Paris, A. | bioRxiv Science

bioRxiv2018

Specific metabolic signatures of fish exposed to cyanobacterial blooms

With the increasing impact of the global warming, occurrences of cyanobacterial blooms in aquatic ecosystems are becoming a main ecological concern around the world. Due to their capacity to produce potential toxic metabolites, interactions between the cyanobacteria/cyanotoxin complex and the other freshwater organisms have been widely studied in the past years. Non-targeted metabolomic analyses have the powerful capacity to study a high number of metabolites at the same time and thus to understand in depth the molecular interactions between various organisms in different environmental scenario and notably during cyanobacterial blooms. In this way during summer 2015, liver metabolomes of two fish species, sampled in peri-urban lakes of the ile-de-France region containing or not high concentrations of cyanobacteria, were studied. The results suggest that similar metabolome changes occur in both fish species exposed to cyanobacterial blooms compared to them not exposed. Metabolites implicated in protein synthesis, protection against ROS, steroid metabolism, cell signaling, energy storage and membrane integrity/stability have shown the most contrasted changes. Furthermore, it seems that metabolomic studies will provide new information and research perspectives in various ecological fields and notably concerning cyanobacteria/fish interactions but also a promising tool for environmental monitoring of water pollutions.

ecology↗

bioRxiv2018

ASICS: an R package for a whole analysis workflow of 1D 1H NMR spectra

In metabolomics, the detection of new biomarkers from NMR spectra is a promising approach. However, this analysis remains difficult due to the lack of a whole workflow that handles spectra pre-processing, automatic identification and quantification of metabolites and statistical analyses.\n\nWe present ASICS, an R package that contains a complete workflow to analyse spectra from NMR experiments. It contains an automatic approach to identify and quantify metabolites in a complex mixture spectrum and uses the results of the quantification in untargeted and targeted statistical analyses. ASICS was shown to improve the precision of quantification in comparison to existing methods on two independant datasets. In addition, ASICS successfully recovered most metabolites that were found important to explain a two level condition describing the samples by a manual and expert analysis based on bucketting. It also found new relevant metabolites involved in metabolic pathways related to risk factors associated with the conditions.\n\nThis workflow is available through the R package ASICS, available on the Bioconductor platform.

systems biology↗