Thomas Pellegrini

Romain Zimmer

Timothée Masquelier

LOW-ACTIVITY SUPERVISED CONVOLUTIONAL SPIKING NEURAL NETWORKS APPLIED TO SPEECH COMMANDS RECOGNITION

Keywords: Spiking neural networks, surrogate gradient, speech command recognition

Deep Neural Networks (DNNs) are the current stateof-the-art models in many speech related tasks. There is a growing interest, though, for more biologically realistic, hardware friendly and energy efficient models, named Spiking Neural Networks (SNNs). Recently, it has been shown that SNNs can be trained efficiently, in a supervised manner, using backpropagation with a surrogate gradient trick. In this work, we report speech command (SC) recognition experiments using supervised SNNs. We explored the Leaky-Integrate-Fire (LIF) neuron model for this task, and show that a model comprised of stacked dilated convolution spiking layers can reach an error rate very close to standard DNNs on the Google SC v1 dataset: 5.5%, while keeping a very sparse spiking activity, below 5%, thank to a new regularization term. We also show that modeling the leakage of the neuron membrane potential is useful, since the LIF model outperformed its non-leaky model counterpart significantly.

INTRODUCTION

Deep Neural Networks (DNNs) are the current stateof-the-art models in many speech related tasks. From a computational neuroscience perspective, DNNs can be seen as rate coding based models [START_REF] Edgar | The impulses produced by sensory nerve-endings: Part ii. the response of a single end-organ[END_REF], in the sense that if a neuron is responsive to a given stimulus, then if we augment the stimulus intensity, the neuron output intensity will also increase. Temporal coding based models [START_REF] Dayan | Theoretical neuroscience: computational and mathematical modeling of neural systems[END_REF] try to also take into account information carried by the temporal structure of the stimulus. In the case of Spiking Neural Networks (SNNs), spike timing and delays between spikes is important in order to retrieve patterns in the spike sequences given as input to a model.

There is a growing interest for SNNs applied to speech recognition tasks, from isolated word and phone recognition [START_REF] Tavanaei | Bioinspired multi-layer spiking neural network extracts discriminative features from speech signals[END_REF][START_REF] Wu | A biologically plausible speech recognition framework based on spiking neural networks[END_REF][START_REF] Zhang | A digital liquid state machine with biologically inspired learning and its application to speech recognition[END_REF][START_REF] Bellec | Long short-term memory and learning-to-learn in networks of spiking neurons[END_REF],to large-vocabulary automatic speech recognition (ASR) very recently [START_REF] Wu | Deep spiking neural networks for large vocabulary automatic speech recognition[END_REF]. Reasons are that the audio speech signal is particularly suited to event-driven models such as SNNs, SNNs are also more biologically realistic than DNNs, hardware friendly and energy efficient models, if implemented on dedicated energy-efficient neuromorphic chips. Furthermore, it has been shown recently that SNNs can be trained efficiently, in a supervised manner, using backpropagation with a surrogate gradient trick [START_REF] Emre O Neftci | Surrogate gradient learning in spiking neural networks: Bringing the power of gradientbased optimization to spiking neural networks[END_REF]. This new approach allows to train SNNs as one would do for DNNs.

In this work, we propose to use supervised SNNs for speech command (SC) recognition. We explore the Leaky Integrate-and-Fire (LIF) neuron model for this task, and show that convolutional SNNs can reach an accuracy very close to the one obtained with state-ofthe-art DNNs, for this task. Our main contributions are the following: i) we propose to use dilated convolution spiking layers, ii) we define a new regularization term to penalize the averaged number of spikes to keep the spiking neuron activity as sparse as possible, iii) we show that the leaky variant of the neuron model outperforms the non-leaky one (NLIF), used in [START_REF] Wu | Deep spiking neural networks for large vocabulary automatic speech recognition[END_REF].

In order to facilitate reproducibility, our code using PyTorch is available online1 .

OVERVIEW OF THE LEAKY INTEGRATE-AND-FIRE MODEL (LIF)

The Leaky Integrate-and-Fire (LIF) model [START_REF] Richard B Stein | Some models of neuronal variability[END_REF] is a phenomenological model of a biological spiking neuron, that describes how the neuron membrane potential U behaves through time, given an input current I comprised of spikes incoming from afferent neurons. Despite its simplicity, this model is very popular in com-putational neuroscience, for studies of neural coding, memory, and network dynamics [START_REF] Gerstner | Spiking neuron models: Single neurons, populations, plasticity[END_REF]. From a machine learning point of view, it was shown to outperform more complex models at predicting real neuron activity [START_REF] Kobayashi | Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold[END_REF][START_REF] Gerstner | How good are neuron models?[END_REF], and it is the basic brick of many recent Spiking Neural Networks (SNNs).

In Integrate-and-Fire (IF) models, a spike (output pulse) δ(tt f )δ being the Dirac δ-function -is generated at the firing time t f when the potential U crosses from below a threshold value b. The potential is then instantly reset to a new value U rest . In the standard leaky variant, the sub-threshold dynamics of the membrane potential of the i th neuron are described by the differential equation Eq. 1 [START_REF] Emre O Neftci | Surrogate gradient learning in spiking neural networks: Bringing the power of gradientbased optimization to spiking neural networks[END_REF]:

τ mem dU i dt = -(U i (t) -U rest ) + RI i (t) (1)
where U i (t) is the membrane potential at time t, U rest is the resting membrane potential, τ mem is the membrane time constant, I i is the current injected into the neuron and R is the membrane resistance. When U i exceeds a threshold B i , the neuron fires and U i is decreased. The -(U i -U rest ) term is the leak term that drives the potential towards U rest .

If there is no leak, the model is called Non-Leaky Integrate and Fire (NLIF) and the corresponding differential equation is:

τ mem dU i dt = RI i (2)
Without loss of generality, we take R = 1 hereafter. The input current can be defined as the projection of the input spikes along the preferred direction of neuron i given by W i , the i th row of W, the synaptic weight matrix:

I i = ∑ j W ij S in j (3)
where S j (t) = ∑ k δ(tt j k ) if neuron j fires at time t = t j 1 , t j 2 , .... With this formulation, the potential will rise instantaneously when input spikes are received. Alternatively, the current can be governed by a leaky integration of these projections, similar to the membrane potential. For the sake of simplicity, we chose to use the instantaneous formulation for the input current.

The differential equations of LIF models can be approximated by linear recurrent equations in discrete time. Introducing a reset term U R i [n] for the potential, the neuron dynamics can now be fully described by the following equations [START_REF] Zimmer | Technical report: supervised training of convolutional spiking neural networks with PyTorch[END_REF].

U R i [n 1] = b i ||W i || 2 S l i [n 1] I i [n] = ∑ j W ij S l 1 j [n] U i [n] = β(U i [n 1] -U R i [n 1]) + I i [n] (4)
S l i [n] = Θ(U i [n] -b i ||W i || 2 )
where β = exp(-∆t τ mem ), with ∆t is a time step, Θ is the Heaviside step function and b i is the threshold of neuron i.

With these equations, LIF neurons can be modeled as Recurrent Neural Network (RNN) cells whose state and output at time step n are given by (U[n], I[n]) and S[n], respectively [START_REF] Emre O Neftci | Surrogate gradient learning in spiking neural networks: Bringing the power of gradientbased optimization to spiking neural networks[END_REF].

