Mohamad Mansouri
email: mohamad.mansouri@eurecom.fr

Beyza Bozdemir
email: beyza.bozdemir@eurecom.fr

Melek Önen
email: melek.onen@eurecom.fr

Orhan Ermis
email: orhan.ermis@eurecom.fr

PAC: Privacy-Preserving Arrhythmia Classification with Neural Networks

Keywords: privacy, neural networks, arrhythmia classification

teaching and research institutions in France or abroad, or from public or private research centers.

Introduction

Artificial intelligence and machine learning have gained a renewed popularity thanks to the recent advances in information technology such as the Internet of Things that help collect, share and process data, easily. This powerful technology helps make better decisions and accurate predictions in many domains including finance, healthcare, etc. In particular, Neural Networks (NN) can support pharmacists and doctors to analyse patients' data and quickly diagnose a particular disease such as heart arrhythmia. Nowadays, this disease can be detected at early stages with the help of smart wearable devices such as Apple Watch 41 that can record electric heart activities using Electro-Cardiograms (ECG) data. Nevertheless, we are experiencing severe data breaches and these cause crucial damages. A recent research2 concludes that in 2018, the global average cost of a data breach is 3.86 million dollars and the healthcare sector is the first sector facing huge costs. ECG data is considered as very sensitive. Therefore, there is an urgent need for tools enabling the protection of such data while still being able to launch predictive analytics and hence improve individuals' lives. These tools will also help stakeholders be compliant with the General Data Protection Regulations (GDPR) 3 .

In this work, we aim at addressing privacy concerns raised by the analysis of the ECG data for arrhythmia classification. Our goal is to enable service providers (data processors) perform classification without discovering the input (the ECG data). On the other hand, we also look into the problem from the service providers' point of view as they care about keeping the design of their services confidential from the users (data subjects or data controllers). Users using these systems/solutions should not be able to discover the details about the underlying system (such as the Neural Network model). The challenge often manifests as a choice between the privacy of the user and the secrecy of the system parameters. We propose to reconcile both parties, namely the service providers and the users and combine the use of neural networks with secure two-party computation (2PC). Since 2PC protocols cannot efficiently support all kinds of operations, we propose to revisit the underlying neural network operations and design a new, customized neural network model that can be executed to classify arrhythmia accurately, and this, without disclosing neither the input ECG data to the service provider nor the neural network parameters to the users.

With this aim, we reduce the input size of neural network by employing Principal Component Analysis (PCA) [START_REF] Castells | Principal Component Analysis in ECG Signal Processing[END_REF]. The proposed methodology is illustrated with a case study whereby some arrhythmia dataset from the PhysioBank database 4 is used. With this dataset, we show that the newly designed model only involves 2 layers with 54 hidden neurons. The resulting model is implemented in a realistic environment and uses the ABY framework [START_REF] Demmler | ABY -A Framework for Efficient Mixed-Protocol Secure Two-Party Computation[END_REF] for 2PC. Experimental results show that the most optimal resulting model reaches an accuracy level of 96.34%. Our solution helps predict the class of one heartbeat in 1 second, approximately. In order to improve the performance of the solution even further, we propose to make predictions in batches and thus help the analyser (the doctor) receive the prediction of a set of heartbeats for a given period (e.g., 30s). We show that by using the Single Instruction Multiple Data (SIMD) packing method offered by ABY, the computational overhead is significantly reduced.

In the next section, we introduce the problem of arrhythmia classification and identify the main challenges to ensure the privacy of the ECG data at the same time. Section 3 focuses on the case study and presents the newly proposed privacy-preserving variant of the neural network that we name PAC together with experimental results. In section 4, we describe the additional optimisation method which consists of executing predictions in batches. Finally, we review the state of the art in Section 5.

Problem Statement

As defined in [START_REF] Kass | Secure Multiparty Computation for Privacy-Preserving Data Mining[END_REF], cardiac arrhythmias are abnormal heart rhythms, which cause the heart to beat too fast (tachycardia) or too slow (bradycardia) and to pump blood less effectively. These irregularities can be detected and classified by an-alyzing the Electro-Cardiogram (ECG) signals of a heart. Doctors classify arrhythmia to several types according to such behaviors of the heart.

In this work, we focus on the classification of heartbeats extracted from ECG signals into different classes of arrhythmia using machine learning techniques. In order to design an efficient arrhythmia classifier, we propose to use Neural Networks (NN). ECG signals representing patients' heartbeats can be considered as sensitive information. Thus, we aim at finding a solution where a service provider can execute the classification model without leaking and even discovering information about the input data. On the other hand, the classification model can also be considered as confidential against users, namely parties who will send their queries for classification. This model itself can also have some business value and therefore be protected. For this respect, we assume that the model should be unknown to the parties sending queries.

Performing some operations over data while these are kept confidential requires the use of advanced cryptographic tools such as homomorphic encryption [START_REF] Gentry | Fully homomorphic encryption using ideal lattices[END_REF][START_REF] Gentry | A Fully Homomorphic Encryption Scheme[END_REF][START_REF] Paillier | Public-Key Cryptosystems Based on Composite Degree Residuosity Classes[END_REF][START_REF] Brakerski | Leveled) fully homomorphic encryption without bootstrapping[END_REF] or secure multi-party computation [23,[START_REF] Malkhi | Fairplay -Secure Two-Party Computation System[END_REF]. While the integration of such tools offers better privacy guarantees, they unfortunately introduce some non-negligible overhead in terms of computation and communication. Furthermore, these tools may not always be compatible with the complex NN operations. Hence, we propose to follow a privacy-by-design approach and consider privacy requirements at the design phase of the neural network. We have identified the following three main challenges when building a neural network model customized for the use of privacy enhancing technologies:

-Large size of the NN: The size of the neural network directly depends on the size of the input and output vectors, the number of layers, and the number of neurons in the model. These parameters have a significant impact on the complexity of the model. Hence, the size of the neural network has to be optimized when designing privacy-preserving variants. Such an optimization, on the other hand, should not have an impact on the actual accuracy of the model. -Complex NN operations: A neural network involves various operations executed by each neuron during the classification phase. These include sophisticated operations such as sigmoid or hyperbolic tangent that may not be easily and efficiently supported by existing cryptographic tools. Hence, the underlying operations should be optimized and sometimes even transformed when designing the privacy-preserving variant of the neural network classification model. -Real numbers instead of integers: Most of the operations in the neural network are executed over real numbers whereas cryptographic tools usually support integers. Therefore, there is a need for either supporting floating point numbers or approximating them to integers. Such an approximation should nevertheless not have a significant impact on the accuracy of the model.

To summarize, when designing a neural network model customized for the use of privacy enhancing technologies, one should address the trade-off between privacy, performance, and accuracy. The dedicated model should involve an optimized number of "simple" operations that advanced cryptographic tools can support while reaching a good accuracy level.

Privacy-preserving Neural Network Arrhythmia

Classifier -A case study with PhysioBank

In this section, we describe the privacy-by-design approach in details and use a publicly available database, namely the PhysioBank database, to build a concrete model as an example and evaluate the accuracy and performance of the newly developed model.

Solution Idea

In order to address the three main challenges identified in the previous section, we propose to build a neural network model from scratch. This approach is illustrated with a case study where a publicly available arrhythmia dataset is used.

The design of the NN model is combined with secure two-party computation (2PC) which enables two parties to jointly compute a function over their private inputs without revealing any extra information about these inputs than what is leaked by the result of the computation. This setting is well motivated and captures many different applications to ensure privacy protection 5 . We propose to use ABY [START_REF] Demmler | ABY -A Framework for Efficient Mixed-Protocol Secure Two-Party Computation[END_REF], a mixed-protocol framework that efficiently combines the use of Arithmetic shares, Boolean shares, and Yao's garbled circuits.

As for the design of the appropriate model, we propose to define a small neural network with two fully connected (FC) layers, one activation layer, and one softmax layer. This architecture seems sufficient to achieve a good accuracy level (see next section). The number of intermediate neurons can be optimized based on several simulations evaluating the accuracy of a model for each case. Additionally, to reduce the number of input neurons, we propose to apply Principal Component Analysis (PCA) [START_REF] Castells | Principal Component Analysis in ECG Signal Processing[END_REF] and filter out the most significant inputs. Furthermore, because of the complexity of the activation functions, we propose to use the square function, only. This operation can be supported by 2PC more efficiently. The design of the new model customized for the use of 2PC should not result in a significant decrease on the accuracy of the classification.

All operations within the newly designed neural network model will be executed through a client-server system, whereby the client who could be considered as the patient (Data Subject) or the hospital (Data Controller) holds the input vector and the server (Data Processor) holds the model's parameters. The underlying protocol should therefore ensure (i) the secrecy of the input supplied by the client which means that the client would like to get the prediction results without leaking any information about the heartbeat signal; (ii) the secrecy of the model parameters supplied by the server while assuming that the client knows the architecture of the model, only; (iii) the secrecy of the prediction results with respect to the server.

The optimized neural network model

In order to ensure data privacy during the arrhythmia classification phase, a dedicated neural network model should be computed. Because the use of cryptographic primitives adds a non-negligible overhead, the complexity of the model should be optimized as much as possible. Hence, the primary goal while building a new prediction model, is to optimize the number of neurons at each layer while keeping an adequate accuracy level. As mentioned in the previous section, the cryptographic tool that we chose to ensure data privacy is 2PC [23] whereby the client holds the input vector, and the server has the NN prediction model which consists of the weight matrices and bias vectors. Similarly to [START_REF] Dowlin | Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy[END_REF] and [START_REF] Chabanne | Privacy-Preserving Classification on Deep Neural Networks[END_REF], nonlinear operations such as the activation functions should be replaced with more efficient operations such as low degree polynomials. In this section, we describe our approach with a case study using the MIT-BIH arrhythmia dataset from the PhysioBank database. The resulting neural network model is presented with an incremental approach. We first extract heartbeats from the Electro-Cardiogram (ECG) signals. Each heartbeat is composed of 180 samples with 90 samples before the R-peak, 1 sample for the R-peak, and, 89 samples after the R-peak. Once heartbeats were extracted, we have performed various filtering operations to create an appropriate dataset to build the neural network model. The PhysioBank database is shown in Table 1 and contains 23 different annotations for the extracted heartbeats. We have decided to only consider 16 out of 23 annotations representing meaningful arrhythmia classes that have significant number of instances in the dataset. Secondly, we realized that normal beats were dominating the dataset (67.3%) and hence resulting in an unbalanced dataset for model training purposes. We have reduced the number of normal beats in order for the model to predict anomalies more accurately while keeping this number sufficiently large so that it reflects reality. Moreover, we have used the over-sampling method to enforce the learning of low frequent classes such as class "e". Table 1 provides details about the final dataset we are actually using. We have removed some heartbeats from the dataset since the classes of V, ", -, Q do not represent any arrhythmia class, and also [, ], S are rare. This dataset is further split such that 80% of the heartbeats are used to train the network and 20% of the heartbeats are used as a test dataset. We propose a model with 2 fully connected (FC) layers, one activation function, and a final softmax function that would provide the resulting arrhythmia class.

We now aim at optimizing the number of neurons in each layer. The number of neurons in the output layer corresponds to the number of arrhythmia classes. As shown in Table 1, we decide to take the first 16 out of the 23 arrhythmia classes in the studied dataset. Hence, the number of neurons in the output layer is set to 16.

