Shikha Singh
email: shikhas@iiitd.ac.in

Angshul Majumdar
email: angshul@iiitd.ac.in

Emilie Chouzenoux
email: emilie.chouzenoux@inria.fr

Giovanni Chierchia
email: giovanni.chierchia@esiee.fr

Semi-supervised Deep Convolutional Transform Learning for Hyperspectral Image Classification

Keywords: hyperspectral classification, supervised learning, deep learning. I

This work addresses the problem of hyperspectral image classification when the number of labeled samples is very small (few shot learning). Our work is based on the recently proposed framework of convolutional transform learning. In this work, we propose a semisupervised version of deep convolutional transform learning. We compare with four recent studies which are tailored for solving the few-shot learning problem in hyperspectral classification. Results show that our method can improve over the state-of-the-art.

INTRODUCTION

In hyperspectral image classification, the usual protocol is to label some samples manually after acquiring the image; this forms the training set. The labeled training data is fed into some classifier (after feature extraction if needed) in order to generate the labels for the remaining unlabeled testing samples. Ideally one would like to manually label as few samples as possible since this is a laborious timeconsuming process that needs expertise.

Deep learning is known to be data hungry. Consequently, initial studies on deep learning [START_REF] Chen | Deep Learning-Based Classification of Hyperspectral Data[END_REF][START_REF] Chen | Spectral-Spatial Classification of Hyperspectral Data Based on Deep Belief Network[END_REF] used more than 80% of the data as a training set; in practice this is infeasible. Over time, deep learning techniques like [START_REF] Liu | Deep Few-Shot Learning for Hyperspectral Image Classification[END_REF][START_REF] Gao | Deep Induction Network for Small Samples Classification of Hyperspectral Images[END_REF] have been able to reduce the data requirement, but even with the new age of deep neural networks (DNN), the number of labeled samples required is usually higher than shallow learning techniques like [START_REF] Karaca | Domain Transform Filter and Spatial-Aware Collaborative Representation for Hyperspectral Image Classification Using Few Labeled Samples[END_REF][START_REF] Xu | Efficient Probabilistic Collaborative Representation-Based Classifier for Hyperspectral Image Classification[END_REF].

Given that DNNs require copious amounts of labeled data for training, the only approach is to augment the number of labeled samples. The standard approach to address this issue had been employing generative models [START_REF] Zhu | Generative Adversarial Networks for Hyperspectral Image Classification[END_REF][START_REF] Wang | Caps-TripleGAN: GAN-Assisted CapsNet for Hyperspectral Image Classification[END_REF] in the recent past and meta-learning [START_REF] Gao | Small sample classification of hyperspectral image using model-agnostic meta-learning algorithm and convolutional neural network[END_REF][START_REF] Gao | Deep Induction Network for Small Samples Classification of Hyperspectral Images[END_REF] in current years. Classical techniques like Gaussian mixture models have also been used for labeled data augmentation [START_REF] Davari | GMM-Based Synthetic Samples for Classification of Hyperspectral Images With Limited Training Data[END_REF]. A thorough review of different data augmentation techniques can be found in [START_REF] Nalepa | Hyperspectral data augmentation[END_REF].

Over the years various deep learning architectures have been employed for the task of hyperspectral image classification. Initial studies used stacked autoencoders [START_REF] Chen | Deep Learning-Based Classification of Hyperspectral Data[END_REF] and deep belief networks [START_REF] Chen | Spectral-Spatial Classification of Hyperspectral Data Based on Deep Belief Network[END_REF] for image classification. Later on, convolutional neural networks (CNN) showed improvement in results [START_REF] Makantasis | Deep supervised learning for hyperspectral data classification through convolutional neural networks[END_REF][START_REF] Jia | Convolutional neural network based classification for hyperspectral data[END_REF]. Later more complex CNNs like ResNet [START_REF] Zhong | Deep residual networks for hyperspectral image classification[END_REF] and DenseNet [START_REF] Bai | SSDC-DenseNet: A Cost-Effective End-to-End Spectral-Spatial Dual-Channel Dense Network for Hyperspectral Image Classification[END_REF] were employed for the said problem. Capsule networks had also been used for hyperspectral classification with varying degrees of success [START_REF] Paoletti | Capsule Networks for Hyperspectral Image Classification[END_REF][START_REF] Yin | Hyperspectral Image Classification Using CapsNet With Well-Initialized Shallow Layers[END_REF]. Even though not a natural choice recurrent neural networks too have been used for hyperspectral image classification [START_REF] Mou | Deep Recurrent Neural Networks for Hyperspectral Image Classification[END_REF]. In the limited scope of this letter, it is not possible to review all deep learning techniques. The interested reader can peruse survey papers [START_REF] Li | Deep Learning for Hyperspectral Image Classification: An Overview[END_REF][START_REF] Audebert | Deep Learning for Classification of Hyperspectral Data: A Comparative Review[END_REF]. The said papers are from two years back and do not include the latest deep learning techniques for hyperspectral image classification based on attention networks [START_REF] Zhu | Residual Spectral-Spatial Attention Network for Hyperspectral Image Classification[END_REF] and graph CNNs [START_REF] Hong | Graph Convolutional Networks for Hyperspectral Image Classification[END_REF].

The unique and interesting aspect about hyperspectral image classification is that the sum of training (labeled) and testing (unlabeled) samples are usually fixed, as discussed at the beginning of this section. This allows for semisupervised learning of the test features [START_REF] Wu | Semi-Supervised Deep Learning Using Pseudo Labels for Hyperspectral Image Classification[END_REF] while training the DNN. This work follows the same paradigm.

Our work is based on the framework of convolutional transform learning (CTL) [START_REF] Maggu | Convolutional Transform Learning[END_REF][START_REF] Maggu | Deep Convolutional Transform Learning[END_REF]. CTL overcomes the major shortcomings of CNN since it can learn convolutional filters in an unsupervised fashion. A recent study proposed a supervised version of shallow convolutional transform learning [START_REF] Singh | Multi-Label Consistent Convolutional Transform Learning: Application to Non-Intrusive Load Monitoring[END_REF]. In this work, we propose a semi-supervised formulation of convolutional transform learning, where the features for both the labeled and the unlabeled data are learnt during training of the inbuilt classifier. During testing, the features corresponding to the unlabeled training data are input to the learnt classifier.

Since the CTL is a relatively new framework, we will discuss it in depth in Section II; in the recent past, the said framework has been used successfully in other hyperspectral imaging applications [START_REF] Goel | K-Means Embedded Deep Transform Learning for Hyperspectral Band Selection[END_REF][START_REF] Goel | Sparse Subspace Clustering Friendly Deep Dictionary Learning for Hyperspectral Image Classification[END_REF]. This is necessary for understanding our formulation discussed in Section III. The experimental evaluation is detailed in Section IV. The conclusion is discussed in Section V.

II.

BACKGROUND

In convolutional transform learning (CTL) a set of filters are learnt such that when operated on the data they produce the corresponding representations. Formally this is expressed as x the representation of the k th sample after applying the m th convolutional filter. There are a total of K samples and M filters. The symbol '  'denotes a convolutional operation with zero padding.

During training, the filters and the representations are learnt by solving the following optimization problem.

( ) ( ) ( ) ( ) () , 2 ( ) ( ) ( ) 2 , 11 2 min log det k mm m mk KM k k k m m m tx km F t s x x TT   ==   - +   +-  (2)
Here ψ is a regularization on the representation and (μ, λ) is positive. T is defined as →  . The penalty on T is borrowed from transform learning [START_REF] Ravishankar | Online Sparsifying Transform Learning-Part I: Algorithms[END_REF].

  1 | ... | m T t t = .
One can see how it is possible to learnt convolutional filters from training data in an unsupervised fashion. CNNs do not have the said penalty on the learnt filters and hence can end up at the trivial solution. Furthermore, without the penalty on T, there is no guarantee that CNNs will learn unique filters.

In matrix-vector form, (2) can be expressed as follows

( ) ( ) 22 , min ) log det FF TX T S X X T T  - +  + - (3)
where The unsupervised formulation for CTL was proposed in [START_REF] Singh | Multi-Label Consistent Convolutional Transform Learning: Application to Non-Intrusive Load Monitoring[END_REF]. Later the label consistency term from [START_REF] Jiang | Label Consistent K-SVD: Learning a Discriminative Dictionary for Recognition[END_REF][START_REF] Maggu | Label-Consistent Transform Learning for Hyperspectral Image Classification[END_REF] was added to this formulation to make it supervised [START_REF] Goel | Sparse Subspace Clustering Friendly Deep Dictionary Learning for Hyperspectral Image Classification[END_REF]. This led to the following formulation.

( ) ( ) (1) ( ) 1 1 | ... | , | ... | T kk K M kK S s s X x x    ==   , (1) (1) 1 ( ) ( ) 1 .
( ) ( ) 22 , 2 min ) log det FF TX F T S X X T T Q MX   - +  + - +- (4)
In ( 4), the last term is the supervision term. Matrix Q consists of one-hot encoded class labels, M is a linear map that projects the representation X to the corresponding class labels. Here η is a positive constant that controls the relative importance of the label consistency term. Note that in [START_REF] Goel | Sparse Subspace Clustering Friendly Deep Dictionary Learning for Hyperspectral Image Classification[END_REF] the problem was multi-label classification and hence this simple label consistency term is sensible.

In [START_REF] Goel | K-Means Embedded Deep Transform Learning for Hyperspectral Band Selection[END_REF] a deeper extension of the unsupervised formulation was proposed leading to deep convolutional transform learning (DCTL). As the name suggests, in the said formulation, multiple convolutional filters were being applied to the samples one after the other to generate the representation. This is expressed as follows,

( ) ( ) ( ) ( )   1 2 3 2 3 2 1 ,, 3 2 1 min )
log det

F T T T X ii F i T T T S X X TT  = - +  +- (5)
Hereabove, Ti refers to the i th layer of convolutional filters. In each layer, the Ti is formed by stacking the filters as columns of a matrix. The formulation ( 5) is shown for 3 layers but can be extended further.

III. PROPOSED FORMULATION

Let us re-iterate the problem of hyperspectral image classification. Once the hyperspectral imagery is captured the task is to label each position / pixel. Let us assume that there are N such positions. The usual protocol is to label a subset of these N (say n) positions manually and train a classifier to label the remaining N-n positions. Whatever the value of n, the total number of positions to be labeled (be it manual or automatic) is always N.

