Transcriptomics,Genomics

Dataset Information

31

Exposure to different copper forms – nanoparticles, nanowires, salt and field aged: gene expression profiling in Enchytraeus crypticus


ABSTRACT: The testing of NMs under the currently available standard toxicity tests does not cover many of the NMs specificities. One of the current recommended approaches forward lays on understanding the mechanisms of action as these can help predicting long term effects and safe-by-design production. Copper nanomaterials (Cu-NMs) usage has been highly increasing with the concern in terms of exposure, effect and associated risks. In the present study we used the high-throughput gene expression tool developed for Enchytraeus crypticus (44Kx4 Agilent microarray) to study to the effect of exposure to several copper forms. The copper treatments include two NMs (spherical and wires) and two copper-salt treatments (CuNO3 spiked and Cu field historical contamination). Testing was done based on reproduction effect concentrations (EC20, EC50) using 3 and 7 days exposure periods. Overall design: Gene expression profile of Enchytraeus crypticus was analysed after 3 and 7 days of exposure to the EC20 and EC50 (effect concentrations on reproduction) of several copper forms (nanoparticles, nanowires, salt and field aged) in a natural soil from Hygum site (Denmark). Three biological replicates per test treatment and control (Hygum soil from control area) were used.

INSTRUMENT(S): Agilent-049615 Enchytraeus crypticus V2.0 44k

SUBMITTER: Susana Isabel Lopes Gomes  

PROVIDER: GSE69792 | GEO | 2017-06-13

SECONDARY ACCESSION(S): PRJNA286782

REPOSITORIES: GEO

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Publications

Variation-preserving normalization unveils blind spots in gene expression profiling.

Roca Carlos P CP   Gomes Susana I L SI   Amorim Mónica J B MJ   Scott-Fordsmand Janeck J JJ  

Scientific reports 20170309


RNA-Seq and gene expression microarrays provide comprehensive profiles of gene activity, but lack of reproducibility has hindered their application. A key challenge in the data analysis is the normalization of gene expression levels, which is currently performed following the implicit assumption that most genes are not differentially expressed. Here, we present a mathematical approach to normalization that makes no assumption of this sort. We have found that variation in gene expression is much  ...[more]

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