Pablo Gimeno
email: pablogj@unizar.es

Alfonso Ortega
email: ortega@unizar.es

Antonio Miguel
email: amiguel@unizar.es

Eduardo Lleida Vivolab

A Study on the Use of wav2vec Representations for Multiclass Audio Segmentation

Keywords: multiclass audio segmentation, unsupervised representation learning, wav2vec

This paper presents a study on the use of new unsupervised representations through wav2vec models seeking to jointly model speech and music fragments of audio signals in a multiclass audio segmentation task. Previous studies have already described the capabilities of deep neural networks in binary and multiclass audio segmentation tasks. Particularly, the separation of speech, music and noise signals through audio segmentation shows competitive results using a combination of perceptual and musical features as input to a neural network. Wav2vec representations have been successfully applied to several speech processing applications. In this study, they are considered for the multiclass audio segmentation task presented in the Albayzín 2010 evaluation. We compare the use of different representations obtained through unsupervised learning with our previous results in this database using a traditional set of features under different conditions. Experimental results show that wav2vec representations can improve the performance of audio segmentation systems for classes containing speech, while showing a degradation in the segmentation of isolated music. This trend is consistent among all experiments developed. On average, the use of unsupervised representation learning leads to a relative improvement close to 6.8% on the segmentation task.

Introduction

The goal of a generic audio segmentation system is to provide a sequence of labels that isolates different regions in an input audio signal according to the characteristics described in a predefined set of classes, e.g., speech, music or noise. So far, a number of different methods have been applied to audio segmentation. Literature reviews usually make a separation between two kind of systems: those based on the segmentation & classification approach, and those based on the segmentation by classification approach [START_REF] Theodorou | An overview of automatic audio segmentation[END_REF]. The first approach is usually known as distance-based due to the use of a distance metric to obtain class boundaries. Then, segments obtained are classified in a posterior stage. In the segmentation by classification approach, systems obtain its segmentation hypotheses as a direct sequence of decisions over the input audio. Traditionally statistical approaches have been applied to audio segmentation tasks [START_REF] Richard | Combined supervised and unsupervised approaches for automatic segmentation of radiophonic audio streams[END_REF] [3], however current state-of-the-art is based on deep learning models [START_REF] Bhattacharjee | Clean vs. overlapped speech-music detection using harmonic-percussive features and multi-task learning[END_REF] [START_REF] Venkatesh | You only hear once: a yolo-like algorithm for audio segmentation and sound event detection[END_REF]. Audio segmentation systems described in this paper are based on the segmentation by classification paradigm, relying mainly on deep learning solutions.

Supervised learning algorithms have played a crucial role in the evolution of deep learning applications, however the process of obtaining labelled data is very costly, and these models are strongly dependent on the amount of data they were trained on. Under these circumstances, unsupervised learning methods seek to reduce the need for labelled data by presenting a model with large amounts of unlabelled data. The model then learns to characterise input data by predicting different aspects of it. First occurrences of this idea happened in discrete data such as text, forcing a model to generate the next items [START_REF] Hochreiter | Long short-term memory[END_REF]. In the case of real valued signals, the initial approach was based on the minimisation of the reconstruction error of the signal [START_REF] Kingma | Auto-encoding variational bayes[END_REF]. Nevertheless, recent works have demonstrated that the use of pretext tasks, where the objective of the system is to solve a prediction as a classification, can lead to better representations. In many works, this target is to select an unseen fragment of the signal among other randomly selected distractor fragments [START_REF] Oord | Representation learning with contrastive predictive coding[END_REF].

Unsupervised learning representation has become an active research field in the audio and speech processing community, with different approaches and techniques being applied in different tasks [9] [10]. This strategy has been successfully developed with competitive performance in tasks such as speech recognition [START_REF] Schneider | wav2vec: Unsupervised pre-training for speech recognition[END_REF] [START_REF] Baevski | wav2vec 2.0: A framework for self-supervised learning of speech representations[END_REF], or speaker verification [START_REF] Sang | Selfsupervised speaker verification with simple siamese network and self-supervised regularization[END_REF]. Concerning audio segmentation applications, we can find examples of these methods being applied successfully in binary audio segmentation tasks, such as speech activity detection [START_REF] Gimeno | Unsupervised representation learning for speech activity detection in the fearless steps challenge 2021[END_REF], showing relevant results specially in scenarios with mismatched train and evaluation data. Another example can be found in [START_REF] Kreuk | Self-supervised contrastive learning for unsupervised phoneme segmentation[END_REF], that applies contrastive learning to a phoneme segmentation task.

Our previous experience in multiclass audio segmentation already demonstrated the capabilities of deep neural networks in the Albayzín 2010 dataset, seeking to separate speech, music, voice and a combination of them [START_REF] Gimeno | Multiclass audio segmentation based on recurrent neural networks for broadcast domain data[END_REF]. This work explored a number of neural architectures based on the use of bidirecional LSTM layers, successfully introducing a temporal pooling mechanism that improved the performance of the system without increasing the number of parameters. This paper builds on the knowledge acquired from these previous experiences, aiming to introduce the recent advances in unsupervised representation learning in the audio segmentation task. While all the previous experiments relied on the use of a combination of different sets of features, namely perceptual features such as log Mel filterbanks, or musical features such as chroma, the approach presented in this paper aims to replace these features with representations obtained through unsupervised learning.

The remainder of the paper is organised as follows: section 2 provides an overview of the full audio segmentation system, from the wav2vec representation learning to the neural network classifiers. Section 3 describes the experimental setup for this paper, focusing on the datasets and the metrics for evaluation. Results are presented and commented in section 4. Finally, an overview of the paper and some conclusions are provided in section 5.

System description 2.1. wav2vec representation learning

The original wav2vec system was presented in [START_REF] Schneider | wav2vec: Unsupervised pre-training for speech recognition[END_REF] in the context of speech recognition, following the ideas described in [START_REF] Oord | Representation learning with contrastive predictive coding[END_REF] about contrastive predictive coding. Our implementation is based on those works but with some variations that we explain in this section. A general overview of the system can be observed in the block diagram depicted in Figure 1. As it can be seen, the system is made of two different parts: a convolutional neural network (CNN) encoder and a recurrent neural network (RNN). The CNN encoder is composed of 10 strided 1D convolutional layers whose task is to map the input sequence Xt into a latent space Zt. This encoder works on top of the 16 kHz raw waveform, providing an output feature vector every 10 ms, as the total downsampling factor of the encoder is 160. The second main part of the system is a RNN, which in our implementation is a single layer gated recurrent unit (GRU) [START_REF] Cho | Learning phrase representations using RNN encoder-decoder for statistical machine translation[END_REF] with a hidden state of dimension 512. The output of the GRU layer for every temporal instant serves as the context embedding Ct that the system uses to predict 8 timesteps via a contrastive loss.

BERT text models [START_REF] Devlin | BERT: Pretraining of deep bidirectional transformers for language understanding[END_REF] and other works such as Decoar [START_REF] Ling | Deep contextualized acoustic representations for semi-supervised speech recognition[END_REF] already proposed the use of bidirectional embeddings in order to estimate previous and posterior timesteps, resulting in a loss term for each direction. Our wav2vec system follows this approach using a bidirectional GRU and computing the final loss as the sum of right and left embedding predictions. Thereby, our context embedding is a 1024-dimensional array that, after training, is extracted and used as the learned features. In the classifier training stage, the wav2vec model is frozen, so no fine tuning is performed.

Our approach to prediction differs slightly from the one presented in [START_REF] Schneider | wav2vec: Unsupervised pre-training for speech recognition[END_REF]. We use a single head with a single hidden layer [START_REF] Chen | A simple framework for contrastive learning of visual representations[END_REF] in order to predict future and past frames. Some of the different wav2vec models evaluated in this work use data augmentation techniques in training time. In those cases, augmented versions of the context embedding Ct serves as input to predict an unmodified reference Zt. We add noises from MUSAN [START_REF] Snyder | Musan: A music, speech, and noise corpus[END_REF] database sampling a uniform distribution in the range [START_REF] Lavner | A decision-tree-based algorithm for speech/music classification and segmentation[END_REF][START_REF] Kreuk | Self-supervised contrastive learning for unsupervised phoneme segmentation[END_REF] dB to obtain the signal to noise ratio (SNR). Furthermore, a variety of room impulse responses (RIR) are simulated using the gpuRIR toolkit [START_REF] Diaz-Guerra | gpuRIR: A python library for room impulse response simulation with gpu acceleration[END_REF]. The influence of data augmentation is evaluated by training different wav2vec models with or without this augmentation pipeline.

