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INTEGRATING TRANSCRIPTOMICS INTO TRIAD-BASED SOIL QUALITY ASSESSMENT

ABSTRACT: This study examined how transcriptomics tools can be included in a Triad-based soil quality assessment to assess the toxicity of soils from river banks polluted by metals. To that end we measured chemical soil properties and used the standardized ISO guideline for ecotoxicological tests and a newly developed microarray for gene expression in the indicator soil arthropod, Folsomia candida. Microarray analysis revealed that the oxidative stress response pathway was significantly affected in all soils except one. The data indicate that changes in cell redox homeostasis are a significant signature of metal stress. Finally, 32 genes showed significant dose-dependent expression with metal concentrations. They are promising genetic markers providing an early indication of the need for higher tier testing in soil quality. One of the least polluted soils showed toxicity in the bioassay that could be removed by sterilization. The gene expression profile for this soil did not show a metal-related signature, confirming that another factor than metals (most likely of biological origin) caused the toxicity. This study demonstrates the feasibility and advantages of integrating transcriptomics into Triad-based soil quality assessment. Combining molecular and organismal life-history trait’s stress responses helps identifying causes of adverse effect in bioassays. Further validation is needed for verifying the set of genes with dose-dependent expression patterns linked with toxic stress.

ORGANISM(S): Folsomia candida

PROVIDER: GSE52393 | GEO | 2014/02/01

SECONDARY ACCESSION(S): PRJNA227772

REPOSITORIES: GEO

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Project description:Background: The high number of heavy metal resistance genes in the soil bacterium Cupriavidus metallidurans CH34 makes it an interesting model organism to study microbial responses to heavy metals. Results: In this study the transcriptional response of this bacterium was measured after challenging it to a wide range of sub-lethal concentrations of various essential or toxic metals. Considering the global transcriptional responses for each challenge as well as by identifying the overlap in upregulated genes between different metal responses, the sixteen metals could be clustered in three different groups. Additionally, next to the assessment of the transcriptional response of already known metal resistance genes, new metal response gene clusters were identified. The majority of the metal response loci showed similar expression profiles when cells were exposed to different metals, suggesting complex cross-talk at transcriptional level between the different metal responses. The highly redundant nature of these metal resistant regions – illustrated by the large number of paralogous genes – combined with the phylogenetic distribution of these metal response regions within evolutionary related and other metal resistant bacteria, provides important insights on the recent evolution of this naturally soil dwelling bacterium towards a highly metal-resistant strain found in harsh and anthropogenic environments. Conclusions: The metal-resistant soil bacterium Cupriavidus metallidurans CH34 displays myriads of gene expression patterns when exposed to a wide range of heavy metals at non-lethal concentrations. The interplay between the different gene expression clusters points towards a complex cross-regulated regulatory network governing heavy metal resistance in C. metallidurans CH34. Keywords: Cupriavidus metallidurans CH34, transcriptional regulation, heavy metal resistance Two-condition experiments. Comparing samples after induction with heavy metals versus non-induced samples. Biological duplicate or triplicate. Each array contains 3 or 4 technical replicates.

Project description:Environmental risk assessment relies heavily on the use of bioassays to assess the environmental impact of chemicals. Gene expression is gaining acceptance as a valuable mechanistic endpoint in bioassays and effect-based screening. Data analysis and its results however, are often complex and not directly applicable in risk assessment. Classifier analysis is a promising method to turn complex gene expression analysis results into answers suitable for risk assessment. We have assembled a large gene expression dataset assembled from multiple studies and experiments in the springtail Folsomia candida, with the aim of selecting a set of genes that can be trained to classify general toxic stress. By performing differential expression analysis prior to classification we were able to select a set of 135 genes which was enriched in stress related processes. This set was then used to classify two test sets comprised of chemical spiked soils, polluted soils and clean soils and compared to another, more traditional feature selection for classification. The gene set presented here outperformed the more traditionally selected gene set. This gene set has the potential to be used as a biomarker to test for adverse effects caused by chemicals in springtails to provide endpoints in environmental risk assessment. The data presented in our manuscript is part of a larger experiment which was performed in single, large loop design. Only the samples used in the study presented here are named while the other samples will remain unnamed. All the data can still be used for normalization after which the analysis presented in the manuscript can be replicated. A single channel, interwoven loop design was used to test animals exposed to a control and to 5 concentrations of cadmium and phenanthrene (cadmium concentrations are: cad_1 till cad_5: 5.8, 14.5, 28.9, 57.9 and 115.8 mg/Kg respectively and phenanthrene concentrations are: phe_1 till Phe_5: 0, 4.6, 11.4, 22.9, 45.8 and 91.6 mg/Kg soil respectively). Exposures lasted for 2 days and used 4 biological replicates per condition each containing 30 grams soil and 30 individuals.

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INTEGRATING TRANSCRIPTOMICS INTO TRIAD-BASED SOIL QUALITY ASSESSMENT

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