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At least 1,243 records · Page 69Linked to original sources
PubMed2003

A statistical perspective on gene expression data analysis.

Rapid advances in biotechnology have resulted in an increasing interest in the use of oligonucleotide and spotted cDNA gene expression microarrays for medical research. These arrays are being widely used to understand the underlying genetic structure of various diseases, with the ultimate goal to provide better diagnosis, prevention and cure. This technology allows for measurement of expression levels from several thousands of genes simultaneously, thus resulting in an enormous amount of data. The role of the statistician is critical to the successful design of gene expression studies, and the analysis and interpretation of the resulting voluminous data. This paper discusses hypotheses common to gene expression studies, and describes some of the statistical methods suitable for addressing these hypotheses. S-plus and SAS codes to perform the statistical methods are provided. Gene expression data from an unpublished oncologic study is used to illustrate these methods.

Cluster Analysis↗
PubMed2004

Role of phenytoin in wound healing: microarray analysis of early transcriptional responses in human dermal fibroblasts.

Wound healing is a complex process involving a number of related genes and receptors. Using cDNA microarrays, we explored the global gene expression profile of phenytoin (20microg/ml) induced changes to human dermal fibroblasts. Microarray data analysis revealed approximately 1500 genes were differentially expressed by 2.5-fold. At 3, 6, 12, and 24h, the transcripts of the major growth factors involved in wound healing and their receptors were increased. This was further confirmed by RT-PCR. Genes encoding other proteins with roles in signal transduction (NFkappaB), extracellular matrix (MMP1) including type I collagen, fibronectin, and laminin were strongly induced at 6h and onwards. Genes involved in cell cycle regulation (CCND1 and CDKN1A) were down-regulated consistent with our finding that phenytoin per se did not have cell proliferation activity. Notably, phenytoin accelerates the autocrine and paracrine activity of growth factors by up-regulating the related receptors.

Cell Cycle↗
PubMed2005

Single base mismatch detection by microsecond voltage pulses.

A single square voltage pulse applied to metal electrodes underneath a silicon dioxide film upon which DNA probes are immobilized allows the discrimination of DNA targets with a single base mismatch during hybridization. Pulse duration, magnitude and slew rate of the voltage pulse are all key factors controlling the rates of electric field assisted hybridization. Although pulses with 1 V, lasting less than 1 ms and with a rise/fall times of 4.5 ns led to maximum hybridization of fully complementary strands, lack of stringency did not allow the discrimination of single base mismatches. However, by choosing pulse conditions that are slightly off the optimum, the selectivity for discriminating single base mismatches could be improved up to a factor approximately 5 when the mismatch was in the middle of the strand and up to approximately 1.5 when the mismatch was on the 5'-end and. These results demonstrate that hybridization with the appropriate electric field pulse provides a new, site-specific, approach to the discrimination of single nucleotide polymorphisms in the sub-millisecond time scale, for addressable DNA microarrays.

Base Pair Mismatch↗
PubMed2005

Dual polarisation interferometry characterisation of DNA immobilisation and hybridisation detection on a silanised support.

Dual polarisation interferometry is an analytical technique that allows the simultaneous determination of thickness, density and mass of a biological layer on a sensing waveguide surface in real time. We evaluated, for the first time, the ability of this technique to characterise the covalent immobilisation of single stranded probe DNA and the selective detection of target DNA hybridisation on a silanised support. Two immobilisation strategies have been evaluated: direct attachment of the probe molecule and a more complex chemistry employing a 1,2 homobifunctional crosslinker molecule. With this technique we demonstrate it was possible to determine probe orientation and measure probe coverage at different stages of the immobilisation process in real time and in a single experiment. In addition, by measuring simultaneously changes in thickness and density of the probe layer upon hybridisation of target DNA, it was possible to directly elucidate the impact that probe mobility had on hybridisation efficiency. Direct covalent attachment of an amine modified 19 mer resulted in a thickness change of 0.68 nm that was consistent with multipoint attachment of the probe molecule to the surface. Blocking with BSA formed a dense layer of protein molecules that absorbed between the probe molecules on the surface. The observed hybridisation efficiency to target DNA was approximately 35%. No further significant reorientation of the probe molecule occurred upon hybridisation. The initial thickness of the probe layer upon attachment to the crosslinker molecule was 0.5 nm. Significant reorientation of the probe molecule surface normal occurred upon hybridisation to target DNA. This indicated that the probe molecule had greater mobility to hybridise to target DNA. The observed hybridisation efficiency for target DNA was approximately 85%. The results show that a probe molecule attached to the surface via a crosslinker group is better able to hybridise to target DNA due to its greater mobility.