Neural network classifiers

Inspired by our previous work on the Albayzín 2010 audio segmentation task [START_REF] Gimeno | Multiclass audio segmentation based on recurrent neural networks for broadcast domain data[END_REF], the neural network architectures used in this work are based on the use of BLSTM layers. Specifically, we evaluate three different variants with increasing complexity that have been proven to show relevant results and that we describe in the following lines:

• 1BLSTM: a single BLSTM layer is used to process the input. Final classification scores are obtained through a linear layer. One segmentation label is emitted for every frame at the input, this is one each 10 milliseconds. Note that the wav2vec model is frozen during the classifier training stage and, therefore, is used only as a feature extractor. All classifier models have been trained using Adam optimiser, with a learning rate that decays exponentially from 10 -3 to 10 -4 during the 20 epochs that data is presented. Cross entropy loss is used as training objective as usually done in classification tasks.

Experimental setup

In this section we describe the datasets and metric used in this work. For the data description we make a separation between unlabeled data used to train the wav2vec system and the annotated data used to train the neural network classifiers. Note that unlike our previous work in [START_REF] Gimeno | Multiclass audio segmentation based on recurrent neural networks for broadcast domain data[END_REF], this work does not follow Albayzín 2010 evaluation conditions because we included several external datasets apart from the one provided for the original challenge.

Data for unsupervised feature representation

We distinguish three different wav2vec models depending on how they have been trained. Particularly, we differentiate if data augmentation has been used or not, and consider two training conditions: a first one trained with a smaller amount of data, and a second one using a larger amount of data in training. With this considerations three different models were evaluated:

• wav2vec base: this model was trained using mainly audio data in Spanish. Data from Albayzin 2018 and 2020 evaluations [START_REF] Lleida | Albayzin 2018 evaluation: the iberspeech-rtve challenge on speech technologies for spanish broadcast media[END_REF] and from the Spanish partition of the Commonvoice 2 dataset was used. This makes a total of around 1200 hours of unsupervised audio for training.

• wav2vec base no aug: this model was trained on the exact same training data as the wav2vec base model with the only difference that no data augmentation of any kind was applied in training time.

• wav2vec extended no aug: A larger amount of data was added to the datasets already described in the base model. Several well known English corpora were included in the training data: LibriSpeech, LibriLight, Tedlium and VoxCeleb 1&2. This makes a total of around 60000 hours of speech audio. Aditionally, a dataset of music scrapped from different sources, and presenting around 1700 hours of audio, was also considered, seeking to improve the modelling capabilities of the system in classes containing music. As done in the previous model, no data augmentation was applied in training time.

Data for multiclass audio segmentation

The multiclass audio segmentation task described in this paper was firstly proposed in the the Albayzín 2010 dataset [START_REF] Butko | Audio segmentation of broadcast news in the albayzin-2010 evaluation: overview, results, and discussion[END_REF]. The full database includes 87 hours of audio sampled at 16 KHz and belonging to the broadcast news domain. The database was separated into two parts, using two thirds of the data for training and the remaining third for testing. The database features five different acoustic classes with the following unbalanced distribution: 37% for clean speech (sp), 5% for music (mu), 15% for speech over music (sm), 40% for speech over noise (sn) and 3% for others (ot). The class "others" is not evaluated in the final test.

Metric & evaluation protocol

In order to evaluate our results we follow the same metric as the one proposed in the original Albazín 2010 evaluation. As shown in the following equation, the metric represents the average error obtained over all the acoustic classes:

Avg error = 1 |C| i∈C dur(missi) + dur(fai) dur(refi) , ( 1
)
where C is the set of acoustic classes defined in the evaluation, dur(missi) is the total duration of all miss errors for the ith acoustic class, dur(fai) is the total duration of all false alarm errors for the ith acoustic class, and dur(refi) is the total duration of the ith acoustic class according to the reference. seeking to avoid uncertainty in class transitions, a 1 second collar is not scored around each reference boundary.

Results

As a comparison to the results obtained using the presented wav2vec representations, we also evaluate the neural network classifiers using a set of traditional features that combines log Mel filterbank energies and chroma features. Namely, we concatenate 80 log Mel coefficients and the log energy of the frame with 12 chroma coefficients. Furthermore, first and second derivatives are computed and concatenated to the input. Experimental results showed that this frontend can lead to competitive results in the multiclass audio segmentation task [START_REF] Gimeno | Multiclass audio segmentation based on recurrent neural networks for broadcast domain data[END_REF].

In a first approximation, seeking to perform an exploratory analysis of the wav2vec representations capabilities, a t-SNE [START_REF] Van Der Maaten | Visualizing data using t-SNE[END_REF] dimensionality reduction of the best performing wav2vec system is computed in order to obtain a 2D visualisation. These features are also compared to the traditional frontend based on log Mel and chroma features. The mentioned representation extracted for the validation partition of the Albayzín 2010 dataset is presented in Figure 2. It can be observed that the use of a wav2vec representation provides a 2D t-SNE plane that is much more easily separable. The t-SNE plane obtained for the log Mel and chroma features shows only a limited number of clusters that can be identified, e.g., for the class others in orange. On the other hand, the wav2vec representation results in a plane where separation between classes is much more distinguishable, and that shows no significant overlap between classes such as speech and music , clean speech and speech and noise. In the case of the wav2vec system, the music class seems to obtain the most disperse representation, with several small sparse subclusters.

Having used a non-linear projection in order to study the class separability provided by the wav2vec representations, we aim now to evaluate these kind of systems in the multiclass audio segmentation task. In the following lines we describe the different experiments carried out with different wav2vec systems and different neural network classififers. Table 1 presents the results obtained using the 1BLSTM classifier on the Albayzín 2010 test partition with different frontend configurations, comparing traditional features and wav2vec representations, in terms of per class error and average error. It can be observed that the wav2vec base and wav2vec base no aug systems underperform when compared to the baseline system that uses log Mel and chroma features. This drop in performance comes mainly motivated by a large increase in the music class classification error. Furthermore, class error in the rest of classes remains similar, making that the average error increases due to both facts mentioned. In the case of the wav2vec base no aug model, which used no data augmentation in the pretraining stage of the wav2vec system, the error in the music and speech with music classes decreases compared to the model using data augmentation. Even though these differences are significant, they do not affect in a major way the average error observed, that remains similar to one from the version using data augmentation. Best results are observed when incorporating more data in the pretraining phase of the wav2vec system. The wav2vec extended noaug version yields a relative improvement of 5.2%, 16.03% and 6.7% respectively in the classes speech, speech with music and speech with music compared to the baseline frontend configuration. The music class error rate still remains high compared to the performance of the baseline system, limiting the overall performance of the wav2vec representations to a 5.3% relative improvement in the average error metric. Tables 2 and3 describe the results obtained using the 2BLSTM pool and 2BLSTM pool + mixup classifiers respectively on the Albayzín 2010 test partition comparing traditional features to the best performing wav2vec representation, in terms of per class error and average error. In general terms, trends observed in previous results hold for the new neural architectures evaluated in results reported in Tables 2 and3. Focusing first on results described in Table 2, that uses the 2BLSTM pool classifier, it can be appreciated that again all the classes but music show an improvement when using the wav2vec representation as input for the neural network. The improvement between the 1BLSTM classifier and the 2BLSTM pool classifier is consistent when using both the log Mel and chroma features and the wav2vec representation.

Concerning experiments depicted in Table 3, we evaluate the mixup augmentation strategy under the same conditions as described in [START_REF] Gimeno | Multiclass audio segmentation based on recurrent neural networks for broadcast domain data[END_REF]: applied on the feature space, and using 3 different α values 0.1, 0.2 and 0.3. The best performance is obtained using a setup with α = 0.2 for both the traditional set of features and the wav2vec representation. Furthermore, all setups showed an improvement when using mixup augmentation and the wav2vec representation. The best result in this paper using a wav2vec system is obtained with the 2BLSTM pool architecture, mixup augmentation and α = 0.2, achieving a relative improvement close to 6.8% when compared to the same setup using log Mel and chroma features. Even though wav2vec models show better performance than traditional features in classes containing speech, experimental results suggest that one limitation of the wav2vec representations evaluated come from its capacity to model music fragments, underperforming traditional features in all the experiments shown. This fact strongly limits the boost in average performance observed, that could be improved by decreasing music class error.

Conclusions

In this paper we have presented a study on the applications of self supervised learned features to the multiclass audio segmentation task. Namely, we have evaluated different wav2vec models seeking to jointly discriminate audio information belonging to speech, music and noise classes in the Albayzín 2010 dataset. Following previous studies that showed competitive performance using a combination of log Mel and chroma features, we apply the same neural network classifier models using the new wav2vec features seeking to obtain a comparable experimental setup. The task is evaluated using the average error among all acoustic classes as done in the original evaluation.

Experimental results demonstrate that the use of wav2vec representations can lead to a significant improvement in the performance of audio segmentation systems for classes containing speech. However, a degradation in performance is observed in the segmentation of isolated music when compared to traditional features. This limits the overall improvement observed to a relative improvement close to 6.8% on the Albayzín 2010 segmentation task. Further research should investigate the learning of more robust audio representations, capable of dealing with speech and music simultaneously. Some research is already working towards that direction [START_REF] Wang | Towards learning universal audio representations[END_REF] [START_REF] Turian | HEAR: Holistic evaluation of audio representations[END_REF].

