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

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 R2s 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.In the field of environmental sciences a high throughput molecular research often called 'ecogenomics' [1,2] has evolved over the past 10 years. The current challenge for ecotoxicology is to benefit most from the outburst of molecular knowledge [3] initially mainly generated by microarray studies, later followed by expressed sequence tag (EST) sequencing and mapping (see for an overview [4]) which in turn is currently being followed up by next generation sequencing of cDNAs (RNA-Seq). Ideally, the integration of "omics" data with traditional