Project description:Small organisms can be used as biomonitoring tools to assess chemicals in the environment. Chemical stressors are especially hard to assess and monitor when present as complex mixtures. Here, Daphnia magna were exposed for 24 hours to five different munitions constituents 2,4,6-trinitrotoluene (TNT), 2,4-dinitrotoluene (2,4-DNT), 2,6-dinitrotoluene (2,6-DNT), trinitrobenzene (TNB), dinitrobenzene (DNB), or 1,3,5-trinitro-1,3,5-triazacyclohexane (RDX) as well as to 8 different munitions mixtures and ground water contaminated with munitions constituents. To better understand possible mixture effects, gene expression changes from all treatments were compared using high-density microarrays. While mixtures and ground water exposures had genes and gene functions in common with single chemical exposures, unique functions were also affected, which was consistent with the non-additivity of chemical effects in these mixtures. The study consisted of three different experiments: (1) exposure to a concentration corresponding to 70% of 1/10th of the LC50 value of six individual MCs (TNT, 2,4-DNT, 2,6-DNT, DNB, TNB, RDX) and a control; (2) exposure to eight different laboratory mixtures of the previously mentioned MCs. Different combinations of MCs including four mixtures (Mixtures 5, 6, 7 and 8) representative of field collected groundwater from LAAP (Louisiana Army Ammunition Plant) were created; and (3) exposure to MC-contaminated ground water field-collected from 3 different wells (85, 108, and 141) at the LAAP. All exposures were conducted for 24h.
Project description:Investigation of mRNA expression (using HiSeq 2500) in response to treatment of Daphnia magna to pyriproxyfen, wetland water, or stormwater samples.
Project description:Daphnia magna is a bio-indicator 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 dataset for 36 chemical exposures. We demonstrate here that we can discriminate between chemicals belonging to different chemical classes including endocrine disruptors, metals and industrial 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.