Quantification of differentially expressed genes in Daphnia magna exposed to rubber wastewater

Thermal stress-stimulated ZnO toxicity inhibits reproduction of freshwater crustacean Daphnia magna

Elevated temperatures due to climate change pose a variety of environmental risks to the freshwater ecosystem. At the same time, zinc oxide (ZnO) has become widely used and has entered the freshwater environment. As thermal stress may potentially impact the physicochemical properties of ZnO, its toxicity to freshwater organisms in the face of global warming is poorly understood. The potential effects on reproductive performances, including oogenesis, are of particular concern. In this study, we investigate the reproductive performances and related mRNA abundance of the zooplankton Daphnia magna under conditions of ZnO exposure and heat stress. The results revealed that ZnO and elevated temperature delayed maturity and juvenile production of D. magna. Histological observations indicated that oogenesis was inhibited, and the number and size of oocytes were reduced in the condition of ZnO exposure under heat stress. Eventual offspring in the same treatment exhibited decreased numbers, size, and quality. Congenital juvenile anomalies were increased, such as deformed eye, and impaired antenna and tail spine. Moreover, both ZnO and elevated temperature treatments inhibited expression levels of reproduction-related genes (vtg, EcR and VMO1) and induced the dmrt93b gene involved in the production of male offspring. Furthermore, we found that D. magna tried to cope with ZnO and thermal stress by upregulating hsp90, HIF-1α and HIF-1β. ZnO and heat stress inhibited the reproductive capacity of D. magna, produced deleterious effects on reproduction-associated physiological pathways, and damaged reproductive outcomes.

A Bayesian model for the identification of differentially expressed genes in Daphnia magna exposed to munition pollutants

In this article we propose a Bayesian hierarchical model for the identification of differentially expressed genes in Daphnia magna organisms exposed to chemical compounds, specifically munition pollutants in water. The model we propose constitutes one of the very first attempts at a rigorous modeling of the biological effects of water purification. We have data acquired from a purification system that comprises four consecutive purification stages, which we refer to as "ponds," of progressively more contaminated water. We model the expected expression of a gene in a pond as the sum of the mean of the same gene in the previous pond plus a gene-pond specific difference. We incorporate a variable selection mechanism for the identification of the differential expressions, with a prior distribution on the probability of a change that accounts for the available information on the concentration of chemical compounds present in the water. We carry out posterior inference via MCMC stochastic search techniques. In the application, we reduce the complexity of the data by grouping genes according to their functional characteristics, based on the KEGG pathway database. This also increases the biological interpretability of the results. Our model successfully identifies a number of pathways that show differential expression between consecutive purification stages. We also find that changes in the transcriptional response are more strongly associated to the presence of certain compounds, with the remaining contributing to a lesser extent. We discuss the sensitivity of these results to the model parameters that measure the influence of the prior information on the posterior inference.

Molecular toxicity identification evaluation (mTIE) approach predicts chemical exposure in Daphnia magna

Daphnia magna is a bioindicator organism accepted by several international water quality regulatory agencies. Current approaches for assessment of water quality rely on acute and chronic toxicity that provide no insight into the cause of toxicity. Recently, molecular approaches, such as genome wide gene expression responses, are enabling an alternative mechanism based approach to toxicity assessment. While these genomic methods are providing important mechanistic insight into toxicity, statistically robust prediction systems that allow the identification of chemical contaminants from the molecular response to exposure are needed. Here we apply advanced machine learning approaches to develop predictive models of contaminant exposure using a D. magna gene expression data set for 36 chemical exposures. We demonstrate here that we can discriminate between chemicals belonging to different chemical classes including endocrine disruptors and inorganic and organic chemicals based on gene expression. We also show that predictive models based on indices of whole pathway transcriptional activity can achieve comparable results while facilitating biological interpretability.

A toxicity monitoring study on identification and reduction of toxicants from a wastewater treatment plant

In this study, toxicity of effluents in a wastewater treatment plant and of receiving water in an adjacent stream was periodically monitored from November 2007 to June 2008, in order to trace and reduce sources of toxicants. The results showed that toxicity of final effluent (FE) changed greatly over different sampling events, and appeared to have impacts on toxicity of downstream water with a significant correlation (r(2)=0.87, p<0.05). In particular, FE toxicity was always higher than that of secondary effluent (SE). Toxicity identification evaluation (TIE) for the FE sample collected in March 2008 showed that FE toxicity was attributed to low quality of Fenton reagent with Zn contamination used for SE treatment. Furthermore, Zn concentrations in FE samples significantly correlated with FE toxicity during the sampling period (r(2)=0.95, p<0.05). After changing the Fenton reagent to one containing low Zn, Zn concentration and toxicity of FE greatly decreased in the following months.