Expression profiling with oligonucleotide arrays: technologies and applications for neurobiology

Helicobacter pylori-secreting protein Tipalpha is a potent inducer of chemokine gene expressions in stomach cancer cells

PURPOSE

Stomach cancer has a high mortality rate in East Asia, and is strongly associated with Helicobacter pylori (H. pylori) infection. H. pylori is known to express chemokine genes in the gastric mucosa, chemokines that are important host immune factors facilitating inflammation and tumor growth. To investigate the mechanism of carcinogenesis in the stomach, it is essential to determine which molecule of H. pylori is involved in induction of chemokines, but this has remained unclear. We previously reported that a tumor necrosis factor-alpha (TNF-alpha) inducing protein (Tipalpha) secreted from H. pylori acts as a tumor promoter in stomach cancer development, and thus started to investigate whether Tipalpha is involved in induction of chemokine genes.

METHODS

Comprehensive gene expression analysis was conducted using DNA microarray and KeyMolnet analyses. The gene expression was quantitatively analyzed by real-time RT-PCR.

RESULTS

Comprehensive and quantitative gene expression analyses revealed that Tipalpha induces gene expression of the chemokines Ccl2, Ccl7, Ccl20, Cxcl1, Cxcl2, Cxcl5 and Cxcl10 extensively and simultaneously in mouse stomach cancer cells, MGT-40. Tipalpha induced high levels of chemokine gene expression, whereas inactive deleted Tipalpha, del-Tipalpha, showed only marginal expression, suggesting a correlation between tumor promotion and chemokine gene expression by Tipalpha. MG-132, a proteasome inhibitor which represses NF-kappaB-activation, inhibited chemokine gene expressions.

CONCLUSION

We report here that Tipalpha of H. pylori gene product is a strong inducer of chemokine gene expressions, providing a new model for stomach cancer development.

Using RNA sample titrations to assess microarray platform performance and normalization techniques

We have assessed the utility of RNA titration samples for evaluating microarray platform performance and the impact of different normalization methods on the results obtained. As part of the MicroArray Quality Control project, we investigated the performance of five commercial microarray platforms using two independent RNA samples and two titration mixtures of these samples. Focusing on 12,091 genes common across all platforms, we determined the ability of each platform to detect the correct titration response across the samples. Global deviations from the response predicted by the titration ratios were observed. These differences could be explained by variations in relative amounts of messenger RNA as a fraction of total RNA between the two independent samples. Overall, both the qualitative and quantitative correspondence across platforms was high. In summary, titration samples may be regarded as a valuable tool, not only for assessing microarray platform performance and different analysis methods, but also for determining some underlying biological features of the samples.

Genomic platforms for cancer research: potential diagnostic and prognostic applications in clinical oncology

The completion of the Human Genome Project and the achievement of similar goals in other organisms have generated a huge amount of free available information with high potential in biomedical sciences. However, the identification of the DNA sequence was only a starting point for genomic research. This research has been facilitated by the development of new powerful genomic tools that allow the use of the wide amount of genomic information generated to address new biological and biomedical questions. One of these widely accepted and accessible technologies is DNA microarrays. Although the most popular use of DNA microarrays is gene expression profiling, due to the continuous advances in microarray technologies, scientists have also successfully used them for multiple applications, including genotyping, re-sequencing, DNA copy number analysis and DNA methylation. In short, DNA microarrays are changing the way cancer research scientists are addressing different biological questions and will allow the translation of genome research to clinical practice.

DNA microarray applications in functional genomics

The successful completion of the Human Genome Project and the achievement of similar goals in other species have generated a huge amount of free available information about the genomic sequence of different organisms, opening the door to a postgenome era where new challenges arise. One of the most ambitious objectives of this new period, addressed by the emerging discipline of functional genomics, attempts to understand the genome and the products it encodes for, and how these gene products interact to produce complex living organisms. This new era is also characterized by the development of new technologies, which have produced genomic tools indispensable for understanding how gene products are regulated in normal and diseased conditions on a global genome scale. One of these technologies is DNA microarrays, turned into a very popular tool in the last years. Although the most common use of DNA microarrays is gene expression profiling, scientists have successfully used them for multiple applications, including genotyping, sequencing, DNA copy number analysis, and DNA-protein interactions, among others. In summary, DNA microarrays are changing the way biomedicine and other disciplines are addressing different biological questions and will allow the translation of genome research to the clinic.

