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The transition metal nickel is used in a wide variety of alloys and medical devices. Nickel can cause a range of toxicities from allergy in humans to tumors when implanted in animals. Several microarray studies have examined nickel toxicity, but so far none have comprehensively profiled expression over an extended period. In this work, male mice were implanted with a single nickel pellet in the muscle of the right leg with the left leg used as a control. At 3 week intervals up to 12 months, nickel concentrations in bioflulids and microarrays of surrounding tissue were used to track gene expression patterns. Pellet biocorrosion resulted in varying levels of systemic nickel over time, with peaks of 600 μg L -1 in serum, while global gene expression was cyclical in nature with immune related genes topping the list of overexpressed genes. IPA and KEGG pathway analyses was used to attribute overall biological function to changes in gene expression levels, supported by GO enrichment analysis. IPA pathways identified sirtuin, mitochondria, and oxidative phosphorylation as top pathways, based predominantly on downregulated genes, whereas immune processes were associated with upregulated genes. Top KEGG pathways identified were lysosome, osteoclast differentiation, and phasgosome. Both pathway approaches identified common immune responses, as well as hypoxia, toll like receptor, and matrix metalloproteinases. Overall, pathway analysis identified a negative impact on energy metabolism, and a positive impact on immune function, in particular the acute phase response. Inside the cell the impacts were on mitochondria and lysosome. New pathways and genes responsive to nickel were identified from the large dataset in this study which represents the first long-term analysis of the effects of chronic nickel exposure on global gene expression.

The stresses that animals experience as a result of modification of their ecological circumstances induce physiological changes that leave a signature in profiles of gene expression. We illustrate this concept in a comparison of free range and confined North American red wolves (Canis rufus). Transcription profiling of peripheral blood samples from 13 red wolf individuals in the Alligator River region of North Carolina revealed a strong signal of differentiation. Four hundred eighty-two out of 2980 transcripts detected on Illumina HumanRef8 oligonucleotide bead arrays were found to differentiate free range and confined wolves at a false discovery rate of 12.8% and P < 0.05. Over-representation of genes in focal adhesion, insulin signalling, proteasomal, and tryptophan metabolism pathways suggests the activation of pro-inflammatory and stress responses in confined animals. Consequently, characterization of differential transcript abundance in an accessible tissue such as peripheral blood identifies biomarkers that could be useful in animal management practices and for evaluating the impact of habitat changes on population health, particularly as attention turns to the impact of climate change on physiology and in turn species distributions.

Gene expression profiling is a widely used technique with data from the majority of published microarray studies being publicly available. These data are being used for meta-analyses and in silico discovery; however, the comparability of toxicogenomic data generated in multiple laboratories has not been critically evaluated. Using the power of prospective multilaboratory investigations, seven centers individually conducted a common toxicogenomics experiment designed to advance understanding of molecular pathways perturbed in liver by an acute toxic dose of N-acetyl-p-aminophenol (APAP) and to uncover reproducible genomic signatures of APAP-induced toxicity. The nonhepatotoxic APAP isomer N-acetyl-m-aminophenol was used to identify gene expression changes unique to APAP. Our data show that c-Myc is induced by APAP and that c-Myc-centered interactomes are the most significant networks of proteins associated with liver injury. Furthermore, sources of error and data variability among Centers and methods to accommodate this variability were identified by coupling gene expression with extensive toxicological evaluation of the toxic responses. We show that phenotypic anchoring of gene expression data is required for biologically meaningful analysis of toxicogenomic experiments.

Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.

Consistency and coherence of gene expression data across multiple sites depends on several factors such as platform (oligo, cDNA, etc.), environmental conditions at each laboratory, and data quality. The Hepatotoxicity Working Group of the International Life Sciences Institute Health and Environmental Sciences Institute consortium on the application of genomics to mechanism-based risk assessment is investigating these factors by comparing high-density gene expression data sets generated on two sets of RNA from methapyrilene (MP) experiments conducted at Abbott Laboratories and Boehringer-Ingelheim Pharmaceuticals, Inc. using a single platform (Affymetrix Rat Genome U34A GeneChip) at seven different sites. This article focuses on the evaluation of data quality and statistical models that facilitate the comparison of such data sets at the probe level. We present methods for exploring and quantitatively assessing differences in the data, with the principal goal being the generation of lists of site-insensitive genes responsive to low and high doses of MP. A combination of numerical and graphical techniques reveals important patterns and partitions of variability in the data, including the magnitude of the site effects. Although the site effects are significantly large in the analysis results, they appear to be primarily additive and therefore can be adjusted in the statistical calculations in a way that does not bias conclusions regarding treatment differences.