In practice, we used a trainable threshold parameter b i , such that:

S i [n] = Θ( U i [n] W i 2 + -b i ) (5)
with b i initialized to 1 and = 10 -8 . We normalize the potential with W i 2 to avoid squashing the value of the gradients during training. Note that we may choose to also optimize the leak parameters β, during training. Impact of learning those and the thresholds will be discussed in Section 10.

In [START_REF] Wu | Deep spiking neural networks for large vocabulary automatic speech recognition[END_REF], the authors based their SNNs on a non-leaky variant of IF models. In this case, solving Eq. 2 and with our notations, Eq. 4 boils down to:

U i [n] = U i [n 1] -U R i [n] + I i [n] (6)
We will report results using this NLIF formulation in Section 10.

SURROGATE GRADIENT

In order to train SNNs in a supervised fashion, just as we do in standard deep learning using the backpropagation algorithm and stochastic gradient descent, we need to address the issue regarding the calculation of the gradient of the threshold function. Indeed, the gradient of the Heaviside function is zero everywhere and is not defined at zero. No gradient would be propagated unless we approximate it by a smooth function. That has been proposed in [START_REF] Emre O Neftci | Surrogate gradient learning in spiking neural networks: Bringing the power of gradientbased optimization to spiking neural networks[END_REF], in the name of a surrogate gradient function. In our work, we approximate the gradient of the Heaviside step function by the gradient of a sigmoid function, with a scale parameter a ≥ 0 to control the quality of the approximation. The sigmoid function we use is:

sig a (x) = 1/(1 + exp (-ax)) ( 7
)
where a is the scale parameter, x a real-valued input. The larger a, the steeper the curve. This hyperparameter can be set empirically.

Hence, when using our SNN, on the forward pass the Heaviside function is used, whereas on the backward pass, the gradients are computed using the sigmoid gradient:

Θ (x) ≈ sig a (x) = a sig a (x) sig a (-x) (8)

SPIKING LAYERS

The proposed SNN is a feed-forward model with multiple spiking layers, and a decision output layer, sometimes called a readout layer. The input of a spiking layer is a spike sequence, also called a spike train, except for the first layer, for which the input is a multidimensional real-valued signal (log-FBANK coefficients). Each layer outputs a spike train except for the readout layer that outputs real values that can be seen as a linear combination of spikes, and on which predictions are made.

For fully-connected spiking layers, the input current at each time step would be a weighted sum of the input spikes emitted by the previous layer at the given moment (or a weighted sum of the input signal if it is the first layer). The state and the output of the cells are updated following the equations given in Section 2.

Just as in standard deep learning, we expect convolution filters to be pertinent for our task, given that our input are spectrograms. We thus defined and implemented convolutional spiking layers, as described in Algorithm 1. In the 2-d case (time×frequency), the input current at each time step is given by a 2-d convolution between a kernel and the input spike train. The output spike train is computed through a for loop on the time steps, using the equations of the LIF model. As a side note, when processing spike trains as input, convolution in time can be seen as propagation delays of the input spikes. It is well known that such delays enrich the network's dynamics [START_REF] Izhikevich | Polychronization: computation with spikes[END_REF] and expressivity [START_REF] Maass | On the Complexity of Learning for Spiking Neurons with Temporal Coding[END_REF]. Apply threshold function to compute S out [n] 10: end for We used the readout layer proposed in [START_REF] Emre O Neftci | Surrogate gradient learning in spiking neural networks: Bringing the power of gradientbased optimization to spiking neural networks[END_REF], with nonfiring neurons (no reset). For classification tasks, the dimension of the output is equal to the number of labels. The label probabilities are given by a Softmax function applied to the maximum value over time of the membrane potential of each neuron.

In practice, we have found that using time-distributed fully connected layer and taking the mean activation of this layer over time as output makes training more stable. Thus, the output is the mean over time of a linear combination of input spikes.

REGULARIZING THE SPIKING ACTIVITY

A desirable property of SNNs would be energy efficiency, meaning that their neuron spiking activity should be as sparse as possible. We would like the number of emitted spikes by each spiking layer as small as possible, while still performing the requested task with high accuracy. This property is also desirable from a biological point of view, since biological neurons are very energy-efficient and emit limited amounts of spikes in a given amounts of time. If sparse, patterns of activity might also be more explainable.

In order to enforce sparse spiking activity, one could apply a 1 or 2 regularization on the total number of spikes emitted by each layer. However, due to the surrogate gradient, some neurons will be penalized even if they have not emitted any spike. As a regularization term, we propose to use the squared number of spikes at each layer l:

L r (l) = 1 2KN ∑ n ∑ k S 2 k [n]
where K is the number of neurons and N is the number of time steps. Using S k [n] 2 instead of S k [n] is a simple way to ensure that the regularization will not be applied to neurons that have not emitted any spikes, i.e. for which S k [n] = 0. Indeed,

dS 2 k [n] dU k [n] = 2S k [n]sig a (U k [n])
which is zero when S k [n] = 0. We will report the regularization impact in the experiment section.

GOOGLE SPEECH COMMANDS DATASET AND PREPROCESSING

The Google Speech Commands dataset [START_REF] Warden | Speech commands: A dataset for limited-vocabulary speech recognition[END_REF] is a dataset of short audio recordings (at most 1 second, sampled at All experiments were conducted on the first version of the dataset. The task considered is to discriminate among 12 classes: silence, unknown, "yes", "no", "up", "down", "left", "right", "on", "off", "stop", "go". Commands that are not in the ten target classes are labeled as unknown and silence training data are extracted from the background noise files provided with the dataset. We used the standard validation and testing subsets provided with the corpus to evaluate the accuracy of our approach.

Forty log-Mel coefficients were extracted from raw signals with LibROSA [START_REF] Mcfee | librosa: v0.4.0[END_REF], using a Mel scale defined between 20 Hz and 4000 Hz. We used a window size of 30 ms and a hop length of 10 ms, which also corresponds to the time step of the simulation ∆t. The resulting input feature maps are of dimension 100 × 40 in channels, time and frequency. Finally, the spectrograms are re-scaled to ensure that the signal in each frequency band has a variance of 1 across time.

MODEL ARCHITECTURE

Fig. 1 shows the architecture of our proposed SNN for the speech commands recognition task.

We used three stacked convolution spiking layers and an output/readout layer, which is a timedistributed fully connected layer with twelve output neurons for the twelve classes to be predicted. Each convolution layer has one β learnable parameter controlling the time constant of the layer, C channels and one learnable threshold parameter b per channel.

Table 1 shows the details of our best model. Kernels are of size H = 4 along the time axis and W = 3 along the frequency axis. All convolutional layers have a stride of 1 and dilation factors of d H and d W along time and frequency axes respectively. The dilation coefficients were chosen so that the receptive fields r grow exponentially according the layer index: 4 × 3, 16 × 9 and 64 × 27 for the three convolution layers, respectively. With these settings, the total number of trainable parameters is 0.13M parameters.

The scale a of the surrogate gradient was empirically set to ten. Values below gave worse results and higher values did not bring improvements.

TRAINING DETAILS

The model was trained using 128-samples minibatches, with the Rectified-Adam optimizer [START_REF] Liu | On the variance of the adaptive learning rate and beyond[END_REF], with a learning rate of 10 -3 , for 20 epochs with one epoch of warmup, an exponential learning rate scheduler with a 0.85 coefficient, and a weight decay of 10 -5 . We used a weighted random sampler to address the unbalanced number of samples of each class. The thresholds b (one per channel) and the β (one per layer) parameters were randomly initialized with normal distributions of respective means 1 and 0.7, and 0.01 standard deviations. Gradient values were clipped to [-5, 5], β to [0, 1] and b to [0, +∞[. For each convolution layer l = 1, ..., L, the spiking activity regularization loss L r (l) was added with a 0.1 weighting coefficient. One training epoch took 15 minutes on a 16-GB Tesla V100.