In order to choose the appropriate number of neurons within the hidden FC layer, we have evaluated the accuracy of models on the validation dataset whereby the number of neurons varies from 2 to 100. We not only evaluate the overall accuracy but compute the confusion matrix that indicates the accuracy with respect to each arrhythmia class. We observe that 38 is a good choice as this implies less complexity in the model as well as its corresponding confusion matrix shows better fairness toward less frequent classes (see [START_REF] Mansouri | Pac: Privacy-preserving arrhythmia classification with neural networks[END_REF] for more details). The accuracy of our model is 96.51%. We represent the model's performance on the test dataset with the confusion matrix as illustrated in Figure 1. The other parameter that affects the complexity of the NN model is the size of the input vector. This inherently reduces the dimension of the first matrix used for the FC layer. The main tool to adequately reduce the number of neurons of the input layer is the principal component analysis technique (PCA) [START_REF] Jolliffe | Principal Component Analysis[END_REF]. PCA uses orthogonal linear transformations to find a projection of all input data (ECG heartbeats) into k dimensions which are defined as the principal components. Hence, PCA outputs the k features with the largest variance. The first k eigenvectors of the covariance matrix (of the dataset) are the target dimensions. The efficiency of using PCA for the ECG analysis domain has also been proved in [START_REF] Castells | Principal Component Analysis in ECG Signal Processing[END_REF]. It also helps reduce the noise in the ECG signals and hence improve the accuracy of the model. This is due to the fact that dimensions with low variance noise are automatically discarded. To identify the appropriate number of eigenvalues we run a simulation with 100 hidden neurons and change the value of the input size n starting from n = 180. The same simulation is executed using the the square operation as for the activation function. From this analysis, we choose to set the input size to 16, mainly because the resulting prediction model provides good accuracy with acceptable complexity. Hence, the number of neurons of the input layer is now set to 16 (please see [START_REF] Mansouri | Pac: Privacy-preserving arrhythmia classification with neural networks[END_REF] for more details). The resulting model: To summarize, the developed model, compatible with the use of 2PC, consists of 2-FC layer involving matrix multiplication, one activation layer implementing a square function and one softmax function. The architecture of the proposed neural network model is shown in Figure 2. The first layer consists of a fully connected layer and its main operations are: Y h = X T .W h + B h . In this equation, X represents the input vector (PCA transform of a heartbeat). This input vector X of size 16 is multiplied with the weight matrix of the hidden layer, W h , of size 16 × 38. This intermediate result is further added to the bias vector of the hidden layer, B h , of size 38. The resulting vector Y h becomes the input of the activation layer which consists of computing the square of each element of Y h . The resulting vector Y h is the final output of the hidden layer. This vector further becomes the input for another FC layer which is defined as:

Y = Y T h .W o + B o .
W o and B o denote the weight matrix and the bias vector, respectively. The output is the vector Y of size 16. Finally, a softmax function is executed over the components y j of Y . The aim of this function is to mainly identify the actual predicted class (the one that shows the greatest probability). The result y is the index of one of the 16 arrhythmia classes.

In total, the prediction phase consists of: Discussion on Principle Component Analysis: As previously mentioned, the NN model is revised and designed from scratch in order to be compatible with 2PC and remain as efficient as possible. To improve the performance of the classification phase, the size of the input is reduced using the Principle component analysis (PCA) method. PCA is a statistical method which identifies patterns, highlights similarity between elements within the dataset and finally reduces the dimension of the feature space. More formally, let S be a dataset and x i an element of it with dimension d. The first step of PCA (executed by the server) consists of computing the mean µ of all the elements x i . Then, the covariance matrix A of S is computed. The eigenvectors and corresponding eigenvalues of matrix A are further evaluated and the first k eigenvectors with the largest eigenvalues are selected. In this particular case, where the dataset consists of 44048 heartbeats of 180 samples, the server obtains he 180×16 matrix of the most relevant eigenvectors. This matrix along with the vector µ is sent to the client who reduces the dimension of its input to 16 by first by subtracting his signal with the mean vector and further multiplying the result with the received matrix.

The use of the PCA transformation at the client side can result in some information leakage. The leakage resulting from the use of PCA mainly are the mean of the dataset and the 180×16 covariance matrix. We argue that the mean of all the signals in the training dataset does not carry any valuable information since the labels of the training signals are not included in the computation of the mean. On the other hand, the matrix of 16 eigenvectors does not correspond to the entire matrix of eigenvectors. Additionally, without the knowledge of the eigenvalues there exist an infinite number of inverse transformations back to the original covariance matrix. Therefore, one cannot discover the training dataset and hence the model from this reduced and transformed matrix.

On the other hand, we can also choose not to leak such information while designing the privacy-preserving NN classification. In this case, we either do not use PCA (which causes high bandwidth and computational cost) or include the PCA steps to the 2PC solution (additional overhead but less costly at the first FC layer). Accordingly, in this work, we propose the following three design approaches for PAC (as shown in Figure 3) and evaluate the performance for each of them:

-Model 1: PCA is not integrated to 2PC (original and most efficient solution implies some leakage), -Model 2: PCA is integrated to 2PC (less efficient but no leakage), -Model 3: PCA is not used (worse performance but no leakage).

SIMD circuits: In addition to reducing the size of the neural network and decreasing the cost of the underlying operations, we also take advantage of Single Instruction Multiple Data (SIMD) circuits which allow the packing of multiple data elements and the execution of operations in parallel. We use this technique to perform the matrix multiplications and additions more efficiently. In more details, since the number of hidden neurons is 38, the client creates the SIMD version of its input X (of size 16) repeated 38 times (i.e. the size of the share is 38 * 16 = 608). Similarly, the server creates a SIMD version of the weight matrices W h (of size 16x38) and W o (of size 38x16) by flattening them to two vectors of 608 elements. Once these versions obtained, one single SIMD multiplication gate can be used to perform element-wise multiplication. The server also creates a SIMD version of the bias vectors and adds them to the vector resulting from the previous SIMD matrix multiplication. The square activation function can also be computed using one SIMD multiplication gate. To implement the argmax function, we transform the SIMD share of the previous layer to a non-SIMD share (i.e., the SIMD share is composed of 1 wire holding all the 16 values of Y while the non-SIMD share is composed of 16 wires each wire will hold one value of Y ).

Moreover, we propose a secure computation of PCA in Model 2. As previously described, the computation of the PCA can eventually introduce a limited leakage of the training dataset. Therefore, in Model 2, we propose to introduce the computation of the PCA vector into the 2PC. The two parties will first collaboratively compute PCA of the signal (using the confidential mean and the 16 eigenvectors) and further perform classification similarly to Model 1. This PCA computation layer adds 181 SIMD addition gates and 1 SIMD multiplication gate.

PAC: Detailed description

As previously mentioned, we propose to use 2PC to obtain the privacy-preserving variant of the arrhythmia prediction model, i.e., PAC. Since the underlying model involves several different operations (such as additions, multiplications and comparisons), we propose to use the ABY framework which supports all basic operations in a flexible manner using Arithmetic, Boolean or Yao's circuits. ABY supports Single Instruction Multiple Data (SIMD) gates. Furthermore, the current ABY implementation6 also supports floating point representation if Boolean circuits are used. Hence, we first implement the privacy-preserving model using Boolean circuits. Nevertheless, floating point representation and the use of Boolean circuits appear to be inefficient. We therefore propose some improvements using fixed point representations that may result in some truncations of the inputs or intermediate values in the circuit. Additionally, since operations executed over Boolean shares are much more expensive than those executed over arithmetic shares, we propose to replace Boolean gates with arithmetic gates as much as possible, hence improve the system. Due to the space limitation, we have not included the model using Boolean circuits and refer to [START_REF] Mansouri | Pac: Privacy-preserving arrhythmia classification with neural networks[END_REF] for more details.

As the multiplication of two fixed-point numbers can yield numbers with a number of bits higher than the two initial numbers, hence to an overflow, these numbers need to be truncated and/or rounded in order to ensure that all intermediate values can be represented in 64 bits. We mainly propose two truncation methods: The first method consists of applying truncation at intermediate stages in the circuit and hence try to continuously keep a good accuracy level whereas the second method truncates the inputs before the prediction process starts, only. In this section, we only present the second truncation method since it shows better performance gains. The description of the first truncation method is given in the full version of the paper. In this truncation approach, only the inputs to the circuits are truncated and this before the actual execution of the circuit. In order to avoid overflows, we multiply X, W o and W h by 10 3 , B h by 10 6 and B o by 10 15 and truncate the fractional part afterwards. We observe that this method is as safe as the maximum number a signed 64-bit integer variable can take is 9.223372037 × 10 18 and the upper bound for the values of Y is 9, 223 and the lower bound is -9, 223. We observe that the risk of overflow is very low.

Thanks to this new approach, the actual circuit only consists of arithmetic gates except at the last stage where an argmax operation needs to be executed. We have tested the accuracy of the new model using the test dataset and we have achieved an accuracy of 96.34% which is very close to the accuracy of the original model (96.51%). The confusion matrix of the new model shows the same accuracies as presented in the original model (see Figure 1).

To evaluate the computational and communication overhead of the model in a real setting, experiments were carried out by a computer which has four 3.60GHz Intel Core i7-7700 processors, 32GB of RAM acting as the server and a laptop which has two 1.70GHz Intel Core i5-4210U processors, 4 GB of RAM acting as the client. On the other hand, the client and the server communicate through a local area network (LAN). The client is connected to the LAN through a wireless access point. A simulation of the bandwidth and the latency of the connection between the client and the server showed the values of 39Mbit/sec for the bandwidth and 3.36 ms for the latency. Furthermore, we run the client and the server on two separate processes communicating through the localhost of the same computer, specifically the one with the four 3.60GHz Intel Cores to evaluate the performance of the model without considering the limitation of the bandwidth. In ABY, we set the security parameter to 128 bits.

Table 2 shows the performance results in terms of prediction time and bandwidth consumption for the original Boolean circuits as well as for the 3 models implementing arithmetic circuits, mainly. Moreover, we have repeated all evaluations on a local set-up, i.e., the localhost on one machine, to give an insight about the overhead incurred by the low bandwidth (please see the full version for details). The prediction time of one heartbeat is measured as the total time adding to the BaseOT time.

We have also evaluated the performance of the prediction model without using any privacy enhancing technologies and making use of Tensorflow7 . It takes 7.29ms to predict one heartbeat in cleartext while this value becomes 117,859ms with PAC (without any truncation). Nevertheless, from Table 2, we observe some significant performance gain in terms of computational and communication cost by employing the truncation method. Compared to the model built with Boolean circuits, the Total time with the second truncation method is reduced by a factor of 108. Model 2 still provides better results than Model 3 of which is built without the use of the PCA method: The time and bandwidth consumption of Model 3 is larger with a factor of 1.8 than Model 2. We have also implemented Model 2 without the argmax layer (the output is a vector of 16-value where its argmax can be computed locally by the client) to show the size and performance of the arithmetic circuit without introducing any Boolean gates. Finally, the time performance presented in the table is highly affected by the low bandwidth of the communication channel between the client and the server. The time performance difference can be easily seen by comparing the results in Table 2 with the one in Table 5 in the full paper which represent the result of when the client and the server resides on the same machine and so no bandwidth limitation can affect the result. We observe that the time consumption of the model evaluated locally on the same machine can reach 39.785 ms which is 27 times less than the remotely evaluated model. This limitation comes from the core of the 2PC protocol which suffers from high bandwidth consumption. We believe this problem can be easily solved by a decent connection between the client and the server.