In this work, we propose a semi-supervised formulation where the n labeled samples (corresponding to n positions) will be used for training a classifier. During training, all the representation / features for all the N samples will be generated. At testing, the N-n unlabeled samples will be input to the learnt classifier for labeling.

We add binary cross-entropy (JBCE) loss for supervision instead of the label consistency term to the deep convolutional transform learning formulation. Note that this loss only pertains to the labeled data; for the unlabeled portion of the data only deep convolutional transform learning formulation is applicable. Our semi-supervised formulation can be expressed as,

  ( ) ( )     ( ) ( )   ( ) ( ) 1 2 3 2 3 2 1 , , , 3 2 1 min
| | | log det , LU L U L U F T T T X X L U i i F i BCE L T T T S S X X X X T T J MX Q   = - +  + - +  (6)
Equivalently -

( )   ( ) ( ) ( ) ( ) ( ) ( ) ( )
( )

1 2 3 3 2 , , , 1
2 3 2 1 Supervised 2 3 2 1 Unsupervised min log det , LU ii F T T T X X i L L L BCE L F U U U F TT T T T S X X J MX Q T T T S X X   = -+ - +  + + - +  
The subscript 'L' corresponds to labeled and 'U' corresponds to unlabelled. The BCE loss is only being defined on the labeled data. In (6) σ refers to the sigmoid function and Q (as defined before) refers to the one-hot encoded label vectors. In a more comprehensible notation, our semi-supervised formulation (6) can be expressed as the cost of 'supervised + unsupervised' terms as follows:

Here we have shown the formulation for 3 layers. One can see that extending it to more layers is straightforward. The problem ( 6) is solved using any standard gradient descent algorithm. Here, we have used Adam [START_REF] Kingma | Adam: A method for stochastic optimization[END_REF]. The activation function used is scaled exponential linear unit (SELU). The schematic diagram for 2-layers semisupervised deep convolutional transform learning is shown in Fig. 1. EXPERIMENTAL RESULTS

We evaluate our proposed technique on two benchmark datasets -Indian Pines 1 and Pavia University2 . The standard pre-processing steps are performed on these datasets prior to classification. For both datasets, an extremely challenging experimental protocol from [START_REF] Jia | Collaborative Representation-Based Multiscale Superpixel Fusion for Hyperspectral Image Classification[END_REF] is followed. The number of labeled samples to be considered is 5 per class; the rest are unlabeledthe goal is to label the unlabeled samples. All the experiments were carried out 100 times, using random splits of labeled and unlabeled samples. The arithmetic means of these 100 sets of experiments are reported in this work.

We compare with recent techniques that have been specifically tailored for limited training samples. Deep few-shot learning (DFSL) [START_REF] Liu | Deep Few-Shot Learning for Hyperspectral Image Classification[END_REF] is based on the meta-learning approach. Dual-path Siamese network (DPSN) [START_REF] Huang | Dual-Path Siamese CNN for Hyperspectral Image Classification With Limited Training Samples[END_REF] uses a Siamese network for classification; to augment labeled data it uses a combination of a generative adversarial network. Dual graph convolutional network (DGCN) [START_REF] Maggu | Convolutional Transform Learning[END_REF] uses a graph convolutional network for classification followed by label propagation. The final work that we compare is Intraclass Similarity Structure Representation (ISSR) [START_REF] Wei | Intraclass Similarity Structure Representation-Based Hyperspectral Imagery Classification With Few Samples[END_REF]. In [START_REF] Wei | Intraclass Similarity Structure Representation-Based Hyperspectral Imagery Classification With Few Samples[END_REF] this work a novel label propagation approach is proposed to increase the number of training samples; the augmented data serves as input to a standard deep CNN for classification.

Our proposed network does not consider spatial information; only the spectral information for every pixel 1 https://paperswithcode.com/dataset/indian-pines is input to the network as 1D data. Our proposed work requires the specification of three parameters. We have used the values μ=λ=.1 and η=.5. As for the size of the filters we have used 7x1, 5x1, 3x1; we also tried with four layers but the results deteriorated owing to overfitting and hence we are not showing the results. The comparative results in terms of overall accuracy (OA), average accuracy (AA), and Kappa coefficient are shown in Table I. One can see that our method performs the best in terms of all the metrics. Among the benchmarks DGCN performs the best; in fact, it is better than our fourlayer architecture. The meta-learning based approach DFSL also performs relatively well but not as good as DGCN. DPSN combines several aspects of deep learning and data augmentation but performs worse than DGCN. The performance of ISSR is the worst; it has a separate data augmentation stage and a classification stage; such a piecemeal approach does not perform at par with the other end-to-end architectures. For visual clarity we show the classification results as images in Fig. 2. It corroborates the numerical findings.

A. Ablation Studies

In the first set of experiments, we analyze the different aspects of our proposed solution. We will see how the results change with depth. We will also see in each depth, how the results vary between semi-supervised and supervised formulations. The former is the proposition of this study and has been discussed in detail. The supervised formulation means that the network is only trained on the labeled samples; the unlabeled samples are not used during training. After training, the learnt filters are used for extracting the features from the input data and subsequent classification. From Tables II and III we see that for the semisupervised formulation the results improve from layers one to three and then dips when one more layer is added. This is probably due to over-fitting. The supervised formulation always yields considerably worse results than the semisupervised one. This is because the volume of data used for training the network is very less leading to over-fitting. Furthermore, for the supervised formulation, the results keep on deteriorating with depth. This too is likely due to over-fitting. With limited data, learning more parameters hampers the generalization ability of the network.

Finally, we show the empirical convergence plots for our proposed algorithm. Owing to limitations in space we only show it for the three-layer architecture. For other layers, the trend is similar. From Fig. 3, one can see that the algorithm convergences in 50 iterations.

V. CONCLUSION

This work proposes a new approach for hyperspectral image classification when the number of labeled samples are limited. The work is based on convolutional transform learning. A semi-supervised architecture for deep convolutional transform learning is proposed. Comparison with state-of-the-art techniques on few shot learning has shown that our proposed method can improve over rest. We believe there is further scope for improvement. The current work only accounts for spectral information while ignoring spatial information. There can be two possibilities for incorporating spatial information. First, we can consider 2D convolutions on patches / superpixels. Second, we can keep the 1D convolutional filters as it is but incorporate the spatial information via graph regularization.

In terms of methodology, we believe that the framework of deep convolutional transform learning can improve by incorporating metric learning. Recent studies like [38] have shown how it can be incorporated into the deep dictionary learning framework; we believe that similar improvements can be achieved in the deep convolutional transform formulation as well.

2 F

 2 == and also promotes diversity among the learnt filters; the other penalty

Fig. 1 .

 1 Fig. 1. Schematic diagram of the proposed approach IV.EXPERIMENTAL RESULTS

Fig. 2 .

 2 Fig. 2. Top -Pavia University; Bottom -Indian Pines. Pictorial view of classification. Left to Right -ISSR, DPSN, DGCN and Proposed

Fig. 3 .

 3 Fig. 3. Cost vs Iteration. Empirical Convergence Plot for 3 Layer Architecture.

  ACKNOWLEDGMENT E.C. and G.C. acknowledge support from the Agence Nationale de la Recherche of France under MAJIC (ANR-17-CE40-0004-01) project.

  ..

	TS	... t s   ... M t s  = 	and Ψ amounts to
		... KK M t s  t s  
	applying the penalty term ψ column-wise on matrix X and
	summing.

TABLE I COMPARISON

 I

	Dataset Metric	ISSR	DPSN	DGCN	DFSL	Proposed
	Pavia	OA	80.09	85.02	88.91	86.24	89.79
		AA	80.26	85.69	89.48	87.41	90.08
		Kappa	.78	.82	.85	.84	0.86
	Indian	OA	70.36	75.32	77.38	75.85	81.02
	Pines	AA	70.95	75.97	77.91	76.30	84.32
		Kappa	.69	.74	.77	.75	0.80

WITH STATE-OF-THE-ART TECHNIQUES

https://paperswithcode.com/dataset/pavia-university


Dr Yasmine Even
email: yasmine.even@univ-brest.fr

Emilien Pousse

Coraline Chapperon

Sébastien Artigaud

Hélène Hégaret

Benoit Bernay

Vianney Pichereau

Jonathan Flye-Sainte-Marie

Fred Jean

immune defense response of the king scallop Pecten maximus in presence of

Keywords: Paralytic shellfish toxins, Alexandrium minutum, Pecten maximus, ecotoxicology, proteomics

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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.

Authors: Yasmine Even 1* , Emilien Pousse 1 , Coraline Chapperon 1 , Sébastien Artigaud 1 , Hélène Hégaret 1 , Benoit Bernay 2 , Vianney Pichereau 1 , Jonathan Flye-Sainte-Marie 1 , Fred Jean 1 .

1: Laboratoire des Sciences de l'Environnement Marin, UMR 6539 CNRS/UBO -Institut Universitaire Européen de la Mer, Technopôle Brest-Iroise, 29280 Plouzané, France 2 : Plateforme Proteogen, US EMerode, Université de Caen Normandie, Esplanade de la paix, 14032 Caen, France

Introduction

King scallop, Pecten maximus is a highly valued seafood product and is of major commercial importance in Europe with global capture production of 60,000 tons in 2019 (FAO Fisheries and Aquaculture Department). This marine bivalve lives in coastal waters at the surface of, or slightly buried in the sediment [START_REF] Baird | Measurement of Condition in Mussels and Oysters[END_REF]. Like many shellfishes feeding on microalgae, king scallops are exposed to harmful algal blooms (HAB) [START_REF] Chauvaud | Variations in food intake of Pecten maximus (L.) from the Bay of Brest (France): Influence of environmental factors and phytoplankton species composition[END_REF][START_REF] Chauvaud | Effects of environmental factors on the daily growth rate of Pecten maximus juveniles in the Bay of Brest (France)[END_REF]. HAB occur worldwide and can negatively affect king scallops including hatchery seeds and juveniles [START_REF] Anderson | The globally distributed genus Alexandrium: Multifaceted roles in marine ecosystems and impacts on human health. Harmful Algae, Harmful Algae--The requirement for species-specific information[END_REF][START_REF] Borcier | Effects of bioactive extracellular compounds and paralytic shellfish toxins produced by Alexandrium minutum on growth and behaviour of juvenile great scallops Pecten maximus[END_REF][START_REF] Li | Effects of toxic dinoflagellate Alexandrium tamarense on the energy budgets and growth of two marine bivalves[END_REF], thus causing significant economic and social impacts. A better understanding of the different effects caused by harmful algae exposures on the king scallop is therefore essential to improve the management of fisheries or aquaculture practices.

