Ammonia exposures on the endangered delta smelt

The delta smelt (Hypomesus transpacificus) is a pelagic fish species endemic to the Sacramento-San Joaquin Estuary in Northern California, listed as endangered under both the USA Federal and Californian State Endangered Species Acts and acts as an indicator of ecosystem health in its habitat range. Interrogative tools are required to successfully monitor effects of contaminants upon the delta smelt, and to research potential causes of population decline in this species. We used microarray technology to investigate genome-wide effects in 57-day old larvae after a 4-day exposure to ammonia; one of multiple contaminants arising from wastewater treatment plants and agricultural runoff. Genomic assessments were carried out between larvae exposed to 10 mg/L total ammonium; the lowest observed effect concentration (LOEC), and controls. Microarray assessments were conducted on larvae exposed for 4-days to 10 mg/L (nominal) ammonium chloride and controls. Assessments were carried out in quadruplicate, using 5 fish per treatment. RNA was extracted from frozen whole, individual organisms, using Trizol Reagent (Invitrogen) as per manufacturer's guidelines. Total RNA from 5 fish was pooled per treatment and cDNA was synthesized from a total of 500ng total RNA, amplified using a SuperScripttm Indirect RNA Amplification System (Invitrogen). Resulting cDNA was labeled with Alexa fluor dyes (Invitrogen) as per manufacturer’s instructions. Two color microarray assessments were carried out on quadruplicate treatments, using 1µg of amplified cDNA for each control vs exposed sample, incorporating dye swaps for each (total 8 samples). Microarray hybridizations were performed using an automated Tecan HS4800 hybridization station. Slides were scanned using a GenePix 4000B scanner (Axon Instruments). Data was analyzed using LIMMA GUI (Linear model for microarray analysis graphical user interface) (Smyth, 2005), written in the R-programming language available through Bioconductor http://www.Bioconductor.org. Data was normalized within using print-tip Lowess and between arrays applying average intensity quantile (Aquantile) normalization methods with background correction (Smyth, 2005). A linear model fit was computed using the duplicates on the arrays and the least-squares method, with Benjamin Hochberg false discovery rate adjustment.