PAC in batches

In this section, we propose to perform arrhythmia predictions in batches, namely, with several heartbeat inputs. This can be justified as the classification of a single heartbeat may not be sufficient to diagnose the disease for a patient and the doctor may need to receive the classification of the n consecutive heartbeats.

We also realize that, the online time becomes relatively short when the proposed design is used and that 21.2% (82.2% in case of evaluation on the same machine) of the Total time corresponds to the BaseOT phase. This phase is only processed once the two parties initiate the protocol. Hence, the overall time may again be decreased by performing predictions in batches (i.e., performing prediction of many ECG signals at once) using the SIMD technique, once again.

Indeed, the client may first record N signals, prepare the inputs, and further store them in a matrix S(N ) (see the full paper [START_REF] Mansouri | Pac: Privacy-preserving arrhythmia classification with neural networks[END_REF]). This matrix is further flattened into a vector of 16.N elements. Similarly, the server transforms the weight matrix vectors The Arithmetic circuit of the NN model is implemented as described in Figure 4 whereby only the structure of the inputs and output differ (ie. SIMD vectors result in larger size). The number of SIMD multiplications does not change since all values are regrouped in one SIMD share and the multiplication is further performed. The number of SIMD additions also remains the same.

Finally, the Boolean circuit which represents the argmax layer is also performed with SIMD gates. Values of each class in each individual output in the vector Y (N ) are grouped in separate SIMD vectors. The same comparison and multiplexers gates described before are used but this time the inputs are SIMD shares. The output of the Boolean circuit is the vector y(N ) whereby each value represents the index of the class of the corresponding individual. We have run experiments with different batch sizes using the local and remote setups. The results are given in Table 3 for the remote setup (see Table 6 in the full paper [START_REF] Mansouri | Pac: Privacy-preserving arrhythmia classification with neural networks[END_REF] for the local setup). We can observe that the number of gates y slightly increases with respect to the number of heartbeats, which is much better than performing prediction on signals individually which will cost y = 34329N gates. Also, the depth is constant regardless of the number of input heartbeats the model predicts.

We observe that the Total time still increases linearly with the number of signals but with a much better rate. More specifically, the batches model can decrease the time consumption with a percentage of 27% (70% in case of local evaluation) compared to performing prediction on signals one by one which takes t = 1082.8N ms (t = 39.7N in case of local evaluation). Finally, the BaseOT time is approximately 290ms and remains constant regardless to the number of input signals the model predicts. This is, again, much better than performing prediction on signals, one by one, where the BaseOT time bt costs bt = 290 ms. Table 3 also shows that the bandwidth grows linear with the number of signals.

To summarize, prediction in batches does improve performance in terms of computational cost but the size of the batch should be bounded according to bandwidth limitations.

Related Work

Existing privacy-preserving neural network classification techniques mostly focus on Convolutional Neural Networks (CNN) with the goal of classifying images and achieve data privacy using homomorphic encryption [START_REF] Dowlin | Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy[END_REF][START_REF] Chabanne | Privacy-Preserving Classification on Deep Neural Networks[END_REF][START_REF] Hesamifard | CryptoDL: Deep Neural Networks over Encrypted Data[END_REF][START_REF] Ibarrondo | FHE-compatible Batch Normalization for Privacy Preserving Deep Learning[END_REF][START_REF] Bourse | Fast homomorphic evaluation of deep discretized neural networks[END_REF][START_REF] Sanyal | TAPAS: tricks to accelerate (encrypted) prediction as a service[END_REF][START_REF] Hesamifard | Privacy-preserving machine learning as a service[END_REF][START_REF] Jiang | Secure outsourced matrix computation and application to neural networks[END_REF][START_REF] Chou | Faster cryptonets: Leveraging sparsity for real-world encrypted inference[END_REF], secure multi-party computation [START_REF] Mohassel | SecureML: A System for Scalable Privacy-Preserving Machine Learning[END_REF][START_REF] Liu | Oblivious Neural Network Predictions via MiniONN Transformations[END_REF][START_REF] Mohassel | Aby 3 : A mixed protocol framework for machine learning[END_REF][START_REF] Rouhani | Deepsecure: scalable provably-secure deep learning[END_REF][START_REF] Riazi | Chameleon: A hybrid secure computation framework for machine learning applications[END_REF][START_REF] Dahl | Private machine learning in tensorflow using secure computation[END_REF][START_REF] Ball | Garbled neural networks are practical[END_REF][START_REF] Chandran | Ezpc: Programmable, efficient, and scalable secure two-party computation for machine learning[END_REF][START_REF] Wagh | Securenn: Efficient and private neural network training[END_REF], both techniques [START_REF] Barni | A privacy-preserving protocol for neural-networkbased computation[END_REF][START_REF] Orlandi | Oblivious neural network computing via homomorphic encryption[END_REF][START_REF] Juvekar | GAZELLE: A low latency framework for secure neural network inference[END_REF], or, trusted hardware [START_REF] Ohrimenko | Oblivious multi-party machine learning on trusted processors[END_REF][START_REF] Hunt | Chiron: Privacypreserving machine learning as a service[END_REF][START_REF] Tramèr | Slalom: Fast, verifiable and private execution of neural networks in trusted hardware[END_REF]. Similarly to our work, these methods aim at reducing the complexity of the underlying neural networks. However, the application scenarios (image classification with CNNs) significantly differ from ours (arrhythmia classification) and their models are more complex and not easily comparable to our solutions.

The closest study to our work is [START_REF] Barni | Privacypreserving ecg classification with branching programs and neural networks[END_REF] which specifically focuses on privacypreserving arrhythmia classification with neural networks. Authors in [START_REF] Barni | Privacypreserving ecg classification with branching programs and neural networks[END_REF] use 2PC combined with a partially homomorphic encryption [START_REF] Paillier | Public-Key Cryptosystems Based on Composite Degree Residuosity Classes[END_REF]. Their protocol is executed between the client who protects the input vector and the server who stores the model. Similarly to our model, their neural network is also composed of two layers: one hidden layer with SATLIN (a symmetric saturating linear) as an activation function and the output layer implementing the argmax function to decide on the arrhythmia class. Although this work uses the same dataset from the PhysioBank datasets as we do, authors achieve an accuracy of 86.3% whereas PAC reaches 96.43% for the classification of each heartbeat. Furthermore, while our model seems slightly more complex than the one in [START_REF] Barni | Privacypreserving ecg classification with branching programs and neural networks[END_REF] (38+16=54 neurons instead of 6+6=12 neurons), it shows better accuracy and performance results: The communication channel used in [START_REF] Barni | Privacypreserving ecg classification with branching programs and neural networks[END_REF] is set to 1 Gbit/sec which is much larger than our bandwidth estimation (39 Mbit/sec). Authors evaluated the timing complexity to be about 7 seconds whereas our solution predicts one heartbeat in 1 second within a more realistic scenario (less performance in the clientside and lower bandwidth). This may be considered acceptable in applications wherein heartbeats are classified at the same pace at which they are produced. Additionally, the accuracy of the predicted heartbeat is higher and further, the number of output neurons is set to 16, PAC detects more arrhythmia classes. Moreover, the solution in [START_REF] Barni | Privacypreserving ecg classification with branching programs and neural networks[END_REF] combines the use of homomorphic encryption with garbled circuits. The use of both techniques renders the prediction protocol more time consuming compared to PAC whereby mostly arithmetic circuits are used. Finally, while both solutions use packing at the encryption stage and thus allow for prediction in batches, our solution additionally parallelizes each operation in the model (using the SIMD packing method, once again) and hence optimizes the timing complexity.

Conclusion

In this paper, we have presented PAC, a methodology for designing Privacypreserving Arrhythmia Classification that keeps users' ECG data confidential against service providers and the neural network model confidential against users. As a case study, we have designed a new model based on the PhysioBank dataset. The proposed model involves a customized two-layer neural network with 54 neurons. This model was built from scratch in order to be compatible with 2PC. The solution is implemented with the ABY framework which required the truncation of input values and model parameters. The second truncation method combined with Arithmetic circuits consists of multiplying the input values with 10 3 and shows significant improvement in terms of performance and accuracy. PAC achieves an accuracy of 96.34% and experimental results show that the prediction of one heartbeat takes approximately 1 second in real world scenarios. We show that more savings can be achieved with the use of SIMD for performing predictions in batches.

Fig. 1 .

 1 Fig. 1. Confusion matrix of the model for each class in the test dataset

Fig. 2 .

 2 Fig. 2. The proposed NN model

Fig. 3 .

 3 Fig. 3. PAC Overview

  W h and W o and obtains vectors of 16.38.N elements. The server also creates the bias vectors B h (N ) and B o (N ) of size 38.N and 16.N , respectively (see their representations in the original paper) by expanding the two original bias vectors B h and B o , respectively.

Fig. 4 .

 4 Fig. 4. Arithmetic circuit representation of the model with Truncation v2

Table 1 .

 1 Heartbeats for Arrhythmia classification and frequency in datasets

			Our Dataset PhysioBank Dataset
	Arrhythmia Class	Symbol #	%	#	%
	Normal beat	N	14985 34.02% 59926	66.593%
	Left bundle branch block beat	L	6450 14.64% 6450	7.168%
	Right bundle branch block beat	R	5794 13.15% 5794	6.439%
	Premature ventricular contraction	V	5712 12.97% 5712	6.347%
	Paced beat	/	5608 12.73% 5608	6.232%
	Atrial premature beat	A	2042 4.64% 2042	2.269%
	Rhythm change	+	1005 2.28% 1005	1.117%
	Fusion of paced and normal beat	f	786 1.78% 786	0.873%
	Fusion of ventricular and normal beat	F	647 1.47% 647	0.719%
	Ventricular flutter wave	!	378 0.86% 378	0.42%
	Nodal (junctional) escape beat	j	184 0.42% 184	0.204%
	Non-conducted P-wave (blocked APB)	x	155 0.35% 155	0.172%
	Aberrated atrial premature beat	a	123 0.28% 123	0.137%
	Ventricular escape beat	E	85 0.19% 85	0.094%
	Nodal (junctional) premature beat	J	68 0.15% 68	0.076%
	Atrial escape beat	e	26 0.06% 13	0.014%
	Signal quality change	V	NA	NA	508	0.565%
	Comment annotation	"	NA	NA	352	0.391%
	Isolated QRS-like artifact	-	NA	NA	112	0.124%
	Unclassifiable beat	Q	NA	NA	29	0.032%
	Start of ventricular flutter/fibrillation	[	NA	NA	5	0.006%
	End of ventricular flutter/fibrillation	]	NA	NA	5	0.006%
	Premature or ectopic supraventricular beat	S	NA	NA	2	0.002%

  16 × 38 + 38 × 16 + 38 = 1254 multiplications, 15 × 38 + 38 + 37 × 16 + 16 = 1216 additions, 16 exponentiations, 16 divisions and 1 argmax operation.

Table 2 .

