Physiological and comparative proteomic analyzes reveal immune defense response of the king scallop Pecten maximus in presence of paralytic shellfish toxin (PST) from Alexandrium minutum

Abstract

The king scallop, Pecten maximus is a highly valuable seafood in Europe. Over the last few years, its culture has been threatened by toxic microalgae during harmful algal blooms, inducing public health concerns. Indeed, phycotoxins accumulated in bivalves can be harmful for human, especially paralytic shellfish toxins (PST) synthesized by the microalgae Alexandrium minutum. Deleterious effects of these toxic algae on bivalves have also been reported. However, its impact on bivalves such as king scallop is far from being completely understood. This study combined ecophysiological and proteomic analyzes to investigate the early response of juvenile king scallops to a short term exposure to PST producing A. minutum. Our data showed that all along the 2-days exposure to A. minutum, king scallops exhibited transient lower filtration and respiration rates and accumulated PST. Significant inter-individual variability of toxin accumulation potential was observed among individuals. Furthermore, we found that ingestion of toxic algae, correlated to toxin accumulation was driven by two factors: 1/ the time it takes king scallop to recover from filtration inhibition and starts to filtrate again, 2/ the filtration level to which king scallop starts again to filtrate after inhibition. Furthermore, at the end of the 2-day exposure to A. minutum, proteomic analyzes revealed an increase of the killer cell lectin-like receptor B1, involved in adaptative immune response. Proteins involved in detoxification and in metabolism were found in lower amount in A. minutum exposed king scallops. Proteomic data also showed differential accumulation in several structure proteins such as β-actin, paramyosin and filamin A, suggesting a remodeling of the mantle tissue when king scallops are subjected to an A. minutum exposure.

Toxin accumulation linked to feeding behavior. (a). Individual toxin concentration in scallop digestive gland (DG) at the end of the exposure (day 4, μg STX 100 g−1 DG) against the total numbers of A. minutum cells consumed for each scallop on days 3&4 per g of scallop (number of cells g−1) for all assays (n=18). The line indicates the adjusted type II regression model. (b). Graph shows clearance rates (L h−1) from TC-A assays (days 1&2 exposition to T. lutea and C. muelleri and days 3&4 exposition to A. minutum) measured from day 1 to day 4 and standardized for a 7g scallop in total mass. Data have been highlighted according to the 3 clusters (low, medium and high accumulation potential), each empty shape representing an individual from the low □, mean △ or high ○ accumulation cluster and filled shapes corresponding to the average values for 5h from the low ■, mean ▲ and high ●.

Highlights

• Harmful microalgae A. minutum transiently inhibits king scallop filtration and respiration activities.
• The A. minutum paralytic shellfish toxin accumulation in king scallop is highly variable between individuals.
• Proteomic analysis of toxic algae A. minutum effect on king scallop: immune response and detoxication.

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Abstract

Studies on the impact of ocean acidification on marine organisms involve exposing organisms to future acidification scenarios, which has limited relevance for coastal calcifiers living in a mosaic of habitats. Identification of tipping points beyond which detrimental effects are observed is a widely generalizable proxy of acidification susceptibility at the population level. This approach is limited to a handful of studies that focus on only a few macro-physiological traits, thus overlooking the whole organism response. Here we develop a framework to analyze the broad macro-physiological and molecular responses over a wide pH range in juvenile oyster. We identify low tipping points for physiological traits at pH 7.3–6.9 that coincide with a major reshuffling in membrane lipids and transcriptome. In contrast, a drop in pH affects shell parameters above tipping points, likely impacting animal fitness. These findings were made possible by the development of an innovative methodology to synthesize and identify the main patterns of variations in large -omic data sets, fitting them to pH and identifying molecular tipping points. We propose the broad application of our framework to the assessment of effects of global change on other organisms.

Graphical abstract

Tipping points of oyster transcriptome. (a–c) Frequency distribution of tipping point for piecewise linear relationships (left side). Linear and log-linear models (no tipping point) are under “Lin” name. Genes are grouped into three clusters of genes that co-vary together. The gray line indicates the distribution frequency of all genes irrespective of clusters. Groups of genes that exhibit neighboring tipping points with distribution frequencies >5% (shown as a dotted line), were grouped together. The segments above the bars indicate the groups of genes on which GO analyses were conducted. In each case, the gene that best represents the cluster according to module membership, gene significance for pH and R2 is presented as a function of pH as an example (right side). Tipping points and their 95% confidence intervals are shown in gray. The significance levels of the slopes are presented using stars (p < .001***, p < .01**, p < .05*). Gene names are as follows: LOC117690205: monocarboxylate transporter 12-like, LOC105317113: 60S ribosomal protein L10a, LOC105331560: protocadherin Fat 4

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