Transcriptomic response of the red tide dinoflagellate, Karenia brevis, to nitrogen and phosphorus depletion and addition.

BACKGROUND: The role of coastal nutrient sources in the persistence of Karenia brevis red tides in coastal waters of Florida is a contentious issue that warrants investigation into the regulation of nutrient responses in this dinoflagellate. In other phytoplankton studied, nutrient status is reflected by the expression levels of N- and P-responsive gene transcripts. In dinoflagellates, however, many processes are regulated post-transcriptionally. All nuclear encoded gene transcripts studied to date possess a 5' trans-spliced leader (SL) sequence suggestive, based on the trypanosome model, of post-transcriptional regulation. The current study therefore sought to determine if the transcriptome of K. brevis is responsive to nitrogen and phosphorus and is informative of nutrient status. RESULTS: Microarray analysis of N-depleted K. brevis cultures revealed an increase in the expression of transcripts involved in N-assimilation (nitrate and ammonium transporters, glutamine synthetases) relative to nutrient replete cells. In contrast, a transcriptional signal of P-starvation was not apparent despite evidence of P-starvation based on their rapid growth response to P-addition. To study transcriptome responses to nutrient addition, the limiting nutrient was added to depleted cells and changes in global gene expression were assessed over the first 48 hours following nutrient addition. Both N- and P-addition resulted in significant changes in approximately 4% of genes on the microarray, using a significance cutoff of 1.7-fold and p ≤ 10-4. By far, the earliest responding genes were dominated in both nutrient treatments by pentatricopeptide repeat (PPR) proteins, which increased in expression up to 3-fold by 1 h following nutrient addition. PPR proteins are nuclear encoded proteins involved in chloroplast and mitochondria RNA processing. Correspondingly, other functions enriched in response to both nutrients were photosystem and ribosomal genes. CONCLUSIONS: Microarray analysis provided transcriptomic evidence for N- but not P-limitation in K. brevis. Transcriptomic responses to the addition of either N or P suggest a concerted program leading to the reactivation of chloroplast functions. Even the earliest responding PPR protein transcripts possess a 5' SL sequence that suggests post-transcriptional control. Given the current state of knowledge of dinoflagellate gene regulation, it is currently unclear how these rapid changes in such transcript levels are achieved.

The transcriptomic responses of the eastern oyster, Crassostrea virginica, to environmental conditions.

Understanding the mechanisms by which organisms adapt to environmental conditions is a fundamental question for ecology and evolution. In this study, we evaluate changes in gene expression of a marine mollusc, the eastern oyster Crassostrea virginica, associated with the physico-chemical conditions and the levels of metals and other contaminants in their environment. The results indicate that transcript signatures can effectively disentangle the complex interactive gene expression responses to the environment and are also capable of disentangling the complex dynamic effects of environmental factors on gene expression. In this context, the mapping of environment to gene and gene to environment is reciprocal and mutually reinforcing. In general, the response of transcripts to the environment is driven by major factors known to affect oyster physiology such as temperature, pH, salinity, and dissolved oxygen, with pollutant levels playing a relatively small role, at least within the range of concentrations found in the studied oyster habitats. Further, the two environmental factors that dominate these effects (temperature and pH) interact in a dynamic and nonlinear fashion to impact gene expression. Transcriptomic data obtained in our study provide insights into the mechanisms of physiological responses to temperature and pH in oysters that are consistent with the known effects of these factors on physiological functions of ectotherms and indicate important linkages between transcriptomics and physiological outcomes. Should these linkages hold in further studies and in other organisms, they may provide a novel integrated approach for assessing the impacts of climate change, ocean acidification and anthropogenic contaminants on aquatic organisms via relatively inexpensive microarray platforms.

Modelling interactions of acid-base balance and respiratory status in the toxicity of metal mixtures in the American oyster Crassostrea virginica.

Heavy metals, such as copper, zinc and cadmium, represent some of the most common and serious pollutants in coastal estuaries. In the present study, we used a combination of linear and artificial neural network (ANN) modelling to detect and explore interactions among low-dose mixtures of these heavy metals and their impacts on fundamental physiological processes in tissues of the Eastern oyster, Crassostrea virginica. Animals were exposed to Cd (0.001-0.400 microM), Zn (0.001-3.059 microM) or Cu (0.002-0.787 microM), either alone or in combination for 1 to 27 days. We measured indicators of acid-base balance (hemolymph pH and total CO(2)), gas exchange (Po(2)), immunocompetence (total hemocyte counts, numbers of invasive bacteria), antioxidant status (glutathione, GSH), oxidative damage (lipid peroxidation; LPx), and metal accumulation in the gill and the hepatopancreas. Linear analysis showed that oxidative membrane damage from tissue accumulation of environmental metals was correlated with impaired acid-base balance in oysters. ANN analysis revealed interactions of metals with hemolymph acid-base chemistry in predicting oxidative damage that were not evident from linear analyses. These results highlight the usefulness of machine learning approaches, such as ANNs, for improving our ability to recognize and understand the effects of sub-acute exposure to contaminant mixtures.

A transcriptomic analysis of land-use impacts on the oyster, Crassostrea virginica, in the South Atlantic bight.

Increasing utilization and human population density in the coastal zone is widely believed to place increasing stresses on the resident biota, but confirmation of this belief is somewhat lacking. While we have solid evidence that highly disturbed estuarine systems have dramatic changes in the resident biota (black and white if you will), we lack tools that distinguish the shades of grey. In part, this lack of ability to distinguish shades of grey stems from the analytical tools that have been applied to studies of estuarine systems, and perhaps more important, is the insensitivity of the biological end points that we have used to assess these impacts. In this study, we will present data on the phenotypic adjustments as measured by transcriptomic signatures of a resilient organism (oysters) to land-use practices in the surrounding watershed using advanced machine-learning algorithms. We will demonstrate that such an approach can reveal subtle and meaningful shifts in oyster gene expression in response to land use. Further, the data show that gill tissues are far more responsive and provide superior discrimination of land-use classes than hepatopancreas and that transcripts encoding proteins involved in energy production, protein synthesis and basic metabolism are more robust indicators of land use than classic biomarkers such as metallothioneins, GST and cytochrome P-450.