Crassostrea gigas accumulates diarrheic shellfish toxins (DSP) associated to Prorocentrum lima of which Okadaic acid (OA) causes specific inhibitions of serine and threonine phosphatases 1 and 2A. Its toxic effects have been extensively reported in bivalve mollusks at cellular and physiological levels, but genomic approaches have been scarcely studied. Acute and sub-chronic exposure effects of P. lima were investigated on farmed juvenile C. gigas (3–5 mm). The Pacific oysters were fed with three dinoflagellate concentrations: 0.3, 3, and 30×10³ cells mL⁻¹ along with a nontoxic control diet of Isochrysis galbana. The effects of P. lima on C. gigas were followed by analyzing expression levels of a total of four genes, three involved in cell cycle regulation and one in immune response by polymerase chain reaction and real time quantitative PCR, where changes in time and cell concentration were found. The highest expression levels were found in oysters fed 3×10³ cells mL⁻¹ at 168 h for the cycle regulator p21 protein (9 fold), chromatin assembly factor 1 p55 subunit (8 fold), elongation factor 2 (2 fold), and lipopolysaccharide/β-1, 3 glucan binding protein (13 fold above base line). Additionally, the transcript level of all the genes decreased in oysters fed wich the mixed diet 30×10³ cells mL⁻¹ of dinoflagellate after 72 h and was lowest in the chromatin assembly factor 1 p55 subunit (0.9 fold below baseline). On C. gigas the whole cell ingestion of P lima caused a clear mRNA modulation expression of the genes involved in cell cycle regulation and immune system. Over-expression could be related to DNA damage, disturbances in cell cycle continuity, probably a genotoxic effect, as well as an activation of its innate immune system as first line of defense.

Crassostrea gigas accumulates paralytic shellfish toxins (PST) associated with red tide species as Gymnodinium catenatum. Previous studies demonstrated bivalves show variable feeding responses to toxic algae at physiological level; recently, only one study has reported biochemical changes in the transcript level of the genes involved in C. gigas stress response. We found that 24 h feeding on toxic dinoflagellate cells (acute exposure) induced a significant decrease in clearance rate and expression level changes of the genes involved in antioxidant defense (copper/zinc superoxide dismutase, Cu/Zn-SOD), cell detoxification (glutathione S-transferase, GST and cytochrome P450, CPY450), intermediate immune response activation (lipopolysaccharide and beta glucan binding protein, LGBP), and stress responses (glutamine synthetase, GS) in Pacific oysters compared to the effects with the non-toxic microalga Isochrysis galbana. A sub-chronic exposure feeding on toxic dinoflagellate cells for seven and fourteen days (30×10³ cells mL⁻¹) showed higher gene expression levels. A significant increase was observed in Cu/Zn-SOD, GST, and LGBP at day 7 and a major increase in GS and CPY450 at day 14. We also observed that oysters fed only with G. catenatum (3×10³ cells mL⁻¹) produced a significant increase on the transcription level than in a mixed diet (3×10³ cells mL⁻¹ of G. catenatum+0.75×10⁶ cells mL⁻¹ I. galbana) in all the analyzed genes. Our results provide gene expression data of PST producer dinoflagellate G. catenatum toxic effects on C. gigas, a commercially important bivalve. Over expressed genes indicate the activation of a potent protective mechanism, whose response depends on both cell concentration and exposure time against these toxic microalgae. Given the importance of dinoflagellate blooms in coastal environments, these results provide a more comprehensive overview of how oysters respond to stress generated by toxic dinoflagellate exposure.

Harmful algal blooms (HABs) are natural phenomena with different effects on the aquatic environment that affect both human economy and health. Several genomic studies have been done to characterize the effects of contaminants on Crassostrea gigas. However, oysters’ molecular response to HABs exposure needs to be studied more. In this study, we challenged C. gigas with Prorocentrum lima, a diarrhoetic toxin producer, under controlled experimental conditions considering dinoflagellate density and exposure time (acute and sub-chronic) as variables. The expression profile of six stress response genes was analysed by semiquantitative RT-PCR: Glutamine synthetase (GS), Glutathione S-transferase (GST), heat shock protein 70 (HSP₇₀), and heat shock protein 90 (HSP₉₀), CuZn superoxide dismutase (SOD) and melanogenic peroxidase (POX). The results revealed that these gene expressions depend on exposure time and cell concentration. In general, an increased expression was observed in all tested cell densities as immediate response to exposure (0–3 h); whereas lower expression levels than control were observed after 6 h. Treatment with 3,000 cells mL^-1 promoted higher expression than control on most genes after 336 h of exposure. This is the first report providing information at molecular level on C. gigas response to dinoflagellate blooms.

Chlorophyll-a (Chl-a) concentration is a key parameter to describe water quality in marine and freshwater environments. Nowadays, several products with Chl-a have derived from satellite imagery, but they are not available or reliable sometimes for coastal and/or small water bodies. Thus, in the last decade several methods have been described to estimate Chl-a with high-resolution (30 m) satellite imagery, such as Landsat, but a standardized method to estimate Chl-a from Landsat imagery has not been accepted yet. Therefore, this study evaluated the predictive performance of regression models (Simple Linear Regression [SLR], Multiple Linear Regression [MLR] and Generalized Additive Models [GAMs]) to estimate Chl-a based on Landsat imagery, using in situ Chl-a data collected (synchronized with the overpass of Landsat 8 satellite) and spectral reflectance in the visible light portion (bands 1–4) and near infrared (band 5). These bands were selected because of Chl-a absorbance/reflectance properties in these wavelengths. According to goodness of fit, GAM outperformed SLR and MLR. However, the model validation showed that MLR performed better in predicting log-transformed Chl-a. Thus, MLR, constructed by using four spectral bands (1, 2, 3, and 5), was considered the best method to predict Chl-a. The coefficients of this model suggested that log-transformed Chl-a concentration had a positive linear relationship with bands 1 (coastal/aerosol), 3 (green), and 5 (NIR). On the other hand, band 2 (blue) suggested a negative relationship, which implied high coherence with Chl-a absorbance/reflectance properties measured in the laboratory, indicating that Landsat 8 images could be applied effectively to estimate Chl-a concentrations in coastal environments.