RESULTS

Fig. 2 shows an example of a log-Mel spectrogram for the word "off" (a), the corresponding output after the first convolutional spiking layer (b), for one of its channels, and the network output predictions (c), after training. As can be seen, the first layer binarizes the input spectrogram ; each channel with a different threshold. In Fig. 2 (c), the twelve curves correspond to the twelve non-normalized score values at the different time steps (X-axis). As can be seen, one class gets high scores fast, at the start of the recording, indicating that the convolution layers integrated the relevant information. Globally speaking, our SNN achieved 5.5% error rate (ER) on the standard test subset. In comparison, the best standard deep learning models reported in the literature achieved error rates from 3% to 6% [START_REF] Coimbra De Andrade | A neural attention model for speech command recognition[END_REF][START_REF] Warden | Speech commands: A dataset for limited-vocabulary speech recognition[END_REF]. In [START_REF] Coimbra De Andrade | A neural attention model for speech command recognition[END_REF], for instance, a ResNet-8 (110k trainable parameters, close to our model size of 131k params), and a ResNet-16 (248k parameters) models reached 5.9% and 4.2% ER on the same dataset. Regarding the unknown class, precision is lower for this class than for the other classes: 80% compared to 94% in average for all the classes. Recall is similar to other classes: about 94%. The silence class is perfectly recognized.

Our implementation keeps track of the number of spikes emitted by each of the convolution layers of the network. On the test subset, the averaged spiking rates are 2.6%, 4.9% and 6.1%, for the first, second and third convolution layers, respectively. Thus, the layer activation is around 5%, which is sparse as desired.

After training, we observe that the β values decrease in average with the convolution layer index: 0.60, 0.35, 0.23, for the three layers. The smaller the beta, the faster the leak and the more the neurons forget the past and reacts to the incoming spikes. The values of the thresholds b also follow this pattern in average: 0.9541, 0.9434, 0.9284. This is consistent with the previous observation that the deeper the layer is in the network, the lower the thresholds are and the more spikes are emitted.

ABLATION STUDIES

In order to estimate the impact of different components of our model, we conducted four complementary experiments: 1) when we use non-dilated convolution layers instead of dilated ones, 2) when we remove the spiking activity regularization, 3) when we simplify the LIF model to be the non-leaky Integrate-and-Fire model (NLIF) from [START_REF] Wu | Deep spiking neural networks for large vocabulary automatic speech recognition[END_REF], 4) when we freeze the leak and threshold parameters: β and b.

All the model variants used in this section are SNNs. We did another experiment where we removed the spiking components of the convolution and readout layers, thus turning the SNN into a standard convolutional neural network (CNN), comprised of dilated convolution layers and a time-distributed readout layer. This CNN performed much worse, with 52.8% ER obtained on the test set, showing that our proposed architecture has been optimized as an SNN, and other design choices should be made to build an efficient CNN.

Table 2 shows the results of these experiments, in terms of accuracy values obtained on the valid and test subsets, and the average number of spikes for the three convolution layers (# spikes 1, 2, 3). The first line of the table reports the numbers for our proposed SNN ("full model"), already discussed in the previous section.

Dilated convolutions: 1-a,b

As can be seen in Table 2, line 1-a, there is a significant increase in ER when using standard instead of dilated convolutions, from 5.5% to 9.1% ER on the test subset. When removing dilation, the receptive fields of the three layers are notably reduced, compared to the ones listed in 1. This explains the accuracy reduction, as confirmed by our second experiment, reported in line 1-b, in which we used the model with no dilation but with convolution kernels with the same receptive fields as in our proposed SNN. This larger model recovers the points lost by the 1-a model, with 5.2% and 6.4% ER on the validation and test sets, respectively. This model is, nevertheless, much larger than our SNN, with 4.22 Million parameters. Dilated convolutions are a way to achieve similar results but with much less parameters. Finally, as a side comment, it is interesting to see that the large model emit much less spikes: below 2% for the first two layers and 4.7% for the third one. When we remove the penalization on the spiking activity, accuracy remains very close to the full model one (line 2). As expected, the averaged numbers of spikes are larger than for the full model: 12.5% for the first two layers, and 6.4% for the third layer. For this last layer, the difference is small (full model: 6.1%), indicating that the regularization weight, that we chose to be constant to 0.1 for the three layers, was too small for that layer. It could be useful, in future experiments, to use layer-dependent weights, although we do not expect accuracy improvements from tweaking those.

Non-leaky neuron model: 3-a,b

As explained in Section 2, the LIF model can be simplified by removing the potential leak term from the model. The resulting NLFI model has been used in [START_REF] Wu | Deep spiking neural networks for large vocabulary automatic speech recognition[END_REF] for acoustic modeling. In this case, the potential is governed by Eq. 6. We replaced Eq. 4 by Eq. 6 in the convolution layer definition of our model, without any other change. Lines 3-a and 3-b lead to the same conclusion, either with or without spike regularization. ER increased significantly compared to using the LIF model (full model): about 13-12% on the test set. It indicates that the β parameters play an important role.

Frozen leak coefficients β and thresholds b

We did three more experiments, freezing either the β values or the threshold b values, or both. We did not report the results in Table 2 since no significant difference has been found compared to the full model ones.

For instance, keeping the initial random values for both β and b led to 5.7% ER. Similar figures were also found for the spiking activity, with a number of spikes that increase with the depth index of the convolution layers. This finding indicates that learning these model parameters is not needed, at least with the initial values we used for them.

CONCLUSIONS

In this work, we explored the LIF neuron model to define dilated convolution spiking layers for a spoken command recognition application. Contrarily to most works using SNNs applied to speech tasks, in which special mechanisms, usually non-trainable, are needed to first encode the speech input features into some type of neural encoding (spike trains) as a first step to then use SNNs (threshold encoding in [START_REF] Zhang | MPD-AL: an efficient membrane potential driven aggregate-label learning algorithm for spiking neurons[END_REF], a specific layer in [START_REF] Wu | Deep spiking neural networks for large vocabulary automatic speech recognition[END_REF], etc.), our approach is unified in the sense that the first convolution layer applied to realvalued speech features is trainable and shares the same definition and implementation than the ones processing spike trains as input. Our proposed SNN, trained with back-propagation through time with surrogate gradient, achieved results competitive with standard deep convolutional neural networks. We defined a regularization term to penalize the averaged number of spikes to keep the spiking neuron activity as sparse as possible, which is a desirable property both from a biological point of view and for a future potential implementation on low-energy dedicated chips.

Finally, we conducted ablation studies in order to estimate the impact of different components of our approach. In particular, an interesting result is that the LIF neuron model outperformed the simpler non-leaky one (NLFI), used in [START_REF] Wu | Deep spiking neural networks for large vocabulary automatic speech recognition[END_REF] for ASR.

In future work, we will try to confirm these results in acoustic modeling for speech recognition.

Algorithm 1

 1 Spiking convolution layer algorithm 1: Inputs: input spike train S in 2: Outputs: output spike train S out 3: Initialize the potential U and S out to zero tensors 4: Convolve S in with kernels w: cS ← conv2d(S in , w) 5: for n=1,. . .,T do 6: Compute R[n] 7: Get input current at time step n: I[n] ← cS[n] 8: Compute U[n]

Fig. 1 .