 2 Performance results for each model

		Boolean Circuits	Truncation v1		Truncation v2
	Proposed NN models	Model 1	Model 1 Model 2	Model 1 without ARGMAX	Model 1 Model 2	Model 3
	Circuit
	Gates	553925	35477	36418	128	34329	34696	34660
	Depth	4513	160	168	5	146	147	146
	Time (ms)
	Total 8	117571.82	1218.613	2776.862	735.357	1082.804	2641.846	4723.203
	Init	0.046	0.076	0.071	0.056	0.062	0.037	0.033
	CircuitGen	0.046	0.074	0.062	0.067	0.078	0.055	0.047
	Network 8	272.867	268.39	94.142	248.92	51.391	89.46	34.221
	BaseOTs	288.047	309.288	310.06	311.387	291.705	294.698	298.294
	Setup 8	107481.557	851.397	2373.818	714.511	817.807	2354.391	4409.689
	OTExtension	106645.796	847.424	2369.377	714.278	816.069	2351.584	4407.521
	Garbling	812.573	2.502	3.268	0.002	1.405	1.851	1.252
	Online	10090.26	367.21	403.042	20.844	264.995	287.453	313.512
	Data Transfer (Sent/Rcv, in KB)
	Total	319269 / 309573 2629 / 2252 7113 / 6651	1910 / 1900	2171 / 2095 6560 / 6461 12266 / 12139
	BaseOTs	48 / 48	48 / 48	48 / 48	48 / 48	48 / 48	48 / 48	48 / 48
	Setup	305915 / 304815 2240 / 2227 6591 / 6579	1881 / 1881	2086 / 2071 6406 / 6391 12025 / 12010
	OTExtension	301095 / 304815 2057 / 2227 6377 / 6579	1881 / 1881	2053 / 2071 6373 / 6391 11992 / 12010
	Garbling	4819 / 0	183 / 0	214 / 0	0 / 0	33 / 0	33 / 0	33 / 0
	Online	13354 / 4757	389 / 25	522 / 72	29 / 19	85 / 24	154 / 70	240 / 129

Table 3 .

 3 Performance results for the multi-signal model

				# Input signals
		1	10	100	200	400
	Circuit
	Gates	33741	39552	40918	42422	45426
	Depth	148	148	148	148	148
	Time (ms)
	Total	1084.713	8002.287	77867.26	160114.6	314311
	Init	0.061	0.09	0.062	0.059	0.058
	CircuitGen	0.041	0.043	0.052	0.053	0.066
	Network	7.115	7.681	7.513	5.34	4.307
	BaseOTs	290.672	294.094	293.867	300.302	285.169
	Setup	814.036	7575.32	75821.76	155921.4	306985
	OTExtension	808.49	7509.797	75149.46	154673.2	304616
	Garbling	5.056	62.455	650.642	1214.046	2310
	Online	270.673	426.961	2045.492	4193.194	7325.77
	Bandwidth (Rcv/Sent in KB)
	Total	2095 / 2167 21010 / 21652 209912 / 216247 419805 / 432465 839588 / 864898
	BaseOTs	48 / 48	48 / 48	48 / 48	48 / 48	48 / 48
	Setup	2071 / 2084 20782 / 20936 207647 / 209107 415276 / 418186 830533 / 836342
	OTExtension	2071 / 2053 20782 / 20612 207647 / 205947 415276 / 411876 830532 / 823732
	Garbling	0 / 31	0 / 315	0 / 3159	0 / 6309	0 / 12609
	Online	24 / 83	227 / 716	2264 / 7139	4528 / 14278	9055 / 28555

https://www.apple.com/lae/apple-watch-series-4/health/

https://www.ibm.com/security/data-breach

https://eur-lex.europa.eu/eli/reg/2016/679/oj

https://www.physionet.org/physiobank/database/mitdb/

Lectures 1&2: Introduction to Secure Computation, Yao's and GMW Protocols, Secure Computation Course at Berkeley University

https://github.com/encryptogroup/ABY

https://www.tensorflow.org/

The Total time corresponds to the Setup time and the Online time. The Network time represents the time for the connection to be established. The Setup time corresponds to the OTExtension time and the Garbling time.

Acknowledgments

This work was partly supported by the PAPAYA project funded by the European Union's Horizon 2020 Research and Innovation Programme, under Grant Agreement no. 786767.


Charles Banliat
email: charles.banliat@inrae.fr

Daniel Le Bourhis
email: daniel.lebourhis@allice.fr

Ophélie Bernardi
email: ophelie.bernardi@inrae.fr

Daniel Tomas
email: daniel.tomas@inrae.fr

Valérie Labas
email: valerie.labas@inrae.fr

Pascal Salvetti
email: pascal.salvetti@allice.fr

Benoît Guyonnet
email: benoit.guyonnet@evolution-xy.fr

Pascal Mermillod
email: pascal.mermillod@inrae.fr

Marie Saint-Dizier
email: marie.saint-dizier@univ-tours.fr

Oviduct Fluid Extracellular Vesicles Change the Phospholipid Composition of Bovine Embryos Developed In Vitro

Keywords: extracellular vesicles, exosomes, microvesicles, tubal fluid, fallopian tube, oviduct, embryo, bovine, cattle, lipidomics

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Understanding how the mother communicates with the early developing embryo is a scientific challenge. In ruminants as in other mammals, the oviduct is the first environment to which the embryo is exposed [START_REF] Coy | Roles of the oviduct in mammalian fertilization[END_REF][START_REF] Hunter | Components of oviduct physiology in eutherian mammals[END_REF]. The oviductal fluid is a complex fluid containing proteins, carbohydrates, lipids, and ions that are regulated in abundance throughout the estrous cycle in domestic mammals [START_REF] Saint-Dizier | Composing the Early Embryonic Microenvironment: Physiology and Regulation of Oviductal Secretions[END_REF]. Recently, extracellular vesicles from the oviductal fluid (oEVs), also called oviductosomes, have been proposed as key participants of the early embryo-maternal dialog [START_REF] Alminana | Extracellular Vesicles in the Oviduct: Progress, Challenges and Implications for the Reproductive[END_REF][START_REF] Bridi | Extracellular Vesicles Mediated Early Embryo-Maternal Interactions[END_REF]. EVs are lipid bilayer-enclosed nanosized vesicles, including exosomes (40-100 nm) and microvesicles (100-1000 nm) [START_REF] Raposo | Extracellular vesicles: Exosomes, microvesicles, and friends[END_REF]. As EVs in other biological fluids, oEVs contain a range of biomolecules such as proteins, small RNAs, metabolites, and genomic DNA [START_REF] Alminana | Extracellular Vesicles in the Oviduct: Progress, Challenges and Implications for the Reproductive[END_REF][START_REF] Gatien | Metabolomic Profile of Oviductal Extracellular Vesicles across the Estrous Cycle in Cattle[END_REF][START_REF] Margolis | The biology of extracellular vesicles: The known unknowns[END_REF][START_REF] Bauersachs | The Oviductal Extracellular Vesicles' RNA Cargo Regulates the Bovine Embryonic Transcriptome[END_REF]. Using fluorescent-labelled oEVs and confocal microscopy, it was shown that bovine blastocysts were able to take up oEVs added to the culture medium and internalize them inside their blastomeres [START_REF] Alminana | Oviduct extracellular vesicles protein content and their role during oviduct-embryo cross-talk[END_REF]. Furthermore, the supplementation of culture media with oEVs has been shown to improve the quality of bovine blastocysts in terms of total cell number, cryotolerance, and hatching rates [START_REF] Alminana | Oviduct extracellular vesicles protein content and their role during oviduct-embryo cross-talk[END_REF][START_REF] Lopera-Vasquez | Extracellular Vesicles from BOEC in In Vitro Embryo Development and Quality[END_REF][START_REF] Lopera-Vasquez | Effect of bovine oviductal extracellular vesicles on embryo development and quality in vitro[END_REF]. Exposure to oEVs was also shown to induce changes in gene expression, miRNA content, and global DNA methylation levels in cattle embryos [START_REF] Bauersachs | The Oviductal Extracellular Vesicles' RNA Cargo Regulates the Bovine Embryonic Transcriptome[END_REF][START_REF] Lopera-Vasquez | Extracellular Vesicles from BOEC in In Vitro Embryo Development and Quality[END_REF][START_REF] Lopera-Vasquez | Effect of bovine oviductal extracellular vesicles on embryo development and quality in vitro[END_REF]. In mice, oEVs from donor oviduct fluid in the transfer medium were shown to improve birth rates after transfer of in-vitro-produced embryos to recipient mothers [START_REF] Qu | Extracellular vesicles derived from donor oviduct fluid improved birth rates after embryo transfer in mice[END_REF].

Phospholipids, including phosphatidylcholines (PC), phosphatidylethanolamines (PE), and sphingomyelins (SM), represent key structural components of cell membranes and also major components of EVs [START_REF] Kastelowitz | Exosomes and microvesicles: Identification and targeting by particle size and lipid chemical probes[END_REF][START_REF] Skotland | An emerging focus on lipids in extracellular vesicles[END_REF]. Glycerophospholipids and sphingolipids are involved in a wide range of cell-signaling pathways and act as precursors to many biomolecules such as lysophosphatidylcholines (LPC), lysoPE (LPE), and eicosanoids [START_REF] Eyster | The membrane and lipids as integral participants in signal transduction: Lipid signal transduction for the non-lipid biochemist[END_REF]. Using intact-cell MALDI-TOF (Matrix assisted laser desorption ionization-Time of flight) mass spectrometry (ICM-MS), we showed earlier that the bovine oviductal fluid contains a mixture of PC, PE, LPC, LPE, and SM varying in abundance throughout the estrous cycle, and it was suggested that oEVs contribute a large part to these phospholipids [START_REF] Banliat | Stage-dependent changes in oviductal phospholipid profiles throughout the estrous cycle in cattle[END_REF]. Furthermore, significant differences in various PC and SM were reported between in-vitro-produced cattle blastocysts and their in-vivo conceived counterparts [START_REF] Sudano | Phosphatidylcholine and sphingomyelin profiles vary in Bos taurus indicus and Bos taurus taurus in vitro-and in vivo-produced blastocysts[END_REF], indicating that the maternal environment modulates the phospholipid composition of oocytes and developing embryos.

We hypothesized that oEVs added to the culture medium would change the lipid composition of developing embryos in a way that might explain the beneficial effects of oEVs on embryo quality. The objective of this study was thus to examine the effect of oEVs on the phospholipid composition of bovine blastocysts.

Results

Oviductal Extracellular Vesicles Had No Effect on Embryo Development

The same experiment was performed in two different laboratories (see Materials and Methods Section and Figure 4). Supplementation with postovulatory oEVs during in vitro culture had no effect on embryo cleavage on day 2 or blastocyst rates from cleaved embryos on days 6, 7, and 8 (Table 1). The rates of blastocyst hatching on day 8 did not differ between groups (control vs. oEVs: 11.3 ± 3.8% vs. 19.3 ± 4.0%; data from Experiment 2). Supplementation with oEVs had no effect on mean cell number per blastocyst on day 8 (85.4 ± 5.3 vs. 97.3 ± 9.8; n = 22 and n = 12, respectively) but tended to increase this number in expanded blastocysts (133.1 ± 5.7 vs. 146.6 ± 5.2; n = 47 and n = 69, respectively; p = 0.09; data from Experiment 1).