Toxic microalgae of the genus Alexandrium, are responsible for a large proportion of HAB events worldwide. Indeed, the dinoflagellates Alexandrium spp. can produce saxitoxin and its derivatives that are paralytic shellfish toxins (PST), responsible for the paralytic shellfish poisoning (PSP) syndrome in human following consumption of contaminated seafood. The PST can be lethal for mammals by inducing paralysis via binding to voltage-dependent sodium channels involved in the nervous influx, thus inhibiting membrane depolarization and blocking proliferation of action potentials [START_REF] Narahashi | Neuroactive agents and nerve membrane conductances[END_REF][START_REF] Reyero | Evidence of saxitoxin derivatives as causative agents in the 1997 mass mortality of monk seals in the Cape Blanc Peninsula[END_REF]. Although less toxic for invertebrates such as bivalves, the consumption of toxic algae such as Alexandrium minutum that synthesizes PST, which accumulate within the digestive gland and soft tissues, can results in several deleterious effects [START_REF] Borcier | Effects of bioactive extracellular compounds and paralytic shellfish toxins produced by Alexandrium minutum on growth and behaviour of juvenile great scallops Pecten maximus[END_REF][START_REF] Castrec | Bioactive extracellular compounds produced by the dinoflagellate Alexandrium minutum are highly detrimental for oysters[END_REF]Contreras et al., 2012aContreras et al., , 2012b;;[START_REF] Fabioux | Exposure to toxic Alexandrium minutum activates the detoxifying and antioxidant systems in gills of the oyster Crassostrea gigas[END_REF][START_REF] Li | Effects of toxic dinoflagellate Alexandrium tamarense on the energy budgets and growth of two marine bivalves[END_REF][START_REF] Pousse | Sources of paralytic shellfish toxin accumulation variability in the Pacific oyster Crassostrea gigas[END_REF]. Assessing the effect of toxic dinoflagellates upon bivalves is of great importance, as it first represents a hazard for human after consumption of contaminated seafood, but also for other marine organisms exposed to these toxic dinoflagellates, therefore impacting socioeconomic activity, especially associated with the closure of fishing areas, when toxin concentration exceeds authorized values [START_REF] Anderson | The globally distributed genus Alexandrium: Multifaceted roles in marine ecosystems and impacts on human health. Harmful Algae, Harmful Algae--The requirement for species-specific information[END_REF][START_REF] Geraci | Humpback Whales (Megaptera novaeangliae) Fatally Poisoned by Dinoflagellate Toxin[END_REF]Hégaret et al., 2009;[START_REF] Reyero | Evidence of saxitoxin derivatives as causative agents in the 1997 mass mortality of monk seals in the Cape Blanc Peninsula[END_REF]. Among the A. minutum species, several strains have been identified with variable amounts of PST produced, as well as variable levels of cytotoxic potency, associated to the production of some uncharacterized bioactive extracellular components (BEC), independent from PST [START_REF] Borcier | Effects of bioactive extracellular compounds and paralytic shellfish toxins produced by Alexandrium minutum on growth and behaviour of juvenile great scallops Pecten maximus[END_REF][START_REF] Castrec | Bioactive extracellular compounds produced by the dinoflagellate Alexandrium minutum are highly detrimental for oysters[END_REF][START_REF] Long | Allelochemicals from Alexandrium minutum induce rapid inhibition of metabolism and modify the membranes from Chaetoceros muelleri[END_REF]. In the latter studies, clear damages of BEC on bivalves or other marine organisms have been demonstrated, whereas toxic effects of PST on bivalves are less well established [START_REF] Mat | Genetic and genotoxic impacts in the oyster Crassostrea gigas exposed to the harmful alga Alexandrium minutum[END_REF][START_REF] Payton | Remodeling of the cycling transcriptome of the oyster Crassostrea gigas by the harmful algae Alexandrium minutum[END_REF].

The effects of A. minutum and its toxins on mussels and oysters have been well studied [START_REF] Bougrier | Paralytic shellfish poison accumulation yields and feeding time activity in the Pacific oyster (Crassostrea gigas) and king scallop (Pecten maximus)[END_REF][START_REF] Castrec | The dinoflagellate Alexandrium minutum affects development of the oyster Crassostrea gigas, through parental or direct exposure[END_REF][START_REF] Fabioux | Exposure to toxic Alexandrium minutum activates the detoxifying and antioxidant systems in gills of the oyster Crassostrea gigas[END_REF]Haberkorn et al., 2010bHaberkorn et al., , 2010a;;[START_REF] Mat | Oyster transcriptome response to Alexandrium exposure is related to saxitoxin load and characterized by disrupted digestion, energy balance, and calcium and sodium signaling[END_REF][START_REF] Payton | Remodeling of the cycling transcriptome of the oyster Crassostrea gigas by the harmful algae Alexandrium minutum[END_REF], however very little is known about its physiological impact on the king scallop. [START_REF] Coquereau | Short-Term Behavioural Responses of the Great Scallop Pecten maximus Exposed to the Toxic Alga Alexandrium minutum Measured by Accelerometry and Passive Acoustics[END_REF] and [START_REF] Borcier | Effects of bioactive extracellular compounds and paralytic shellfish toxins produced by Alexandrium minutum on growth and behaviour of juvenile great scallops Pecten maximus[END_REF] respectively demonstrated that an exposure to A. minutum, depending on the strain, could lead to modified valve movement, but also a decreasing filtration rate and shell growth as well as histological damages and altered escape response. No studies have, however, investigated the ecophysiological and associated proteomic responses of the king scallop following an exposure to this toxic microalgae. In order to better understand protective mechanisms activated in bivalves when exposed to toxic algae, we assessed the response of king scallop juveniles to an environmental stress, corresponding to a short term exposure to a PST producing strain of A. minutum, by characterizing physiological and proteome modifications in king scallops. More specifically, we measured toxin accumulation in king scallops and its consequences on physiological parameters such as filtration and respiration. We further performed proteomic analyzes on mantle tissues to evaluate the responses of juvenile king scallops to A. minutum exposure at the protein levels.

Material and methods

Animals and sample collection

King scallop (P. maximus) juveniles were obtained from the Tinduff hatchery (Plougastel-Daoulas, France). Average individual length was 35.9 ± 2.1 mm (𝑚𝑒𝑎𝑛 ± 𝑆𝐷, 𝑛 = 35). In order to make sure that toxin accumulation in king scallops is not due to contaminations prior to our experiment, individuals were acclimated a week prior experiments into seawater (35 PSU) flow-through tanks and fed ad libitum with a standard 50/50 mix of algae Tisochrisis lutea (T-iso) and Chaetoceros muelleri which are common aquaculture feed.

Algal cultures

T. lutea (strain CCAP 927/14) and C. muelleri (strain CCAP1010/3) were produced in continuous ( 10L bioreactor) and batch cultures (2 L and 6 L glass bottles) in Conway medium [START_REF] Walne | Effect of the PSP-causing dinoflagellate, Alexandrium sp. on the initial feeding response of Crassostrea gigas[END_REF] made with UV sterilized 1-µm filtered seawater (35 PSU) that was aerated with a mix of air and CO2 [START_REF] Walne | Studies on the food value of nineteen genera of algae to juvenile bivalves of the genera Ostrea, Crassostrea, Mercenaria and Mytilus[END_REF].

Cultures of A. minutum (strain Daoulas 1257) were grown in 2 L and 6 L glass bottles with L1 medium [START_REF] Guillard | Stichochrysis immobilis is a diatom, not a chrysophyte[END_REF] made with 1-µm filtered seawater (35 PSU). Air but no CO2 was added to the cultures. Room temperature was maintained at 18ºC with a 12:12 photoperiod. The mean toxicity of A. minutum strain Daoulas 1257 was 52.8 fg STX equivalent cell -1 (equivalent to 0.63 fmol cell-1; [START_REF] Pousse | Sources of paralytic shellfish toxin accumulation variability in the Pacific oyster Crassostrea gigas[END_REF] at the end of the exponential growth phase. This strain was reported to not produce any bioactive extracellular compounds responsible for toxic effects in bivalves [START_REF] Castrec | Bioactive extracellular compounds produced by the dinoflagellate Alexandrium minutum are highly detrimental for oysters[END_REF] or allelopathic effects [START_REF] Long | Allelochemicals from Alexandrium minutum induce rapid inhibition of metabolism and modify the membranes from Chaetoceros muelleri[END_REF].

Exposure to A. minutum and sampling

In order to examine the potential physiological effects of A. minutum strain Daoulas 1257 and its PST upon king scallops, experimental trials were divided into 2 phases. Each phase lasted 2 days.

During the first phase, each group of king scallops (n=25) were fed with a normal diet characterized by an equal algal mix of non-toxic algae (50/50, T. lutea and C. muelleri, for algal concentrations see Fig. 1).

During the second phase, king scallop individuals were exposed to 2 different conditions:

1) absence of food: no algae (NA); experiment named TC-NA hereafter (n=7).

2) toxic diet: A. minutum strain Daoulas 1257 (A); experiment repeated 3 times and named TC-A1, TC-A2, TC-A3 (n=18); the concentration of A. minutum distributed to king scallops was respectively 560, 440 and 500 cells mL -1 (Fig. 1).

During the 4 days, the flow rate in each chamber was adjusted to 30 mL min -1 . At the end of the 4 days of experiment, juvenile king scallops were individually measured and weighted (total, shell and humid flesh mass) and mantles were collected and stored at -80°C for further proteomic analyzes. For specimen exposed to A. minutum the digestive gland was collected, weighted and stored at -80°C to perform a PST accumulation quantification (Fig. 1).