 1 Fig. 1. Proposed SNN architecture.

Fig. 2 .

 2 Fig. 2. Example for the spoken word "off". (a) Input features given to the SNN, (b) the corresponding spike train emitted by one channel of the first layer, (c) the model output, X-axis: time, Y-axis: predictions for the twelve classes.

Table 1 .

 1 Convolutional

	Conv. layer C H W d H d W	r
	1	64 4	3	1	1	4 × 3
	2	64 4	3	4	3	16 × 9
	3	64 4	3	16	9	64 × 27

spiking layer characteristics. C: number of channels, H, W: kernel size in time and frequency, d H , d W : dilation coefficients in time and frequency, r: receptive fields.

Table 2 .

 2 Results and ablation studies. ER: error rate (%), # spikes i: averaged percentage of spikes emitted by the i th convolution layer, during inference on the test subset. NLIF: Non-Leaky Integrate-and-Fire variant[START_REF] Wu | Deep spiking neural networks for large vocabulary automatic speech recognition[END_REF].

		Model size	valid ER test ER #spikes 1 #spikes 2 #spikes 3
	(Million params)	(%)	(%)	(%)	(%)	(%)
	ResNet-8 [19]	0.11		5.9
	Full SNN model (ours)	0.13	6.6	5.5	2.6	4.9	6.1
	1-a) w/o dilation	0.13	15.8	9.1	4.1	3.7	6.0
	1-b) w/o dilation, large kernels	4.22	5.2	6.4	1.7	1.3	4.7
	2) w/o spik. regul.	0.13	5.8	5.7	12.5	12.5	6.4
	3-a) NLIF, w/ spik. regul.	0.13	16.3	12.8	9.0	6.0	7.2
	3-b) NLIF, w/o spik. regul.	0.13	18.6	12.1	11.9	7.2	8.3
	10.2. Spiking activity regularization: 2

https://github.com/romainzimmer/s2net

This work was partially supported by the French ANR agency within the LUDAU project (ANR-18-CE23-0005-01) and the French "Investing for the Future -PIA3" AI Interdisciplinary Institute AN-ITI (Grant agreement ANR-19-PI3A-0004). We used HPC resources from CALMIP (Grant 2020-p20022).


Thierry Meinnel
email: thierry.meinnel@i2bc.paris-saclay.fr

Cyril Dian

Carmela Giglione
email: carmela.giglione@i2bc.paris-saclay.fr

Myristoylation, an ancient protein modification mirroring eukaryogenesis and evolution

Keywords: acetylation, glycine, membrane, myristoylation, quality control, subcellular compartments

N-myristoylation (MYR) is a crucial fatty acylation catalyzed by N-myristoyltransferases (NMTs) that is likely to have appeared over two billion years ago. Proteome-wide approaches have now delivered an exhaustive list of substrates undergoing MYR across approximately 2% of any proteome, with constituents, several unexpected, associated with different membrane compartments. A set of <10 proteins conserved in eukaryotes probably represents the original set of N-myristoylated targets, marking major changes occurring throughout eukaryogenesis.

Recent findings have revealed unexpected mechanisms and reactivity, suggesting competition with other acylations that are likely to influence cellular homeostasis and the steady state of the modification landscape. Here, we review recent advances in NMT catalysis, substrate specificity, and MYR proteomics, and discuss concepts regarding MYR during evolution.

Lipidated proteins

Plasma membranes (PMs) are composed of extrinsic and intrinsic proteins (52%) and lipids (40%), the latter sustaining the overall cellular architecture. Membrane-penetrating extrinsic proteins often possess covalently linked lipids, usually fatty acids, which allow the protein to contact other intra-and extracellular proteins [START_REF] Meinnel | Protein lipidation meets proteomics[END_REF]. Protein lipidation involves amides (i.e. N-αmyristoylation, MYR, see Glossary and glycosylphosphatidylinositol (GPI) anchors), thioesters (i.e. S-palmitoylation, PAL), and thioethers (i.e. isoprenylation and farnesylation) [START_REF] Resh | Fatty acylation of proteins: The long and the short of it[END_REF]. Of these, MYR is a frequent and conserved modification specific to eukaryotes that targets major cellular components. Mapping the proteins undergoing MYR has proven challenging due to their difficult handling characteristics and amphiphilic, chimeric nature. Recently, high-end technologies have allowed the first lipidated proteome, the myristoylome, to be described in detail in various organisms recapitulating the tree of life [START_REF] Dacks | Evolutionary origins and specialisation of membrane transport[END_REF]. These and other studies on myristoylome composition and genesis have also revealed (i) an unexpected novel mechanism of action of Nmyristoyltransferase (NMT), (ii) NMT substrate selectivity, and (iii) the capacity of NMT to act on N-terminal lysines (Lys) as well as the usual glycines (Gly). This review highlights the series of ground-breaking discoveries that have recently significantly advanced our knowledge about this long-studied enzyme. This includes an overview of a new NMT catalytic mechanism, substrate specificity, and proteomics, and a discussion of how a reduced set of targets is closely related to eukaryogenesis and eukaryote evolution.

How NMTs catalyze MYR with high selectivity

NMTs are GNAT members closely related to Nα-acetyltransferases

Seventy-four crystal structures have now revealed that the C-terminal 400 amino acid-long NMT catalytic core displays a conserved 3D GCN5-related N-acetyltransferase (GNAT) core.

GNATs also include the catalytic subunits of Nα-acetyltransferases (NAAs) [START_REF] Aksnes | Co-translational, post-translational, and non-catalytic roles of Nterminal acetyltransferases[END_REF], with Naa10 being the closest to NMT as it modifies N-terminal Gly [START_REF] Arnesen | Proteomics analyses reveal the evolutionary conservation and divergence of N-terminal acetyltransferases from yeast and humans[END_REF][START_REF] Bienvenut | Comparative large scale characterization of plant versus mammal proteins reveals similar and idiosyncratic N-alpha-acetylation features[END_REF][START_REF] Castrec | Structural and genomic decoding of human and plant myristoylomes reveals a definitive recognition pattern[END_REF]. NMTs have two adjacent GNAT domains, most likely to have arisen through duplication of a Naa10-like GNAT domain [START_REF] Bhatnagar | Biology and enzymology of protein N-myristoylation[END_REF]. Prokaryotes do not possess NMTs, which seem to have arisen as eukaryotes evolved from the last archaeal common ancestor (LACA) to the last eukaryotic common ancestor (LECA). Therefore, NMTs are associated with events during eukaryogenesis.

How NMTs promote high substrate specificity

NMTs are classified as glycylpeptide N-tetradecanoyltransferases because MYR was thought to only modify N-terminal Gly residues. As Gly usually arises from co-translational methionine excision, the N-myristoylated (MYRed) Gly is Gly2 (Met1 being aa1, and aax referring to the following amino acids at position x). Post-translational MYR has also been reported (see below)

Structural approaches

Structures of human NMT1 (HsNMT1) co-crystallized with reactive substrates, including polypeptide substrates, have recently become available [START_REF] Castrec | Structural and genomic decoding of human and plant myristoylomes reveals a definitive recognition pattern[END_REF]. These peptide-bound structures are different to those with inhibitors [START_REF] Farazi | Structures of Saccharomyces cerevisiae N-myristoyltransferase with bound myristoylCoA and peptide provide insights about substrate recognition and catalysis[END_REF], especially at the level of the recognition of peptide side chains in dedicated cavities (Figure 1A). These cavities are formed with the second GNAT fold, which is adapted to host and select the first six residues (Gly2 to aa6). Peptides with a Lys or Arg at position aa7 form a salt bridge with conserved Asp residues at the cavity entrance. The peptidesubstrate backbone adopts an extended structure and only promotes significant contacts with the enzyme at the outer side (aa7-9), explaining why octapeptides are optimal for NMT-driven MYR.

