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PubMed2005

Identification of discrete chromosomal deletion by binary recursive partitioning of microarray differential expression data.

DNA copy number abnormalities (CNA) are characteristic of tumours, and are also found in association with congenital anomalies and mental retardation. The ultimate impact of copy number abnormalities is manifested by the altered expression of the encoded genes. We previously developed a statistical method for the detection of simple chromosomal amplification using microarray expression data. In this study, we significantly advanced those analytical techniques to allow detection of localised chromosomal deletions based on differential gene expression data. Using three cell lines with known chromosomal deletions as model system, mRNA expression in those cells was compared with that observed in diploid cell lines of matched tissue origin. Results show that genes from deleted chromosomal regions are substantially over-represented (p<0.000001 by chi2) among genes identified as underexpressed in deletion cell lines relative to normal matching cells. Using a likelihood based statistical model, we were able to identify the breakpoint of the chromosomal deletion and match with the karyotype data in each cell line. In one such cell line, our analyses refined a previously identified 10p chromosomal deletion region. The deletion region was mapped to between 10p14 and 10p12, which was further confirmed by subtelomeric fluorescence in situ hybridisation. These data show that microarray differential expression data can be used to detect and map the boundaries of submicroscopic chromosomal deletions.

Cell Line↗
PubMed2004

Microarray analysis reveals differences in gene expression of circulating CD8(+) T cells in melanoma patients and healthy donors.

Circulating T cells from many cancer patients are known to be dysfunctional and undergo spontaneous apoptosis. We used microarray technology to determine whether gene expression differences exist in T cells from melanoma patients versus healthy subjects, which may underlie these abnormalities. To maximize the resolution of our data, we sort purified CD8(+) subsets and amplified the extracted RNA for microarray analysis. These analyses show subtle but statistically significant expression differences for 10 genes in T cells from melanoma patients versus healthy controls, which were additionally confirmed by quantitative real-time PCR analysis. Whereas none of these genes are members of the classical apoptosis pathways, several may be linked to apoptosis. To additionally investigate the significance of these 10 genes, we combined them into a classifier and found that they provide a much better discrimination between melanoma and healthy T cells as compared with a classifier built uniquely with classical apoptosis-related genes. These results suggest the possible engagement of an alternative apoptosis pathway in circulating T cells from cancer patients.

Cluster Analysis↗
PubMed2006

SimArray: a user-friendly and user-configurable microarray design tool.

BACKGROUND: Microarrays were first developed to assess gene expression but are now also used to map protein-binding sites and to assess allelic variation between individuals. Regardless of the intended application, efficient production and appropriate array design are key determinants of experimental success. Inefficient production can make larger-scale studies prohibitively expensive, whereas poor array design makes normalisation and data analysis problematic. RESULTS: We have developed a user-friendly tool, SimArray, which generates a randomised spot layout, computes a maximum meta-grid area, and estimates the print time, in response to user-specified design decisions. Selected parameters include: the number of probes to be printed; the microtitre plate format; the printing pin configuration, and the achievable spot density. SimArray is compatible with all current robotic spotters that employ 96-, 384- or 1536-well microtitre plates, and can be configured to reflect most production environments. Print time and maximum meta-grid area estimates facilitate evaluation of each array design for its suitability. Randomisation of the spot layout facilitates correction of systematic biases by normalisation. CONCLUSION: SimArray is intended to help both established researchers and those new to the microarray field to develop microarray designs with randomised spot layouts that are compatible with their specific production environment. SimArray is an open-source program and is available from http://www.flychip.org.uk/SimArray/.

Documentation↗
PubMed2006

Biclustering of gene expression data by Non-smooth Non-negative Matrix Factorization.

BACKGROUND: The extended use of microarray technologies has enabled the generation and accumulation of gene expression datasets that contain expression levels of thousands of genes across tens or hundreds of different experimental conditions. One of the major challenges in the analysis of such datasets is to discover local structures composed by sets of genes that show coherent expression patterns across subsets of experimental conditions. These patterns may provide clues about the main biological processes associated to different physiological states. RESULTS: In this work we present a methodology able to cluster genes and conditions highly related in sub-portions of the data. Our approach is based on a new data mining technique, Non-smooth Non-Negative Matrix Factorization (nsNMF), able to identify localized patterns in large datasets. We assessed the potential of this methodology analyzing several synthetic datasets as well as two large and heterogeneous sets of gene expression profiles. In all cases the method was able to identify localized features related to sets of genes that show consistent expression patterns across subsets of experimental conditions. The uncovered structures showed a clear biological meaning in terms of relationships among functional annotations of genes and the phenotypes or physiological states of the associated conditions. CONCLUSION: The proposed approach can be a useful tool to analyze large and heterogeneous gene expression datasets. The method is able to identify complex relationships among genes and conditions that are difficult to identify by standard clustering algorithms.

