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Results for “Oligonucleotide Array Sequence Analysis”

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PubMed2002

Reaction of keratinocytes to exogenous DNA.

Keratinocytes have the ability to take up oligonucleotides (ODN) and plasmid DNA probably by receptor-mediated endocytosis or macropinocytosis. Despite the use of DNA for antisense and gene therapy, little is known about the regulation of genes following exposure to nucleic acids. To systematically identify gene regulation in keratinocytes upon exposure to ODN, we screened human cytokine DNA arrays containing 383 different genes and found interleukin-1 alpha (IL-1 alpha), IL-1 beta, integrin beta1, alpha-tubulin and follistatin greatly induced, while most genes were unaffected. The time course and concentration dependence for IL-1 alpha and follistatin was confirmed by the standard Northern blot technique and found to be induced by picomolar or femtomolar concentrations of ODN. ODN of different length and sequence induced comparable amounts of IL-1 alpha and follistatin. Their induction was independent of the negative charge and of several proinflammatory compounds and proteins such as lipopolysaccharides, IL-1 beta or IFN-gamma, but was partly inhibited by activin A. In summary, our study revealed several genes of the acute phase protein family that were induced in a non-sequence-specific manner following the exposure of normal human keratinocytes to ODN. Therefore, it is tempting to speculate that, upon internalization, ODN bind to an intracellular receptor (e.g. Toll-like receptor 9), which mediates signaling.

Activins↗
PubMed2003

Clinical application of oligonucleotide probe array for full-length gene sequencing of TP53 in colon cancer.

OBJECTIVE: TP53 mutations are the most frequent genetic alterations in colon cancer. We studied whether the recently developed oligonucleotide microarray technique, GeneChip p53 assay, can be applied to sensitive detection of TP53 gene mutations in surgical specimens from colon cancer patients. METHODS: TP53 gene mutations in exons 2-11 in 20 colon cancers and the corresponding histopathologically normal mucosa at the surgical margins were assessed by GeneChip p53 assay, and the results were further evaluated by direct sequencing of the involved exon or by mutant-allele-specific amplification (MASA). The expression of TP53 protein was also evaluated immunohistochemically and the result was compared with the gene alteration. RESULTS: The GeneChip p53 assay detected TP53 mutations in 65% of primary cancers; 61% of the mutations were within the evolutionarily conserved regions, and 46% of the mutations were within the zinc-binding domains (regions of loop 2 and loop 3). Direct sequencing confirmed these mutations. Immunohistochemical examination detected TP53 protein overexpression in 47% of primary cancers, but this protein did not accumulate with all types of TP53 mutations. In addition, the GeneChip assay detected a mutation identical to that in the primary tumor in 2 samples from the surgical margins, and MASA confirmed both mutations, implying the presence of occult cancer cells. CONCLUSION: The GeneChip p53 assay is sufficiently sensitive to detect TP53 mutations in surgical specimens from colon cancers and may be applicable to screening examination in clinical laboratories as a routine procedure.

Adult↗
PubMed2006

Gene expression profiles on hypoxia and reoxygenation in rat gastric epithelial cells: a high-density DNA microarray analysis.

Previous investigations have demonstrated that the cellular signaling induced by hypoxia-reoxygenation is a major pathway contributing to gastric mucosal injury induced by stress, non-steroidal anti-inflammatory drugs, and Helicobacter pylori. The aim of the present study was to perform a gene expression analysis of the gastric mucosal cellular response and to define the protective molecules in hypoxia and reoxygenation using a high-density DNA microarray analysis. Normal rat gastric mucosal (RGM-1) cells were subjected to hypoxia for 2 h, and reoxygenation was initiated by placing the cells in an environment of normoxia for 2, 4, or 8 h. Total RNA was extracted, and differences in the gene expression profiles between the normoxia and hypoxia groups or among the different durations of reoxygenation were investigated using a high-density DNA microarray. HIF-1- and apoptosis-related genes were modulated by hypoxia. Moreover, inflammation-, stress-, and wound- healing-related genes were regulated by reoxygenation following hypoxia. In particular, the expression of heat shock protein-70, amphiregulin and cyclooxygenase-2 were upregulated during reoxygenation following hypoxia, suggesting that these upregulations may play an important role in maintaining cell survival and supporting cell function.

Animals↗
PubMed2006

Genome wide measurement of DNA copy number changes in neuroblastoma: dissecting amplicons and mapping losses, gains and breakpoints.