After slow freezing and thawing, blastocyst re-expanding (control vs. oEVs: 100% vs. 94.7% and 94.7 vs. 84.2% at 24 and 48 h, respectively) and hatching (control vs. oEVs: 31.6% vs. 57.9% and 84.2% vs. 84.2% at 24 and 48 h, respectively) did not differ between groups (n = 19 blastocysts/group; data from Experiment 1).

Oviductal Extracellular Vesicles Changed Embryo Phospholipid Profiles in a Stage-Specific Manner

The lipidomic analysis of fresh individual blastocysts (n = 25) and expanded blastocysts (n = 53) on day 8 detected a total of 259 peaks in the 350-900 m/z mass range, corresponding mostly after annotation to PC, PE, LPC, and SM (see all masses and corresponding annotations in Supplementary data A).

The hierarchical clustering of differential m/z (p-value < 0.05) between oEV-treated and control groups showed a clear separation between blastocysts and expanded blastocysts and evidenced specific lipidomic profiles due to oEV supplementation at each stage (Figure 1; see individual profiles for blastocysts and expanded blastocysts in Supplementary data B).

Int. J. Mol. Sci. 2020, 21, x FOR PEER REVIEW 3 of 13

Oviductal Extracellular Vesicles Changed Embryo Phospholipid Profiles in a Stage-Specific Manner

The lipidomic analysis of fresh individual blastocysts (n = 25) and expanded blastocysts (n = 53) on day 8 detected a total of 259 peaks in the 350-900 m/z mass range, corresponding mostly after annotation to PC, PE, LPC, and SM (see all masses and corresponding annotations in Supplementary data A).

The hierarchical clustering of differential m/z (p-value < 0.05) between oEV-treated and control groups showed a clear separation between blastocysts and expanded blastocysts and evidenced specific lipidomic profiles due to oEV supplementation at each stage (Figure 1; see individual profiles for blastocysts and expanded blastocysts in Supplementary data B). Each line corresponds to one molecular species. For a given species, green lines represent higher abundance while red lines represent lower abundance compared with other conditions. Black lines represent the median abundance values. The proximity between the conditions and lipid profiles are shown by the hierarchical trees on the top and left of the heatmap, respectively. A total of 25 blastocysts (16 control, 9 oEV-treated) and 53 expanded blastocysts (28 control, 25 oEV-treated) were analyzed.

The Volcano plot analysis with a fold-change threshold of 1.5 between oEV-treated and control embryos evidenced 18 and 28 differentially abundant lipid m/z in blastocysts and expanded blastocysts, respectively (Figure 2). In total, 61% (11/18) and 64% (18/28) of differentially abundant m/z were increased in oEV-treated embryos compared with controls in blastocysts and expanded blastocysts, respectively. Among those, six molecular species between 756 and 788 m/z, all overabundant in oEV-treated embryos, were shared between blastocysts and expanded blastocysts and annotated as PE (36:1), PC (34:2), SM (d38:1), PC (34:1), PC (35:1), and PC (36:1). Furthermore, overabundant masses due to oEVs supplementation included a majority of PC, PE, and SM with longchain fatty acids; whereas LPC/LPE (with one fatty acid group) were identified only among m/z found less abundant following oEV supplementation. Each line corresponds to one molecular species. For a given species, green lines represent higher abundance while red lines represent lower abundance compared with other conditions. Black lines represent the median abundance values. The proximity between the conditions and lipid profiles are shown by the hierarchical trees on the top and left of the heatmap, respectively. A total of 25 blastocysts (16 control, 9 oEV-treated) and 53 expanded blastocysts (28 control, 25 oEV-treated) were analyzed.

The Volcano plot analysis with a fold-change threshold of 1.5 between oEV-treated and control embryos evidenced 18 and 28 differentially abundant lipid m/z in blastocysts and expanded blastocysts, respectively (Figure 2). In total, 61% (11/18) and 64% (18/28) of differentially abundant m/z were increased in oEV-treated embryos compared with controls in blastocysts and expanded blastocysts, respectively. Among those, six molecular species between 756 and 788 m/z, all overabundant in oEV-treated embryos, were shared between blastocysts and expanded blastocysts and annotated as PE (36:1), PC (34:2), SM (d38:1), PC (34:1), PC (35:1), and PC (36:1). Furthermore, overabundant masses due to oEVs supplementation included a majority of PC, PE, and SM with long-chain fatty acids; whereas LPC/LPE (with one fatty acid group) were identified only among m/z found less abundant following oEV supplementation. Relative abundance of differential masses ordered in increasing m/z values confirmed that less abundant m/z in oEV-exposed vs. control embryos included a majority of low lipid m/z; whereas overabundant masses were almost exclusively phospholipids of high molecular weight, with a cut-off around 700 and 600 m/z in blastocysts and expanded blastocysts, respectively (Figure 3).

Relative abundance of differential masses ordered in increasing m/z values confirmed that less abundant m/z in oEV-exposed vs. control embryos included a majority of low lipid m/z; whereas overabundant masses were almost exclusively phospholipids of high molecular weight, with a cutoff around 700 and 600 m/z in blastocysts and expanded blastocysts, respectively (Figure 3).

Majority of Overabundant Masses in oEV-Treated Embryos Were Also Detected in oEVs

In order to determine if the m/z found more abundant in oEV-treated compared with control embryos might originate from oEVs, the oEV sample used for in vitro embryo development (postovulatory stage, ipsilateral to ovulation) was analyzed by ICM-MS (6 replicates). A total of 234 lipid peaks in the 350-900 m/z mass range, including mostly LPC, PC, PE, and SM, were detected in oEVs (see details in Supplementary data A). Of the 11 and 18 m/z found increased after oEV exposure in blastocysts and expanded blastocysts, respectively, 11 (100%) and 6 (33%) were also detected in oEVs. The lipid species shared between oEVs and embryos are indicated by an asterisk in Figure 2.

Discussion

The oviductal fluid is the first interface between the mother and the early developing embryo. Oviduct fluid EVs have been proposed as nanocarriers of molecules for gametes and embryos [START_REF] Alminana | Extracellular Vesicles in the Oviduct: Progress, Challenges and Implications for the Reproductive[END_REF][START_REF] Bridi | Extracellular Vesicles Mediated Early Embryo-Maternal Interactions[END_REF][START_REF] Alminana | Extracellular vesicles: Multi-signal messengers in the gametes/embryo-oviduct cross-talk[END_REF], but the regulation of the embryonic lipidome by oEVs remains to be explored. Here, for the first time, we show that exposure to oEVs during in vitro culture changed the phospholipid composition of

Majority of Overabundant Masses in oEV-Treated Embryos Were Also Detected in oEVs

In order to determine if the m/z found more abundant in oEV-treated compared with control embryos might originate from oEVs, the oEV sample used for in vitro embryo development (postovulatory stage, ipsilateral to ovulation) was analyzed by ICM-MS (6 replicates). A total of 234 lipid peaks in the 350-900 m/z mass range, including mostly LPC, PC, PE, and SM, were detected in oEVs (see details in Supplementary data A). Of the 11 and 18 m/z found increased after oEV exposure in blastocysts and expanded blastocysts, respectively, 11 (100%) and 6 (33%) were also detected in oEVs. The lipid species shared between oEVs and embryos are indicated by an asterisk in Figure 2.

Discussion

The oviductal fluid is the first interface between the mother and the early developing embryo. Oviduct fluid EVs have been proposed as nanocarriers of molecules for gametes and embryos [START_REF] Alminana | Extracellular Vesicles in the Oviduct: Progress, Challenges and Implications for the Reproductive[END_REF][START_REF] Bridi | Extracellular Vesicles Mediated Early Embryo-Maternal Interactions[END_REF][START_REF] Alminana | Extracellular vesicles: Multi-signal messengers in the gametes/embryo-oviduct cross-talk[END_REF], but the regulation of the embryonic lipidome by oEVs remains to be explored. Here, for the first time, we show that exposure to oEVs during in vitro culture changed the phospholipid composition of developing embryos in favor of PC, PE, and SM of high molecular masses. Oviductal EVs had specific impact on blastocyst phospholipids before and after blastocoel expansion.

Based on two successive experiments on more than 1500 zygotes, we did not observe any significant improvement in the blastocyst yield after supplementation of the culture medium with oEVs. This is in accord with some previous studies that used oEVs derived from bovine oviduct epithelial cells in vitro [START_REF] Lopera-Vasquez | Extracellular Vesicles from BOEC in In Vitro Embryo Development and Quality[END_REF] and oviductal fluid [START_REF] Lopera-Vasquez | Effect of bovine oviductal extracellular vesicles on embryo development and quality in vitro[END_REF]. In the latter, a detrimental effect of oEVs on blastocyst development was even observed at day 7 that was compensated on days 8 and 9 [START_REF] Lopera-Vasquez | Effect of bovine oviductal extracellular vesicles on embryo development and quality in vitro[END_REF] By contrast, a previous study reported an improvement of blastocyst rates due to oEV supplementation but with a more marked effect on day 9 than on days 7 and 8 [START_REF] Alminana | Oviduct extracellular vesicles protein content and their role during oviduct-embryo cross-talk[END_REF]. Here, in order to analyze blastocyst quality and phospholipid composition, we stopped embryo development at day 8, preventing us from assessing a potential later effect on embryo development. In the study from Alminana et al. [START_REF] Alminana | Oviduct extracellular vesicles protein content and their role during oviduct-embryo cross-talk[END_REF] and ours, frozen-thawed oEVs collected from oviductal fluids at the postovulatory stage were used. Furthermore, as previously reported [START_REF] Gatien | Metabolomic Profile of Oviductal Extracellular Vesicles across the Estrous Cycle in Cattle[END_REF], the proportions of exosomes (30-100 nm: 76%) and of microvesicles (100-500 nm: 24%), as measured by transmission electron microscopy in the oEV samples used, were very similar in the two studies. However, the differences between the two studies may be due to differences in oEV content in the culture media: based on preliminary (unpublished) data comparing different doses of oEVs proteins (0.005, 0.05, 0.5 mg/mL) in the culture medium, the 0.05 dose tended to increase the rates of blastocysts at days 7-8 compared to controls without oEVs and was chosen for the present study, whereas Alminana et al. [START_REF] Alminana | Oviduct extracellular vesicles protein content and their role during oviduct-embryo cross-talk[END_REF] used final concentrations of 0.2-0.4 mg of proteins/mL.