Algorithms↗
PubMed2006

An alternative method to amplify RNA without loss of signal conservation for expression analysis with a proteinase DNA microarray in the ArrayTube format.

BACKGROUND: Recent developments in DNA microarray technology led to a variety of open and closed devices and systems including high and low density microarrays for high-throughput screening applications as well as microarrays of lower density for specific diagnostic purposes. Beside predefined microarrays for specific applications manufacturers offer the production of custom-designed microarrays adapted to customers' wishes. Array based assays demand complex procedures including several steps for sample preparation (RNA extraction, amplification and sample labelling), hybridization and detection, thus leading to a high variability between several approaches and resulting in the necessity of extensive standardization and normalization procedures. RESULTS: In the present work a custom designed human proteinase DNA microarray of lower density in ArrayTube format was established. This highly economic open platform only requires standard laboratory equipment and allows the study of the molecular regulation of cell behaviour by proteinases. We established a procedure for sample preparation and hybridization and verified the array based gene expression profile by quantitative real-time PCR (QRT-PCR). Moreover, we compared the results with the well established Affymetrix microarray. By application of standard labelling procedures with e.g. Klenow fragment exo-, single primer amplification (SPA) or In Vitro Transcription (IVT) we noticed a loss of signal conservation for some genes. To overcome this problem we developed a protocol in accordance with the SPA protocol, in which we included target specific primers designed individually for each spotted oligomer. Here we present a complete array based assay in which only the specific transcripts of interest are amplified in parallel and in a linear manner. The array represents a proof of principle which can be adapted to other species as well. CONCLUSION: As the designed protocol for amplifying mRNA starts from as little as 100 ng total RNA, it presents an alternative method for detecting even low expressed genes by microarray experiments in a highly reproducible and sensitive manner. Preservation of signal integrity is demonstrated out by QRT-PCR measurements. The little amounts of total RNA necessary for the analyses make this method applicable for investigations with limited material as in clinical samples from, for example, organ or tumour biopsies. Those are arguments in favour of the high potential of our assay compared to established procedures for amplification within the field of diagnostic expression profiling. Nevertheless, the screening character of microarray data must be mentioned, and independent methods should verify the results.

Cells, Cultured↗
PubMed2006

Different approaches for noninvasive prenatal diagnosis of genetic diseases based on PNA-mediated enriched PCR.

The aim of this work was to develop advanced and accessible protocols for noninvasive prenatal diagnosis of genetic diseases. We are evaluating different technologies for mutation detection, based on fluorescent probe hybridization of the amplified product and pyrosequencing, a technique that relies on the incorporation of nucleotides in a primer-directed polymerase extension reaction. In a previous investigation, we have already proven that these approaches are sufficiently sensitive to detect a few copies of a minority-mutated allele in the presence of an excess of wild-type DNA, In this work, in order to further enhance the sensitivity, we have employed a mutant enrichment amplification strategy based on the use of peptide nucleic acids (PNAs). These DNA analogues bind wild-type DNA, thus interfering with its amplification while still allowing the mutant DNA to become detectable. We have synthesized different PNAs, which are highly effective in clamping wild-type DNA in the beta-globin gene region, where four beta-thalassemia mutations are located (IVSI.110, CD39, IVSI.1, IVSI.6) plus HbS. The fluorescence microchip readout allows us to monitor the extent of wild-type allele inhibition, thus facilitating the assessment of the optimal PNA concentration.

DNA↗
PubMed2004

Papillary and follicular thyroid carcinomas show distinctly different microarray expression profiles and can be distinguished by a minimum of five genes.