Experimental set up for physiological measurements

Experiments were run through a physiological measurement system designed to allow the simultaneous measurements of king scallops respiration and clearance rates (see experimental device described in Flye- Sainte-Marie et al., 2007;[START_REF] Savina | A comparative ecophysiological study of two infaunal filter-feeding bivalves: Paphia rhomboı̈des and Glycymeris glycymeris[END_REF]. Seven king scallop juveniles were each placed into a transparent open-flow 540 mL chamber (n=7) connected to the system. An 8 th chamber remained empty, i.e. without any individual, and was used as a control. Chambers were supplied with thermoregulated (ca. 15.7°C) filtered (1 µm) seawater at 35 PSU. Concentrated algal culture was constantly added to the system at the inlet of the "mixing tank" to reach concentrations between 24 000 and 40 000 cells mL -1 and between 440 and 560 cells mL -1 for T-iso/Chaeto and A. minutum respectively. These concentrations were adjusted as a function of the fluorescence in the control chamber as in [START_REF] Pousse | Sources of paralytic shellfish toxin accumulation variability in the Pacific oyster Crassostrea gigas[END_REF]. The seawater inflow was controlled within each chamber by 2 peristaltic pumps placed upstream (each peristaltic pump controlled the seawater flow of 4 chambers) and maintained at a rate of ca. 30 mL min -1 . Algal concentrations were measured in the control chamber for each experiment. The 8-tank experimental device is equipped with a multiparameter probe (WTW MultiLine 3430, Fisher Scientific, Suwanee, GA, USA) measuring both dissolved oxygen and temperature, and a fluorometer (WETstar chlorophyll a, WET Labs, Philomat, USA) quantifying fluorescence in seawater outflow coming from each chamber successively (by alternating control and experimental chambers). Measures lasted 15 min within each chamber with a record every 10 sec. A full set of measurements was thus conducted over 210 min. Real time data could be visualized using a graphical user-interface developed specifically for this system and recorded data were sent to the controller and stored continuously. In order to prevent the development of biofilm that could cause a bias in respiration measurements, the full system was emptied and cleaned with peracetic acid and hydrogen peroxide, rinsed with hot freshwater and then with filtered seawater between the 2 phases of each experimental trial. Juvenile king scallops were kept in filtered seawater during this procedure.

Physiological parameters 2.5.1. Respiration rate (RR)

The average respiration rate (RR, mg O2 h -1 ) of juvenile king scallops was assessed using the following equation:

𝑅𝑅 = -(0 2 (𝑐𝑜𝑛𝑡𝑟𝑜𝑙) -0 2 (𝑠𝑐𝑎𝑙𝑙𝑜𝑝)) × 𝐹𝑅
where:

-0 2 (𝑐𝑜𝑛𝑡𝑟𝑜𝑙) corresponds to the average oxygen concentration (mg O2 L -1 ) in the control chamber recorded prior and after the measurements made in the experimental chamber, and -0 2 (𝑠𝑐𝑎𝑙𝑙𝑜𝑝) is the average oxygen concentration (mg O2 L -1 ) measured in the experimental chamber and FR is the seawater flow rate (L h -1 ) through the chambers.

Clearance rate (CR)

Clearance rate (CR in L h -1 ) can be defined as the volume of water cleared of particle per unit of time by an individual. Throughout the experiment, seawater outflow from the different chambers was sampled and algal concentration was measured using a Coulter Counter Multisizer. Fluorescence values were converted to cell concentration (cells L -1 ). CR was calculated as follows:

𝐶𝑅 = -(𝐹𝐶(𝑐𝑜𝑛𝑡𝑟𝑜𝑙) -𝐹𝐶(𝑠𝑐𝑎𝑙𝑙𝑜𝑝)) 𝐹𝐶(𝑐𝑜𝑛𝑡𝑟𝑜𝑙) × 𝐹𝑅
where 𝐹𝐶(𝑐𝑜𝑛𝑡𝑟𝑜𝑙) is the average cell concentration (cells L -1 ) recorded in the control chamber before and after the measurements made in experimental chambers, 𝐹𝐶(𝑠𝑐𝑎𝑙𝑙𝑜𝑝) corresponds to the average cell concentration (cells L -1 ) measured in the experimental chamber and 𝐹𝑅 is the seawater flow rate (L h -1 ) through the chambers.

Both respiration and clearance rates were standardized using the following equation [START_REF] Bayne | Feeding and digestion by the mussel Mytilus edulis L. (Bivalvia: Mollusca) in mixtures of silt and algal cells at low concentrations[END_REF]:

𝑌 𝑠 = (𝑊 𝑠 𝑊 𝑒 ⁄ ) 𝑏 × 𝑌 𝑒 .
Where Ys is the physiological rate (respiration or clearance rate in this study) for the standard total mass Ws, Ye is the physiological rate of the individual that occupied the experimental chamber with a total mass of We and b is the allometric coefficient for the clearance (0.67) and respiration (0.75) rates [START_REF] Bayne | Feeding and digestion by the mussel Mytilus edulis L. (Bivalvia: Mollusca) in mixtures of silt and algal cells at low concentrations[END_REF]. Physiological rates were standardized for a juvenile king scallop with a total mass of 7 g.

Type II linear regressions with range major axis method were applied to adjust linear relationships between the number of consumed algal cells and toxin concentration by using the R package "Lmodel2" [START_REF] Legendre | Lmodel2: Model II Regression[END_REF].

Clearance rate inhibition index (CRII)

The clearance rate inhibition index (CRII) was calculated for each individual, using standardized CR of day 2 (fed with non-toxic algae) and standardized CR of day 4 (exposed to toxic algae). The CRII was used to quantify CR inhibition due to A. minutum and was calculated as follows [START_REF] Pousse | Sources of paralytic shellfish toxin accumulation variability in the Pacific oyster Crassostrea gigas[END_REF]:

𝐶𝑅𝐼𝐼 = 1 - 𝐶𝑅(𝑑𝑎𝑦2) 𝐶𝑅(𝑑𝑎𝑦4)

Time for filtration recovery

The time for filtration recovery was measured from the beginning of A. minutum exposure to the time when individual clearance rate reached 0.5 L h -1 . This threshold was chosen arbitrarily to detect low levels of filtration activity, without considering erratic filtration behaviors. When individuals CR did not reach this threshold, a time of 40h was applied.

Toxin accumulation

The PST accumulation was estimated individually using juvenile king scallop digestive gland. HCl was added to digestive gland samples (1:1 w:w) that were then ground using a beadblaster, boiled for 5 min at 104ºC and centrifuged for 10 min. PSTs were subsequently estimated using a Saxitoxin PSP ELISA kit (Abraxis), following instructions from the manufacturer as described in [START_REF] Lassudrie | Physiological responses of Manila clams Venerupis (=Ruditapes) philippinarum with varying parasite Perkinsus olseni burden to toxic algal Alexandrium ostenfeldii exposure[END_REF]. The acid hydrolysis can induce chemical conversion of some PST analogues to STX [START_REF] Vale | In Vitro and in Vivo Evaluation of Paralytic Shellfish Poisoning Toxin Potency and the Influence of the pH of Extraction[END_REF]. This Abraxis PSP ELISA assay recognizes mostly STX, and other PSTs only to varying degrees (cross-reactivities of 100% for STX and from 29% to 0% for other PSTs). Thus, toxicity of the digestive glands was expressed as µg of PST per 100 g of wet DG weight.

Other ecophysiological parameters measured 2.7.1. Toxin accumulation potential

The concentration of toxic algae delivered to king scallops slightly differed in the 3 assays (Fig. 1).

Therefore, in order to be able to compare toxin accumulation in all individuals exposed to A. minutum,

we calculated the toxin accumulation potential of each individual by dividing the toxin concentration accumulated in digestive glands (DG) at the end of day 4 by the number of A. minutum cells distributed during days 3&4 for each individual (µg of PST per 100 g of DG per toxic algal cell delivered).

Number of A. minutum cells consumed (x10 6 per 100g of DG) and toxins ingested -TI)

From the individual unstandardized CR obtained and the algal concentrations measured in the experimental chambers, the number of A. minutum cells consumed can be estimated. To do so, the mean algal concentration (cell L -1 ) to which a king scallop has been exposed to during a recording cycle (3.5 hours) was multiplied to the unstandardized clearance rate (L h -1 ) calculated for the corresponding cycle and by 3.5 (h), the duration of each recording cycle. All recording cycles were then added together and divided by the DG mass (g) to obtain the whole quantity of A. minutum cells consumed per DG mass during the experiment.

To calculate the overall toxins ingested (TI, µg of STX per 100 g of DG), the number of cells consumed was multiplied by the cellular toxins concentration corresponding to the A. minutum strain used in this experiment (52.8x10 -9 µg STX cell -1 ).

Toxin accumulation efficiency (TAE)

The toxin accumulation efficiency (TAE) reflects the balance between toxin uptake and elimination processes. It corresponds to the proportion of toxins accumulated (TA, i.e. measured) in king scallop DG during days 3&4 relative to that ingested (TI), and is calculated as follows [START_REF] Bougrier | Paralytic shellfish poison accumulation yields and feeding time activity in the Pacific oyster (Crassostrea gigas) and king scallop (Pecten maximus)[END_REF][START_REF] Mafra | Domoic acid uptake and elimination kinetics in oysters and mussels in relation to body size and anatomical distribution of toxin[END_REF][START_REF] Pousse | Sources of paralytic shellfish toxin accumulation variability in the Pacific oyster Crassostrea gigas[END_REF]:

𝑇𝐴𝐸 = 100 × 𝑇𝐴 𝑇𝐼

Protein extraction

Frozen mantle tissue was crushed with a mixer mill (MM400; RETSCH, Haan, Germany) and kept frozen using liquid nitrogen. For each animal, 100 mg of the obtained mantle tissue powder was homogenized in 100 mM Tris-HCl (pH 6.8) with 1% of protease inhibitor mix (GE Healthcare, Little Chalfont, UK) and centrifuged (50 000 g, 5 min, 4°C). Samples were precipitated overnight at 4°C using TCA 20% (1/1:v/v). After centrifugation (20 000 g, 30 min, 4°C), pellets were washed with 70% acetone and re-suspended in thiourea/urea/CHAPS buffer (2 M urea, 7 M thiourea, 4% CHAPS, 1% DTT) containing 1% ampholytes (IPG Buffer, pH 4-7; GE Healthcare, Little Chalfont, UK).

Protein concentrations were determined using the Bradford derived method and all samples were adjusted to 800 µg of proteins in 250µL.

Two-dimensional electrophoresis (2-DE)

Prior to isoelectric focusing, IPG (immobilized pH gradient) strips (pH 4-7, 13 cm; GE Healthcare, Little Chalfont, UK) were passively rehydrated with 250 µL of protein solution in wells for 14h.