A tendency to increase the total cell numbers in expanded blastocysts after oEV exposure was observed. These results are in line with previously reported positive effects of oEVs on growth and expansion of in-vitro produced cattle embryos [START_REF] Alminana | Extracellular Vesicles in the Oviduct: Progress, Challenges and Implications for the Reproductive[END_REF][START_REF] Alminana | Oviduct extracellular vesicles protein content and their role during oviduct-embryo cross-talk[END_REF]. Slow freezing in a two-step protocol is currently the method mostly used for cattle embryo cryopreservation in France. We expected a positive effect of oEVs on embryo cryosurvival after slow freezing, as previously reported by others after vitrification [START_REF] Lopera-Vasquez | Extracellular Vesicles from BOEC in In Vitro Embryo Development and Quality[END_REF][START_REF] Lopera-Vasquez | Effect of bovine oviductal extracellular vesicles on embryo development and quality in vitro[END_REF]. However, there was no detectable effect of pre-exposure to oEVs on blastocysts re-expansion and hatching at 24 or 48 h after thawing. It is to note that thawed blastocysts were cultured for 2 days in the presence of 1% of postovulatory cow serum. An uptake of serum EVs added for 24 h in the culture medium was previously shown to occur in bovine blastocysts [START_REF] Melo-Baez | Extracellular vesicles from serum in culture media are internalized by bovine embryos produced in vitro[END_REF]. It may be that serum EVs masked the possible effects of oEVs during this culture period. It is also worth noting that the survival rates were globally very high (>94% and 84% of re-expansion at 24 and 48 h) in both groups, precluding any evaluation of the effects of oEV in suboptimal conditions. This is probably due to the selection of expanded blastocysts of excellent morphology (grade 1) for cryopreservation. Previous studies examining the effect of liposomes (containing only phospholipids and cholesterol) or of soybean lecithin (mix of phospholipids) on in vitro embryo development have resulted in slight improvements of embryo survival after slow freezing in one [START_REF] Guyader-Joly | Effect of lecithin on in vitro and in vivo survival of in vitro produced bovine blastocysts after cryopreservation[END_REF] but not all [START_REF] Pugh | Cryopreservation of in vitro-produced bovine embryos: Effects of protein type and concentration during freezing or of liposomes during culture on post-thaw survival[END_REF] studies, and failed to evidence any effect on blastocyst hatching and pregnancy rates after transfer to the uterus of recipient cows [START_REF] Guyader-Joly | Effect of lecithin on in vitro and in vivo survival of in vitro produced bovine blastocysts after cryopreservation[END_REF]. Thus, the effects of oEVs on cattle embryo cryosurvival after slow freezing remains to be evaluated.

The mechanisms involved in cellular communications mediated by EVs include fusion between EV membranes and the plasma membrane of the target cells, as well as EV endocytosis via specific target cell receptors [START_REF] Bridi | Extracellular Vesicles Mediated Early Embryo-Maternal Interactions[END_REF]. Previous studies evidenced that oEVs are able to fuse with the membrane of spermatozoa in mice [START_REF] Al-Dossary | Oviductosome-Sperm Membrane Interaction in Cargo Delivery: Detection of fusion and underlying molecular players using three-dimensional super-resolution structured illumination microscopy (SR-SIM)[END_REF] and cats [START_REF] Ferraz | Oviductal extracellular vesicles interact with the spermatozoon's head and mid-piece and improves its motility and fertilizing ability in the domestic cat[END_REF] in vitro, leading to the transfer of oEV membrane components to the sperm membrane [START_REF] Al-Dossary | Oviductosome-Sperm Membrane Interaction in Cargo Delivery: Detection of fusion and underlying molecular players using three-dimensional super-resolution structured illumination microscopy (SR-SIM)[END_REF][START_REF] Al-Dossary | Role of exosomes in the reproductive tract Oviductosomes mediate interactions of oviductal secretion with gametes/early embryo[END_REF]. Previous confocal microcopy observations in our laboratory showed that oEVs added in the culture medium of bovine embryos are able to cross the zona pellucida and localize into the cytoplasm of embryo blastomeres [START_REF] Alminana | Oviduct extracellular vesicles protein content and their role during oviduct-embryo cross-talk[END_REF]. Similarly, uterine EVs were shown to be internalized by trophectoderm cells of embryos at later stages of development in sheep [START_REF] Burns | Extracellular Vesicles Originate from the Conceptus and Uterus During Early Pregnancy in Sheep[END_REF] and humans [START_REF] Vilella | Hsa-miR-30d, secreted by the human endometrium, is taken up by the pre-implantation embryo and might modify its transcriptome[END_REF][START_REF] Greening | Human Endometrial Exosomes Contain Hormone-Specific Cargo Modulating Trophoblast Adhesive Capacity: Insights into Endometrial-Embryo Interactions[END_REF]. Based on these observations, we made the hypothesis that the oEVs added to the culture medium are captured by developing embryos, leading to the incorporation of their phospholipids in the plasma membrane and/or cytoplasm of blastomeres. In line with this hypothesis, the majority (>61%) of differentially abundant m/z were overabundant in oEV-exposed embryos compared to controls. In order to evaluate which phospholipids could be brought by oEVs through internalization, we analyzed the lipidomic profile of the pool of oEVs used for the supplementation of the culture medium and detected PC, SM, and PE as major phospholipid species, in accordance with a recent lipidomic analysis of EVs isolated from the uterine fluid of ewes [START_REF] O'neil | Characterization and regulation of extracellular vesicles in the lumen of the ovine uterus[END_REF]. A perspective of this study is to characterize the phospholipid profiles of oEVs across the cycle with a higher number of replicates. The present analysis enabled us to identify up to 100% (11/11) of overabundant masses in oEV-exposed blastocysts as also present in oEVs, which is consistent with the assumption of an internalization of oEV-derived phospholipids by developing embryos. However, this percentage was much lower (33%, 6/18) after blastocoel expansion. Furthermore, around one third of differentially abundant masses (39% and 36% in blastocysts and expanded blastocysts, respectively) were decreased in embryos after oEV supplementation, indicating that mechanisms other than the simple internalization of oEV-phospholipid cargos inside embryonic cells were involved in these changes. Of note, EVs can carry a large panel of lipid mediators and molecules potentially involved in lipid metabolism including free fatty acids, eicosanoids, proteins, and nucleic acids [START_REF] Skotland | An emerging focus on lipids in extracellular vesicles[END_REF][START_REF] Record | Extracellular vesicles: Lipids as key components of their biogenesis and functions[END_REF]. Previous transcriptomic analyses showed that oEV supplementation during in vitro culture altered the expression of genes involved in lipid metabolism and regulation of lipid metabolic processes in cattle blastocysts [START_REF] Bauersachs | The Oviductal Extracellular Vesicles' RNA Cargo Regulates the Bovine Embryonic Transcriptome[END_REF][START_REF] Lopera-Vasquez | Effect of bovine oviductal extracellular vesicles on embryo development and quality in vitro[END_REF]. Furthermore, various miRNAs involved in fatty acid biosynthesis and metabolism were found highly abundant in bovine oEVs isolated during the periovulatory period compared to the luteal phase of cycle [START_REF] Alminana | Deciphering the oviductal extracellular vesicles content across the estrous cycle: Implications for the gametes-oviduct interactions and the environment of the potential embryo[END_REF]. Therefore, the exposure to oEVs during in vitro culture might have led to both the reorganization of blastomere plasma membrane with oEV-derived phospholipids and to the modulation of embryonic lipid metabolism by small RNAs and proteins brought by oEVs. Further experiments using EV imaging [START_REF] Chuo | Imaging extracellular vesicles: Current and emerging methods[END_REF] would be necessary to confirm the EV uptake and precisely determine if this uptake varies according to the developmental stage.

The embryos exposed to oEVs displayed a shift toward PC, PE, and SM of higher molecular weight, i.e., possessing fatty acids of longer chains, whereas PC and PE species of lower molecular weight and LPC were detected among less abundant phospholipids compared to control embryos. Phospholipids are important components of the bovine oviductal [START_REF] Killian | Changes in phospholipids, cholesterol and protein content of oviduct fluid of cows during the oestrous cycle[END_REF][START_REF] Grippo | Cholesterol, phospholipid and phospholipase activity of ampullary and isthmic fluid from the bovine oviduct[END_REF][START_REF] Banliat | Intraoviductal concentrations of steroid hormones during in vitro culture changed phospholipid profiles and cryotolerance of bovine embryos[END_REF] and uterine [START_REF] Ribeiro | Role of lipids on elongation of the preimplantation conceptus in ruminants[END_REF][START_REF] Simintiras | Progesterone alters the bovine uterine fluid lipidome during the period of elongation[END_REF][START_REF] Moraes | Analysis of the uterine lumen in fertility-classified heifers: I. Glucose, prostaglandins, and lipids[END_REF] fluids and may serve as an energy source and as signaling precursors for the gametes and developing embryos. However, there is currently very few information on their roles for the establishment of pregnancy in ruminants. Phospholipids that are regulated in abundance in the oviductal fluid between the periovulatory and luteal stages of cycle are potential candidates for a role in early embryo development. It is noteworthy that molecular species such as PC (34:1), PC (36:4), and PC (36:3) (at m/z 760.59, 782.56, and 784.56, respectively) that were all found more abundant at the periovulatory period than at the luteal stage of the cycle [START_REF] Banliat | Stage-dependent changes in oviductal phospholipid profiles throughout the estrous cycle in cattle[END_REF] were also identified as overabundant in oEV-exposed blastocysts. Furthermore, several species of PC/SM and PE identified in uterine EVs were reported to be discriminating (by hierarchical clustering) between pregnant and cyclic ewes at day 14, i.e., after blastocyst hatching and elongation [START_REF] O'neil | Characterization and regulation of extracellular vesicles in the lumen of the ovine uterus[END_REF]. In addition, the PCs detected as increased by pregnancy in the uterine lumen of ewes at day 17, including PC (34:1), PC (34:2), PC (36:1), and PC (36:2) [START_REF] Moraes | Analysis of the uterine lumen in fertility-classified heifers: I. Glucose, prostaglandins, and lipids[END_REF], were also found increased in abundance in oEV-exposed blastocysts in the present study.

There is evidence that embryos developed in contact with oviduct epithelial cells, oviduct fluid, and/or oEVs are of higher quality than unexposed controls in terms of morphology, cryotolerance, and gene expression [START_REF] Lopera-Vasquez | Effect of bovine oviductal extracellular vesicles on embryo development and quality in vitro[END_REF][START_REF] Cordova | Development rate and gene expression of IVP bovine embryos cocultured with bovine oviduct epithelial cells at early or late stage of preimplantation development[END_REF][START_REF] Lopera-Vasquez | Effect of bovine oviductal fluid on development and quality of bovine embryos produced in vitro[END_REF][START_REF] Maillo | Maternal-embryo interaction in the bovine oviduct: Evidence from in vivo and in vitro studies[END_REF]. However, the mechanisms by which oviduct fluid components enhance the quality of developing embryos are poorly known. We previously showed that the steroid hormones progesterone, estradiol, and cortisol added at intraoviductal concentrations to the culture medium altered the phospholipid composition and cryosurvival of bovine embryos [START_REF] Banliat | Intraoviductal concentrations of steroid hormones during in vitro culture changed phospholipid profiles and cryotolerance of bovine embryos[END_REF]. Several phospholipids higher than 700 m/z that were more abundant in embryos exposed to physiological concentrations of steroid hormones-including PC (34:2), PC (36:3), SM (d40:2), PC (36:2), and PC (38:4) at m/z 758.56, 784.56, 785.58, 786.6, and 810.6, respectively-were also found overabundant in blastocysts exposed to oEVs, whereas PC (29:2) or PE (32:2) at m/z 688.43 was found less abundant in both conditions [START_REF] Banliat | Intraoviductal concentrations of steroid hormones during in vitro culture changed phospholipid profiles and cryotolerance of bovine embryos[END_REF]. Two previous studies compared the phospholipid composition of bovine blastocysts conceived in vivo, which have a high likelihood of pregnancy after uterine transfer to a recipient cow; and those produced in vitro, which have lower developmental potential [START_REF] Sudano | Phosphatidylcholine and sphingomyelin profiles vary in Bos taurus indicus and Bos taurus taurus in vitro-and in vivo-produced blastocysts[END_REF][START_REF] Annes | Lipid characterization of in vitro-produced bovine embryos with distinct kinetics of development[END_REF]. In both studies, the MS data were acquired in the mass range above m/z 700, precluding any comparison below this threshold. However, Sudano et al. [START_REF] Sudano | Phosphatidylcholine and sphingomyelin profiles vary in Bos taurus indicus and Bos taurus taurus in vitro-and in vivo-produced blastocysts[END_REF] evidenced nine phospholipids differentially abundant between in-vivo-and in-vitro-developed bovine blastocysts, among which eight were found more abundant in in-vivo blastocysts. Of note, three PCs (PC (34:2), PC (36:2), and PC (36:1) of m/z 758.56, 786.6, and 788.6, respectively) found here more abundant in oEV-exposed blastocysts were also overabundant among in-vivo-conceived blastocysts [START_REF] Sudano | Phosphatidylcholine and sphingomyelin profiles vary in Bos taurus indicus and Bos taurus taurus in vitro-and in vivo-produced blastocysts[END_REF]. By contrast, in the study from Annes et al. [START_REF] Annes | Lipid characterization of in vitro-produced bovine embryos with distinct kinetics of development[END_REF], a majority of phospholipids (9/12)-including the molecular species PC (32:1), PC (34:2), PC (38:4)-were absent or less abundant in in-vivo-derived blastocysts than in in-vitro-produced ones, whereas we detected them at higher abundance in oEV-exposed blastocysts or expanded blastocysts than in controls. However, in accordance with Sudano et al. [START_REF] Sudano | Phosphatidylcholine and sphingomyelin profiles vary in Bos taurus indicus and Bos taurus taurus in vitro-and in vivo-produced blastocysts[END_REF], PC (36:2) was more abundant in in-vivo and in-vitro oEV-exposed developed blastocysts [START_REF] Annes | Lipid characterization of in vitro-produced bovine embryos with distinct kinetics of development[END_REF]. Further studies are now needed to know whether exposure to oEVs in vitro could lead to higher chances of implantation and pregnancy after intrauterine transfer.

