A microarray study of MPP(+)-treated PC12 Cells: Mechanisms of toxicity (MOT) analysis using bioinformatics tools

BACKGROUND: This paper describes a microarray study including data quality control, data analysis and the analysis of the mechanism of toxicity (MOT) induced by 1-methyl-4-phenylpyridinium (MPP(+)) in a rat adrenal pheochromocytoma cell line (PC12 cells) using bioinformatics tools. MPP(+ )depletes dopamine content and elicits cell death in PC12 cells. However, the mechanism of MPP(+)-induced neurotoxicity is still unclear. RESULTS: In this study, Agilent rat oligo 22K microarrays were used to examine alterations in gene expression of PC12 cells after 500 μM MPP(+ )treatment. Relative gene expression of control and treated cells represented by spot intensities on the array chips was analyzed using bioinformatics tools. Raw data from each array were input into the NCTR ArrayTrack database, and normalized using a Lowess normalization method. Data quality was monitored in ArrayTrack. The means of the averaged log ratio of the paired samples were used to identify the fold changes of gene expression in PC12 cells after MPP(+ )treatment. Our data showed that 106 genes and ESTs (Expressed Sequence Tags) were changed 2-fold and above with MPP(+ )treatment; among these, 75 genes had gene symbols and 59 genes had known functions according to the Agilent gene Refguide and ArrayTrack-linked gene library. The mechanism of MPP(+)-induced toxicity in PC12 cells was analyzed based on their genes functions, biological process, pathways and previous published literatures. CONCLUSION: Multiple pathways were suggested to be involved in the mechanism of MPP(+)-induced toxicity, including oxidative stress, DNA and protein damage, cell cycling arrest, and apoptosis

Detection and correction of probe-level artefacts on microarrays

<p>Abstract</p> <p>Background</p> <p>A recent large-scale analysis of Gene Expression Omnibus (GEO) data found frequent evidence for spatial defects in a substantial fraction of Affymetrix microarrays in the GEO. Nevertheless, in contrast to quality assessment, artefact detection is not widely used in standard gene expression analysis pipelines. Furthermore, although approaches have been proposed to detect diverse types of spatial noise on arrays, the correction of these artefacts is mostly left to either summarization methods or the corresponding arrays are completely discarded.</p> <p>Results</p> <p>We show that state-of-the-art robust summarization procedures are vulnerable to artefacts on arrays and cannot appropriately correct for these. To address this problem, we present a simple approach to detect artefacts with high recall and precision, which we further improve by taking into account the spatial layout of arrays. Finally, we propose two correction methods for these artefacts that either substitute values of defective probes using probeset information or filter corrupted probes. We show that our approach can identify and correct defective probe measurements appropriately and outperforms existing tools.</p> <p>Conclusions</p> <p>While summarization is insufficient to correct for defective probes, this problem can be addressed in a straightforward way by the methods we present for identification and correction of defective probes. As these methods output CEL files with corrected probe values that serve as input to standard normalization and summarization procedures, they can be easily integrated into existing microarray analysis pipelines as an additional pre-processing step. An R package is freely available from <url>http://www.bio.ifi.lmu.de/artefact-correction</url>.</p