Isoelectric focusing was conducted using the following protocol: 250 V for 15 min, 500 V for 2 h, gradient voltage increased to 1000 V for 1 h, gradient voltage increased to 8000 V for 2.5 h, 8000 V for 2 h and finally reduced to 500 V (Ettan IPGphor3; GE Healthcare, Little Chalfont, UK). After isoelectrofocalisation, strips were incubated in equilibration buffer (50 mM Tris-HCl pH 8.8, 6 M urea, 30% glycerol, 2% SDS and 0.002% Bromophenol Blue) for two periods of 15 min, the first one completed with 1 g L -1 dithiothreitol and the second time with 48 g L -1 iodoacetamide. The IPG strips were then placed on top of 12% polyacrylamide gels (SDS-PAGE) and were run in a 10°C thermoregulated electrophoresis unit (SE 600 Ruby; Amersham Biosciences, Amersham, UK) at 10 mA per gel for 1 h and then 30 mA per gel until complete migration. Gels were subsequently stained with Coomassie Blue (PhastGel R350, GE Healthcare) and unspecific coloration was destained with an aqueous solution containing 30% methanol and 7% acetic acid. The resulting gels were scanned with a transparency scanner (G:BoxChemi XL 1.4; SynGene) in gray scale with 16-bit depth and a resolution of 100 dpi.

Gel image and statistical analyzes for protein abundance

Images were aligned and spots were detected and quantified using the Progenesis SameSpots software (version 3.3, Nonlinear Dynamics) with manual alignment completed by automated algorithm. All detected spots were manually checked and artifact spots were removed. Data were exported as raw values and statistical analyzes were conducted in R (R Core Team, 2020) using the prot2D [START_REF] Artigaud | Identifying differentially expressed proteins in twodimensional electrophoresis experiments: inputs from transcriptomics statistical tools[END_REF] and limma packages [START_REF] Ritchie | limma powers differential expression analyses for RNA-sequencing and microarray studies[END_REF] from the Bioconductor suite [START_REF] Gentleman | Bioconductor: open software development for computational biology and bioinformatics[END_REF]. Data were normalized (quantile normalization) and the samples were paired compared between exposed and non-exposed to A. minutum conditions using moderated t-test with 7 replicates per condition. For comparisons, we used a moderated t-test, a modified t-test for which the standard errors have been moderated across spots, increasing the reliability of the test [START_REF] Artigaud | Identifying differentially expressed proteins in twodimensional electrophoresis experiments: inputs from transcriptomics statistical tools[END_REF].

Once the values of moderated t-test were calculated, a global correction by false discovery rate (fdr) was applied, in order to take into account multiple comparisons issues and paired-comparison correction. Spots with a fdr threshold lower than 0.1 and an absolute fold change higher than 1.5 were considered as differentially expressed.

Mass spectrometry

Proteins that changed significantly in abundance in response to A. minutum exposure were excised from gels and prepared for analyzes by mass spectrometry (MS) as described in [START_REF] Artigaud | Proteomic responses to hypoxia at different temperatures in the great scallop (Pecten maximus)[END_REF]. Gel pieces were washed with 50 mM ammonium bicarbonate (BICAM), dehydrated in 100% acetonitrile (ACN) and vacuum dried. Gel pieces were rehydrated with BICAM containing 0.5 µg of porcine recombinant trypsin (sequencing grade; Promega, Madison, Wisconsin, USA) and incubated overnight at 37°C. Peptides were extracted from the gels by alternative washing with 50 mM BICAM and ACN, and with 5% formic acid and ACN. Between each step, the supernatants were pooled, and finally concentrated by evaporation using a centrifugal evaporator (Concentrator 5301; Eppendorf, Hamburg, Germany).

MS experiments were carried out on an AB Sciex 5800 proteomics analyzer equipped with TOF-TOF ion optics and OptiBeam™ on-axis laser irradiation with 1000 Hz repetition rate. The system was calibrated before analysis with a mixture of des-Arg-bradykinin, angiotensin I, Glu1-fibrinopeptide B, ACTH (18-39) and ACTH (7-38), and mass precision was better than 50 ppm in reflectron mode.

A laser intensity of 3400 was typically employed for ionizing. MS spectra were acquired in the positive reflector mode by summarizing 1000 single spectra (5 × 200) in the 700-4000 Da mass range.

MS/MS spectra from the twenty most intense ions were acquired in the positive MS/MS reflector mode by summarizing a maximum of 2500 single spectra (10 × 250) with a laser intensity of 4300.

For tandem MS experiments, the acceleration voltage was 1 kV, and air was used as the collision gas.

Gas pressure medium was selected as settings.

The fragmentation pattern based on the occurrence of y, b and a ions was used to determine peptide sequences. Database searching was performed using the Mascot 2.5.1 program (Matrix Science). A custom EST database was used by combining P. maximus sequences from Illumina RNAseq sequenced from mantle tissues [START_REF] Artigaud | Deep sequencing of the mantle transcriptome of the great scallop Pecten maximus[END_REF] and from hemocyte cells [START_REF] Pauletto | Deep transcriptome sequencing of Pecten maximus hemocytes: A genomic resource for bivalve immunology[END_REF].

The variable modifications allowed were as follows: methionine oxidation and dioxidation, acetyl (K) and carbamidomethyl (C). Trypsin was selected as enzyme and 3 missed cleavages were allowed.

Mass accuracy was set to 300 ppm and 0.6 Da for the MS and MS/MS modes, respectively.

Statistical analyzes for toxin accumulation clusters

According to their accumulation potential, individuals were segregated into groups by applying an hierarchical clustering function with the Ward's method [START_REF] Pousse | Sources of paralytic shellfish toxin accumulation variability in the Pacific oyster Crassostrea gigas[END_REF]. The function 'hclust' available in the R package 'stats' (R Core team, 2020) was applied on the accumulation potential calculated for each individual. Three clusters were defined from the clustering function accordingly to [START_REF] Pousse | Sources of paralytic shellfish toxin accumulation variability in the Pacific oyster Crassostrea gigas[END_REF], and [START_REF] Mat | Oyster transcriptome response to Alexandrium exposure is related to saxitoxin load and characterized by disrupted digestion, energy balance, and calcium and sodium signaling[END_REF] who described phenotypic and genotypic differences in oysters divided into 3 accumulation clusters. This method defined 3 clusters of king scallops differentially accumulating toxins and named high, medium and low according to their high, intermediate and low accumulation potential, respectively (Fig. 2b).

Results

Inter-individual variability in toxin accumulated and toxin accumulation potential in king

scallops exposed to A. minutum.

All king scallops exposed to A. minutum for 2 days accumulated toxins and no mortality was observed. For the 3 assays TC-A1, TC-A2 & TC-A3 juvenile king scallops were exposed to different concentrations of A. minutum (Fig. 1) allowing us to observe a variation in the toxin loads accumulated ranging from 7 to 220 µg STX per 100 g of DG.

After a 2-day exposure to A. minutum toxic algae, our data showed a high variability in toxin accumulation potential between individuals.

Furthermore, according to their accumulation potential, and after applying a hierarchical clustering function with the Ward's method, individuals were segregated into 3 clusters of king scallops differentially accumulating toxins: high (n=5: TC-A1=1, TC-A2=4), intermediate (n=8: TC-A1=3, TC-A2=1, TC-A3=4) and low (n=5: TC-A1=2, TC-A2=2, TC-A3=1) accumulation potential groups (Fig. 2). It is noteworthy that the 3 clusters are irrespective of the 3 replicates.

Within only a 2-day exposure to A. minutum, the inter-individual variability in toxin accumulation was high, with an average twice higher in the high accumulation cluster than in the low one.

Links between feeding behavior and toxin accumulation

Our results demonstrate a significant linear correlation (R²=0.65) between toxin accumulation in DG and the total numbers of A. minutum cells ingested by each king scallop for the 2 days of toxic algae exposure (Fig. 3a).

In all 3 accumulation potential clusters, clearance activity rapidly dropped to almost stop at day 3, when king scallops were exposed to A. minutum, and this low clearance rate persisted at day 4 for the 3 clusters (Fig. 3b). Therefore, for all 3 clusters the clearance rates observed during days 3&4 in the presence of A. minutum were significantly lower compared to the ones in the presence of non-toxic algae on days 1&2 (p-value<0.005). However, no significant inter-individual difference in clearance rate was observed in the 3 accumulation potential clusters when exposed to toxic algae.

A significant inverse relationship between CRII and the concentration of toxins in king scallop DG (p-value=0.014, Fig. 4a) could be observed. Similar observation was made between CRII and toxin accumulation potential (p-value=0.038, Fig. 4c). No significant correlation could be observed between the CRII and the three different clusters, although a positive trend was visible and should be further analyzed using a higher number of individuals.

Data showed that the time for filtration recovery was positively correlated with the toxin concentrations (p-value=0.014, Fig. 4b), but showed significant inverse relationship with the potential of king scallop to accumulate toxins (p-value=0.002, Fig. 4d). However, King scallop toxin accumulation efficiency (TAE) did not correlate with accumulation potential (Fig. 5).

Transient inhibition in king scallop respiration rates when exposed to toxic algae A. minutum.

A significantly lower respiration rate was observed on day 3 at the beginning of A. minutum exposure compared to T-iso/C.muelleri feeding days (days1&2), in correlation to clearance rate results.

However, on day 4, in presence of A. minutum, the respiration rate came back to its initial level (observed in presence of non-toxic algae during the first 2 days of experiment) (p-value<0.04 ; figure 6a&b). On days 1&2, in presence of non-toxic algae, the respiration rate was not significantly different for the 3 toxin accumulation potential clusters. Similarly, all 3 clusters of king scallops displayed decreased respiration activity on day 3 right after contact with A. minutum (less than 4h), and recovered on day 4 (Fig. 6b).

Proteomic analyzes: differentially accumulated protein spots between king scallops exposed and non-exposed to A. minutum.

In order to complete the above ecophysiological data, the proteomic response was studied by comparing TC-NA (n=7) and TC-A groups (n=7). In total, 13 protein spots were found to be differentially accumulated in the 2-DE gels between A. minutum exposed and non-exposed king scallop mantle tissues (Fig. 7). Twelve of the 13 differentially regulated protein spots were successfully identified. They corresponded to 6 different proteins (Table 1). Over the 6 identified proteins, 2 of them displayed higher accumulation levels and 4 were less accumulated in exposed ). This may correspond to different phosphorylation states of the proteins (Fig. 7).

Discussion

In the present study, both ecophysiological and proteomic approaches were used to study the ecotoxicological response of juvenile king scallops, P. maximus, to the toxic microalgae A. minutum.