Figure 1A shows the various NMT pockets. Pocket 1 specifically recognizes the long linear hydrophobic chain of myristate. The first ten carbons are embedded within a linear pocket formed from the side chains borne by the βe-αC-βf adjacent secondary structures of the first domain [START_REF] Bhatnagar | The structure of myristoyl-CoA:protein N-myristoyltransferase[END_REF]. Each side chain's hydrophobic character is conserved, optimizing the pocket for myristate recognition and excluding bulkier fatty acids (e.g., palmitate (C:16) or stearate (C:18)) and branched fatty acids from isoprenoids such as farnesyl (C:15). The use of a linear fatty acid, not synthesized in Archaea, might indicate that NMTs eventually acquired their lipid specificity after engulfing bacterial partners that introduced the pathway (LACA; [START_REF] Koonin | Origin of eukaryotes from within archaea, archaeal eukaryome and bursts of gene gain: eukaryogenesis just made easier?[END_REF]).

Narrow pocket 2 is involved in NH2Gly2 recognition, pocket 3 favors small-and medium-sized residues [START_REF] Castrec | Structural and genomic decoding of human and plant myristoylomes reveals a definitive recognition pattern[END_REF], and large pockets 4/5 can accommodate almost any chain. The narrow constriction at the end of the peptide recognition pocket 6 favors small residues, especially Ser. While the emerging peptide recognition pattern (Figure 1) defines global specificity, it does not fully define the actual substrate landscape in a given proteome.

Metabolic labeling

Chemical biology strategies coupled with mass spectrometry have been used to investigate myristoylomes. The approaches involve, for instance, metabolic strategies that combine fatty acid analogs as precursors of myristoyl-CoA (MyrCoA) bearing a bio-orthogonal alkyne with NMTspecific inhibitors [START_REF] Lanyon-Hogg | Dynamic protein acylation: new substrates, mechanisms, and drug targets[END_REF] to identify NMT substrates in one shot in various organisms (Table 1, Key Table ). These approaches were subsequently completed with subcellular fractionation approaches in plants. Combining the 364 sequences from these studies revealed a similar profile (Figure 1B) to those observed in each of the nine organisms [START_REF] Castrec | Structural and genomic decoding of human and plant myristoylomes reveals a definitive recognition pattern[END_REF][START_REF] Thinon | Global profiling of co-and post-translationally N-myristoylated proteomes in human cells[END_REF][START_REF] Wright | Validation of N-myristoyltransferase as an antimalarial drug target using an integrated chemical biology approach[END_REF][START_REF] Broncel | Multifunctional reagents for quantitative proteome-wide analysis of protein modification in human cells and dynamic profiling of protein lipidation during vertebrate development[END_REF][START_REF] Wright | Global analysis of protein N-myristoylation and exploration of Nmyristoyltransferase as a drug target in the neglected human pathogen Leishmania donovani[END_REF][START_REF] Wright | Global profiling and inhibition of protein lipidation in vector and host stages of the sleeping sickness parasite Trypanosoma brucei[END_REF][START_REF] Roberts | The N-myristoylome of Trypanosoma cruzi[END_REF][START_REF] Majeran | Targeted profiling of A. thaliana sub-proteomes illuminates new coand post-translationally N-terminal Myristoylated proteins[END_REF].

Peptide arrays and artificial intelligence complete the myristoylomes MYR co-translational activity can be mimicked in vitro with short peptides recapitulating nascent chains combined with an NMT-catalyzed MYR assay [START_REF] Towler | The biology and enzymology of eukaryotic protein acylation[END_REF][START_REF] Boutin | Myristoylation[END_REF][START_REF] Boisson | Unexpected protein families including cell defense components feature in the N-myristoylome of a higher eukaryote[END_REF]. Providing insights into NMT specificity based on a reduced, non-random dataset, these approaches are biased and poorly predictive [START_REF] Traverso | High-throughput profiling of N-myristoylation substrate specificity across species including pathogens[END_REF]. Recent strategies have leveraged large arrays of peptides derived from the Ntermini of open reading frames starting with Gly from Arabidopsis thaliana and Homo sapiens proteomes. These data have validated the quasi-entire set of positives in both organisms (Table 1) and provided crucial information on the negative set. About 20% of Gly-starting proteins were MYRed. Human NMT1 and NMT2 and the functional NMT from A. thaliana displayed identical substrate specificity. Nevertheless, Saccharomyces cerevisiae and Plasmodium falciparum NMTs had restricted specificities in vitro and in vivo, partly assigned to negative residues when occurring at aa8 and/or aa9 [START_REF] Boisson | Unexpected protein families including cell defense components feature in the N-myristoylome of a higher eukaryote[END_REF][START_REF] Traverso | High-throughput profiling of N-myristoylation substrate specificity across species including pathogens[END_REF][START_REF] Pierre | N-Myristoylation regulates the SnRK1 pathway in Arabidopsis[END_REF].

This sequence dataset was used to check NMT selectivity. The pattern (Figure 1C), although similar to that obtained with global approaches, was more robust due to the higher number of positive sequences and the 60% negative sequences. This allowed a better understanding of their selectivity and why some proteins, despite displaying favorable residues (e.g., N3/S6), are not substrates (Box 1).

Revisiting NMT catalysis

Early studies on NMT catalysis [START_REF] Bhatnagar | Biology and enzymology of protein N-myristoylation[END_REF] revealed an ordered mechanism with initial MyrCoA binding. Although similar to all other GNATs, the NMT mechanism was distinct, with the peptide's ammonium group making direct contact with the carboxy-terminal group to trap the proton and allow the amino group to react. However, the widely-spaced reactive ammonium and thioester bond of MyrCoA suggested that it must fully flip to approach and bind to it. Recent 3D structures of HsNMT1 in complex with canonical substrates delivered sequential snapshots of the entire catalytic mechanism spanning the enzymatic pathway from substrates, intermediates, and products. In these models, the peptide substrate is differently positioned in pockets 2-4, while the ammonium creates a water-mediated bond with the base [START_REF] Castrec | Structural and genomic decoding of human and plant myristoylomes reveals a definitive recognition pattern[END_REF]. The reaction is thus easier to achieve without major conformational changes around the alpha carbon of Gly2. These structures reveal a conserved water channel connecting the deeply buried active site to the surface and the key role of the Ab-loop, on which NMT catalysis relies (Figure 2).