Figure 1 :

 1 Figure 1: Block diagram of the wav2vec representation learning approach used in this work.

Figure 2 :

 2 Figure 2: t-SNE 2D representation of the Albayzín 2010 validation subset for the traditional set of features using log Mel and chroma (left), and the best performing wav2vec system (right).

Table 1 :

 1 Error per class and average class error for the 1BLSTM classifier on the Albayzín 2010 test partition for different frontend configurations.

	Features	mu	Class err.(%) sp sm	sn	Avg err.(%)
	80 Mel
	+chroma	16.28 28.82 26.32 31.94 25.84
	+deriv
	wav2vec base	21.46 28.09 26.72 30.35 26.65
	wav2vec
	base	20.37 28.21 25.87 31.04 26.37
	no aug
	wav2vec
	extended	19.65 27.32 22.10 28.80 24.47
	noaug

Table 2 :

 2 Error per class and average class error for the 2BLSTM pool classifier on the Albayzín 2010 test partition comparing traditional features and the best performing wav2vec representation.

	Features	mu	Class err.(%) sp sm	sn	Avg err.(%)
	Mel,chr, deriv	15.55 29.16 24.34 30.51 24.89
	w2v extended na	19.29 24.75 21.96 26.69 23.24

Table 3 :

 3 Error per class and average class error for the 2BLSTM pool + mixup classifier on the Albayzín 2010 test partition comparing traditional features and the best performing wav2vec representation.

	Features	α	mu	Class err.(%) sp sm	sn	Avg err.(%)
	Mel,chr, deriv	0.1	15.21 27.99 23.05 29.34 23.90
	w2v
	extended		19.27 24.87 20.40 26.62 22.79
	na
	Mel,chr, deriv	0.2	14.64 28.20 22.01 29.01 23.56
	w2v
	extended		17.41 24.58 20.01 26.23 22.06
	na
	Mel,chr, deriv	0.3	16.03 26.36 23.89 28.82 23.62
	w2v
	extended		18.66 25.70 20.78 26.65 22.95
	na

Acknowledgements

This work was supported in part by the European Union's Horizon 2020 research and innovation programme under Marie Skłodowska-Curie Grant 101007666; in part by MCIN/AEI/10.13039/501100011033 and by the European Union "NextGenerationEU"/PRTR under Grant PDC2021-120846-C41, and in part by the Government of Aragón (Grant Group T36 20R).


Jordi Haubrich

Jurriaan M Zwier

Fabienne Charrier-Savournin

Laurent Prézeau
email: laurent.prezeau@igf.cnrs.fr

Jean-Philippe Pin
email: jean-philippe.pin@igf.cnrs.fr.

Different EGF-induced receptor dimer conformations for signaling and internalization

Keywords: Epidermal Growth Factor Receptor, activation, TK inhibitors, internalization, conformation

The structural basis of the activation and internalization of EGF receptors (EGFR) is still a matter of debate despite the importance of this target in cancer treatment. Whether agonists induce dimer formation or act on pre-formed dimers remain discussed. Here we provide direct evidence that EGF-induced EGFR dimer formation as best illustrated by the very large increase in FRET between snap-tagged EGFR subunits induced by agonists. We confirm that Erlotinib-related TK inhibitors also induce dimer formation despite the inactive state of the binding domain. Surprisingly, TK inhibitors do not inhibit EGF-induced EGFR internalization despite their ability to fully block EGFR signaling. Only Erlotinib-related TK inhibitors promoting asymmetric dimers could slow down this process, while the lapatinib-related ones have almost no effect. These results reveal that the conformation of the intracellular TK dimer, rather than the known EGFR signaling is critical for EGFR internalization. These results also illustrate clear differences in the mode of action of TK inhibitors on the EGFR and open novel possibilities to control EGFR signaling for cancer treatment.

Introduction

Receptor tyrosine kinases (RTKs) are single pass membrane receptors that act by phosphorylating target proteins on tyrosine residues. These receptors are key in controlling cell division and differentiation, and can be critical in tumorigenesis. Among these, the Epidermal Growth Factor Receptor (EGFR) is one of the most-studied being expressed in many tissues and playing a vital role in biological processes such as apoptosis, cell growth and differentiation, migration and more [START_REF] Wee | Epidermal Growth Factor Receptor Cell Proliferation Signaling Pathways[END_REF]. EGFR is actually the target of lots of therapeutic biomolecules, especially for the treatment of cancers [START_REF] Roskoski | Small molecule inhibitors targeting the EGFR/ErbB family of proteintyrosine kinases in human cancers[END_REF]. Unfortunately, EGFR-targeting therapies rapidly induce resistance that requires new treatment to overcome them [START_REF] Chhouri | Mechanisms of Acquired Resistance and Tolerance to EGFR Targeted Therapy in Non-Small Cell Lung Cancer[END_REF][START_REF] Shi | Emerging strategies to overcome resistance to third-generation EGFR inhibitors[END_REF].

The EGFR has seven endogenous agonists, that are all small proteins [START_REF] Harris | EGF receptor ligands[END_REF]. Upon binding to the extracellular domain, EGFR activators promote allosteric processes between two assembled EGFRs leading to the association of the intracellular tyrosine kinase (TK) domains, and the activation of one of them. EGFR activity is exclusively occurring when the two TK domains adopt an asymmetric conformation, i.e. there is an enzymatically inactive (Activator) and active (Receiver) domain [START_REF] Kovacs | Analysis of the Role of the C-Terminal Tail in the Regulation of the Epidermal Growth Factor Receptor[END_REF]. The Receiver domain initiates ATP-dependent phosphorylation of tyrosine residues on the intracellular domain of the Activator.

Phosphorylation of its carboxy-tail residues leads to downstream signaling events such as activation of ERK1/2 and PI3K/AKT/mTOR (Mendoza et al, 2011a;Mendoza et al, 2011b).

However, the precise mechanism of EGFR activation is not fully understood, and several steps are still highly debated [START_REF] Maruyama | Mechanisms of activation of receptor tyrosine kinases: monomers or dimers[END_REF]. Indeed, at least three different mechanisms have been reported for EGFR activation. First, a ligand-induced dimerization has been described (Zhou et al, 1993), as example using single molecule analysis at very low receptor density avoiding preformed EGFR dimer [START_REF] Chung | Spatial control of EGF receptor activation by reversible dimerization on living cells[END_REF][START_REF] Huang | Molecular basis for multimerization in the activation of the epidermal growth factor receptor[END_REF]. It has also been proposed that at physiological receptor expression levels, preformed dimers are reorganized upon agonist binding, leading to full receptor activation [START_REF] Chung | Spatial control of EGF receptor activation by reversible dimerization on living cells[END_REF][START_REF] Purba | Allosteric activation of preformed EGF receptor dimers by a single ligand binding event[END_REF][START_REF] Purba | Activation of the EGF Receptor by Ligand Binding and Oncogenic Mutations: The "Rotation Model[END_REF][START_REF] Zanetti-Domingues | The architecture of EGFR's basal complexes reveals autoinhibition mechanisms in dimers and oligomers[END_REF]. Third, it has recently been reported that monomeric EGFR can be directly activated by EGF [START_REF] Srinivasan | Ligand-induced transmembrane conformational coupling in monomeric EGFR[END_REF]. Elucidation of the mechanism is crucial for the design of new molecules (like allosteric biomolecules) controlling receptor activity, desensitization and internalization. Indeed, as observed for many other receptors, different ligand-stabilized conformation may lead to different properties of the targeted receptor [START_REF] Trenker | Receptor tyrosine kinase activation: From the ligand perspective[END_REF]. Indeed, EGFR ligands with different properties have been identified, and understanding their mode of action may open novel possibilities for drug design [START_REF] Huang | A molecular mechanism for the generation of ligand-dependent differential outputs by the epidermal growth factor receptor[END_REF].

In the present study we aimed at better understanding the EGFR structural basis for it kinase activation, its signaling and internalization properties. Using time-resolved (TR) Förster resonance energy transfer (TR-FRET) technologies, we provide clear evidence that EGFRs are mainly in a monomeric form, and undergo dimerization upon agonist activation. We provide further evidence that the first-generation TK inhibitors and dacomitinib also induce EGFR dimer formation, through a direct association of the TK domains, independently of the conformation of the extracellular domain. Surprisingly, TK inhibitors do not inhibit EGF-induced EGFR internalization, demonstrating EGFR kinase activity is not required for this process. Importantly, TK inhibitors inducing dimers slow down EGF-induced internalization of the EGFR, revealing a link between TK inhibitorinduced EGFR conformation and EGFR trafficking. These data reveal that differential effects induced by TK inhibitors can result from different conformation of the ligand-induced EGFR dimers.