Performance evaluation of commercial short-oligonucleotide microarrays and the impact of noise in making cross-platform correlations

BACKGROUND

Despite the widespread use of microarrays, much ambiguity regarding data analysis, interpretation and correlation of the different technologies exists. There is a considerable amount of interest in correlating results obtained between different microarray platforms. To date, only a few cross-platform evaluations have been published and unfortunately, no guidelines have been established on the best methods of making such correlations. To address this issue we conducted a thorough evaluation of two commercial microarray platforms to determine an appropriate methodology for making cross-platform correlations.

RESULTS

In this study, expression measurements for 10,763 genes uniquely represented on Affymetrix U133A/B GeneChips and Amersham CodeLink UniSet Human 20 K microarrays were compared. For each microarray platform, five technical replicates, derived from the same total RNA samples, were labeled, hybridized, and quantified according to each manufacturers' standard protocols. The correlation coefficient (r) of differential expression ratios for the entire set of 10,763 overlapping genes was 0.62 between platforms. However, the correlation improved significantly (r = 0.79) when genes within noise were excluded. In addition to levels of inter-platform correlation, we evaluated precision, statistical-significance profiles, power, and noise levels for each microarray platform. Accuracy of differential expression was measured against real-time PCR for 25 genes and both platforms correlated well with r values of 0.92 and 0.79 for CodeLink and GeneChip, respectively.

CONCLUSIONS

As a result of this study, we recommend using only genes called 'present' in cross-platform correlations. However, as in this study, a large number of genes may be lost from the correlation due to differing levels of noise between platforms. This is an important consideration given the apparent difference in sensitivity of the two platforms. Data from microarray analysis need to be interpreted cautiously and therefore, we provide guidelines for making cross-platform correlations. In all, this study represents the most comprehensive and specifically designed comparison of short-oligonucleotide microarray platforms to date using the largest set of overlapping genes.

A molecular description of brain trauma pathophysiology using microarray technology: an overview

It has been estimated that 50% of human transcriptome, the collection of mRNA in a cell, is expressed in the brain, making it one of the most complex organs to understand in terms of genomic responses to injury. The availability of genome sequences for several organisms coupled with the increasing affordability of microarray technologies makes it feasible to monitor the mRNA levels of thousands of genes simultaneously. In this paper, we provide an overview of findings using both cDNA- and oligonucleotide-based microarray analyses after experimental traumatic brain injury (TBI). Specifically, the utility of this methodology as a means of cataloging the biochemical sequelae of brain trauma and elucidating novel genes or pathways for further study is discussed. Furthermore, we offer future directions for the continued evaluation of microarray results and discuss the usefulness of microarray techniques as a testing format for determining the efficacy of mechanism-based therapies.

Data mining of gene expression changes in Alzheimer brain

Genome-wide transcription profiling is a powerful technique for studying the enormous complexity of cellular states. Moreover, when applied to disease tissue it may reveal quantitative and qualitative alterations in gene expression that give information on the context or underlying basis for the disease and may provide a new diagnostic approach. However, the data obtained from high-density microarrays is highly complex and poses considerable challenges in data mining. The data requires care in both pre-processing and the application of data mining techniques. This paper addresses the problem of dealing with microarray data that come from two known classes (Alzheimer and normal). We have applied three separate techniques to discover genes associated with Alzheimer disease (AD). The 67 genes identified in this study included a total of 17 genes that are already known to be associated with Alzheimer's or other neurological diseases. This is higher than any of the previously published Alzheimer's studies. Twenty known genes, not previously associated with the disease, have been identified as well as 30 uncharacterized expressed sequence tags (ESTs). Given the success in identifying genes already associated with AD, we can have some confidence in the involvement of the latter genes and ESTs. From these studies we can attempt to define therapeutic strategies that would prevent the loss of specific components of neuronal function in susceptible patients or be in a position to stimulate the replacement of lost cellular function in damaged neurons. Although our study is based on a relatively small number of patients (four AD and five normal), we think our approach sets the stage for a major step in using gene expression data for disease modeling (i.e. classification and diagnosis). It can also contribute to the future of gene function identification, pathology, toxicogenomics, and pharmacogenomics.