In the past few years high throughput methods for assessment of DNA copy number alterations have witnessed rapid progress. Both 'in house' developed BAC, cDNA, oligonucleotide and commercial arrays are now available and widely applied in the study of the human genome, particularly in the context of disease. Cancer cells are known to exhibit DNA losses, gains and amplifications affecting tumor suppressor genes and proto-oncogenes. Moreover, these patterns of genomic imbalances may be associated with particular tumor types or subtypes and may have prognostic value. Here we summarize recent array CGH findings in neuroblastoma, a pediatric tumor of the sympathetic nervous system. A total of 176 primary tumors and 53 cell lines have been analyzed on different platforms. Through these studies the genomic content and boundaries of deletions, gains and amplifications were characterized with unprecedented accuracy. Furthermore, in conjunction with cytogenetic findings, array CGH allows the mapping of breakpoints of unbalanced translocations at a very high resolution.

Cell Line, Tumor↗
PubMed2003

Genome-wide array analysis of normal and malformed human hearts.

BACKGROUND: We present the first genome-wide cDNA array analysis of human congenitally malformed hearts and attempted to partially elucidate these complex phenotypes. Most congenital heart defects, which account for the largest number of birth defects in humans, represent complex genetic disorders. As a consequence of the malformation, abnormal hemodynamic features occur and cause an adaptation process of the heart. METHODS AND RESULTS: The statistical analysis of our data suggests distinct gene expression profiles associated with tetralogy of Fallot, ventricular septal defect, and right ventricular hypertrophy. Applying correspondence analysis, we could associate specific gene functions to specific phenotypes. Furthermore, our study design allows the suggestion that alterations associated with primary genetic abnormalities can be distinguished from those associated with the adaptive response of the heart to the malformation (right ventricular pressure overload hypertrophy). We provide evidence for the molecular transition of the hypertrophic right ventricle to normal left ventricular characteristics. Furthermore, we present data on chamber-specific gene expression. CONCLUSIONS: Our findings propose that array analysis of malformed human hearts opens a new window to understand the complex genetic network of cardiac development and adaptation. For detailed access, see the online-only Data Supplement.

Adaptation, Physiological↗
PubMed2005

Assembly fabrication of oligonucleotide arrays.

In this paper, a simple, reliable and flexible method for fabricating oligonucleotide arrays integrating in in-situ synthesis with a spotting technique is described. In this approach, different oligonucleotide sequences were synthesized on coded modification glass slides using combinatorial chemistry and a mature phosphoramidite chemistry protocol. The slides were then sliced into smaller pieces. Finally, an oligonucleotide array was fabricated by arbitrarily assembling the different coded pieces onto another solid support. A 5 x 5 array including four different sequences of the P16 gene and a control (blank) was successfully assembled. The results indicated that the hybridization fluorescence intensities from the same sequences located at different places on the array were homogeneous and uniform. Background fluorescence was much lower. The fluorescence intensity ratio of a matched sequence to a one-base mismatched sequence, a two-base mismatched sequence and a three-base mismatched sequence was 0.499, 0.236 and 0.04, respectively. The results for the same sequence at different spots in the chip were reproducible with the relative standard deviation ranging from 6.64% to 10.2% (n = 5). This method has the advantages of high probe-density of in-situ synthesis, and off-chip flexibility. Moreover, it does not need any immobilization process to bond oligonucleotides on the substrate.

Base Sequence↗
PubMed2003

Keratinocyte growth factor and the transcription factors C/EBP alpha, C/EBP delta, and SREBP-1c regulate fatty acid synthesis in alveolar type II cells.

Strategies to stimulate endogenous surfactant production require a detailed understanding of the regulation of lipogenesis in alveolar type II cells. We developed culture conditions in which keratinocyte growth factor (KGF) stimulates fatty acid and phospholipid synthesis. KGF stimulated acetate incorporation into phosphatidylcholine, disaturated phosphatidylcholine, and phosphatidylglycerol more than 5% rat serum alone. To determine the mRNA levels of lipogenic enzymes and transport proteins, we analyzed gene expression by oligonucleotide microarrays. KGF increased the mRNA levels for fatty acid synthase, stearoyl-CoA desaturase-1 (SCD-1), and epidermal fatty acid-binding protein more than rat serum alone. In addition, KGF increased the mRNA levels of the transcription factors CCAAT/enhancer-binding protein alpha (C/EBPalpha) and C/EBPdelta as well as SREBP-1c (ADD-1), but not PPARgamma. These changes in C/EBPalpha and C/EBPdelta were confirmed by in situ hybridization. SCD-1 was also found to be highly expressed in alveolar type II cells in vivo. Furthermore, KGF increased protein levels of fatty acid synthase, C/EBPalpha, C/EBPdelta, SREBP-1, epidermal fatty acid-binding protein, and SCD. Finally, the liver X receptor agonist T0901317 increased acetate incorporation and SREBP-1 but not SREBP-2 protein levels. In summary, KGF stimulates lipogenesis in type II cells by a coordinated expression of lipogenic enzymes and transport proteins regulated by C/EBP isoforms and SREBP-1c.