Results

Intersubunit FRET induced by agonists and group I TK inhibitors through the TK domain.

Intersubunit FRET was measured after randomly labelling the SNAP-tags with SNAP-Lumi4-Tb (donor) and SNAP-Green (acceptor) (Fig. 1A) [START_REF] Doumazane | A new approach to analyze cell surface protein complexes reveals specific heterodimeric metabotropic glutamate receptors[END_REF] in monoclonal stable cell lines expressing SNAP-EGFR (Supp Fig. 1) or SNAP-EGFRNSCLC. While a very low FRET was measured under basal condition (Fig. 1B, insert), agonists and the group I TK inhibitors, dacomitinib, erlotinib and PD153035 induced a large increase in intersubunit FRET (Fig. 1B, C and Suppl. Fig. 2 and3A). Since FRET efficacy is related to distance of the FRETpair, total FRET levels may change depending on the conformational changes and on the number of molecules in FRET. While the Group I TK inhibitor dacomitinib induced an effect similar to that of EGF, the other Group I TKIs erlotinib and PD153035 induced a lower TR-FRET signal than EGF, and similar or lower than the effect of the partial agonist TGF-a (Fig. 1B), raising the question whether this is due to a different conformation or to a lower number of induced dimers. In contrast, the other TK inhibitors, GW583340 and lapatinib (group II inhibitors), osimertinib, cetuximab (i.e. an inhibitory antibody) and AG1024 (i.e. an insulin-like growth factor receptor inhibitor) did not induce intersubunit FRET (Fig. 1C and Suppl. Fig. 2 and3A).

The FRET between two EGFRs induced by the group I TK inhibitor occurs independently of the dimerization of the extracellular domain, as this effect could still be observed with an EGFR mutant with a disrupted dimerization arm (EGFRdel242-249) (Fig. 1D and Suppl. Fig. S3C). As expected, the agonists had no effect on this mutant. On the contrary, EGF and not erlotinib induced intersubunit FRET on a receptor deleted of its intracellular domain (EGFRdelCter) [START_REF] Scholler | HTS-compatible FRET-based conformational sensors clarify membrane receptor activation[END_REF] (Fig. 1D and Suppl. Fig. S3D). This showed that erlotinib induces intersubunit FRET of the EGFR through the TK domain independently of the dimerization arm. This proposal was confirmed for the full-length EGFR, when exposure of the dimerization arm was prevented by cetuximab (Fig. 2A). Notably, the TR-FRET signal induced by erlotinib was not affected by cetuximab, while the effect of EGF was fully inhibited (Fig. 2A). As control, we verified that the absence of effect of cetuximab is not due to a specific conformation stabilized by erlotinib that could prevent cetuximab binding. Indeed, cetuximab could still inhibit the binding of the antibody Ab528 labelled with d2 (Ab528-d2) in the presence of erlotinib (Fig. 2B,C). Indeed, Ab528 and cetuximab both compete to EGF but also to each other, because they bind at overlapping sites on the dimerization arm [START_REF] Makabe | Anti-EGFR antibody 528 binds to domain III of EGFR at a site shifted from the cetuximab epitope[END_REF], Thus, Ab528 prevents EGFinduced EGFR dimerization as cetuximab does. These data are consistent with the increase in intersubunit FRET being the result of ligand-induced dimerization rather than conformational changes of the extracellular domain.

Group I TK inhibitors induce less dimers than agonists

Whether EGF acts by allowing dimer formation, or through conformational changes within pre-formed dimers is still a matter of debate [START_REF] Purba | Activation of the EGF Receptor by Ligand Binding and Oncogenic Mutations: The "Rotation Model[END_REF]. The absence of effect of cetuximab, or of the deletion of the dimerization arm on TK inhibitor-induced intersubunit FRET is more consistent with ligand-induced dimerization.

To further study this possibility, we examined why some TK inhibitors induced a lower maximal FRET signal than the full agonist EGF. This can be due either to a different conformation of the extracellular part of the EGFR dimer, which would be consistent with pre-formed dimers, or to a lower proportion of receptors in FRET than with agonist. To clarify this point, we analyzed the excited-state lifetime of the sensitized acceptor emission (τDA) as an indication of the conformational state of the receptor, as τDA is related to the distance between the fluorophores.

As a control for this approach, we analyzed the SNAP-metabotropic glutamate (mGlu) 4 -CLIP-mGlu2 receptor heterodimer, a prototypical preformed dimer both subunits being linked by a disulphide bond [START_REF] Rondard | Coupling of agonist binding to effector domain activation in metabotropic glutamate-like receptors[END_REF]. In that case, a change in FRET cannot be due to a different proportion of dimers, and then only rely on a change in distance due to conformational changes. The C termini of the subunits were modified by addition of the endoplasmic reticulum retention sequences of the GABAB2 (C2) and GABAB1 (C1) to the respective receptors, so that only heterodimeric receptors were expressed at the cell surface (Suppl. Fig. S4A) [START_REF] Kniazeff | Closed state of both binding domains of homodimeric mGlu receptors is required for full activity[END_REF]. We labelled each mGlu2-4 heterodimer subunit with SNAP-Lumi4-Tb (donor) and CLIP-Green (acceptor), and measured the FRET signal.

In principle, the excited-state lifetime of the FRET donor emission (τD) is in the millisecond range, and decreases when the donor's excited state is subject to other deactivation pathways such as FRET. As the τDA is proportional to the distance between a FRET donor and acceptor, distinct FRET donor-acceptor distances can be determined. The τDA is measured in a time-resolved manner to discriminate between non-specific and specific signal [START_REF] Heyduk | Luminescence energy transfer with lanthanide chelates: interpretation of sensitized acceptor decay amplitudes[END_REF].

In the basal mGlu2-4 condition, τDA was 343 ± 25 µs, corresponding to a high FRET conformation. The τDA was largely increased to 564 ± 22 µs in the presence of the mGlu2 agonist LY379268, indicating a low FRET conformation (Fig. 3A andB) [START_REF] Doumazane | Illuminating the activation mechanisms and allosteric properties of metabotropic glutamate receptors[END_REF][START_REF] Scholler | HTS-compatible FRET-based conformational sensors clarify membrane receptor activation[END_REF]. These data confirm that a conformational change within constitutive dimers can be detected by measuring the τDA values.

For the SNAP-EGFR, the τDA measured in the presence of agonists or group I TK inhibitors were not significantly different (Fig. 3C andD, Suppl. Table S3), despite differences in TR-FRET intensity (Suppl. Fig. 4B). This suggests that the larger TR-FRET level measured with agonists is due to a higher proportion of receptors in FRET, rather than to a distinct donor-acceptor distance. Such data are then consistent with TK inhibitors stabilizing less EGFR dimers than agonists.

Inhibition of EGF-induced phosphorylation of EGFR and ERK1/2 by TK inhibitors

We then evaluated the efficacy of the inhibitors for EGF-induced phosphorylation of either EGFR or ERK1/2. Phosphorylation of tyrosine residue 1068 of the EGFR (Y1068) and of threonine residue 202 and tyrosine residue 204 of ERK1/2, as detected by the HTRF® signals in an antibody-based sandwich assay (Fig. 4A and Suppl. Fig. 5), are inhibited efficiently by group I, group II TK inhibitors and cetuximab and less efficaciously by group III TK inhibitor osimertinib. Conversely, irrelevant TK inhibitor AG1024 did not inhibit phosphorylation of the EGFR or ERK1/2 (Fig. 4B, C and Suppl. Fig. 5B,D).

TK inhibitors stabilizing dimers slow down EGF-induced EGFR internalization

Then, we setup an internalization assay for the EGFR based on diffusion-enhanced resonance energy transfer (DERET) [START_REF] Levoye | A Broad G Protein-Coupled Receptor Internalization Assay that Combines SNAP-Tag Labeling, Diffusion-Enhanced Resonance Energy Transfer, and a Highly Emissive Terbium Cryptate[END_REF]. In principle, SNAP-EGFR is labelled with Lumi4-Tb and an excess of fluorescein is added to each well, thereby generating DERET and quenching the Lumi4-Tb emission. Upon internalization of the EGFR, the Lumi4-Tb signal is recovered (Fig. 4D) allowing the detection of EGFR internalization in living cells over time.

We observed a basal internalization in the absence of agonist, but EGF largely increases EGFR internalization, that reaches a plateau after 39 min, followed by a decline, suggesting receptor recycling to the cell surface. Cetuximab fully inhibited EGF-induced internalization but not the basal internalization (Suppl. Fig. 7). Surprisingly, TK inhibitors did not inhibit EGFR internalization (Fig. 4F and Suppl. Fig. 7) as group I TK inhibitors only slowed down EGF-induced EGFR internalization (Fig. 4E), whereas others had no effect (Suppl. Fig. 7).