Myristoylomes, MYR targets, and evolutionary boundaries

The five main NMT subtypes in eukaryotes NMTs display an average of 50% identity, and phylogenetic clustering of NMTs recapitulate the eukaryotic tree (Supplemental Figure 1), falling into plant, animal, apicomplexa/diatom/ameba, and fungi/englenozoa/microsporidian classes. Unicellular organisms and invertebrates have one NMT gene, while land plants and vertebrates have two (NMT1, NMT2) [START_REF] Giang | A second mammalian N-myristoyltransferase[END_REF]. The two land plant NMTs cannot be distinguished from each other and probably correspond to early gene duplication without true speciation. Vertebrate NMT1/2 display very similar substrate specificity [START_REF] Castrec | Structural and genomic decoding of human and plant myristoylomes reveals a definitive recognition pattern[END_REF][START_REF] Rundle | Characterization of type I and type II myristoyl-CoA:protein Nmyristoyltransferases with the Acyl-CoAs found on heterogeneously acylated retinal proteins[END_REF]. Interestingly, caspase cleavage of the N-terminal extension, previously thought to favor ribosome binding, results in the targeting of human NMT2 to membranes and NMT1 to the cytosol [START_REF] Perinpanayagam | Regulation of co-and post-translational myristoylation of proteins during apoptosis: interplay of N-myristoyltransferases and caspases[END_REF].

NMT-catalyzed MYR is essential for cell compartmentation

In silico analyses predicted 46 targets in the S. cerevisiae myristoylome, a dozen of which were essential during stationary phase when inactivated (Table 1) [START_REF] Bhatnagar | Biology and enzymology of protein N-myristoylation[END_REF][START_REF] Ashrafi | A role for Saccharomyces cerevisiae fatty acid activation protein E in regulating N-myristoylation during entry into stationary phase[END_REF]. In A. thaliana, three essential targets were identified during development [START_REF] Pierre | N-Myristoylation regulates the SnRK1 pathway in Arabidopsis[END_REF][START_REF] Bayer | Paternal control of embryonic patterning in Arabidopsis thaliana[END_REF][START_REF] Renna | Golgi traffic and integrity depend on N-myristoyl transferase-1 in Arabidopsis[END_REF], and inhibition of Rpt2 MYR induced phenotypes indicating decreased proteasome activity in yeast [START_REF] Kimura | N-myristoylation of the Rpt2 subunit regulates intracellular localization of the yeast 26S proteasome[END_REF]. Another important MYRed protein is the protein kinase Vps15, which is a PI3 kinase complex I subunit involved in macroautophagy in eukaryotes. All these targets shuttle between the cytosol and other compartments including the nucleus (Rpt2/Sip2/Ptc2), the PM (Gpa1), and the endosome (Arf1/Arf2/Vps20).

The weak binding energy provided by a myristate moiety suggests that MYR cannot maintain stable protein-membrane interactions by itself [START_REF] Traverso | Roles of N-Terminal fatty acid acylations in membrane compartment partitioning: Arabidopsis h-type thioredoxins as a case study[END_REF]. MYR can, however, target proteins to endomembranes, and cytosolic partitioning was demonstrated to be correlated with NMT's catalytic efficiency in A. thaliana [START_REF] Traverso | Roles of N-Terminal fatty acid acylations in membrane compartment partitioning: Arabidopsis h-type thioredoxins as a case study[END_REF]. Therefore, MYR of less efficient NMT substrates is partial and varies between MYRed proteins [START_REF] Castrec | Structural and genomic decoding of human and plant myristoylomes reveals a definitive recognition pattern[END_REF]. While the hydrophobic myristate group is crucial for membrane localization, its N-terminus position plays a key role. For instance, C-terminal prenylation does not compensate for the endocytic function of a non-MYRed ADP-ribosylation factor (ARF) variant [START_REF] Souza-Schorey | Myristoylation is required for the intracellular localization and endocytic function of ARF6[END_REF].

MYR's involvement in protein shuttling and reversible membrane binding is regulated through various signals which allow the protein to associate or dissociate from the lipid bilayer [START_REF] Resh | Trafficking and signaling by fatty-acylated and prenylated proteins[END_REF]. The switch may involve, for instance, (i) GTP hydrolysis inducing conformational changes in ARFs [START_REF] Goldberg | Structural basis for activation of ARF GTPase: mechanism of guanine nucleotide exchange and GTP-myristoyl switching[END_REF],

(ii) Ca 2+ binding to EF-hand calcium-sensor proteins [START_REF] Ames | Molecular structure and target recognition of neuronal calcium sensor proteins[END_REF], (iii) phosphorylation of a polybasic motif involved in electrostatic interactions with membrane phospholipids, or (iv) depalmitoylation of vicinal cysteine residues. Such reversible myristoyl switches control MYR-dependent subcellular protein trafficking, distributing MYRed proteins to different compartments. For instance, the mammalian protein kinase A undergoes nuclear-cytoplasmic shuttling through significant deamination of N3 in muscles due to electronegative charges caused by the new D3 [START_REF] Pepperkok | Intracellular distribution of mammalian protein kinase A catalytic subunit altered by conserved Asn2 deamidation[END_REF].

The PM localization induced by cysteine residue PAL in the vicinity of the MYR site was initially discovered for the mammalian homolog of Gpa1 [START_REF] Parenti | A novel N-terminal motif for palmitoylation of G-protein α subunits[END_REF][START_REF] Degtyarev | The G protein .alpha.s subunit incorporates [3H]palmitic acid and mutation of cysteine-3 prevents this modification[END_REF] and later in yeast [START_REF] Song | Partial constitutive activation of pheromone responses by a palmitoylation-site mutant of a G protein alpha subunit in yeast[END_REF]. This phenomenon was recently confirmed by hierarchical clustering of protein accumulation profiles, which revealed a clear shift in MYRed protein abundance toward PM fractions in the presence of second targeting signals including PAL or polybasic tracks [START_REF] Majeran | Targeted profiling of A. thaliana sub-proteomes illuminates new coand post-translationally N-terminal Myristoylated proteins[END_REF]. Yeast only features four such proteins including Gpa2, and only Gpa1 is essential (Table 1). The proximal PAL of Gpa1 is therefore likely to be an ancient function linking MYR, PAL, and PM localization, probably later in evolution than endosomes and relating to multi-cellularity and its required intercellular communication. Recent studies on PAL and MYR co-occurrence in plants and humans have shown that PAL on cysteine residues in the immediate vicinity of the MYRed Gly are readily detectable, with co-occurring cysteine codons at positions 3-7 significantly more frequent in MYRed proteins. 50% of MYRed targets are expected to be palmitoylated (PALed) in plants and 23% in humans [START_REF] Castrec | Structural and genomic decoding of human and plant myristoylomes reveals a definitive recognition pattern[END_REF].

Perspectives with respect to eukaryogenesis

The myristoylomes of nine organisms displaying NMTs of all classes (Table 1) allow us to draw conclusions about the specific features promoted by NMTs throughout evolution. Figure 3A shows that larger proteomes correspond to larger myristoylomes, which always constitute about 2% of the proteome. The yeast myristoylome is an outlier, probably due to the gene loss characterizing fungal evolution [START_REF] Dacks | Evolutionary origins and specialisation of membrane transport[END_REF]. Nevertheless, Trypanosomatidae and Apicomplexa, which also lost genes through parasitism [START_REF] Dacks | Evolutionary origins and specialisation of membrane transport[END_REF], do not have similarly small myristoylomes, so the small myristoylome observed in yeast might be partly linked to the restricted substrate specificity of yeast NMT [START_REF] Pierre | N-Myristoylation regulates the SnRK1 pathway in Arabidopsis[END_REF]. This is similar to yeast N-acetylation patterns, which are similarly subtly reduced, especially as no N-acetylation Gly-starting proteins have been identified. Note that fungi still display branch-specific essential MYR targets such as the GTPase EGO (Table 1) involved in microautophagy.