Animals↗
PubMed2005

Platelet genomics and proteomics in human health and disease.

Proteomic and genomic technologies provide powerful tools for characterizing the multitude of events that occur in the anucleate platelet. These technologies are beginning to define the complete platelet transcriptome and proteome as well as the protein-protein interactions critical for platelet function. The integration of these results provides the opportunity to identify those proteins involved in discrete facets of platelet function. Here we summarize the findings of platelet proteome and transcriptome studies and their application to diseases of platelet function.

Blood Platelets↗
PubMed2005

Development and evaluation of 16S rDNA microarray for detecting bacterial pathogens in cerebrospinal fluid.

The rapid identification of bacteria in cerebrospinal fluid (CSF) is very important for patient management and antimicrobial therapies. We developed a 16S DNA microarray-based method that targets 16S rDNA and can directly detect bacteria from CSF without cultivation. Universal primers and specific probes were designed from the 16S rDNA sequence data retrieved directly from the GenBank database. The specificity of the assay is obtained through a combination of microarray hybridization and enzymatic labeling of the constructed specific probes. Cultivation-dependent assays were used as reference methods in the development and evaluation of the method. With the exception of Mycobacterium tuberculosis and Proteus mirabilis, forty-five positive blood culture media were successfully differentiated. When this procedure was applied directly to 100 CSF specimens, 29 specimens from 16 patients were positive by bacterial culture and 3 culture-positive CSF specimens produced no hybridized signals. The remaining 26 specimens were correctly identified, including one with mixed infection. The accuracy, sensitivity, and specificity of the assay can be increased further by designing more oligonucleotides for the microarray. This method is versatile and makes it possible to detect more bacteria in a single assay and discriminate different bacterial genera.

Bacteria↗
PubMed2004

A power law global error model for the identification of differentially expressed genes in microarray data.

BACKGROUND: High-density oligonucleotide microarray technology enables the discovery of genes that are transcriptionally modulated in different biological samples due to physiology, disease or intervention. Methods for the identification of these so-called "differentially expressed genes" (DEG) would largely benefit from a deeper knowledge of the intrinsic measurement variability. Though it is clear that variance of repeated measures is highly dependent on the average expression level of a given gene, there is still a lack of consensus on how signal reproducibility is linked to signal intensity. The aim of this study was to empirically model the variance versus mean dependence in microarray data to improve the performance of existing methods for identifying DEG. RESULTS: In the present work we used data generated by our lab as well as publicly available data sets to show that dispersion of repeated measures depends on location of the measures themselves following a power law. This enables us to construct a power law global error model (PLGEM) that is applicable to various Affymetrix GeneChip data sets. A new DEG identification method is therefore proposed, consisting of a statistic designed to make explicit use of model-derived measurement spread estimates and a resampling-based hypothesis testing algorithm. CONCLUSIONS: The new method provides a control of the false positive rate, a good sensitivity vs. specificity trade-off and consistent results with varying number of replicates and even using single samples.

Algorithms↗
PubMed2005

MASQOT: a method for cDNA microarray spot quality control.

BACKGROUND: cDNA microarray technology has emerged as a major player in the parallel detection of biomolecules, but still suffers from fundamental technical problems. Identifying and removing unreliable data is crucial to prevent the risk of receiving illusive analysis results. Visual assessment of spot quality is still a common procedure, despite the time-consuming work of manually inspecting spots in the range of hundreds of thousands or more. RESULTS: A novel methodology for cDNA microarray spot quality control is outlined. Multivariate discriminant analysis was used to assess spot quality based on existing and novel descriptors. The presented methodology displays high reproducibility and was found superior in identifying unreliable data compared to other evaluated methodologies. CONCLUSION: The proposed methodology for cDNA microarray spot quality control generates non-discrete values of spot quality which can be utilized as weights in subsequent analysis procedures as well as to discard spots of undesired quality using the suggested threshold values. The MASQOT approach provides a consistent assessment of spot quality and can be considered an alternative to the labor-intensive manual quality assessment process.

Data Interpretation, Statistical↗
PubMed2005

An algorithm for the determination and quantification of components of nucleic acid mixtures based on single sequencing reactions.