Biosensing Techniques↗
PubMed2005

Secretome analysis of microarray data reveals extracellular events associated with proliferative potential in a cell line model of breast disease.

It is widely believed that breast cancer develops in a multistep process with premalignant lesions preceding invasive carcinoma. The characterization of molecular events associated with premalignant progression would improve our understanding of carcinogenesis and greatly benefit the development of early detection methods and chemoprevention strategies. However, the molecular biology of precancerous breast disease is poorly understood. To better characterize extracellular events associated with disease progression, such as cell-cell and cell-extracellular matrix (ECM) signaling, we analyzed gene expression profiles for the set of genes coding for secreted proteins (the secretome) in a cell line model of human proliferative breast disease (PBD). PBD describes a series of preneoplastic changes in the inner lining of milk glands associated with a dramatic increase in the risk of breast cancer. We used a series of cell lines with increasing proliferative propensity, and cell cultures were grown on matrigel to emulate in vivo growth and ECM interactions. Microarray analysis identified two clusters of secretome genes with expression profiles correlating to PBD progression. Some of the identified genes have previously been associated with breast malignancies, and our results suggest that changes in expression for these genes begin in the premalignant stage, offering potential use for early detection and as chemotherapeutic targets. RT-PCR validation demonstrates the reliability of the microarray results.

Breast Neoplasms↗
PubMed2004

Gene expression profiles of apoptotic neurons.

The multigenic program underlying neuronal apoptosis is mostly unknown. To study the program, we used genome-scale screening by oligonucleotide microarrays during serum and potassium deprivation-induced apoptosis of cerebellar granule neurons. From the 8740 genes interrogated by the arrays, 423 genes were found to be regulated at both the transcriptional and the posttranscriptional level and segregated into distinct clusters. Semantic clustering based on gene ontologies showed coordinated expression of genes with common biological functions and metabolic pathways. Among the genes implicated in apoptotic cerebellar granule neurons, 70 were in common with those differentially expressed in cortical neurons exposed to amyloid beta-protein, indicating the existence of common mechanisms responsible for neuronal cell death. Our results offer a genomic view of the changes that accompany neuronal apoptosis and yield new insights into the underlying molecular basis.

Amyloid beta-Peptides↗
PubMed2001

The main biological determinants of tumor line taxonomy elucidated by a principal component analysis of microarray data.

By using principal components analysis (PCA) we demonstrate here that the information relevant to tumor line classification linked to the activity of 1375 genes expressed in 60 tumor cell lines can be reproduced by only five independent components. These components can be interpreted as cell motility and migration, cellular trafficking and endo/exocytosis, and epithelial character. PCA, at odds with cluster analysis methods routinely used in microarray analysis, allows for the participation of individual genes to multiple biochemical pathways, while assigning to each cell line a quantitative score reflecting fundamental biological functions.

Cell Movement↗
PubMed2006

Clustering methods for analyzing large data sets: gonad development, a study case.

With the development of data set repositories, it is now possible to collate high numbers of related results by gathering data from experiments carried out in different laboratories and addressing similar questions or using a single type of biological material under different conditions. To address the challenge posed by the heterogeneous nature of multiple data sources, this chapter presents several methods used routinely for assessing the quality of data (i.e., reproducibility of replicates and similarity between experimental points obtained under identical or similar biological conditions). As gene clustering on large data sets is not straightforward, this chapter also presents a rapid gene clustering method that involves translating variation profiles from an ordered set of comparisons into chains of symbols. In addition, it shows that lists of genes assembled based on the presence of a common term in their functional description can be used to find the most informative comparisons and to construct from them exemplar chains of symbols that are useful for clustering similar genes. Finally, this symbolic approach is extended to the overall set of biological conditions under study and shows how the resultant collection of variation profiles can be used to construct transcriptional networks, which in turn can be used as powerful tools for gene clustering.

Animals↗
PubMed2000

The transcriptome of Arabidopsis thaliana during systemic acquired resistance.

Infected plants undergo transcriptional reprogramming during initiation of both local defence and systemic acquired resistance (SAR). We monitored gene-expression changes in Arabidopsis thaliana under 14 different SAR-inducing or SAR-repressing conditions using a DNA microarray representing approximately 25-30% of all A. thaliana genes. We derived groups of genes with common regulation patterns, or regulons. The regulon containing PR-1, a reliable marker gene for SAR in A. thaliana, contains known PR genes and novel genes likely to function during SAR and disease resistance. We identified a common promoter element in genes of this regulon that binds members of a plant-specific transcription factor family. Our results extend expression profiling to definition of regulatory networks and gene discovery in plants.

Arabidopsis↗
PubMed2005

How does gene expression clustering work?