Table 1 shows that a subset of eight yeast myristoylome proteins are also present throughout all cell compartments of the myristoylomes of other organisms. The myristoylome has, therefore, exploited all compartments elaborated by eukaryogenesis (Table 2). In addition, the major eukaryotic sensing events also involve MYRed targets (Table 2). As a result, the myristoylome illustrates the various steps of eukaryogenesis and explains why myristoylome size closely mirrors that of the proteome (Figure 3A).

Proteome-wide approaches reveal unexpected targets

These high-throughput data have revealed that MYR targets unexpected compartments and functions, especially catalytic processes, cell organization, and sensing (Figure 3B,C) but not gene expression and processes characterizing the first eukaryotic signature proteins (ESPs) [START_REF] Koumandou | Molecular paleontology and complexity in the last eukaryotic common ancestor[END_REF][START_REF] Spang | Complex archaea that bridge the gap between prokaryotes and eukaryotes[END_REF].

Therefore, NMT probably appeared after these early ESP emerged in Archaea, likely in LACA. They are much more relevant to the compartmentalization events underpinning eukaryogenesis.

Kinases, small GTPases, proteostasis-related phosphatases, hydrolases, transferases, and calcium sensors feature often as targets (Figure 3B,C). These unexpected processes are eukaryote specific and further illustrate the tight link between MYR and eukaryogenesis, as detailed below.

The immune system and MHC presentation

The mammalian immune system is equipped with virus detection machinery via virussynthesized MYRed peptides [START_REF] Morita | Lipopeptides: a novel antigen repertoire presented by major histocompatibility complex class I molecules[END_REF]. This was first shown for HIV-Nef N-terminal pentapeptides, which add to the antigen repertoire presented by major histocompatibility complex (MHC) class I molecules [START_REF] Morita | Lipopeptides: a novel antigen repertoire presented by major histocompatibility complex class I molecules[END_REF]. The crystal structure of the complex reveals a groove suitable for 4-9 residue peptides [START_REF] Morita | Crystal structure of the N-myristoylated lipopeptide-bound MHC class I complex[END_REF][START_REF] Yamamoto | Identification and structure of an MHC class I-encoded protein with the potential to present N-myristoylated 4-mer peptides to T cells[END_REF].

As MYRed peptides are derived from protein components associated with viral or bacterial pathogenicity, this raises the possibility of exploiting MYR for vaccines [START_REF] Morita | Lipopeptides: a novel antigen repertoire presented by major histocompatibility complex class I molecules[END_REF]. The relationship between MYR and immunity is now firmly established in plants and animals [START_REF] Rowe | The myristoylation of TRIF-related adaptor molecule is essential for Toll-like receptor 4 signal transduction[END_REF][START_REF] Udenwobele | Myristoylation: an important protein modification in the immune response[END_REF]. When impaired in humans, such as in rheumatoid arthritis, the lack of MYR of AMPK eventually triggers inflammation due to failed lysosomal targeting [START_REF] Wen | N-myristoyltransferase deficiency impairs activation of kinase AMPK and promotes synovial tissue inflammation[END_REF].

Cell motility

Cilia

The cilium is a sensory organelle occurring in vertebrates and invertebrates, including euglenozoa. Protein trafficking to cilia implies the presence of lipid microdomains that help overcome the lipid diffusion barrier and relies on polarized trafficking of post-Golgi vesicles [START_REF] Nachury | Trafficking to the ciliary membrane: how to get across the periciliary diffusion barrier?[END_REF].

Lipid modification of the cilial proteome is widespread and includes MYRed proteins [START_REF] Roy | Lipid modifications in cilia biology[END_REF]. Of note is the MYRed cystin/nephrocystin. Cilial interactions are mediated by the UNC119 cargo, which appears to be a major coordinator of MYRed protein trafficking, including a Src family kinase (SFK) and nephrocystin [START_REF] Constantine | Uncoordinated (UNC)119: coordinating the trafficking of myristoylated proteins[END_REF]. Both are targeted into cilia via UNC119-mediated trafficking [START_REF] Stephen | The ciliary machinery is repurposed for T cell immune synapse trafficking of LCK[END_REF].

The X-linked human retinitis pigmentosa gene XRP2 encodes a GTPase-activating protein involved in Golgi-cilia trafficking, leading to UNC119 release [START_REF] Wright | An ARL3-UNC119-RP2 GTPase cycle targets myristoylated NPHP3 to the primary cilium[END_REF]. XRP2 has MYR and PAL modification sites featuring Cys3 and Ser6, with deletion of the latter causing disease by inhibiting MYR and further PM localization [START_REF] Chapple | Mutations in the N-terminus of the X-linked retinitis pigmentosa protein RP2 interfere with the normal targeting of the protein to the plasma membrane[END_REF]. Finally, calflagin is a MYRed/PALed calcium-binding protein in kinetoplastids. PAL on Cys3 is required for cilial sorting, while MYR alone is sufficient for membrane targeting [START_REF] Emmer | Identification of a palmitoyl acyltransferase required for protein sorting to the flagellar membrane[END_REF] The glideosome of apicomplexa The complex life cycles of human parasites like apicomplexa alternate between asexual/sexual and parasitic/free stages in various hosts or organs. The parasitic stage involves complex host cell interactions. Parasites elaborate dedicated secretory compartments such as micronemes and rhoptries to promote the required motility. The gliding machinery allows parasites to cross cell barriers to exit infected cells in a process analogous to but distinct from that caused by cilia.

MYR is strongly involved in gliding motility [START_REF] Doerig | Post-translational protein modifications in malaria parasites[END_REF][START_REF] Schlott | N-Myristoylation as a drug target in malaria: exploring the role of Nmyristoyltransferase substrates in the inhibitor mode of action[END_REF]. In the malaria parasite P. falciparum, this involves GAP45, which is MYRed [START_REF] Rees-Channer | Dual acylation of the 45 kDa gliding-associated protein (GAP45) in Plasmodium falciparum merozoites[END_REF]. Of the rhoptry components, ARO and the cGMP-dependent protein kinase PKG are retrieved, while the ISP1 and ISP3 are major MYRed proteins [START_REF] Beck | A novel family of Toxoplasma IMC proteins displays a hierarchical organization and functions in coordinating parasite division[END_REF] that link the machinery to actin and myosin to induce traction [START_REF] Frénal | Gliding motility powers invasion and egress in Apicomplexa[END_REF]. Finally, gliding motility signaling requires the same calcium-dependent protein kinases found in plants, which are almost systematically MYRed and PALed [START_REF] Frénal | Gliding motility powers invasion and egress in Apicomplexa[END_REF].