PURPOSE: We have previously conducted independent microarray expression analyses of the two most common types of nonmedullary thyroid carcinoma, namely papillary thyroid carcinoma (PTC) and follicular thyroid carcinoma (FTC). In this study, we sought to combine our data sets to shed light on the similarities and differences between these tumor types. MATERIALS AND METHODS: Microarray data from six PTCs, nine FTCs, and 13 normal thyroid samples were normalized to remove interlaboratory variability and then analyzed by unsupervised clustering, t test, and by comparison of absolute and change calls. Expression changes in four genes not previously implicated in thyroid carcinogenesis were verified by reverse transcriptase polymerase chain reaction on these same samples, together with eight additional FTC tumors. RESULTS: PTCs showed two distinct groups of genes that were either over- or underexpressed compared with normal thyroid, whereas the predominant changes in FTCs were of decreased expression. Five genes could collectively distinguish the two tumor types. PTCs showed overexpression of CITED1, claudin-10 (CLDN10), and insulin-like growth factor binding protein 6 (IGFBP6) but showed no change in expression of caveolin-1 (CAV1) or -2 (CAV2); conversely, FTCs did not express CLDN10 and had decreased expression of IGFBP6 and/or CAV1 and CAV2. CONCLUSION: PTC and FTC show distinctive microarray expression profiles, suggesting that either they have different molecular origins or they diverge distinctly from a common origin. Furthermore, if verified in a larger series of tumors, these genes could, in combination with known tumor-specific chromosome translocations, form the basis of a valuable diagnostic tool.

Adenocarcinoma, Follicular↗
PubMed2003

Gene expression profiling of rat uterus at different stages of parturition.

A fuller understanding of the process of parturition is needed in view of the current lack of efficient treatment for preterm labor. Using DNA microarrays, we have analyzed patterns of uterine gene expression at d 0 and 20 of pregnancy, at term in labor or not in labor, and at 1 d post partum. Of the 8740 genes analyzed, 562 genes undergoing significant changes were grouped into 5 distinct clusters, each containing many genes not previously known to be involved with uterine functions. Cluster 1 genes were up-regulated at labor and encompassed immune defense and immediate early response genes, including transcription factors NGFI-B/nurr77 and estrogen-responsive gene 1. Cluster 3 genes were acutely suppressed at labor and included extracellular matrix products and genes related to hormonal signaling, implying novel intrauterine mechanisms regulating intracellular cyclic GMP and local steroid hormone concentrations. At labor, more genes were suppressed than activated, indicating that, for the process of labor induction, gene suppression is at least equally important as the more extensively studied processes of gene activation. The study also points to the existence of novel uterine signaling pathways, including Wnt/frizzled and receptor activator of nuclear factor-kappaB (RANK) and its ligand, as well as the involvement of novel signaling molecules such as estrogen-responsive gene 1, decay-accelerating factor 1, and ebnerin. The present results provide the basis for further studies that will enlarge our knowledge of the mechanisms underlying labor and parturition under physiological and pathophysiological conditions.

Animals↗
PubMed2005

Development of an automation system for single nucleotide polymorphisms genotyping using bio-strand, a new three-dimensional microarray.

Previously, we developed a novel three-dimensional microarray system called Bio-Strand, which may be used in various applications including single nucleotide polymorphisms genotyping. In Bio-Strand, samples for detection are immobilized on a one-dimensional thread, which is wound around a cylinder-shaped core to form a three-dimensional thread-and-core structure. The thread-and-core structure is then inserted into a plastic pipette tip, where hybridization and detection are performed. In this study, we have developed an automation system, NIAGALA Bio-Station SDx, which enables automated hybridization and detection during the genotyping procedure using Bio-Strand. Using this system, we have performed the single nucleotide polymorphism (SNP) genotyping of CYP2C, one of the important human cytochrome P450 genes and the results were completely consistent with the genotyping results determined by the TaqMan method.

Algorithms↗
PubMed2005

Data clustering in life sciences.

Clustering has a wide range of applications in life sciences and over the years has been used in many areas ranging from the analysis of clinical information, phylogeny, genomics, and proteomics. The primary goal of this article is to provide an overview of the various issues involved in clustering large biological datasets, describe the merits and underlying assumptions of some of the commonly used clustering approaches, and provide insights on how to cluster datasets arising in various areas within life sciences. We also provide a brief introduction to CLUTO, a general purpose toolkit for clustering various datasets, with an emphasis on its applications to problems and analysis requirements within life sciences.

Algorithms↗
PubMed2005

Enzymatic mutation detection technologies.

Mutation is as necessary for life as fidelity is in DNA replication. The study of mutations reveals the normal functions of genes, messages, proteins, the causes of many diseases, and the variability of responses among individuals. Indeed, recent mutations that have not yet become polymorphisms are often deleterious and pertinent to the disease history of afflicted individuals. This review discusses the principles behind a variety of methods for the detection of mutations and factors that should be considered in future methods design. One enzymatic approach in particular using orthologs of the CEL I nuclease that show high specificity for all mismatches, appears to be easy and robust. Further developments of this and other methods will allow mutation detection to become an integral component of individualized medicine.