High inter-individual variability in toxin accumulation due to feeding behavior has previously been suggested in oysters [START_REF] Bougrier | Paralytic shellfish poison accumulation yields and feeding time activity in the Pacific oyster (Crassostrea gigas) and king scallop (Pecten maximus)[END_REF][START_REF] Haberkorn | Relationship between valve activity, microalgae concentration in the water and toxin accumulation in the digestive gland of the Pacific oyster Crassostrea gigas exposed to Alexandrium minutum[END_REF][START_REF] Mat | Genetic and genotoxic impacts in the oyster Crassostrea gigas exposed to the harmful alga Alexandrium minutum[END_REF][START_REF] Pousse | Sources of paralytic shellfish toxin accumulation variability in the Pacific oyster Crassostrea gigas[END_REF]. The first aim of this study was to test whether this hypothesis could be expanded to other bivalve species such as P. maximus. Here we show that a 2-day exposure to A. minutum affects the physiology (behavior and biochemistry) of king scallops. Ecophysiological data highlight 1/ the interindividual variability in PST accumulation between individual king scallops, linked to feeding behavior; 2/ the A. minutum induced inhibition of clearance and of respiration rates, followed by a recovery; 3/ the influence of level and time for filtration recovery on toxin accumulation. Finally, proteomic data revealed that A. minutum exposure caused a differential accumulation in proteins involved in cell/tissue structure, metabolism, detoxification and immune response. First of all, we found a significant correlation between toxin accumulation in DG and the total number of A. minutum cells ingested by each king scallop. This result suggests that the cells consumed by king scallops during the 2 days of A. minutum exposure contributed to the majority of toxin accumulation measured in king scallop DG. Therefore we are confident that toxin accumulation in king scallops is not due to contaminations prior to our experiment. Furthermore, despite their common cohort origin and their identical rearing conditions, a high inter-individual variability in toxin accumulation was found in king scallop juveniles. We cannot exclude that such feature could be due to low numbers of king scallop individuals. However, a high inter-individual variability in toxin accumulation has also been observed in C. gigas under controlled laboratory conditions [START_REF] Mat | Genetic and genotoxic impacts in the oyster Crassostrea gigas exposed to the harmful alga Alexandrium minutum[END_REF][START_REF] Pousse | Sources of paralytic shellfish toxin accumulation variability in the Pacific oyster Crassostrea gigas[END_REF]. As for oysters [START_REF] Boullot | Molecular Characterization of Voltage-Gated Sodium Channels and Their Relations with Paralytic Shellfish Toxin Bioaccumulation in the Pacific Oyster Crassostrea gigas[END_REF][START_REF] Mat | Oyster transcriptome response to Alexandrium exposure is related to saxitoxin load and characterized by disrupted digestion, energy balance, and calcium and sodium signaling[END_REF][START_REF] Pousse | Sources of paralytic shellfish toxin accumulation variability in the Pacific oyster Crassostrea gigas[END_REF], the 3 different profiles in toxin accumulation potential obtained from the Ward's method showed different ecophysiological responses within tested king scallop juveniles. Furthermore, as for oysters and some other tested bivalves [START_REF] Bougrier | Paralytic shellfish poison accumulation yields and feeding time activity in the Pacific oyster (Crassostrea gigas) and king scallop (Pecten maximus)[END_REF]Contreras et al., 2012b;[START_REF] Pousse | Sources of paralytic shellfish toxin accumulation variability in the Pacific oyster Crassostrea gigas[END_REF], we found a correlation between the numbers of toxic algal cells consumed by king scallops and the final toxin concentrations in DG. Our findings also demonstrate that in king scallops, inter-individual differences in toxin accumulation are mainly due to filter-feeding behavior, as observed in several studies on oysters C. gigas [START_REF] Bougrier | Paralytic shellfish poison accumulation yields and feeding time activity in the Pacific oyster (Crassostrea gigas) and king scallop (Pecten maximus)[END_REF][START_REF] Haberkorn | Relationship between valve activity, microalgae concentration in the water and toxin accumulation in the digestive gland of the Pacific oyster Crassostrea gigas exposed to Alexandrium minutum[END_REF][START_REF] Mat | Oyster transcriptome response to Alexandrium exposure is related to saxitoxin load and characterized by disrupted digestion, energy balance, and calcium and sodium signaling[END_REF][START_REF] Pousse | Sources of paralytic shellfish toxin accumulation variability in the Pacific oyster Crassostrea gigas[END_REF]. Indeed, when exposed to A. minutum, king scallop exhibited inter-individual variability in the time for filtration recovery and the inhibition of clearance rate (CRII) affecting PST accumulation. Such filter-feeding behavior variability among king scallop individuals toward toxic algae has been observed in several bivalve species (Hégaret et al., 2009;[START_REF] Leverone | Comparative effects of the toxic dinoflagellate Karenia brevis on clearance rates in juveniles of four bivalve molluscs from Florida, USA[END_REF]. As for oysters, we could hypothesize that the high inter-individual variability found among king scallop individuals might be linked to differences in toxin sensitivity [START_REF] Pousse | Sources of paralytic shellfish toxin accumulation variability in the Pacific oyster Crassostrea gigas[END_REF]. More precisely, king scallop toxin accumulation efficiency (TAE), which is another explanatory parameter allowing to understand the inter-individual variability in toxin accumulation was analyzed. Therefore, TAE represents the balance between toxin uptake (i.e. the amount of toxins incorporated) and elimination processes and can depend on pre-ingestion selection, toxin depuration or assimilation [START_REF] Bougrier | Paralytic shellfish poison accumulation yields and feeding time activity in the Pacific oyster (Crassostrea gigas) and king scallop (Pecten maximus)[END_REF][START_REF] Mafra | Domoic acid uptake and elimination kinetics in oysters and mussels in relation to body size and anatomical distribution of toxin[END_REF][START_REF] Pousse | Sources of paralytic shellfish toxin accumulation variability in the Pacific oyster Crassostrea gigas[END_REF]. The TAE values we found for A. minutum exposed king scallops (16-59%, average: 32%) are comparable to those calculated for mussels (30-60%, [START_REF] Mafra | Domoic acid uptake and elimination kinetics in oysters and mussels in relation to body size and anatomical distribution of toxin[END_REF] and oysters (10-40%, [START_REF] Mafra | Domoic acid uptake and elimination kinetics in oysters and mussels in relation to body size and anatomical distribution of toxin[END_REF]35%, Pousse et al., 2018) when exposed to Pseudo-nitzschia or A.

minutum for at least 2 days. Species exhibiting high TAE have been suggested to be less sensitive to STX and tend to accumulate it more (For review see [START_REF] Bricelj | Paralytic Shellfish Toxins in Bivalve Molluscs: Occurrence, Transfer Kinetics, and Biotransformation[END_REF].

In contrast, this study demonstrated opposite results for P. maximus compare to C. gigas [START_REF] Pousse | Sources of paralytic shellfish toxin accumulation variability in the Pacific oyster Crassostrea gigas[END_REF]. Indeed, toxin accumulation potential was not influenced by king scallop size/weight or by TAE, which suggests differences in some of the factors influencing toxin accumulation potential among bivalve species. In king scallop, the only factor found to drive toxin accumulation is feeding behavior. Also, in our study, the animal size does not seem to be a factor influencing toxin accumulation. Whereas inter-individual differences in toxin accumulation related to body size have been observed among bivalve species, results similar to our findings have been reported for domoic acid in king scallops [START_REF] Mafra | Domoic acid uptake and elimination kinetics in oysters and mussels in relation to body size and anatomical distribution of toxin[END_REF][START_REF] Moroño | The effect of mussel size, temperature, seston volume, food quality and volume-specific toxin concentration on the uptake rate of PSP toxins by mussels (Mytilus galloprovincialis Lmk)[END_REF].

Low clearance and respiration activities of king scallops when exposed to toxic A. minutum

As soon as they were exposed to A. minutum, all tested king scallops nearly stopped their feeding activity. This reaction toward toxin producing algae has already been highlighted in some, but not all, shellfish species (Contreras et al., 2012b;Hégaret et al., 2009b;[START_REF] Leverone | Comparative effects of the toxic dinoflagellate Karenia brevis on clearance rates in juveniles of four bivalve molluscs from Florida, USA[END_REF][START_REF] Pousse | Sources of paralytic shellfish toxin accumulation variability in the Pacific oyster Crassostrea gigas[END_REF][START_REF] Seger | Uptake of Paralytic Shellfish Toxins by Blacklip Abalone (Haliotis rubra rubra Leach) from direct exposure to Alexandrium catenella microalgal cells and toxic aquaculture feed[END_REF]. Although most of the king scallops re-started filtration again within the 5h following the beginning of A. minutum exposure, filtration recovery remained very low in comparison to oysters subjected to similar conditions [START_REF] Pousse | Sources of paralytic shellfish toxin accumulation variability in the Pacific oyster Crassostrea gigas[END_REF]. This result suggests a higher sensitivity of king scallops toward this toxic algae. [START_REF] Coquereau | Short-Term Behavioural Responses of the Great Scallop Pecten maximus Exposed to the Toxic Alga Alexandrium minutum Measured by Accelerometry and Passive Acoustics[END_REF] maximus from this study, was the most sensitive species toward the toxic algae Karenia brevis [START_REF] Leverone | Comparative effects of the toxic dinoflagellate Karenia brevis on clearance rates in juveniles of four bivalve molluscs from Florida, USA[END_REF]. Modification of king scallop feeding behavior was immediate when exposed to toxic algae and this reaction was also observed in oyster [START_REF] Pousse | Sources of paralytic shellfish toxin accumulation variability in the Pacific oyster Crassostrea gigas[END_REF][START_REF] Tran | Behavioral responses of Crassostrea gigas exposed to the harmful algae Alexandrium minutum[END_REF].

Therefore, rather than a toxin induced response, the avoidance of toxic particles is the most plausible explanation, as suggested in previous studies [START_REF] Coquereau | Short-Term Behavioural Responses of the Great Scallop Pecten maximus Exposed to the Toxic Alga Alexandrium minutum Measured by Accelerometry and Passive Acoustics[END_REF][START_REF] Lassus | Paralytic shellfish poison outbreaks in the Penzé estuary: Environmental factors affecting toxin uptake in the oyster, Crassostrea gigas[END_REF][START_REF] Lassus | Effect of a continuous supply of the toxic dinoflagellate Alexandrium minutum halim on the feeding behavior of the pacific oyster (Crassostrea gigas thunberg)[END_REF][START_REF] Pousse | Sources of paralytic shellfish toxin accumulation variability in the Pacific oyster Crassostrea gigas[END_REF][START_REF] Walne | Effect of the PSP-causing dinoflagellate, Alexandrium sp. on the initial feeding response of Crassostrea gigas[END_REF].

