Department Environmental Toxicology

Integrative network toxicogenomics

In recent years, OMICS (transcriptomics, proteomics, metabolomics) datasets combined with bioinformatic methods, such as differential gene expression analysis and network inference, have proven very useful for discovering biological networks. For several model species, a large quantity of omics data gathered after exposure to environmental stressors has become available in public repositories, however the datasets have mostly only been analysis one study at a time. In this project, we pooled the available gene expression datasets for two environmental model species, the green alga Chlamydomonas reinhardtii and the tropical zebrafish Danio rerio, and built gene co-expression networks. Under the assumption that genes that are regulated together function is the same biological processes, we then queried the Chlamydomonas network around genes known to be involved in toxicity to silver to find other genes with a similar function. We are currently testing knockouts of several of these genes under silver exposure to evaluate whether the used methodology can be useful for discovery of genes responsible for tolerance and sensitivity of environmental pollutants.

Co-expression network of Chlamydomonas reinhardtii in the neighbourhood of copper responsive genes. Nodes (genes) are colored according to differential expression under copper or silver exposure, colored lines between nodes are shortest network paths


Eawag discretionary fund