Soil Metatranscriptome
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REPOSITORIES: ENA
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Project description:soil metatranscriptome Raw sequence reads
| PRJNA412619 | ENA
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
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:Metatranscriptome analysis reveals the putative venom toxin repertoire of the biofouling hydroid Ectopleura larynx
2023-09-29 | E-MTAB-13150 | biostudies-arrayexpress
Project description:To study the soil mcirobial functional communities and the nutrient cycles couplings changes after exposure to different contaminant
2020-12-31 | GSE135153 | GEO
Project description:Soil is an inherently complex matrix and as such, we believe when performing culture-independent microbial community analyses using the 'omics' suite of tools, all biomolecules investigated should be co-extracted from the same biological sample. To this end, we developed a robust, cost-effective DNA, RNA and protein co-extraction method for soil. The samples deposited here represent 3 biological replicates from one of eight soil types tested in this work.
2019-07-15 | PXD007077 | Pride
Project description:Three-day metatranscriptome of surface gravel plain soils from the Central Namib Desert. Samples were collected at four times (6:00, 12:00, 18:00 and 24:00h) on each day (n=12). rRNA-depleted RNA was used to construct stranded libraries with the ScriptSeq v2 complete kit (Epicentre) adding unique barcodes in TruSeq adapters (ScriptSeq Index PCR primers, set 1, Epicentre). Libraries were single-end sequenced in a NextSeq 500 v2 sequencer, with read length of 75bp.
2018-08-01 | E-MTAB-6601 | biostudies-arrayexpress
Project description:Evaluation of different strategies to interpret metaproteomics data acquired on soil samples from a floodplain along the Seine River (France) incorporating sample-specific metagenomics data, soil genome catalogue database, and generic sequence database.
2022-02-17 | PXD026798 | Pride
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. Gene expression was measured in springtails after exposure of 2 days to soil containing either EC10 or EC50 of 6 different metals. The exposure experiment was performed in two separate series (1 and 2), both containing a separate non-spiked (LUFA 2.2) soil control. Also, two field soil samples were tested. The samples were divided into a separate training set and a validation set for USC classifier analysis.
2010-02-04 | E-GEOD-19353 | biostudies-arrayexpress