Gene Coexpression Networks Drive and Predict Reproductive Effects in Daphnia in Response to Environmental Disturbances

In ponds, lakes and other water bodies, organisms face a multitude of environmental challenges which include chemical pollution and harmful algal blooms. To better understand and protect our water bodies, we need to be able to model and predict how organisms grow and reproduce under these environmental challenges. Here, we use gene expression patterns in combination with network methodology and statistical modelling to predict the reproduction of waterfleas after exposure to insecticides and cyanobacteria at environmentally relevant concentrations. Our developed models were able to predict reproduction of waterfleas under these different conditions. In particular, the models were able to predict the combined effect of combinations of insecticides and cyanobacteria on the reproduction of the waterfleas. These results provide a valuable mechanistic framework that consists of using gene expression data to quantify higher level effects.


Scientific abstract

Increasing effects of anthropogenic stressors and those of natural origin on aquatic ecosystems have intensified the need for predictive and functional models of their effects. Here, we use gene expression patterns in combination with weighted gene coexpression networks and generalized additive models to predict effects on reproduction in the aquatic microcrustacean Daphnia. We developed models to predict effects on reproduction upon exposure to different cyanobacteria, different insecticides and binary mixtures of cyanobacteria and insecticides. Models developed specifically for groups of stressors (e.g., either cyanobacteria or insecticides) performed better than general models developed on all data. Furthermore, models developed using in silico generated mixture gene expression profiles from single stressor data were able to better predict effects on reproduction compared to models derived from the mixture exposures themselves. Our results highlight the potential of gene expression data to quantify effects of complex exposures at higher level organismal effects without prior mechanistic knowledge or complex exposure data. 


Full reference (link):

Asselman J, Pfrender ME, Lopez JA, Shaw JR, De Schamphelaere KAC. Gene Coexpression networks drive and predict reproductive effects in Daphnia in response to environmental disturbances. Environ Sci Technol, Article ASAP.