BACKGROUND: Determination and quantification of nucleic acid components in a mixture is usually accomplished by microarray approaches, where the mixtures are hybridized against specific probes. As an alternative, we propose here that a single sequencing reaction from a mixture of nucleic acids holds enough information to potentially distinguish the different components, provided it is known which components can occur in the mixture. RESULTS: We describe an algorithm that is based on a set of linear equations which can be solved when the sequencing profiles of the individual components are known and when the number of sequenced nucleotides is larger than the number of components in the mixture. We have implemented the procedure for one type of sequencing approach, pyrosequencing, which produces a stepwise output of peaks that is particularly suitable for the procedure. As an example we use signature sequences from ribosomal RNA to distinguish and quantify several different species in a mixture. Using simulations, we show that the procedure may also be applicable for dideoxy sequencing on capillary sequencers, requiring only some instrument specific adaptations of protocols and software. CONCLUSION: The parallel sequencing approach described here may become a simple and cheap alternative to microarray experiments which aim at routine re-determination and quantification of known nucleic acid components from environmental samples or tissue samples.

Algorithms↗
PubMed2005

Individual sequences in large sets of gene sequences may be distinguished efficiently by combinations of shared sub-sequences.

BACKGROUND: Most current DNA diagnostic tests for identifying organisms use specific oligonucleotide probes that are complementary in sequence to, and hence only hybridize with the DNA of one target species. By contrast, in traditional taxonomy, specimens are usually identified by 'dichotomous keys' that use combinations of characters shared by different members of the target set. Using one specific character for each target is the least efficient strategy for identification. Using combinations of shared bisectionally-distributed characters is much more efficient, and this strategy is most efficient when they separate the targets in a progressively binary way. RESULTS: We have developed a practical method for finding minimal sets of sub-sequences that identify individual sequences, and could be targeted by combinations of probes, so that the efficient strategy of traditional taxonomic identification could be used in DNA diagnosis. The sizes of minimal sub-sequence sets depended mostly on sequence diversity and sub-sequence length and interactions between these parameters. We found that 201 distinct cytochrome oxidase subunit-1 (CO1) genes from moths (Lepidoptera) were distinguished using only 15 sub-sequences 20 nucleotides long, whereas only 8-10 sub-sequences 6-10 nucleotides long were required to distinguish the CO1 genes of 92 species from the 9 largest orders of insects. CONCLUSION: The presence/absence of sub-sequences in a set of gene sequences can be used like the questions in a traditional dichotomous taxonomic key; hybridisation probes complementary to such sub-sequences should provide a very efficient means for identifying individual species, subtypes or genotypes. Sequence diversity and sub-sequence length are the major factors that determine the numbers of distinguishing sub-sequences in any set of sequences.

Algorithms↗
PubMed2006

Evaluation of methods for oligonucleotide array data via quantitative real-time PCR.

BACKGROUND: There are currently many different methods for processing and summarizing probe-level data from Affymetrix oligonucleotide arrays. It is of great interest to validate these methods and identify those that are most effective. There is no single best way to do this validation, and a variety of approaches is needed. Moreover, gene expression data are collected to answer a variety of scientific questions, and the same method may not be best for all questions. Only a handful of validation studies have been done so far, most of which rely on spike-in datasets and focus on the question of detecting differential expression. Here we seek methods that excel at estimating relative expression. We evaluate methods by identifying those that give the strongest linear association between expression measurements by array and the "gold-standard" assay. Quantitative reverse-transcription polymerase chain reaction (qRT-PCR) is generally considered the "gold-standard" assay for measuring gene expression by biologists and is often used to confirm findings from microarray data. Here we use qRT-PCR measurements to validate methods for the components of processing oligo array data: background adjustment, normalization, mismatch adjustment, and probeset summary. An advantage of our approach over spike-in studies is that methods are validated on a real dataset that was collected to address a scientific question. RESULTS: We initially identify three of six popular methods that consistently produced the best agreement between oligo array and RT-PCR data for medium- and high-intensity genes. The three methods are generally known as MAS5, gcRMA, and the dChip mismatch mode. For medium- and high-intensity genes, we identified use of data from mismatch probes (as in MAS5 and dChip mismatch) and a sequence-based method of background adjustment (as in gcRMA) as the most important factors in methods' performances. However, we found poor reliability for methods using mismatch probes for low-intensity genes, which is in agreement with previous studies. CONCLUSION: We advocate use of sequence-based background adjustment in lieu of mismatch adjustment to achieve the best results across the intensity spectrum. No method of normalization or probeset summary showed any consistent advantages.

