Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1208264 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:We investigated the toxicity of soil samples derived from a former municipal landfill site in the South of the Netherlands, where a bioremediation project is running aiming at reusing the site for recreation. Both an organic soil extract and the original soil sample was investigated using the ISO standardised Folsomia soil ecotoxicological testing and gene expression analysis. The 28 day survival/reproduction test revealed that the ecologically more relevant original soil sample was more toxic than the organic soil extract. Microarray analysis showed that the more toxic soil samples induced gene regulatory changes in twice as less genes compared to the soil extract. Consequently gene regulatory changes were highly dependent on sample type, and were to a lesser extent caused by exposure level. An important biological process shared among the two sample types was the detoxification pathway for xenobiotics (biotransformation I, II and III) suggesting a link between compound type and observed adverse effects. Finally, we were able to retrieve a selected group of genes that show highly significant dose-dependent gene expression and thus were tightly linked with adverse effects on reproduction. Expression of four cytochrome P450 genes showed highest correlation values with reproduction, and maybe promising genetic markers for soil quality. However, a more elaborate set of environmental soil samples is needed to validate the correlation between gene expression induction and adverse phenotypic effects.
2012-04-16 | GSE37154 | GEO
Project description:<p>Functional ecology and biodiversity lack integrative reference data that combine the assessment of traits at different levels. Here, we integrate data from 16 field samples of complex thallose liverworts (order Marchantiales) collected from biological soil crust communities in Southern Sweden and Germany at three levels: (1) bioimaging (morphometric measurements from macro- and microscopy), (2) metabolomics (molecular computations performed on liquid chromatography high-resolution mass-spectrometry (UPLC/ESI-QTOF-MS) with data-dependent acquisition (DDA-MS) data) and (3) DNA marker sequencing. These data are used to construct a reference framework including a proposed naming scheme for the estimation of molecular traits and for demonstrating the systematic and standardized extraction of phenotypic and molecular traits for integration into the plant trait database TRY. Phylogenetic trees and partitioning around medoids are used to demonstrate the assessment of evolutionary relationships and the trait space connecting ecological hypotheses and documenting knowledge gains across domains. With our reference framework we want to encourage the combined assessment, reuse and integration of phenotypic and molecular traits into functional biodiversity research and related disciplines.</p>
2024-06-05 | MTBLS2239 | MetaboLights
Project description:Environmental pollution is a worldwide problem, and metals are the largest group of contaminants in soil. Microarray toxicogenomic studies with ecologically relevant organisms such as springtails, supplement traditional ecotoxicological research, but are presently rather descriptive. Classifier analysis, a more analytical application of the microarray technique, is able to predict biological classes of unknown samples. We used the uncorrelated shrunken centroid (USC) method to classify gene expression profiles of the springtail Folsomia candida exposed to soil spiked with six different metals (barium, cadmium, cobalt, chromium, lead, and zinc). We identified a gene set (classifier) of 188 genes that can discriminate between six different metals present in soil, which allowed us to predict the correct classes for samples of an independent test set with an accuracy of 83% (error rate = 0.17). This study shows further that in order to apply classifier analysis to actual contaminated field soil samples, more insight and information is needed on the transcriptional responses of soil organisms to different soil types (properties) and mixtures of contaminants.
2010-02-05 | GSE19353 | GEO