Cross-species gene expression analysis of species specific differences in the preclinical assessment of pharmaceutical compounds.

Animals are frequently used as model systems for determination of safety and efficacy in pharmaceutical research and development. However, significant quantitative and qualitative differences exist between humans and the animal models used in research. This is as a result of genetic variation between human and the laboratory animal. Therefore the development of a system that would allow the assessment of all molecular differences between species after drug exposure would have a significant impact on drug evaluation for toxicity and efficacy. Here we describe a cross-species microarray methodology that identifies and selects orthologous probes after cross-species sequence comparison to develop an orthologous cross-species gene expression analysis tool. The assumptions made by the use of this orthologous gene expression strategy for cross-species extrapolation is that; conserved changes in gene expression equate to conserved pharmacodynamic endpoints. This assumption is supported by the fact that evolution and selection have maintained the structure and function of many biochemical pathways over time, resulting in the conservation of many important processes. We demonstrate this cross-species methodology by investigating species specific differences of the peroxisome proliferator-activator receptor (PPAR) α response in rat and human.

Effects of Alzheimer's peptide and alpha1-antichymotrypsin on astrocyte gene expression.

We employed gene array technology to investigate the effects of alpha1-antichymotrypsin (ACT), soluble or fibrillar Alzheimer's peptide (Abeta(1-42)) alone and the combination of ACT/Abeta(1-42) on human astrocytes. Using a 1.2-fold change as significance threshold, 398 astrocyte genes showed altered expression in response to these treatments compared to controls. Of the 276 genes affected by the ACT/soluble Abeta(1-42) combination, 195 (70.6%) were suppressed. The ACT/fibrillar Abeta(1-42) combination affected expression of 64 genes of which 58 (90.5%) were up-regulated. The most prominent gene expression changes in response to the ACT/soluble Abeta(1-42), were the down-regulation of at least 60 genes involved in transcription, signal transduction, apoptosis and neurogenesis. The ACT/fibril Abeta(1-42) increased the expression of genes involved in transcription regulation and signal transduction. Surprisingly, gene expression of astrocytes exposed to soluble or fibrillar Abeta(1-42) alone was largely unaffected. Thus, the molecular forms generated by the combination of ACT/Abeta(1-42) alter expression of astrocyte genes more profoundly in breadth and magnitude than soluble or fibrillar Abeta(1-42) alone, suggesting that pathogenic effects of Abeta(1-42) may occur as a consequence of its association with other proteins.

How to decide? Different methods of calculating gene expression from short oligonucleotide array data will give different results.

BACKGROUND: Short oligonucleotide arrays for transcript profiling have been available for several years. Generally, raw data from these arrays are analysed with the aid of the Microarray Analysis Suite or GeneChip Operating Software (MAS or GCOS) from Affymetrix. Recently, more methods to analyse the raw data have become available. Ideally all these methods should come up with more or less the same results. We set out to evaluate the different methods and include work on our own data set, in order to test which method gives the most reliable results. RESULTS: Calculating gene expression with 6 different algorithms (MAS5, dChip PMMM, dChip PM, RMA, GC-RMA and PDNN) using the same (Arabidopsis) data, results in different calculated gene expression levels. Consequently, depending on the method used, different genes will be identified as differentially regulated. Surprisingly, there was only 27 to 36% overlap between the different methods. Furthermore, 47.5% of the genes/probe sets showed good correlation between the mismatch and perfect match intensities. CONCLUSION: After comparing six algorithms, RMA gave the most reproducible results and showed the highest correlation coefficients with Real Time RT-PCR data on genes identified as differentially expressed by all methods. However, we were not able to verify, by Real Time RT-PCR, the microarray results for most genes that were solely calculated by RMA. Furthermore, we conclude that subtraction of the mismatch intensity from the perfect match intensity results most likely in a significant underestimation for at least 47.5% of the expression values. Not one algorithm produced significant expression values for genes present in quantities below 1 pmol. If the only purpose of the microarray experiment is to find new candidate genes, and too many genes are found, then mutual exclusion of the genes predicted by contrasting methods can be used to narrow down the list of new candidate genes by 64 to 73%.

NASCArrays: a repository for microarray data generated by NASC's transcriptomics service.

NASC operates an Affymetrix 'GeneChip' (microarray) service for the Arabidopsis thaliana community. All data produced by the service are publicly available through our microarray data base 'NASCArrays' published at http://affymetrix. arabidopsis.info. The data are accessible through text searching and a series of data mining tools. All data are annotated with sample preparation details, and the original Affymetrix data are available for download. The database aims to be MIAME supportive and provide a coordinated resource for re searchers interested in the transcriptome of Arabidopsis. Using this database, data produced will be shared with other databases worldwide.