Algorithms↗
PubMed2006

Multivariate curve resolution of time course microarray data.

BACKGROUND: Modeling of gene expression data from time course experiments often involves the use of linear models such as those obtained from principal component analysis (PCA), independent component analysis (ICA), or other methods. Such methods do not generally yield factors with a clear biological interpretation. Moreover, implicit assumptions about the measurement errors often limit the application of these methods to log-transformed data, destroying linear structure in the untransformed expression data. RESULTS: In this work, a method for the linear decomposition of gene expression data by multivariate curve resolution (MCR) is introduced. The MCR method is based on an alternating least-squares (ALS) algorithm implemented with a weighted least squares approach. The new method, MCR-WALS, extracts a small number of basis functions from untransformed microarray data using only non-negativity constraints. Measurement error information can be incorporated into the modeling process and missing data can be imputed. The utility of the method is demonstrated through its application to yeast cell cycle data. CONCLUSION: Profiles extracted by MCR-WALS exhibit a strong correlation with cell cycle-associated genes, but also suggest new insights into the regulation of those genes. The unique features of the MCR-WALS algorithm are its freedom from assumptions about the underlying linear model other than the non-negativity of gene expression, its ability to analyze non-log-transformed data, and its use of measurement error information to obtain a weighted model and accommodate missing measurements.

Algorithms↗
PubMed2006

Selecting normalization genes for small diagnostic microarrays.

BACKGROUND: Normalization of gene expression microarrays carrying thousands of genes is based on assumptions that do not hold for diagnostic microarrays carrying only few genes. Thus, applying standard microarray normalization strategies to diagnostic microarrays causes new normalization problems. RESULTS: In this paper we point out the differences of normalizing large microarrays and small diagnostic microarrays. We suggest to include additional normalization genes on the small diagnostic microarrays and propose two strategies for selecting them from genomewide microarray studies. The first is a data driven univariate selection of normalization genes. The second is multivariate and based on finding a balanced diagnostic signature. Finally, we compare both methods to standard normalization protocols known from large microarrays. CONCLUSION: Not including additional genes for normalization on small microarrays leads to a loss of diagnostic information. Using house keeping genes from the literature for normalization fails to work for certain datasets. While a data driven selection of additional normalization genes works well, the best results were obtained using a balanced signature.

Algorithms↗
PubMed2006

Design of a combinatorial DNA microarray for protein-DNA interaction studies.

BACKGROUND: Discovery of precise specificity of transcription factors is an important step on the way to understanding the complex mechanisms of gene regulation in eukaryotes. Recently, double-stranded protein-binding microarrays were developed as a potentially scalable approach to tackle transcription factor binding site identification. RESULTS: Here we present an algorithmic approach to experimental design of a microarray that allows for testing full specificity of a transcription factor binding to all possible DNA binding sites of a given length, with optimally efficient use of the array. This design is universal, works for any factor that binds a sequence motif and is not species-specific. Furthermore, simulation results show that data produced with the designed arrays is easier to analyze and would result in more precise identification of binding sites. CONCLUSION: In this study, we present a design of a double stranded DNA microarray for protein-DNA interaction studies and show that our algorithm allows optimally efficient use of the arrays for this purpose. We believe such a design will prove useful for transcription factor binding site identification and other biological problems.

3' Flanking Region↗
PubMed2006

Gene selection algorithms for microarray data based on least squares support vector machine.

BACKGROUND: In discriminant analysis of microarray data, usually a small number of samples are expressed by a large number of genes. It is not only difficult but also unnecessary to conduct the discriminant analysis with all the genes. Hence, gene selection is usually performed to select important genes. RESULTS: A gene selection method searches for an optimal or near optimal subset of genes with respect to a given evaluation criterion. In this paper, we propose a new evaluation criterion, named the leave-one-out calculation (LOOC, A list of abbreviations appears just above the list of references) measure. A gene selection method, named leave-one-out calculation sequential forward selection (LOOCSFS) algorithm, is then presented by combining the LOOC measure with the sequential forward selection scheme. Further, a novel gene selection algorithm, the gradient-based leave-one-out gene selection (GLGS) algorithm, is also proposed. Both of the gene selection algorithms originate from an efficient and exact calculation of the leave-one-out cross-validation error of the least squares support vector machine (LS-SVM). The proposed approaches are applied to two microarray datasets and compared to other well-known gene selection methods using codes available from the second author. CONCLUSION: The proposed gene selection approaches can provide gene subsets leading to more accurate classification results, while their computational complexity is comparable to the existing methods. The GLGS algorithm can also better scale to datasets with a very large number of genes.

Algorithms↗