Dataset Information

High throughput nano-liter RT-qPCR to classify soil contamination using a soil arthropod.

ABSTRACT: To incorporate genomics data into environmental assessments a mechanistic perspective of interactions between chemicals and induced biological processes needs to be developed. Since chemical compounds with structural similarity often induce comparable biological responses in exposed animals, gene expression signatures can serve as a starting point for the assessment of chemicals and their toxicity, but only when relevant and stable gene panels are available. To design such a panel, we isolated differentially expressed gene fragments from the soil arthropod Folsomia candida, a species often used for ecotoxicological testing. Animals were exposed to two chemically distinct compounds, being a metal (cadmium) and a polycyclic aromatic hydrocarbon (phenanthrene). We investigated the affected molecular responses resulting from either treatment and developed and validated 44 qPCR assays for their responses using a high throughput nano-liter RT-qPCR platform for the analysis of the samples.Suppressive subtractive hybridization (SSH) was used to retrieve stress-related gene fragments. SSH libraries revealed pathways involved in mitochondrial dysfunction and protein degradation for cadmium and biotransformation for phenanthrene to be overrepresented. Amongst a small cluster of SSH-derived cadmium responsive markers were an inflammatory response protein and an endo-glucanase. Conversely, cytochrome P450 family 6 or 9 was specifically induced by phenanthrene. Differential expressions of these candidate biomarkers were also highly significant in the independently generated test sample set. Toxicity levels in different training samples were not reflected by any of the markers' intensity of expressions. Though, a model based on partial least squares differential analysis (PLS-DA) (with RMSEPs between 9 and 22% and R(2)s between 0.82 and 0.97) using gene expressions of 25 important qPCR assays correctly predicted the nature of exposures of test samples.For the application of molecular bio-indication in environmental assessments, multivariate analyses obviously have an added value over univariate methods. Our results suggest that compound discrimination can be achieved by PLS-DA, based on a hard classification of the within-class rankings of samples from a test set. This study clearly shows that the use of high throughput RT-qPCR could be a valuable tool in ecotoxicology combining high throughput with analytical sensitivity.

SUBMITTER: de Boer ME

PROVIDER: S-EPMC3060125 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature

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High throughput nano-liter RT-qPCR to classify soil contamination using a soil arthropod.

de Boer Muriel E ME Berg Sandra S Timmermans Martijn J T N MJ den Dunnen Johan T JT van Straalen Nico M NM Ellers Jacintha J Roelofs Dick D

BMC molecular biology 20110301

<h4>Background</h4>To incorporate genomics data into environmental assessments a mechanistic perspective of interactions between chemicals and induced biological processes needs to be developed. Since chemical compounds with structural similarity often induce comparable biological responses in exposed animals, gene expression signatures can serve as a starting point for the assessment of chemicals and their toxicity, but only when relevant and stable gene panels are available. To design such a p ...[more]

PMID: 21362169

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Project description:BackgroundNormalization is critical for accurate gene expression analysis. A significant challenge in the quantitation of gene expression from biofluids samples is the inability to quantify RNA concentration prior to analysis, underscoring the need for robust normalization tools for this sample type. In this investigation, we evaluated various methods of normalization to determine the optimal approach for quantifying microRNA (miRNA) expression from biofluids and tissue samples when using the TaqMan® Megaplex™ high-throughput RT-qPCR platform with low RNA inputs.FindingsWe compared seven normalization methods in the analysis of variation of miRNA expression from biofluid and tissue samples. We developed a novel variant of the common mean-centering normalization strategy, herein referred to as mean-centering restricted (MCR) normalization, which is adapted to the TaqMan Megaplex RT-qPCR platform, but is likely applicable to other high-throughput RT-qPCR-based platforms. Our results indicate that MCR normalization performs comparable to or better than both standard mean-centering and other normalization methods. We also propose an extension of this method to be used when migrating biomarker signatures from Megaplex to singleplex RT-qPCR platforms, based on the identification of a small number of normalizer miRNAs that closely track the mean of expressed miRNAs.ConclusionsWe developed the MCR method for normalizing miRNA expression from biofluids samples when using the TaqMan Megaplex RT-qPCR platform. Our results suggest that normalization based on the mean of all fully observed (fully detected) miRNAs minimizes technical variance in normalized expression values, and that a small number of normalizer miRNAs can be selected when migrating from Megaplex to singleplex assays. In our study, we find that normalization methods that focus on a restricted set of miRNAs tend to perform better than methods that focus on all miRNAs, including those with non-determined (missing) values. This methodology will likely be most relevant for studies in which a significant number of miRNAs are not detected.

Project description:BackgroundQuantification and normalization of RT-qPCR data critically depends on the expression of so called reference genes. Our goal was to develop a strategy for the selection of reference genes that utilizes microarray data analysis and combines known approaches for gene stability evaluation and to select a set of appropriate reference genes for research and clinical analysis of breast samples with different receptor and cancer status using this strategy.MethodsA preliminary search of reference genes was based on high-throughput analysis of microarray datasets. The final selection and validation of the candidate genes were based on the RT-qPCR data analysis using several known methods for expression stability evaluation: comparative ∆Ct method, geNorm, NormFinder and Haller equivalence test.ResultsA set of five reference genes was identified: ACTB, RPS23, HUWE1, EEF1A1 and SF3A1. The initial selection was based on the analysis of publically available well-annotated microarray datasets containing different breast cancers and normal breast epithelium from breast cancer patients and epithelium from cancer-free patients. The final selection and validation were performed using RT-qPCR data from 39 breast cancer biopsy samples. Three genes from the final set were identified by the means of microarray analysis and were novel in the context of breast cancer assay. We showed that the selected set of reference genes is more stable in comparison not only with individual genes, but also with a system of reference genes used in commercial OncotypeDX test.ConclusionA selection of reference genes for RT-qPCR can be efficiently performed by combining a preliminary search based on the high-throughput analysis of microarray datasets and final selection and validation based on the analysis of RT-qPCR data with a simultaneous examination of different expression stability measures. The identified set of reference genes proved to be less variable and thus potentially more efficient for research and clinical analysis of breast samples comparing to individual genes and the set of reference genes used in OncotypeDX assay.

Dataset Information

High throughput nano-liter RT-qPCR to classify soil contamination using a soil arthropod.

Publications

High throughput nano-liter RT-qPCR to classify soil contamination using a soil arthropod.

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