can be formalized as a classification problem based on machine learning technique. Usually, both labeled positive and negative samples are needed to train the classifier. For the problem of gene function prediction, however, the available information is only about positive samples. In other words, we know which genes have the function of interested, while it is generally unclear which genes do not have the function, i.e. the negative samples. If all the genes outside of the target functional family are seen as negative samples, the imbalanced problem will arise because there are only a relatively small number of genes annotated in each family. Furthermore, the classifier may be degraded by the false negatives in the heuristically generated negative samples.     

Whole genome association mapping by incompatibilities and local perfect phylogenies

Background  

With current technology, vast amounts of data can be cheaply and efficiently produced in association studies, and to prevent data analysis to become the bottleneck of studies, fast and efficient analysis methods that scale to such data set sizes must be developed.     

Selection of reference genes for gene expression studies in human neutrophils by real-time PCR

Background  

Reference genes, which are often referred to housekeeping genes, are frequently used to normalize mRNA levels between different samples. However the expression level of these genes may vary among tissues or cells, and may change under certain circumstances. Thus the selection of reference gene(s) is critical for gene expression studies. For this purpose, 10 commonly used housekeeping genes were investigated in isolated human neutrophils.     

Association of <Emphasis Type="Italic">Bordetella</Emphasis> dermonecrotic toxin with the extracellular matrix

Background  

Bordetella dermonecrotic toxin (DNT) causes the turbinate atrophy in swine atrophic rhinitis, caused by a Bordetella bronchiseptica infection of pigs, by inhibiting osteoblastic differentiation. The toxin is not actively secreted from the bacteria, and is presumed to be present in only small amounts in infected areas. How such small amounts can affect target tissues is unknown.