In conclusion, this study provides the first analysis of the embryonic lipidome regulated by oEVs during in vitro development. Exposure to oEVs induced significant changes in the phospholipid composition of resulting embryos toward more abundant PC, PE, and SM with long-chain fatty acids. These changes were probably mediated by both the incorporation of oEV phospholipids into embryo membranes and the modulation of embryonic lipid metabolism by other oEV cargos such as mRNAs, miRNAs, and proteins.

Materials and Methods

In Vitro Embryo Production and oEVs Supplementation

Bovine ovaries were collected at a local slaughterhouse, transported to the laboratory at 32-35 • C, then cumulus oocyte complexes (COCs) were collected by aspirating follicles of 2-8 mm in diameter. The in-vitro maturation (IVM)/in-vitro fertilization (IVF) protocols used in the present study have been described previously [START_REF] Banliat | Intraoviductal concentrations of steroid hormones during in vitro culture changed phospholipid profiles and cryotolerance of bovine embryos[END_REF]. Briefly, groups of 40-50 COCs with more than three compact layers of cumulus cells were matured in 25 mM bicarbonate-buffered TCM 199 supplemented with 10% fetal calf serum (FCS; GIBCO BRL), 10 µg/mL follicle-stimulating hormone (FSH), 10 µg/mL luteinizing hormone (LH; Stimufol; SPRL Reprobiol), 1 µg/mL oestradiol 17β, and 50 µg/mL gentamycin for 22 h at 38.5 • C with 5% CO 2 . Matured oocytes were then fertilized in a modified Tyrode's bicarbonate buffered solution (fert-TALP; pH 7.6) containing 10 mg/mL heparin sodium salt (H3125; Sigma), 6 mg/mL BSA, 20 mM penicillamine (P4875; Sigma), 10 mM hypotaurine (H1384; Sigma), and 1 mM adrenaline (E4250; Sigma). A single ejaculate from one Normande bull (Evolution cooperative, France) of proven fertility was used in all the IVF experiments. Frozen-thawed Bovipure-Bovidilute (Nidacon)-treated spermatozoa were coincubated with COCs at an insemination dosage of 2 × 10 6 spermatozoa/mL for 22 h at 38.5 • C in a humidified atmosphere with 5% CO 2 . The day of IVF was defined as Day 0. On Day 1, all presumptive zygotes were denuded and cultured for 8 days in 30 µL of synthetic oviductal fluid (SOF) medium [START_REF] Holm | High bovine blastocyst development in a static in vitro production system using SOFaa medium supplemented with sodium citrate and myo-inositol with or without serum-proteins[END_REF] supplemented with 6 g/L BSA under paraffin oil (Origio, CooperSurgical, Trumbull, USA) at 38.5 • C in a humidified atmosphere containing 5% O 2 and 5% CO 2 .

The oEVs used were obtained from a pool of bovine oviductal fluids collected at a local commercial slaughterhouse and isolated by centrifugation (12,000× g, 15 min) and two successive ultracentrifugations (100,000× g, 90 min), then characterized by transmission electron microscopy and western blotting as detailed in our previous study [START_REF] Gatien | Metabolomic Profile of Oviductal Extracellular Vesicles across the Estrous Cycle in Cattle[END_REF]. Only oviducts at the postovulatory stage of cycle (recently formed corpus luteum, absence of follicles >10 mm; estimated Days 1-5 postovulation) and ipsilateral to ovulation, i.e., at the expected time and site of embryo development, were used. The same pool of oEVs isolated from 12 postovulatory ipsilateral oviducts was used for Experiments 1-2 and ICM-MS analysis. The oEVs sample was assayed for protein concentration (BCA method), aliquoted in small volumes and stored at -80 • C. In Experiments 1 and 2, oEVs were added to the culture medium at a final concentration of 0.05 mg of proteins/mL, starting on Day 1 with no renewal of the culture medium. In both Experiments, the number of cleaved embryos was recorded on day 2 and numbers of blastocysts, expanded blastocysts, and hatched blastocysts on days 6, 7, and 8 over 14 replicates in total (7 replicates per Experiment). Expanded blastocysts were distinguished from blastocysts according to the criteria of the International Embryo Transfer Society (IETS; larger blastocoel volume, thinner zona pellucida). In Experiment 1, subgroups of day-7-expanded blastocysts of grade 1 (according to IETS criteria) were submitted to slow freezing and thawing to assess their cryotolerance. Briefly, embryos were washed twice in the commercial Embryo holding medium (EHM, IMV Technologies, L'Aigle, France) then placed by groups of 3-6 in the Embryo freezing medium (EFM, IMV Technologies) containing 1.5 M ethylene glycol and 0.1 M of sucrose for 10 min, then the mixture was placed in a straw. Freezing at 0.3 °C/min was performed up to -32 °C thanks to a Freeze Control cryochamber (Cryologic, Blackburn, Australia), then straws were immersed in liquid nitrogen. For thawing, straws were put in a water bath at 35 °C for 30 s. Embryos were washed for 5 min in EHM then cultured for 2 days in 30 µL of SOF medium supplemented with 1% of postovulatory cow serum and 6 g/L of BSA at 38.5 °C under humidified atmosphere with 5% CO2 and 5% O2. Numbers of re-expanded and hatched blastocysts were recorded at 24 and 48 h after thawing. Percentages of hatching were calculated from the total numbers of frozen-thawed blastocysts. Four replicates with 3-6 expanded blastocysts per replicate were conducted. In addition, in Experiment 1, blastocysts and expanded blastocysts at day 8 were fixed in phosphate-buffered saline (PBS) containing 4% paraformaldehyde (PBS-PAF 4%, 20 min, 34 °C) for evaluation of total cell numbers under confocal microcopy, as described in detail in our previous study [START_REF] Banliat | Intraoviductal concentrations of steroid hormones during in vitro culture changed phospholipid profiles and cryotolerance of bovine embryos[END_REF]. For statistical analysis, rates of development, blastocyst hatching, and re-expansion/hatching after thawing were compared between groups by Mann-Whitney tests (nonparametric data). Cell numbers per blastocyst were compared by Student t-tests (parametric data). In both Experiments, the number of cleaved embryos was recorded on day 2 and numbers of blastocysts, expanded blastocysts, and hatched blastocysts on days 6, 7, and 8 over 14 replicates in total (7 replicates per Experiment). Expanded blastocysts were distinguished from blastocysts according to the criteria of the International Embryo Transfer Society (IETS; larger blastocoel volume, thinner zona pellucida). In Experiment 1, subgroups of day-7-expanded blastocysts of grade 1 (according to IETS criteria) were submitted to slow freezing and thawing to assess their cryotolerance. Briefly, embryos were washed twice in the commercial Embryo holding medium (EHM, IMV Technologies, L'Aigle, France) then placed by groups of 3-6 in the Embryo freezing medium (EFM, IMV Technologies) containing 1.5 M ethylene glycol and 0.1 M of sucrose for 10 min, then the mixture was placed in a straw. Freezing at 0.3 • C/min was performed up to -32 • C thanks to a Freeze Control cryochamber (Cryologic, Blackburn, Australia), then straws were immersed in liquid nitrogen. For thawing, straws were put in a water bath at 35 • C for 30 s. Embryos were washed for 5 min in EHM then cultured for 2 days in 30 µL of SOF medium supplemented with 1% of postovulatory cow serum and 6 g/L of BSA at 38.5 • C under humidified atmosphere with 5% CO 2 and 5% O 2 . Numbers of re-expanded and hatched blastocysts were recorded at 24 and 48 h after thawing. Percentages of hatching were calculated from the total numbers of frozen-thawed blastocysts. Four replicates with 3-6 expanded blastocysts per replicate were conducted. In addition, in Experiment 1, blastocysts and expanded blastocysts at day 8 were fixed in phosphate-buffered saline (PBS) containing 4% paraformaldehyde (PBS-PAF 4%, 20 min, 34 • C) for evaluation of total cell numbers under confocal microcopy, as described in detail in our previous study [START_REF] Banliat | Intraoviductal concentrations of steroid hormones during in vitro culture changed phospholipid profiles and cryotolerance of bovine embryos[END_REF]. For statistical analysis, rates of development, blastocyst hatching, and re-expansion/hatching after thawing were compared between groups by Mann-Whitney tests (nonparametric data). Cell numbers per blastocyst were compared by Student t-tests (parametric data).