Base Pair Mismatch↗
PubMed2006

Dimension reduction for classification with gene expression microarray data.

An important application of gene expression microarray data is classification of biological samples or prediction of clinical and other outcomes. One necessary part of multivariate statistical analysis in such applications is dimension reduction. This paper provides a comparison study of three dimension reduction techniques, namely partial least squares (PLS), sliced inverse regression (SIR) and principal component analysis (PCA), and evaluates the relative performance of classification procedures incorporating those methods. A five-step assessment procedure is designed for the purpose. Predictive accuracy and computational efficiency of the methods are examined. Two gene expression data sets for tumor classification are used in the study.

Colonic Neoplasms↗
PubMed2005

Functional grouping of yeast genes via biclustering microarray data.

Biclustering algorithm on Gibbs sampling strategy is a recruit in the field of the analysis of gene expression data of microarray experiments. Its feasibility and validity still need to be researched not only for synthetic datasets but also for real datasets. Here we investigated a biclustering algorithm on a microarray dataset of Yeast genome through building a database for storing microarray datasets and MIPS data, and running the scripts on Matlab platform to discover gene patterns. In contrast with standard clusterings that reveal genes behaving similarly over all the conditions, biclustering groups genes over only a subset of conditions for which those genes have a sharp probability distribution. It has the key advantage of providing a transparent probabilistic interpretation of the biclusters. Its basic strategy of Gibbs sampling does not suffer from the problem of local minima that often characterizes expectation maximization, so that the patterns should be more global and accurate. Also we tested it with the known explanation of genes in MIPS, objectively to demonstrate the effectiveness and deficiencies of biclustering approach, and the functions of a few unknown ORFs in some bicluster can be deduced in the present research. In addition, the result of similarity searching in Blast-Search can be an assistant evidence for its effectivity.

Cluster Analysis↗
PubMed2003

Identification of signature genes by microarray for acute myeloid leukemia without maturation and acute promyelocytic leukemia with t(15;17)(q22;q12)(PML/RARalpha).

Acute myeloid leukemia (AML) has distinct subgroups characterized by different maturation and specific chromosomal translocation. In order to gain insight into the gene expression activities in AML, we carried out a gene expression profiling study with 21 AML samples using cDNA microarrays, focusing on acute promyelocytic leukemia with specific translocation t(15;17)(q22;q12) [French-American-British or FAB-M3 with t(15;17)] and AML without maturation (FAB-M1) characterized by morphologically and phenotypically immature AML blasts and no recurrent chromosomal abnormalities. Using a multivariate sigma-classifier algorithm, we identified 33 strong feature genes that distinguish FAB-M3 with t(15;17) from other AML samples, and 24 strong feature genes that classify FAB-M1. A direct comparison between FAB-M3 with t(15;17) and FAB-M1 led to selection of 13 strong feature genes. Those genes include some known to be related to leukemogenesis and cell differentiation. RIN1, a gene in the ras pathway, was up-regulated in FAB-M3 with t(15;17). Growth factor-binding protein 2 gene was down-regulated in FAB-M1. Huntingtin gene was up-regulated in FAB-M1. Others include syndecan 4, interleukin-2 receptor beta, folate receptor beta, low affinity immunoglobulin gamma, Fc receptor IIC precursor, insulin-like growth factor binding protein 2, and myeloperoxidase, which are involved in cell differentiation. Overexpression of myeloperoxidase in FAB-M3 cells with t(15;17) compared to FAB-M1 cells is consistent with the conventional cytochemical staining pattern. Thus, the study revealed that a morphologically-defined FAB-M1 subtype has a distinct gene expression signature that contributes to its cell differentiation and proliferation as well as FAB-M3 with a recurrent cytogenetic abnormality t(15;17)(q22;q12).

Algorithms↗
PubMed2003

Microarrays for bacterial detection and microbial community analysis.

Several types of microarrays have recently been developed and evaluated for bacterial detection and microbial community analysis. These studies demonstrated that specific, sensitive and quantitative detection could be obtained with both functional gene arrays and community genome arrays. Although single-base mismatch can be differentiated with phylogenetic oligonucleotide arrays, reliable specific detection at the single-base level is still problematic. Microarray-based hybridization approaches are also useful for defining genome diversity and bacterial relatedness. However, more rigorous and systematic assessment and development are needed to realize the full potential of microarrays for microbial detection and community analysis.

Bacteria↗