Clustering is often one of the first steps in gene expression analysis. How do clustering algorithms work, which ones should we use and what can we expect from them?

Algorithms↗
PubMed2004

A highly informative SNP linkage panel for human genetic studies.

We have developed a highly informative set of single-nucleotide polymorphism (SNP) assays designed for linkage mapping of the human genome. These assays were developed on a robust multiplexed assay system to provide a combination of very high accuracy and data completeness with high throughput for linkage studies. The linkage panel is comprised of approximately 4,700 SNPs with 0.39 average minor allele frequency and 624-kb average spacing. Based on almost 2 million genotypes, data quality was shown to be extremely high, with a 99.94% call rate, >99.99% reproducibility and 99.995% genotypes consistent with mendelian inheritance. We constructed a genetic map with an average 1.5-cM resolution using series of 28 CEPH pedigrees. The relative information content of this panel was higher than those of commonly used STR marker panels. The potent combination of this SNP linkage panel with the multiplexed assay system provides a previously unattainable level of performance for linkage studies.

Algorithms↗
PubMed2004

Genotyping over 100,000 SNPs on a pair of oligonucleotide arrays.

We present a genotyping method for simultaneously scoring 116,204 SNPs using oligonucleotide arrays. At call rates >99%, reproducibility is >99.97% and accuracy, as measured by inheritance in trios and concordance with the HapMap Project, is >99.7%. Average intermarker distance is 23.6 kb, and 92% of the genome is within 100 kb of a SNP marker. Average heterozygosity is 0.30, with 105,511 SNPs having minor allele frequencies >5%.

Algorithms↗
PubMed2002

Large-scale gene expression analysis in molecular target discovery.

The evolution of simple arrays consisting of a few genes to ones composed of thousands of genes and/or ESTs has allowed investigators unprecedented views of the molecular mechanisms within cells. Due to the enormous quantities of information derived from microarray analysis, new types of problems have surfaced, such as where to store all of the data. The ability to solve database or statistical problems has required the bench biologist to collaborate with database developers, software designers and statisticians to determine solutions for storage, analysis and interpretation of microarray data. The collaborative effort between these extremely diverse disciplines has led to the development of creative database query and gene expression analysis tools, producing significant reductions in the time required by researchers to filter through the datasets and discover the key processes perturbed by the diseases of interest. Both unsupervised and supervised analysis methods have been applied to gene expression data leading to the discovery of novel therapeutic targets and diagnostic markers. Furthermore, tumor classification based on their respective molecular fingerprints has led to the classification of cancer subtypes and the discovery of novel molecular taxonomies that may eventually lead to improved patient stratification and superior therapeutic strategies.

Databases, Factual↗
PubMed2002

Global gene expression profiling in Barrett's esophagus and esophageal cancer: a comparative analysis using cDNA microarrays.

In order to identify and contrast global gene expression profiles defining the premalignant syndrome, Barrett's esophagus, as well as frank esophageal cancer, we utilized cDNA microarray technology in conjunction with bioinformatics tools. We hybridized microarrays, each containing 8000 cDNA clones, to RNAs extracted from 13 esophageal surgical or endoscopic biopsy specimens (seven Barrett's metaplasias and six esophageal carcinomas). Hierarchical cluster analysis was performed on these results and displayed using a color-coded graphic representation (Treeview). The esophageal samples clustered naturally into two principal groups, each possessing unique global gene expression profiles. After retrieving histologic reports for these tissues, we found that one main cluster contained all seven Barrett's samples, while the remaining principal cluster comprised the six esophageal cancers. The cancers also clustered according to histopathological subtype. Thus, squamous cell carcinomas (SCCAs) constituted one group, adenocarcinomas (ADCAs) clustered separately, and one signet-ring carcinoma was in its own cluster, distinct from the ADCA cluster. We conclude that cDNA microarrays and bioinformatics show promise in the classification of esophageal malignant and premalignant diseases, and that these methods can be applied to small biopsy samples.

Barrett Esophagus↗
PubMed2004

Use of laminar flow patterning for miniaturised biochemical assays.

Laminar flow in microfluidic chambers was used to construct low (one dimensional) density arrays suitable for miniaturized biochemical assays. By varying the ratio of flows of two guiding streams flanking a sample stream, precise focusing and positioning of the latter was achieved, and reactive species carried in the sample stream were deposited on functionalized chip surfaces as discrete 50 microm wide lanes. Using different model systems we have confirmed the method's suitability for qualitative screening and quantification tasks in receptor-ligand assays, recording biotin-streptavidin interactions, DNA-hybridization and DNA-triplex formation. The system is simple, fast, reproducible, flexible, and has small sample requirements.

Flow Injection Analysis↗