Despite not inhibiting internalization, it is clear that group I TK inhibitors were more efficacious in slowing down EGFR internalization than the group II and III TK inhibitors and irrelevant TK inhibitor AG1024 (Fig. 4F and Suppl. Fig. 7). We compared internalization of EGFR with fluorescence microscopy, showing comparable results (Suppl. Fig. 6).

The combined data of the 4 assays revealed a different pharmacological profile of each group of compounds for the wild-type EGFR as represented in Figure 5 (Suppl. Fig. S8). Moreover, bias plots for the agonists demonstrated that EGF and TGF-α are more potently inducing internalization and phosphorylation of ERK1/2 (Suppl. Fig. S9). Among the TK inhibitors, only PD153035 more potently inhibited phosphorylation of Y1068 than ERK1/2 (Suppl. Fig. S10).

NSCLC-mutated EGFR is insensitive to TK inhibitor-induced dimerization

EGFRNSCLC has an increased activity within the asymmetric dimer [START_REF] Ruan | Altered conformational landscape and dimerization dependency underpins the activation of EGFR by alphaC-beta4 loop insertion mutations[END_REF] and it was suggested that the presence of EGFRNSCLC could increase dimer formation [START_REF] Zanetti-Domingues | The architecture of EGFR's basal complexes reveals autoinhibition mechanisms in dimers and oligomers[END_REF]. To investigate if EGFRNSCLC has an increased propensity to induce dimer formation, we developed an intersubunit FRET assay specifically detecting heterodimers of wild-type EGFR and EGFRNSCLC or homodimers of EGFRNSCLC (Fig. 6A). For both the heterodimer and the homodimers, EGF induced dimer formation with a similar potency as for wild-type EGFR (Fig. 6B).

As the EGFRNSCLC is resistant to erlotinib-like TK inhibitors due to the T790M substitution in the ATP binding pocket [START_REF] Brewer | Mechanism for activation of mutated epidermal growth factor receptors in lung cancer[END_REF], we were curious whether erlotinib could induce dimer formation in presence of this mutant. Interestingly, erlotinib could not induce formation of dimers containing EGFRNSCLC, either with both subunits mutated, or suggesting that both subunits of an asymmetric TK domain need to be bound by TK inhibitors to induce TK domain dimer formation (Fig. 6C and Suppl. Fig. S11).

Discussion

Although of clinical importance, several aspects of EGFR activation and the mode of action of various TK inhibitors remain unclear. This study brings clear evidence for the agonist-induced dimer formation model of EGFR activation. It also reveals that formation of dimers can be induced by some TK inhibitors through interaction of the TK domains, resulting from a specific conformation of the TK dimer. Eventually, our data show that EGFinduced internalization is mainly driven by a specific conformation of the EGFR dimer, and not by receptor activity, allowing some, but not all TK inhibitors to slow down this process.

Despite years of research on EGFR two models of receptor activation were still discussed [START_REF] Purba | Activation of the EGF Receptor by Ligand Binding and Oncogenic Mutations: The "Rotation Model[END_REF]. One model proposes that agonists like EGF act by promoting dimer formation [START_REF] Chung | Spatial control of EGF receptor activation by reversible dimerization on living cells[END_REF], while the other proposes that agonists stabilize a specific conformation of a pre-formed EGFR dimer [START_REF] Maruyama | Mechanisms of activation of receptor tyrosine kinases: monomers or dimers[END_REF].

Our data strongly support the first model. Indeed, through FRET measurements between N-terminal tags, with cell surface receptors labelled exclusively, we detected a very low FRET signal under basal condition in a cell line stably expressing SNAP-EGFR (Suppl. Fig. S1). This signal is largely increased (9-fold) in the presence of agonist (Fig. 1B). The low FRET under basal condition is unlikely due to pre-formed dimers with a conformation leading to a large distance between the N-termini carrying the SNAP-tags, for two main reasons. First, although a very low FRET was measured under the basal condition, the estimated tDA is only twice of that measured with the active dimer (Fig. 3C), consistent with the low FRET resulting mainly from a very low number of dimers. Such finding is also consistent with the distances between the N-termini of the proposed inactive and active dimers [START_REF] Zanetti-Domingues | The architecture of EGFR's basal complexes reveals autoinhibition mechanisms in dimers and oligomers[END_REF]. Second, when dimers were stabilized through their TK domains using some TK inhibitors, the extracellular domains do not appear to interact via the dimerization arm, and are then likely in an inactive conformation possibly similar to that proposed by others for the pre-formed dimers [START_REF] Zanetti-Domingues | The architecture of EGFR's basal complexes reveals autoinhibition mechanisms in dimers and oligomers[END_REF]. If so, such pre-formed dimers should generate a FRET signal with a tDA similar to that obtained with the EGF bound EGFR dimers. Accordingly, our data are consistent with a very small proportion of the EGFR subunits at the cell surface involved in FRET, either because of a low proportion of pre-formed dimers, or because of random collisions of EGFR monomers (by-stander FRET).

Consistent with previous studies [START_REF] Bublil | Kinase-mediated quasidimers of EGFR[END_REF][START_REF] Coban | Effect of phosphorylation on EGFR dimer stability probed by single-molecule dynamics and FRET/FLIM[END_REF][START_REF] Gan | The epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor AG1478 increases the formation of inactive untethered EGFR dimers. Implications for combination therapy with monoclonal antibody 806[END_REF], some TK inhibitors can also induce dimer formation. Because the dimerization arm is not necessary, it suggests a main role of the TK domain interaction in this process. It is interesting to note that TK inhibitors inducing dimer formation can bind either to the Activator-like or the Receiver like TK domain [START_REF] Park | Erlotinib binds both inactive and active conformations of the EGFR tyrosine kinase domain[END_REF] (Fig 7), thereby allowing the formation of asymmetric TK domain dimers, as expected in the active form of the EGFR dimer [START_REF] Ferguson | Structure-based view of epidermal growth factor receptor regulation[END_REF]. The proportion of the Activator and Receiver forms may then dictate the number of possible dimers -i.e. the higher the proportion of one species (i.e. Activator or Receiver), the lower the number of possible Activator-Receiver dimers. This explains why TK inhibitors are not being able to promote the formation of the same amounts of dimers as EGF (Suppl. Fig. S4). In contrast, TK inhibitors stabilizing a specific conformation of the TK domain would not favor dimer formation according to this model, in agreement with what observed here. This reveals the critical importance of the effect of TK inhibitors on the TK domain conformation on their capacity to stabilize EGFR dimers.

As already reported [START_REF] Bjorkelund | Resolving the EGF-EGFR interaction characteristics through a multiple-temperature, multiple-inhibitor, real-time interaction analysis approach[END_REF], EGFR can rapidly engage into internalization upon agonist activation, though the number of internalized receptors rapidly declined after a peak, possibly due to receptor recycling to the cell surface. Such a process has been known to limit EGFR signaling and can involves clathrin coated pits [START_REF] Goh | Multiple mechanisms collectively regulate clathrin-mediated endocytosis of the epidermal growth factor receptor[END_REF][START_REF] Gong | Dynamin2 downregulation delays EGFR endocytic trafficking and promotes EGFR signaling and invasion in hepatocellular carcinoma[END_REF][START_REF] Tomas | EGF receptor trafficking: consequences for signaling and cancer[END_REF][START_REF] Vieira | Control of EGF receptor signaling by clathrinmediated endocytosis[END_REF]. Internalized EGFR have also been shown to still generate specific signals before most of them are being degraded [START_REF] Goh | Multiple mechanisms collectively regulate clathrin-mediated endocytosis of the epidermal growth factor receptor[END_REF][START_REF] Tomas | EGF receptor trafficking: consequences for signaling and cancer[END_REF]. This agonist-induced EGFR internalization has soon been assumed to result from EGFR activity [START_REF] Tomas | EGF receptor trafficking: consequences for signaling and cancer[END_REF][START_REF] Wiley | The role of tyrosine kinase activity in endocytosis, compartmentation, and down-regulation of the epidermal growth factor receptor[END_REF]. Indeed, deletion of the TK domain, or the M721 mutation that fully inhibits TK activity prevent agonist-induced EGFR internalization [START_REF] Tomas | EGF receptor trafficking: consequences for signaling and cancer[END_REF][START_REF] Wiley | The role of tyrosine kinase activity in endocytosis, compartmentation, and down-regulation of the epidermal growth factor receptor[END_REF]. Surprisingly, we show here that this process is not prevented by any of the TK inhibitors tested despite their full inhibitory effect on agonist-induced receptor phosphorylation and ERK activation. As such, the internalization process may likely be the result of a specific conformation of the dimer. Such a hypothesis is still compatible with the absence of internalization of a TK domain deleted receptor. As for the M721 mutation that also prevents internalization, it remains possible that this mutation also affects the general conformation of the TK dimer within the activated receptor, but this remains to be clarified. Interestingly, the differential effect of TK inhibitors -those not promoting dimers having no effect on the internalization process while those promoting dimers slow down this processhighlights an essential role of the TK dimer conformation. However, it is surprising to see that TK inhibitors promoting EGFR dimer formation, do not promote internalization.