Toxin accumulation dependence on filtration recovery duration and level

It is now well established that bivalve species, such as oysters, mussels or king scallops have different feeding patterns. Indeed, our findings showing that toxin accumulation efficiency does not influence toxin accumulation in king scallop differs from what has been previously shown in oysters [START_REF] Pousse | Sources of paralytic shellfish toxin accumulation variability in the Pacific oyster Crassostrea gigas[END_REF]. This suggests that there is no clear effects from pre-ingestion mechanisms or toxin absorption/depuration on the inter-individual variability in toxin accumulation profile in king scallop.

Our results also highlight that king scallops accumulating more toxins and displaying higher toxin accumulation potential are more likely to start filtration earlier and to exhibit a lower CRII (Fig. 5).

This suggests that the high inter-individual variability observed in toxin accumulation due to feeding behavior was driven by two factors: i) the time taken by king scallops to resume filtration after its inhibition and ii) the level of filtration recovery. Such results have recently been observed in C. gigas [START_REF] Pousse | Sources of paralytic shellfish toxin accumulation variability in the Pacific oyster Crassostrea gigas[END_REF]. Therefore, the variability in toxin accumulation due to differential behavior in reducing filtration activity could be a general mechanism in bivalves when facing toxic microalgae, although extra analyzes on other species should be performed to confirm this purpose. In addition, long-term exposure experiments would be necessary to test whether feeding behaviors are still discriminant in toxin accumulation variability. Furthermore, because environmental conditions were the same for all tested individuals, we can reject the assumption of an environmental cause. Therefore, further studies analyzing the mechanisms involved in such inter-individual variabilities, for instance genomic and/or transcriptomic analyzes associated with ecophysiological data, could highlight potential genetic profiles among bivalve species and could link genes to this specific behavior toward toxic algae. In addition, epigenetic variations could also be investigated to determine the factors and mechanisms influencing such differential behavior.

Our data show that respiration rates follow the same evolution as filtration rates until day 3, with a rapid decrease in the presence of toxic algae. However, whereas the filtration profile remains low, the overall respiration activity increases at day 4. Generally, respiration rates increase exponentially with increasing rates of assimilation [START_REF] Bayne | Effects of seston concentration on feeding, digestion and growth in the mussel Mytilus edulis[END_REF]. Comparing the present respiration rates with the feeding rates over the 4-day experiment shows that a decoupling appears to occur at day 4. Respiration rates rises have already been observed in bivalves exposed to environmental stress such as ocean acidification [START_REF] Pousse | Energetic response of Atlantic surfclam Spisula solidissima to ocean acidification[END_REF], low/high salinity [START_REF] Peteiro | Responses to salinity stress in bivalves: Evidence of ontogenetic changes in energetic physiology on Cerastoderma edule[END_REF] or toxic algae [START_REF] Li | Effects of toxic dinoflagellate Alexandrium tamarense on the energy budgets and growth of two marine bivalves[END_REF]. In the present context, one hypothesis for this increase in respiration would be that the ingestion of A. minutum triggers a metabolic response corresponding to an immune reaction toward the toxic algae and its toxins increasing king scallops respiration rate. It is for instance well known that the consumption of toxic algae by bivalves induces an inflammatory response implying hemocytes degranulation and diapedesis into the digestive tract to encapsulate toxic algae [START_REF] Galimany | Experimental exposure of the blue mussel (Mytilus edulis, L.) to the toxic dinoflagellate Alexandrium fundyense: Histopathology, immune responses, and recovery[END_REF]Hégaret et al., 2009a).

The effects of A. minutum on king scallops at the proteomic level

Cytoskeletal structure

Over the 6 successfully identified proteins, 3 of them are cell structure proteins involved in cytoskeleton composition. It is frequent to find cytoskeletal proteins differentially regulated in proteomic studies because they are highly abundant or conserved proteins among species [START_REF] Monsinjon | Proteomic applications in ecotoxicology[END_REF]. In our study, Filamin A and paramyosin were down regulated, whereas -actin was upregulated in toxin-exposed king scallops. These 3 proteins are main components of the cytoskeleton orchestrating cell shape, adhesion and motility. More specifically, filamin A cross-links to F-actin proteins, including -actin, giving to the cells a dynamic three-dimensional structure [START_REF] Nakamura | The filamins[END_REF][START_REF] Popowicz | Filamins: promiscuous organizers of the cytoskeleton[END_REF][START_REF] Spector | New anti-actin drugs in the study of the organization and function of the actin cytoskeleton[END_REF]. Therefore, modifications of cytoskeleton composition in toxin-exposed mantle cells may depict a cytoskeleton disintegration or restructuration. It is now well established that several phycotoxins display toxicity toward cytoskeleton, especially actin. Indeed, the main molecular target of several microalgal toxins is cytoskeleton. In vitro studies have demonstrated that pectonotoxin-2 was disrupting actin organization in several cell types [START_REF] Spector | New anti-actin drugs in the study of the organization and function of the actin cytoskeleton[END_REF]. Furthermore, DSP toxins such as okadaic acid have also been reported to disturb cytoskeleton dynamic and integrity in several organisms including bivalves [START_REF] Huang | Proteomic profile in Perna viridis after exposed to Prorocentrum lima, a dinoflagellate producing DSP toxins[END_REF][START_REF] Vilariño | Induction of actin cytoskeleton rearrangement by methyl okadaatecomparison with okadaic acid[END_REF][START_REF] Yoon | Dynein cleavage and microtubule accumulation in okadaic acid-treated neurons[END_REF]. Furthermore, [START_REF] Hégaret | Toxic dinoflagellates (Alexandriumfundyense and A. catenella) have minimal apparent effects on oyster hemocytes[END_REF] observed adductor-muscle paralysis in some oysters Crassostrea virginica exposed to another PST producer, Alexandrium fundyense, which could be related to cytoskeleton mis-functioning.

Muscular contraction is an important pathway related to digestion that appeared affected by toxins.

Indeed, [START_REF] Mat | Oyster transcriptome response to Alexandrium exposure is related to saxitoxin load and characterized by disrupted digestion, energy balance, and calcium and sodium signaling[END_REF] also observed over-expression of calmodulin, which regulates binding between myosin and actin in smooth muscles, whereas the muscarinic acetylcholine receptor (M3R), important in the contractile response in smooth muscle, particularly in gastrointestinal smooth muscles, was down-regulated in C. gigas with high PST loads [START_REF] Mat | Oyster transcriptome response to Alexandrium exposure is related to saxitoxin load and characterized by disrupted digestion, energy balance, and calcium and sodium signaling[END_REF].

Detoxification

Even though no correspondence could be clearly established between toxin accumulation profiles and proteomic analyzes, mass spectrometry results brought insights on the functioning of ecotoxicological response. Firstly, not surprising in toxin-challenged experiments, a protein involved in detoxification was identified. The Major Vault Protein (MVP) is the main component of ribonucleoprotein particles called vault [START_REF] Tanaka | The Structure of Rat Liver Vault at 3.5 Angstrom Resolution[END_REF]. Vaults were suggested to be involved in signaling, innate immunity and detoxification [START_REF] Berger | Vaults and the major vault protein: Novel roles in signal pathway regulation and immunity[END_REF]. In particular, MVP is considered as a major multidrug resistance protein since several studies have found high expression of MVP correlated with chemotherapy resistance [START_REF] Kickhoefer | Vaults Are Up-regulated in Multidrug-resistant Cancer Cell Lines[END_REF][START_REF] Scheper | Overexpression of a M(r) 110,000 vesicular protein in non-P-glycoprotein-mediated multidrug resistance[END_REF]. Additional studies have demonstrated that MVP could have a role in drug molecule export from nucleus to cytoplasmic vesicles for sequestration [START_REF] Herlevsen | Depletion of major vault protein increases doxorubicin sensitivity and nuclear accumulation and disrupts its sequestration in lysosomes[END_REF][START_REF] Suprenant | The major vault protein is related to the toxic anion resistance protein (TelA) family[END_REF]. Furthermore, other proteomic studies have shown an increase of MVP in mussel, Mytilus galloprovincialis, when exposed to aquatic pollutants such as Ag nanoparticles or a mixture of Cu and benzo(a)pyrene [START_REF] Gomes | Differential protein expression in mussels Mytilus galloprovincialis exposed to nano and ionic Ag[END_REF][START_REF] Maria | Impact of benzo(a)pyrene, Cu and their mixture on the proteomic response of Mytilus galloprovincialis[END_REF]. Therefore, it appears that MVP would be involved in xenobiotic detoxification. Surprisingly in our case, MVP is decreased when king scallops are exposed to PST producing A. minutum, which suggests that there might be a mechanism blocking the MVP-dependent detoxification pathway. Possibly, A. minutum could be responsible for this blockage by secretion of molecules inhibiting detoxification pathways. Similar to our results, Tomanek and Zuzow have found a lower abundance of two isoforms of VMP in Mytilus galloprovincialis subjected to temperature stress and have suggested that it could be part of an antiapoptotic response [START_REF] Tomanek | The proteomic response of the mussel congeners Mytilus galloprovincialis and M. trossulus to acute heat stress: implications for thermal tolerance limits and metabolic costs of thermal stress[END_REF], therefore corresponding to a more general stress response rather than a specific detoxification process.

Glycolysis balance

The fructose-bisphosphate aldolase is one of the major proteins involved in glycolysis and gluconeogenesis pathways. In our study we found a lower concentration of fructose-bisphosphate aldolase in king scallops exposed to A. minutum than in the control group. This result suggests a subsequent decrease of glycolysis in mantle tissue of PST challenged king scallops due to the accumulation of toxins in this tissue. Lower levels of fructose-bisphosphate aldolase have been found

in the kuruma prawn, Marsupenaeus japonicus, under stressful, hypoxic conditions [START_REF] Abe | Metabolic responses and arginine kinase expression under hypoxic stress of the kuruma prawn Marsupenaeus japonicus[END_REF].