Lipidomic Profiling of Blastocysts and oEVs by Intact Cell MALDI-TOF Mass Spectrometry

Lipidomic Profiling of Blastocysts and oEVs by Intact Cell MALDI-TOF Mass Spectrometry

Fresh day-8 blastocysts and expanded blastocysts were washed twice in Tris-sucrose buffer (TSB: 20 mM Tris-HCl, pH 6.8, and 260 mM sucrose) before being individually analyzed for lipidomic profiling by ICM-MS, as described previously [START_REF] Banliat | Intraoviductal concentrations of steroid hormones during in vitro culture changed phospholipid profiles and cryotolerance of bovine embryos[END_REF]. Briefly, using a videomicroscope, individual embryos were immediately spotted onto a plate (Bruker Daltonics, Germany) and the excess of TSB was removed. A total of 0.5 µL of Methanol 100% was deposed on embryo to promote lipid extraction. After evaporation of the methanol, 2 × 0.5 µL of 2,5-Dihydroxybenzoic acid (DHB) matrix at 15 mg/mL dissolved in 90% acetonitrile/9.8% water/0.2% TFA was overlaid on embryos. For oEV analysis, 0.5 µL of oEV sample (at 11.3 mg of proteins per mL) was spotted with 0.5 µL of DHB matrix; a total of six spots were deposited on the MALDI plate. For all samples, the matrix was allowed to evaporate at room temperature for 30 min. Profiles of embryos and oEVs were acquired using a RapifleX Tissuetyper MALDI-TOF instrument (Bruker Daltonics, Bremen, Germany) equipped with a Smartbeam 3D Nd:YAG (355 mm) laser. Each spectrum was obtained in positive and reflector ion modes, at 10-kHz laser repetition rate, with a sampling rate of 1.25 GS s-1, and collected as a sum of 1000 laser shots in 3 shot steps (total of 3000 spectra). Spectra were accumulated in a random walk-on spot within the 350-900 m/z range. A total of 25 blastocysts (16 control, 9 oEV-treated) and 53 expanded blastocysts (28 control, 25 oEV-treated) were analyzed. All embryo and oEVs spots were analyzed in triplicate. External calibration was followed using a mixture of small molecules (1 µL of DHB matrix with 1 µL of calibrant solution containing Caffein, MRFA peptide, LeuEnkephalin, Reserpin, Bradykinin (fragments 2-9), Angiotensin, and Glu-1 Fibrinopeptide B).

Analysis of Lipidomic Data

The analysis of ICM-MS data was performed using the MALDIquant package adapted for mass spectrometry data [START_REF] Gibb | MALDIquant: A versatile R package for the analysis of mass spectrometry data[END_REF] of the R software (version 3.6.2; free software from the R. foundation), as previously described by our team [START_REF] Banliat | Stage-dependent changes in oviductal phospholipid profiles throughout the estrous cycle in cattle[END_REF]. To increase mass accuracy (mass error < 0.05%), an internal calibration was subsequently applied to all spectra using flexAnalysis 4.0 software (Bruker, Bremen, Germany) and FlexAnalysis Batch Process (Bruker, Compass 2.0). For all data, a lock mass correction was performed using known PC (34:1) at 760.5851 m/z. The profile spectra were treated for baseline subtraction (SNIP method), smoothing by the Savitzky-Golay algorithm and realignment using prominent peaks and normalization on intensity using the total ion count method. Peaks were detected using a total average spectrum with a signal:background noise > 4. The precision of the ICM-MS acquisitions was determined by calculating the coefficient of variation (CV) from the normalized peak intensity values of the 3-5 technical replicates for each sample. The average CV obtained was 14.6% and 24.8% for analyses on embryos and oEVs, respectively. The MS data did not pass the normality and homogeneity tests. Thus, the nonparametric Kruskal-Wallis test was used for comparison between all groups and the Mann-Whitney test for comparison between treated and control groups at each stage. M/z values were considered differentially abundant between groups with a p-value < 0.05. Hierarchical clustering of differential m/z values was performed using Spearman correlations and the gplot package (v 3.0.1.2) of FactoMineR (v 2.1) of the R. software. The differential peaks were annotated based on theoretical masses ± 0.05 m/z using the LIPID MAPS ® lipidomics gateway database [START_REF]LIPID MAPS ® Lipidomics Gateway[END_REF] or using a home databank compiled from the MS identification of lipids in bovine oviductal and follicular fluids [START_REF] Banliat | Stage-dependent changes in oviductal phospholipid profiles throughout the estrous cycle in cattle[END_REF][START_REF] Bertevello | Lipid Identification and Transcriptional Analysis of Controlling Enzymes in Bovine Ovarian Follicle[END_REF].

Figure 1 .

 1 Figure 1. Heatmap representation of hierarchical clustering of the differentially abundant lipid m/z according to the embryonic stage (blastocyst vs. expanded blastocyst) and exposure to oEVs (+oEVs).Each line corresponds to one molecular species. For a given species, green lines represent higher abundance while red lines represent lower abundance compared with other conditions. Black lines represent the median abundance values. The proximity between the conditions and lipid profiles are shown by the hierarchical trees on the top and left of the heatmap, respectively. A total of 25 blastocysts (16 control, 9 oEV-treated) and 53 expanded blastocysts (28 control, 25 oEV-treated) were analyzed.

Figure 1 .

 1 Figure 1. Heatmap representation of hierarchical clustering of the differentially abundant lipid m/z according to the embryonic stage (blastocyst vs. expanded blastocyst) and exposure to oEVs (+oEVs).Each line corresponds to one molecular species. For a given species, green lines represent higher abundance while red lines represent lower abundance compared with other conditions. Black lines represent the median abundance values. The proximity between the conditions and lipid profiles are shown by the hierarchical trees on the top and left of the heatmap, respectively. A total of 25 blastocysts (16 control, 9 oEV-treated) and 53 expanded blastocysts (28 control, 25 oEV-treated) were analyzed.

Figure 2 .

 2 Figure 2. Volcano plots of lipid m/z significantly affected by oEV supplementation in blastocysts (1) and expanded blastocysts (2) (p-value < 0.05; fold-change >1.5 or <0.67). The fold-change of treated vs. control group was plotted against the -log10 p-value. Dots in red, orange, and blue indicate significantly altered PC or PE, SM, and LPC or LPE, respectively. Tables on the right indicate the differentially abundant masses that increased (1A and 2A) or decreased (1B and 2B) in oEV-treated embryos compared to controls. PC, phosphatidylcholine; PE, phosphatidylethanolamine; SM, sphingomyelin; LPC, lysophosphatidylcholine; LPE, lysoPE. Note that the first number in parentheses refer to the total number of carbons and the second to the number of double bonds in all chains. *Phospholipids also detected in oEVs (see Section 2.3).

Figure 2 .

 2 Figure 2. Volcano plots of lipid m/z significantly affected by oEV supplementation in blastocysts (1) and expanded blastocysts (2) (p-value < 0.05; fold-change >1.5 or <0.67). The fold-change of treated vs. control group was plotted against the -log10 p-value. Dots in red, orange, and blue indicate significantly altered PC or PE, SM, and LPC or LPE, respectively. Tables on the right indicate the differentially abundant masses that increased (1A,2A) or decreased (1B,2B) in oEV-treated embryos compared to controls. PC, phosphatidylcholine; PE, phosphatidylethanolamine; SM, sphingomyelin; LPC, lysophosphatidylcholine; LPE, lysoPE. Note that the first number in parentheses refer to the total number of carbons and the second to the number of double bonds in all chains. * Phospholipids also detected in oEVs (see Section 2.3).

Figure 3 .

 3 Figure 3. Relative abundance (mean ± SEM) of lipids significantly affected by oEV supplementation in blastocysts (top) and expanded blastocysts (down) and ordered in increasing m/z values (p-value < 0.05; fold-change > 1.5 or <0.67). The m/z cut-off at which lower lipid masses were less abundant after oEV supplementation and a majority of higher masses were more abundant is shown by a vertical purple dotted line. See all intensity values and p-values in supplementary data A.

Figure 3 .

 3 Figure 3. Relative abundance (mean ± SEM) of lipids significantly affected by oEV supplementation in blastocysts (top) and expanded blastocysts (down) and ordered in increasing m/z values (p-value < 0.05; fold-change > 1.5 or <0.67). The m/z cut-off at which lower lipid masses were less abundant after oEV supplementation and a majority of higher masses were more abundant is shown by a vertical purple dotted line. See all intensity values and p-values in supplementary data A.

4. 2 .

 2 Assessment of Embryo Development and Quality Two successive experiments were conducted by two teams of operators (including one person in common in the two experiments) in two different IVF laboratories (Experiment 1 in the experimental unit of Allice; Experiment 2 in the PRC unit of INRAE, Nouzilly, France), allowing us to provide enough embryos for the evaluation of developmental rates (Experiments 1 and 2), embryo quality (Experiment 1), and lipidomic profiles (Experiment 2), as summarized in Figure 4. Due to the collection of expanded blastocysts at day 7 in Experiment 1, blastocyst hatching was evaluated only in Experiment 2. Int. J. Mol. Sci. 2020, 21, x FOR PEER REVIEW 9 of 13culture medium at a final concentration of 0.05 mg of proteins/mL, starting on Day 1 with no renewal of the culture medium.4.2. Assessment of Embryo Development and QualityTwo successive experiments were conducted by two teams of operators (including one person in common in the two experiments) in two different IVF laboratories (Experiment 1 in the experimental unit of Allice; Experiment 2 in the PRC unit of INRAE, Nouzilly, France), allowing us to provide enough embryos for the evaluation of developmental rates (Experiments 1 and 2), embryo quality (Experiment 1), and lipidomic profiles (Experiment 2), as summarized in Figure4. Due to the collection of expanded blastocysts at day 7 in Experiment 1, blastocyst hatching was evaluated only in Experiment 2.

Figure 4 .

 4 Figure 4. Experimental design.

Figure 4 .

 4 Figure 4. Experimental design.

Table 1 .

 1 Developmental rates of bovine embryos exposed to oviduct fluid extracellular vesicles (oEVs) during their development in vitro.

	No

Experiment Treatment Number of Cumulus-Oocyte- Complexes (COCs) Cleaved on Day 2 Blastocyst Yield, N (Mean ± SEM, %) N (Mean ± SEM, %) Day 6 Day 7 Day 8

		Control	374	318 (85.3 ± 3.2)	10 (3.6 ± 1.1)	67 (21.5 ± 3.8)	105 (33.1 ± 3.8)
	Experiment 1	+oEV (treated)	374	316 (85.2 ± 2.4)	18 (4.2 ± 0.6)	85 (27.2 ± 2.8)	115 (40.1 ± 2.6)
		Control	587	468 (80.2 ± 2.0)	21 (4.4 ± 1.4)	77 (15.6 ± 2.8)	85 (20.7 ± 1.8)
	Experiment 2	+oEV (treated)	583	484 (83.8 ± 1.7)	20 (4.5 ± 0.9)	67 (15.9 ± 2.4)	65 (17.4 ± 1.9)

Int. J. Mol. Sci. 2020, 21, 5326; doi:10.3390/ijms21155326 www.mdpi.com/journal/ijms

Acknowledgments:

The authors are grateful to Marc Chodkiewicz for critical reading of this paper. M.S.D. thanks the Centre National de la Recherche Scientifique (CNRS) for full-time delegation in the PRC Unit.

Funding: This research was funded by APIS-GENE under the project name "Embryomimétisme", with additional funding from INRAE and four breeding companies (Union Evolution, Auriva, Origen+, Elitest). Charles Banliat was funded by Union Evolution and the Association Nationale de la Recherche Technologie (ANRT) as recipient of the Convention Industrielle de Formation par la Recherche N • 2017/0684. The RapiFlex MALDI-TOF instrument (Up-Phenotymass project) was financed by the CPER (Contrat Plan Etat Région) Biomédicaments project supported by the "Conseil Régional du Centre", the "Ministère de l'Education Nationale et de la Jeunesse" and the French National Institute for Agriculture, Food and Environment (INRAE).

Supplementary Materials:

The supplementary materials are available online at http://www.mdpi.com/1422-0067/ 21/15/5326/s1.

Author

Conflicts of Interest:

The authors declare no conflicts of interest.

Abbreviations