To explain these apparently contradictory results, one should consider the possible conformation of the intracellular part of the receptor. Indeed, TK inhibitors that stabilize the Activator state of the TK domain, such as lapatinib, do not promote dimer formation, and do not affect EGF-induced internalization (Fig 7). This suggests that a symmetric EGFR dimer in which both TK domains are in the inactive Activator state, are perfectly prone to internalization after direct association of the extracellular domains. In contrast, TK inhibitors like erlotinib that can stabilize either the Activator or the Receiver state (Fig 7), promote dimer formation likely through a stable asymmetric TK domain dimer composed of an Activator and Receiver TK domain. They do not induce receptor internalization, while they only slow down EGF-induced internalization. As such, one is tempted to propose that the asymmetric Activator-Receiver dimer is not prone to internalization. Such hypothesis explains our results, but would certainly need further support to be fully validated.

Previously, agonist-induced conformational models of the EGFR [START_REF] Freed | EGFR Ligands Differentially Stabilize Receptor Dimers to Specify Signaling Kinetics[END_REF][START_REF] Hajdu | Comprehensive Model for Epidermal Growth Factor Receptor Ligand Binding Involving Conformational States of the Extracellular and the Kinase Domains[END_REF] and the role of agonist binding kinetics in ligand bias have been described [START_REF] Kiyatkin | Kinetics of receptor tyrosine kinase activation define ERK signaling dynamics[END_REF][START_REF] Wilson | Functional selectivity of EGF family peptide growth factors: implications for cancer[END_REF]. For TK inhibitors, such studies are less well known, whereas it could improve understanding of their mode of action. Generation of pharmacological profiles and bias plots has recently been proposed as mode to improve EGFR drug design [START_REF] Karl | Ligand bias in receptor tyrosine kinase signaling[END_REF]. Techniques for determining pharmacological profiles or ligand bias are more common in the research field of G protein-coupled receptors (GPCRs) and contributing to GPCRs being the most targeted family of receptors by drugs on the market [START_REF] Hauser | Trends in GPCR drug discovery: new agents, targets and indications[END_REF][START_REF] Kenakin | Functional selectivity and biased receptor signaling[END_REF]. In this study, such techniques have been adapted for an analysis of the EGFR signaling.

Efforts to expose functional selectivity induced by EGFR ligands indicated that agonists have different signaling kinetics by stabilizing different conformations of the extracellular domain [START_REF] Freed | EGFR Ligands Differentially Stabilize Receptor Dimers to Specify Signaling Kinetics[END_REF]. Bias plots for agonists EGF and TGF-α reveal that they are more potent for phosphorylation of ERK and internalization than dimerization and phosphorylation of Y1068 (Suppl. Fig. S9). Moreover, we found that most TK inhibitors inhibit phosphorylation of Y1068 and ERK with similar potency, except for PD153035, which more potently inhibits phosphorylation of Y1068 (Suppl. Fig. S10C). Some TK inhibitors may impact EGF binding through allosteric modulation of the EGFR conformation [START_REF] Hajdu | Comprehensive Model for Epidermal Growth Factor Receptor Ligand Binding Involving Conformational States of the Extracellular and the Kinase Domains[END_REF][START_REF] Macdonald-Obermann | Allosteric regulation of epidermal growth factor (EGF) receptor ligand binding by tyrosine kinase inhibitors[END_REF]. We show that TK inhibitors stabilize distinct dimers, resulting in altered EGFR trafficking. Previous reports linked dimerization to improved cellular survival [START_REF] Coban | Effect of phosphorylation on EGFR dimer stability probed by single-molecule dynamics and FRET/FLIM[END_REF] and decreased efficacy for some TK inhibitors [START_REF] Oashi | Monomer Preference of EGFR Tyrosine Kinase Inhibitors Influences the Synergistic Efficacy of Combination Therapy with Cetuximab[END_REF]. Moreover, disruption of dimerization could be an antitumoral mechanism as well [START_REF] Cho | Cetuximab response of lung cancer-derived EGF receptor mutants is associated with asymmetric dimerization[END_REF], confirming its significance. The role of internalization in tumor survival is not fully clear, nonetheless it has been suggested that wild-type NSCLC patients (i.e. no resistance mutations) could benefit from blocking clathrin-mediated endocytosis of EGFR [START_REF] Jo | EGFR endocytosis is a novel therapeutic target in lung cancer with wild-type EGFR[END_REF][START_REF] Kim | Clathrin-mediated EGFR endocytosis as a potential therapeutic strategy for overcoming primary resistance of EGFR TKI in wild-type EGFR non-small cell lung cancer[END_REF]. This implies that ligands that reduce EGFR internalization (i.e. dacomitinib, erlotinib, PD153035) could induce positive outcomes for wild-type EGFR in NSCLC patients.

To our best knowledge there are no reports of TK inhibitors inducing dimerization of the EGFRNSCLC or heterodimers of wild-type EGFR and EGFRNSCLC. In our model we do not observe pre-formed homo-and heterodimers containing EGFRNSCLC, as the dimers are mainly observed upon EGF or TGF-a activation. The potencies for dimerization are not significantly different from wild-type EGFR (Suppl. Table S2). None of the tested TK inhibitors induces dimerization, suggesting there is a direct or indirect loss of potency for the heterodimer due to altered binding (45,46) or increased affinity for ATP [START_REF] Yun | The T790M mutation in EGFR kinase causes drug resistance by increasing the affinity for ATP[END_REF]. In tumor cells of NSCLC patients, different populations of EGFR heterodimers could exist, among them wild-type EGFR-EGFRNSCLC [START_REF] Brewer | Mechanism for activation of mutated epidermal growth factor receptors in lung cancer[END_REF] and EGFR-ErbB2 heterodimers (Zhao & Xia, 2020). Investigations on the effect of drugs on these heterodimeric receptors, could help improving treatment strategy as they may function as additional drug targets. Another approach could be the use of allosteric modulators to decrease off-target effects [START_REF] Jia | Overcoming EGFR(T790M) and EGFR(C797S) resistance with mutant-selective allosteric inhibitors[END_REF]. The use of allosteric compounds like EAI045 for treating NSCLC has recently been reviewed [START_REF] Maity | Advances in targeting EGFR allosteric site as anti-NSCLC therapy to overcome the drug resistance[END_REF].

Overall, this study reveals the importance of the conformational state of the TK domains within the EGFR dimer in signaling and trafficking. As TK inhibitors have various effect on such conformations, this may explain their biased effects on dimerization and internalization, properties that likely have some importance in the cellular physiology of EGFRs. Eventually, promoting EGFR internalization in the presence of TKI can be a novel approach to limit EGFR signaling for treatment.

Materials and methods

Reagents and the protocols for cell culture, cell preparation for experiments, generation of SNAP-EGFRNSCLC and CLIP-EGFR plasmids, lipofectamine transfection for experiments, generation of HEK293 cells stably expressing SNAP-EGFR and SNAP-EGFRNSCLC, intersubunit FRET, binding and displacement of Ab528-d2, ERK1/2 phosphorylation, phosphorylation of tyrosine residue 1068 of the EGFR (Y1068), internalization, fluorescence microscopy and data analysis can be found in the supplementary information.

Monoclonal stable cell lines stably expressing SNAP-EGFR or SNAP-EGFRNSCLC were generated by lipofectamine transfection. Positive cells were selected by G418 and single cells were sorted with fluorescenceactivated cell sorting. An estimated 256,000 individual SNAP-EGFRs (i.e. 4-fold lower than A341 squamous carcinoma) are present on the cell surface and expression levels were stable over time (Suppl. Fig. S1).