In the same way, it is possible that glycolysis in king scallop is down-regulated when exposed to cytotoxic A. minutum. In another hand, transcriptomic studies have shown higher amounts of fructose-bisphosphate aldolase transcripts in oyster exhibiting higher toxin accumulation [START_REF] Mat | Oyster transcriptome response to Alexandrium exposure is related to saxitoxin load and characterized by disrupted digestion, energy balance, and calcium and sodium signaling[END_REF]. Here again, there might be differences in metabolic responses against toxic algae exposure within bivalves species. This opposite result could also come from differences in the experimental design (as described above) suggesting that the age of animals or the BEC could have an impact on the metabolic response.

Immune system regulation

Finally, we show that the killer cell lectin-like receptor subfamily B member 1B (KLRB1B also called NKR-P1B) is more abundantly accumulated in A. minutum exposed king scallops. The NKR-P1B is part of the receptor family regulating the cytotoxic activity of Natural Killer cells (NK). NK cells are sentinels focused on the early detection of pathogens and their inhibitory receptor NKR-P1B plays a key role in protecting healthy tissues from NK cell-mediated lysis [START_REF] Balaji | Recognition of host Clr-b by the inhibitory NKR-P1B receptor provides a basis for missing-self recognition[END_REF]. NKR-P1B is specifically expressed on the surface of NK cells in animals possessing adaptive immunity. Whereas no adaptive immunity has been described in bivalves, several studies have demonstrated that blue mussel previously exposed to toxic microalgae accumulated less PST than the ones exposed for the first time [START_REF] Shumway | The effects of the toxic dinoflagellate Protogonyaulax tamarensis on the feeding and behaviour of bivalve molluscs[END_REF]. In the same way, recent studies have suggested the existence of an immune memory in oysters [START_REF] Lafont | Transgenerational plasticity and antiviral immunity in the Pacific oyster (Crassostrea gigas) against Ostreid herpesvirus 1 (OsHV-1)[END_REF][START_REF] Lafont | Long-lasting antiviral innate immune priming in the Lophotrochozoan Pacific oyster, Crassostrea gigas[END_REF]. Furthermore, Araya and collaborators have shown the presence of NKR in hemocytes of soft-shell clams, Mya arenaria, suggesting a potential cytotoxic activity from hemocytes [START_REF] Araya | Identification and expression of immune-related genes in hemocytes of soft-shell clams, Mya arenaria, challenged with Vibrio splendidus[END_REF]. Other studies have also observed cytotoxic activity in blue mussel, Mytilus edulis, suggesting that hemocytes may act as NK cells [START_REF] Hannam | Immune modulation in the blue mussel Mytilus edulis exposed to North Sea produced water[END_REF]. Our findings that NKR-P1B is more abundant in toxic algae exposed king scallops suggest a down-regulation of the hemocyte cytotoxic activity through specific receptors. Very little is known about receptor mediated immune response and their corresponding intracellular signaling pathways in bivalves. Further research at the molecular level would allow to better characterize receptors, signaling molecules and pathways orchestrating the immune response in order to better understand the mechanisms regulating the cytotoxic activity found in bivalves.

Conclusion

Our data show for the first time that juvenile king scallops P. maximus have an important inter-individual variability in toxin accumulation driven by its feeding behavior. Our study further highlights the effects of delay and level for filtration recovery on toxin accumulation in king scallops. Furthermore, proteomic analyzes suggest an effect of toxic algae A. minutum on immune response, cytoskeleton remodeling, detoxication and metabolism of king scallops. conditions. Details on identified proteins are provided in Table 1. For each condition N=7.

Figures

  samples, as compared to non-exposed ones. The differentially expressed proteins are involved in cell or tissue structure (beta-actin, filamin A & myosin), in immune response (killer cell lectin-like receptor), energetic metabolism (fructose-bisphosphate aldolase) and detoxification (major vault protein). Several of the identified protein spots appeared as a characteristic horizontal line of spots on the 2-DE electrophoregrams (corresponding to a change in iso-electric point of proteins) as observed for -actin (spots 8, 9, 10 & 12), paramyosin (spots 2 & 11) and killer cell lectin-like receptor (spots 5 & 6

1 .

 1 Inter-individual variability in king scallops toxin accumulation depends on filter-feeding behavior when exposed to A. minutum.

Figure 1 :

 1 Figure 1: Scheme depicting the experimental design.

Figure 2 :

 2 Figure 2: Interindividual variability of toxin accumulation potential

Figure 3 :

 3 Figure 3: Toxin accumulation linked to feeding behavior. a. Individual toxin concentration in

Figure 4 :

 4 Figure 4: Toxin accumulation and accumulation potential in great scallop depend on the time

Figure 5 :

 5 Figure 5: No correlation between accumulation potential and toxin accumulation efficiency at

Figure 6 :

 6 Figure 6: Evolution of standardized respiration rates over the 4 experimental days. a. Graph

Figure 7 :

 7 Figure 7: Representative 2-DE gels (pH 4-7, SDS-PAGE 12%) of Pecten maximus mantle

  Figure 2

  demonstrated indeed that a 2-hour exposure of king scallop to A. minutum caused an increase in valve movements, especially closure and expulsion. Similarly,[START_REF] Borcier | Effects of bioactive extracellular compounds and paralytic shellfish toxins produced by Alexandrium minutum on growth and behaviour of juvenile great scallops Pecten maximus[END_REF] recorded less filtration and shell growth after immediate exposure to A. minutum. Furthermore,[START_REF] Leverone | Comparative effects of the toxic dinoflagellate Karenia brevis on clearance rates in juveniles of four bivalve molluscs from Florida, USA[END_REF] have shown that among all tested bivalves including eastern oyster (C. virginica), northern quahog (M. mercenaria) and green mussel (P. viridis), the bay scallop Argopecten irradians, which is the closest species to king scallop P.

Table 1 : MS/MS identified proteins differentially accumulated in A. minutum exposed scallops compared to control non-exposed group.

 1 List of Pecten maximus mantle proteins differentially expressed between the 2 conditions (TC-A/TC-NA) and identified by MS/MS. Values correspond to the Log2 Fold Change (FC) for the normalized volumes of spots between scallops in toxic algae (TC-A) and in no algae (TC-NA) conditions. Protein abundance changed significantly between the 2 943 conditions (moderate t-test paired-comparison, fdr < 0.1). 944

	Name	MW	pHi # peptides Peptide sequences	FC	Spot #
					SFFLLPGER
					LLHADQEIR
	Major vault protein	96 kDa 5.7	8	TFKDDFGVVR KEVVIDETIR AIPLDENEGIYVR	-0.6
					TAGDEWLFEGPGTYIPR
					SVQLAIEITTNSQEATAR
					IPPYYYLHVLDQNLNVTR
					ATVLCLSR
					ATEQVLAFTYK
					KPWPLTFSFGR
	Fructose-bisphosphate	39 kDa 6.0	10	GILAADESTGSVGKR FAPINVENTEENR	-0.7
	aldolase				IWQGKDENVAAGQK FAPINVENTEENRR
					ETPSYQAMLENANVLAR
					VDKGVVPLMGTDNECTTQGLDGLSER
					TVPPAVAGVTFLSGGQSEEDASINLNAINTDSGR
					GYSFTTTAER
	Beta-actin	42 kDa 5.3	6	VAPEEHPVLLTEAPLNPK IWHHTFYNELR SYELPDGQVITIGNER	1.2
					DLYANTVLSGGTTMFPGIADR
					TTGIVLDSGDGVTHTVPIYEGYALPHAILR
					GYSFTTTAER
	Beta-actin	42 kDa		6	VAPEEHPVLLTEAPLNPK IWHHTFYNELR SYELPDGQVITIGNER	0.9	12
					DLYANTVLSGGTTMFPGIADR
					TTGIVLDSGDGVTHTVPIYEGYALPHAILR
					GYSFTTTAER
	Beta-actin	42 kDa		6	IWHHTFYNELR SYELPDGQVITIGNER	0.5
					VAPEEHPVLLTEAPLNPK
					DLYANTVLSGGTTMFPGIADR
					TTGIVLDSGDGVTHTVPIYEGYALPHAILR
					GYSFTTTAER
	Beta-actin	42 kDa		6	IWHHTFYNELR SYELPDGQVITIGNER	0.5
					VAPEEHPVLLTEAPLNPK
					DLYANTVLSGGTTMFPGIADR
					TTGIVLDSGDGVTHTVPIYEGYALPHAILR
	Beta-actin	42 kDa		3	GYSFTTTAER SYELPDGQVITIGNER	0.6	10
					VAPEEHPVLLTEAPLNPK
					LEEAEAFALR
					VSLQAEVEDLR
	Paramyosin	99 kDa 5.6	7	SQLQVTLDDFKR IRDLEGELEAEQR	-1.1
					LSDELRQEQENYK
					LSEIQIQVNVLVNDKR
					DLELASAQYEAQESSTR
					LEEAEAFALR
					VSLQAEVEDLR
					YEEESEAASILR
	Paramyosin	99 kDa		10	SQLQVTLDDFKR IRDLEGELEAEQR LSDELRQEQENYK	-0.9	11
					LSEIQIQVNVLVNDKR
					DLELASAQYEAQESSTR
					QNLQVQLSALQSDYDNLNAR
					LTQENFDLQHQVQELDAANAGLAK
					VYVTPSIGDAR
	Filamin A	282 kDa 5.5	5	GEINQPCEFNIYTR YAGSYVAGSPFK FNDEHIPQSPYR	-0.9
					VTYKPTEPGNYVINIK
	Killer cell lectin-like receptor subfamily B member 1B	31 kDa 4.5	6	TEWSTAINR LVEFQTNEEAQFVMR TLSGFENEIR VSTSDIVYTGR	0.7
					TWGSGEPDGGTQTCGCTR
					NDAYVWVFLSNDEPVDTAVR
					TEWSTAINR
	Killer cell lectin-like receptor subfamily B member 1B	31 kDa		7	TLSGFENEIR FEESSLTSEVVR VSTSDIVYTGR ILKNEQAELR	0.5
					LVEFQTNEEAQFVMR
					TWGSGEPDGGTQTCGCTR

Acknowledgements

This project was supported by the National Research Agency ANR CESA (ACCUTOX project ANR-13-CESA-0019). Coraline Chapperon was supported by a postdoctoral fellowship from the Conseil Départemental du Finistère. The authors gratefully acknowledge Isabelle Quéau and Adeline Bidault who provided technical help for ecophysiological and proteomic experiments.

Conflict of Interest Disclosures

The authors declare no competing financial interests. 24.10 3 cells mL -1 24.10 3 cells mL -1 40.10 3 cells mL -1 560 cells mL -1 440 cells mL -1