Excited state-lifetime of sensitized acceptor emission to determine receptor conformations

At 24 hours after lipofectamine transfection (for SNAP-mGlu4-C2-KKXX and CLIP-mGlu2-C1-KKXX constructs) or transfer of cells stably expressing SNAP-EGFR into a 96-wells plate, medium was replaced by icecold DMEM containing SNAP-Lumi4-Tb (100 nM) and SNAP-Green (125 nM) for SNAP-labelling and CLIP-Lumi4-Tb (1 µM) and CLIP-Green (1 µM) for CLIP-labelling. Cells were incubated for 90 minutes at 4 °C and carefully washed four times with ice-cold Tag-Lite buffer. LY379268 (100 µM), EGF (100 nM), TGF-α (100 nM), dacomitinib (10 µM), erlotinib (10 µM), PD153035 (10 µM), AG1478 (10 µM), GW583340 (5 µM), lapatinib (10 µM), osimertinib (10 µM), cetuximab (10 nM) or vehicle was incubated in ice-cold Tag-Lite buffer for 30 minutes at 4 °C. Luminescence decay at 520 nm was measured after 150 flashes/well with the UV-pulsed nitrogen laser (337 nm) of the PHERAstar FS microplate reader. Decay was measured from 50 to 5000 µs and was fitted using the biexponential decay function in GraphPad Prism software (version 9.0.1.), which is the preferred model in an extra sum-of-squares F-test compared to a mono-exponential decay function. The excited-state lifetime of the sensitized acceptor emission (τDA) was calculated with a least-squares fit. The apparent amplitude of slow (As) and fast (Af) components of the biexponential decay may vary due to adaptation to multiple conformations or interactions with the antenna, increasing the complexity of its decay [START_REF] Selvin | Principles and biophysical applications of lanthanide-based probes[END_REF]. Typically, the apparent Af was larger than As whereas this value is likely overestimated and should be corrected [START_REF] Heyduk | Luminescence energy transfer with lanthanide chelates: interpretation of sensitized acceptor decay amplitudes[END_REF]. The true fraction of the slow decay species (αDA) is based on the resonance energy transfer rate constant, as described by Heyduk et al. [START_REF] Heyduk | Luminescence energy transfer with lanthanide chelates: interpretation of sensitized acceptor decay amplitudes[END_REF]. After applying this correction, αDA is in the range of 0.75-0.88 for all conditions (Suppl. Table S1).

ERK 1/2 activation and EGFR-P-Y1068 HTRF ® assays

Measurement of ERK1/2 activation was performed using the Cellul'Erk immunoassay kit from REVVITY according to the manufacturer recommendations (REVVITY, Codolet, France). The assay is based on the use of a combination of a cryptate(donor)-labeled anti-ERK monoclonal antibody and a d2(acceptor)-labeled antiphospho-ERK monoclonal antibody. After stimulating the cells with the indicated ligands, cells were lysed and the lysates were transferred to 384 well plates, where both antibodies were added. The plates were read for TR-FRET signal 2 hours later. When both antibodies are bound to the receptor c-terminal domain, the UV excitation of the donor will generate a d2 signal. Similarly, pY1068-EGFR assays is based on the use two antibodies recognizing either all states of the C-term domain of EGFR or the C-terminal domain containing a phosphorylated form of Y1068 (REVVITY, Codolet, France). Readings were performed on a PheraStar FS reader (BMG, Champigny-sur-Marne, France).

Internalization assay

Briefly, the SNAP-tagged EGF receptor transfected cells were seeded in 96 well plates, and 24 hours later, they were washed with cold Krebs buffer and incubated on ice with BG-Lumi4 Tb at a concentration of 100 nM during a one hour period. The cells were washed and incubated with indicated drugs and diluted in fluorescein buffer (24 µM) at 37°C during readings, which were performed on a PheraStar FS reader (BMG, Champigny-sur-Marne, France).

FiguresFig. 1 .Fig. 2 .Fig. 3 .

 123 Figures

Fig. 4 .

 4 Fig.4. Phosphorylation of EGFR and ERK1/2, and internalization of EGFR. (A) Scheme of antibody-based sandwich assays to measure EGFR and ERK1/2 phosphorylation. (B) Inhibition of EGF-induced (10 nM) EGFR phosphorylation by 1 µM of dacomitinib, erlotinib, PD153035, lapatinib, osimertinib and AG1024, 0.3 µM GW583340 or 100 nm cetuximab. Inhibitors were pre-incubated for 30 minutes and phosphorylation was measured 30 minutes after stimulation with EGF. Inhibitory effect induced by addition of the inhibitors on the EGF action were analyzed using a Brown-Forsythe test followed by a one-way ANOVA. (C) Inhibition of EGF-induced (10 nM) ERK1/2 phosphorylation by 1 µM of dacomitinib, erlotinib, PD153035, lapatinib, osimertinib and AG1024, 0.3 µM GW583340 or 100 nm cetuximab. Inhibitors were pre-incubated for 30 minutes and phosphorylation was measured 5 minutes after stimulation with EGF. For B) and C) Effect of all inhibitors is highly significant (****), as analyzed with a Brown-Forsythe test followed by a one-way ANOVA, except for the non-specific inhibitor AG1024 (NS), compared to the EGF effect. (D) Scheme of DERET-based EGFR internalization assay. (E) Representative data for EGFR internalization assay in presence of 10 nM EGF (blue), EGF and 1 µM erlotinib (red) or vehicle (gray). (F) EGF-induced EGFR internalization in presence of 1 µM of dacomitinib, erlotinib, PD153035, lapatinib, osimertinib and AG1024, 0.3 µM GW583340 or 100 nm cetuximab. Inhibitors were pre-incubated for 2 hours and EGF-internalization was measured after 39 minutes. Statistical analysis was performed using a Brown-Forsythe test followed by a one-way ANOVA. Data in B-C and F are mean ± SEM of at least three individual experiments. Data in E is represented as mean ± SD.

Fig. 5 .Fig. 6 .Fig. 7 .

 567 Fig. 5. Pharmacological profile of TK inhibitors. (A) Group I TK inhibitors dacomitinib (blue), erlotinib (red), PD153035 (green). (B) Group II TK inhibitors GW583340 (purple) and lapatinib (orange). (C) Group III TK inhibitor osimertinib (gray). (D) Cetuximab (brown). All data are from figures 1-4.

ACKNOWLEDGEMENTS. We thank the ARPEGE platform facilities at the Institut de Génomique

Fonctionnelle for all fluorescence-based assays and the MRI platform facilities at the Institut de Génétique Humaine for FACS and microscopy assays. We thank dr. Robert Quast for discussions. J.-P.P. was supported by la Fondation pour la Recherche Médicale (ref. DEQ20170336747), Cisbio Bioassays (EIDOS collaborative team IGF-CISBIO, ref. 039293), the Fond Unique Interministériel of the French government (FUI, Cell2Lead project), the Agence Nationale pour la Recherche (ANR 18-CE11-0004-01) and LabEx MAbImprove (ref. NR-10-LABX-5301). J.-P.P. and L.P. were further supported by the Centre National de la Recherche Scientifique and the Institut National de la Santé et de la Recherche Médicale. J.H. was supported by a fellowship from la Région Occitanie and Cisbio Bioassays (TransACT, ref. 156544) and la Ligue contre le cancer (PhD grant, ref. IP/SC-16487).

Data Availability Statement

The data were obtained using molecular and cell signaling assays, mostly based on TR-FRET measurements, and formatted into tables and graphs, and few microscope-generated images. All raw data and analyzed tables, graphs and images have been downloaded in the repository site of the "Université de Montpellier" and made available at the following URL: https://doi.org/10.57745/HASFBQ,.

Disclosure and competing interest statement


Hadrien Lalagüe
email: hadrienlalague@yahoo.fr

Julien Pétillon

Light attraction hypothesis in Arachnids: a new test in neotropical forests

Keywords: French Guiana, pitfall trap, phototaxis, diameter effect

Patterns of phototaxis are incompletely characterized in arachnids and hardly generalizable because of large variations in orders, families, species, environments, and methods employed. In a neotropical forest of French Guiana, we tested the effect of both light and diameter on pitfall trap catches. Light had a significant effect on capture rates of all arachnids and on Araneae alone, with more individuals caught in lit traps. Without light, pitfall diameter had no effect on capture rates, while in lit traps, significantly fewer individuals were captured only in smaller traps. Light trapping is thus a promising tool to complete inventories in tropical forests. This field experiment eventually calls for further studies of the mechanisms by which arachnids are attracted by light, especially by unraveling an actual phototaxis from indirect effects like prey attraction.

Accepted manuscript

Artificial light acts as a signal that alters the behavior of a wide range of animal species [START_REF] Gaston | The ecological impacts of nighttime light pollution: A mechanistic appraisal[END_REF], mainly by directly triggering attraction or repulsion. Phototaxis behavior was largely investigated in insects which revealed a majority of positive phototaxis cases at adult stages (e.g., [START_REF] Kim | Advances in insect phototaxis and application to pest management: a review[END_REF]). Yet, little is known about the phototactic responses in arachnids. A well-known example of positive phototaxis has been demonstrated for two species of camel spiders (Solifugae: [START_REF] Linsenmair | Zur Lichtorientierung der Walzenspinnen (Arachnida, Solifugae)[END_REF]). This feature was then used to lure and trap them in three studies [START_REF] Conrad | Observations on hunting behavior of juvenile Chanbria (Solifugae: Eremobatidae)[END_REF][START_REF] Cushing | Capturing the elusive camel spider (Arachnida: Solifugae): Effective methods for attracting and capturing solifuges[END_REF][START_REF] Graham | A test of the light attraction hypothesis in camel spiders of the Mojave Desert (Arachnida: Solifugae)[END_REF]). In the last study, [START_REF] Graham | A test of the light attraction hypothesis in camel spiders of the Mojave Desert (Arachnida: Solifugae)[END_REF] evaluated the efficiency of trapping under lit and unlit conditions and showed that lit traps captured a higher number of camel spiders than the unlit traps. Similarly, a higher abundance and diversity were found in grassland plots under artificial light compared to unlit plots for several arthropod orders, including Araneae [START_REF] Mcmunn | Artificial Light Increases Local Predator Abundance, Predation Rates, and Herbivory[END_REF]. By contrast, light had no effect on most wandering wolf spiders Rabidosa rabida (Walckenaer, 1837) with only 24.2% of individuals showing a positive phototaxis [START_REF] Lizotte | Nocturnal capture of fireflies by lycosid spiders: visual versus vibratory stimuli[END_REF]. Other studies that investigated the effect of artificial light on the behavior of nocturnal arachnids mainly focused on orb-web spiders and demonstrated both positive [START_REF] Heiling | Why do nocturnal orb-web spiders (Araneidae) search for light?[END_REF][START_REF] Heiling | The importance of being larger: Intraspecific competition for Accepted manuscript prime web sites in orb-web spiders (Araneae, Araneidae)[END_REF] and negative phototaxis [START_REF] Nakamura | Phototactic behavior of nocturnal and diurnal spiders: negative and positive phototaxes[END_REF][START_REF] Gomes | Orb-weaving spiders are fewer but larger and catch more prey in lit bridge panels from a natural artificial light experiment[END_REF].

Most previous studies took place in temperate zones and open environments (desert, grasslands, riversides, urbanized area), none being undertaken in tropical rainforest forests that are characterized by a very dark understory and where the high vegetation density may decrease the artificial light range. In such a close environment, it is unlikely that arachnids navigate at night by traveling at constant angle to the moon or stars. Therefore, the use of artificial light is not expected to disorient or attract them.

In this study, we investigated the light attraction hypothesis on Arachnida in a neotropical rainforest with a protocol slightly modified from that of [START_REF] Graham | A test of the light attraction hypothesis in camel spiders of the Mojave Desert (Arachnida: Solifugae)[END_REF]. The sampling took place in the natural reserve Trésor on the Mountains of Kaw in French Guiana (4°36'31.204818"N, 52°16'47.531959"W). To minimize the impact of sampling in this nature reserve, only adults and juveniles from Mygalomorphae of interest were collected, while all the others were identified at the order or sub-order level, counted and released. To capture live specimens, we used pitfall traps with their inner walls covered by polytetrafluoroethylene (PTFE) that drastically decreases the adhesion of spider's scopula and prevents escape [START_REF] Poerschke | Adhesion of Individual Attachment Setae of the Spider Cupiennius salei to Substrates With Different Roughness and Surface Energy[END_REF]. Tests prior to the field sampling were successfully conducted in laboratory using four adults of Ephebopus murinus Accepted manuscript (Walckenaer, 1837) and Ctenus sp. (Theraphosidae and Ctenidae families, respectively). Pitfall traps were arranged in lines of three traps each. Two lines of pitfall traps were set per plot, with two plots in each of the 3 locations: a talweg close to a small river, mature forest on a hill plateau and disturbed forest close to a road. One of the two lines of pitfall traps per plot was equipped with an incandescent 4W light from a CDC (Centers for Disease Control) trap positioned 60cm above the central pitfall trap and powered by a rechargeable battery. This kind of light is known to attract a large spectrum of airborne insects [START_REF] Wakefield | Experimentally comparing the attractiveness of domestic lights to insects: Do LEDs attract fewer insects than conventional light types?[END_REF]. The three locations were separated on average by 192 m (SD = 15), the plots within location were separated on average by 75 m (SD = 23) and the lines of pitfall traps within plot were separated on average by 25 m (SD = 12). The distance between plots and lines of pitfall traps was depended on the vegetation density to ensure that light was not visible from the unlit line of pitfall traps. Each line of pitfall traps was covered by an 8 x 3 m polyethylene tarp to protect them from rain. A polyethylene drift-fence of 8 m and 30 cm high was set across the line of pitfall traps to maximize the capture rate [START_REF] Boetzl | It's a matter of design-how pitfall trap design affects trap samples and possible predictions[END_REF].

Three diameters of pitfall traps, 20, 30, and 40 cm with the same depth of 20 cm, were used in each line. We tested such large pitfall traps because [START_REF] Brennan | Determination of an optimal pitfall trap size for sampling spiders in a Western Austalian Jarrah forest[END_REF] showed a correlation between pitfall diameter and capture rate within a range of 4.3 to 17.4 cm, and because large Mygalomorphae are not captured with the traps of 10 cm of diameter previously used in French Guiana [START_REF] Privet | Spider assemblage structure in a neotropical rainforest-inselberg complex: Ecological and methodological insights from a small-scale intensive survey[END_REF][START_REF] Privet | Relative effciency of pitfall trapping vs. nocturnal hand collecting in assessing soil-dwelling spider diversity along a structural gradient of neotropical habitats[END_REF]. Dead leaves were placed on the bottom to provide refuge and minimize possible interactions between trapped specimens. All traps were checked every morning for five consecutive days, and the number of trapped specimens was recorded in each pitfall trap. As we had no specific hypothesis regarding location effects, and to increase sample size, we show results of a model with the data from the three locations combined. For all arachnids, adding location to the model did not change the outcome regarding the effect of light.

Analyses were conducted with R 4.1.2 (R Core Team, 2021) and the package stats. Normality of the datasets was checked with Shapiro-Wilk normality test and equality of the variance was checked with Fisher's F-Test (function var.test). For all arachnids we used Student's t-test (function t.test). For Araneae alone and Mygalomorpahe alone, as one of the group was not normally distributed (lit group and unlit group respectively), nonparametric Wilcoxon rank sum test was used (function wilcox.test). In this study, we hypothesized that light has no effect on the capture rate and that larger pitfall trap diameters has higher capture rate.

Accepted manuscript

A total of 202 arachnids were trapped, mostly composed of Opiliones (49.5%), followed by Araneae (44%,). Only 14 (6.9%) Mygalopmorphae, 11 (5.4%) Scorpiones were captured, and only one specimen (0.5%) of Amblypygi and Uropygi were captured. Concerning arachnids, the number of captures was higher in lit traps (M = 21.2; SD = 7.4) than in unlit traps (M = 12.5; SD = 5.6); Student's t-test t(10) = -2.29, P = 0.045). Concerning the Araneae alone, the number of captures was higher in lit traps (M = 9.8, SD = 4.9) than in unlit traps (M = 5.0, SD = 2.4); Wilcoxon rank sum W = 4, P = 0.028). Probably due to the low sample size, no significant effect was found for Mygalomorphae (W = 14, P = 0.55), despite the same tendency of lit pitfall trap capturing a greater number of individuals (M = 1.3; SD = 1.5) than in unlit pitfall traps (M = 1.0; SD = 1.7). It is also interesting to note that, among the 11 Scorpiones, 8 were trapped in the lit pitfall traps. The higher number of arachnids in lit pitfall traps may be explained either by direct positive phototaxis or by higher abundance of prey, as it is well known that many groups of insects are attracted by artificial lights. As an example, McMunn et al. (2019) also found a higher abundance of arthropod predators in plots under artificial light compared to unlit plots.

Without light, pitfall trap diameter had no effect on the capture rate, which is contrary to the results of [START_REF] Brennan | Determination of an optimal pitfall trap size for sampling spiders in a Western Austalian Jarrah forest[END_REF] who found a positive relationship between capture rate and pitfall trap diameter (ranging in their study from 4.3, to 17.4cm). With light, a significant difference was observed between the diameters of 20 vs. 30 cm (Student's t-test t(10) = 3.68, P = 0.004), with fewer arachnids captured in pitfall traps of the smaller diameter. Even if the difference was not significant between the 30 and 40 cm diameters, it is interesting that the larger diameter did not collect more arachnids than the medium diameter. The presence of the light right above the central pitfall trap of medium diameter may explain the higher capture rate compared to pitfall traps farther from the light source. This result strengthens the strong effect light has on tropical arachnids. It remains uncertain whether the higher capture rate under the light treatment was directly due to phototaxis or indirectly due to an increase in insect activity in the vicinity producing both substrate and airborne vibrations. Nevertheless, these preliminary results are promising for further studies in the diversity of arachnids in tropical rainforests.

ACKNOWLEDGEMENTS

Accepted manuscript

This work was funded by the Réserve Naturelle Régionale Trésor. We thank the agents of the reserve the valuable help before and during the fieldwork. The study was authorized with the sampling permit 2021-6854. We thank the Institut Pasteur de Guyane and Stanislas Talaga for the loan of the CDC traps. We finally thank Seyna Smoes, Mathieu Robain, Nathan Zuanon and Guillaume Mazeau, the students from the FTH classes managed by AgroParisTech for their help during the fieldwork, and Yael Lubin and two anonymous referees for comments on an earlier draft.

Accepted manuscript