Mitochondria

There are >20 MYRed proteins in the human mitochondrion, either induced by Met removal or caspase cleavage. Several target the cytosolic side of the outer mitochondrial membrane (OMM). These include NADH-cytochrome b5 reductase, Sam50 (a sorting and assembly machinery complex component), MOSC1/2 through co-translational MYR [START_REF] Colombo | N-myristoylation determines dual targeting of mammalian NADHcytochrome b5 reductase to ER and mitochondrial outer membranes by a mechanism of kinetic partitioning[END_REF], and several posttranslationally MYRed proteins (see below). TOM40 is a MYRed subunit of the mitochondrial import receptor subunit lying within the OMM but with an N-terminus protruding at the outer side [START_REF] Utsumi | Identification and characterization of protein N-myristoylation occurring on four human mitochondrial proteins, SAMM50, TOMM40, MIC19, and MIC25[END_REF][START_REF] Araiso | Structure of the mitochondrial import gate reveals distinct preprotein paths[END_REF]. It was unexpected that MYRed proteins could exist within the intermembrane space, including the MIC19 and MIC25 components of the mitochondrial contact site complex (MICOS, also found in yeast; Table 1), which is crucial for maintaining crista integrity and mitochondrial function [START_REF] Schorr | Integrative functions of the mitochondrial contact site and cristae organizing system[END_REF], and other proteins of the inner membrane space such as complex I subunits of the respiratory chain including B18/NDUFB7 and NDUF4 [START_REF] Castrec | Structural and genomic decoding of human and plant myristoylomes reveals a definitive recognition pattern[END_REF]. How they reach the outer membrane without a mitochondrial targeting signal is unknown. A similar feature was noted in plants with (i) the yeast homolog Mia40 (a mitochondrial intermembrane space assembly machinery component) lying at the inner membrane next to MICOS and interacting with TOM at the OMM [START_REF] Van Der Laan | Mitochondrial contact site and cristae organizing system[END_REF], and (ii) two prohibins involved in crista morphogenesis [START_REF] Castrec | Structural and genomic decoding of human and plant myristoylomes reveals a definitive recognition pattern[END_REF][START_REF] Majeran | Targeted profiling of A. thaliana sub-proteomes illuminates new coand post-translationally N-terminal Myristoylated proteins[END_REF].

One hypothesis on how a MYRed protein might access the intermembrane space comes from studies of yeast MIC19, which undergoes MYR in the cytosol. By interacting with Tom20 of the TOM complex at the OMM, MIC19 would be imported into the inner mitochondrial membrane (IMM) by so-called entropy pushing [START_REF] Ueda | Myristoyl group-aided protein import into the mitochondrial intermembrane space[END_REF]. By contrast, delivery of the MYRed Tom40, which features several transmembrane helices which insert into the OMM, involves Hsp90 [START_REF] Humphries | Dissection of the mitochondrial import and assembly pathway for human Tom40[END_REF].

Finally, it is interesting that ER-mitochondrial crosstalk involves both lipid exchange and Ca 2+ regulation, with the MYRed calcium sensor NCS1 homolog to the yeast form described in Table 1 involved [START_REF] Angebault | ER-mitochondria cross-talk is regulated by the Ca(2+) sensor NCS1 and is impaired in Wolfram syndrome[END_REF]. The link between mitochondrial contact sites, MICOS, calcium signaling, and MYRed protein import [START_REF] Van Der Laan | Mitochondrial contact site and cristae organizing system[END_REF] needs further detailed examination, especially as these are some of the most conserved MYRed targets (Table 1).

MYR and proteostasis

The plant myristoylome reveals about 50 proteins annotated as RING finger, F-box, or ubiquitin domain proteins and E3 ubiquitin ligases [START_REF] Castrec | Structural and genomic decoding of human and plant myristoylomes reveals a definitive recognition pattern[END_REF][START_REF] Majeran | Targeted profiling of A. thaliana sub-proteomes illuminates new coand post-translationally N-terminal Myristoylated proteins[END_REF], some of which localize to the nucleus [START_REF] Majeran | Targeted profiling of A. thaliana sub-proteomes illuminates new coand post-translationally N-terminal Myristoylated proteins[END_REF]. Among Arabidopsis proteins, the SKIP2 subunit of the SCF(ASK-cullin-F-box) E3 ubiquitin ligase complex was found to serve as a substrate receptor for E3 ligases. Interestingly, Toxoplasma TgFBO1 is an inner membrane complex MYRed component with an F-box domain that might bind SKIP1 [START_REF] Baptista | Toxoplasma F-box protein 1 is required for daughter cell scaffold function during parasite replication[END_REF]. In humans, ten such MYRed E3 cullins were identified [START_REF] Castrec | Structural and genomic decoding of human and plant myristoylomes reveals a definitive recognition pattern[END_REF][START_REF] Thinon | N-Myristoyltransferase Inhibition Induces ER-Stress, Cell Cycle Arrest, and Apoptosis in Cancer Cells[END_REF]. The identification of major MYRed components of protein degradation reveals MYR as an important feature of proteostasis (see also glossary and below).

Apoptosis

Induction of apoptosis leads to the activation of numerous caspase proteases. In some cases, caspase cleavage may expose internal cryptic MYR sites to the action of NMTs. MYR-dependent relocalization to the OMM of fragments of BID and actin [START_REF] Zha | Posttranslational N-myristoylation of BID as a molecular switch for targeting mitochondria and apoptosis[END_REF][START_REF] Utsumi | C-terminal 15 kDa fragment of cytoskeletal actin is posttranslationally N-myristoylated upon caspase-mediated cleavage and targeted to mitochondria[END_REF] has been reported. BID switching contributes to enhanced cytochrome c release and cell death [START_REF] Zha | Posttranslational N-myristoylation of BID as a molecular switch for targeting mitochondria and apoptosis[END_REF]. A Huntingtin fragment also relocalized to the autophagosome due to MYR [START_REF] Martin | Identification of a post-translationally myristoylated autophagyinducing domain released by caspase cleavage of huntingtin[END_REF], while the MYRed gelsolin fragment is cytosolic [START_REF] Sakurai | Posttranslational N-myristoylation is required for the antiapoptotic activity of human tGelsolin, the C-terminal caspase cleavage product of human gelsolin[END_REF], and the MYRed C-terminal fragments of PAK2 and PKCε are redirected to internal membranes and that of filensin to the PM [START_REF] Vilas | Posttranslational myristoylation of caspase-activated p21-activated protein kinase 2 (PAK2) potentiates late apoptotic events[END_REF][START_REF] Martin | Tandem reporter assay for myristoylated proteins posttranslationally (TRAMPP) identifies novel substrates for post-translational myristoylation: PKCepsilon, a case study[END_REF][START_REF] Tapodi | BFSP1 C-terminal domains released by post-translational processing events can alter significantly the calcium regulation of AQP0 water permeability[END_REF]. A global approach has identified another 40 proteins MYRed following caspase cleavage. The MYR consensus is oversimplified while the caspase consensus strongly emerges (Figure 1D), suggesting that post-translational MYR was acquired later in the course of eukaryotic evolution and has not yet converged.

Ferroptosis suppressor protein 1 (FSP1/AIFM2) is a MYRed protein that localizes to the PM and behaves as an oxidoreductase, converting ubiquinone to ubiquinol to inhibit ferroptosis [START_REF] Bersuker | The CoQ oxidoreductase FSP1 acts parallel to GPX4 to inhibit ferroptosis[END_REF][START_REF] Doll | FSP1 is a glutathione-independent ferroptosis suppressor[END_REF]. This programmed cell death mechanism is conserved in plants [START_REF] Conrad | Regulation of lipid peroxidation and ferroptosis in diverse species[END_REF] and leads to lipid damage and membrane degradation. Glutathione peroxidase GPX4 in vertebrates together with glutathione prevent lipid peroxidation. While the MYRed FSP1/AIFM2 acts as a parallel system to GPX4, of note, two of the eight GPX isoforms in plants (GPX4/5) are MYRed [START_REF] Majeran | Targeted profiling of A. thaliana sub-proteomes illuminates new coand post-translationally N-terminal Myristoylated proteins[END_REF] so might act similarly to FSP1/AIFM2 directly at the PM. Also, plants have a large array of thioredoxins and glutaredoxins featuring MYR [START_REF] Traverso | Roles of N-Terminal fatty acid acylations in membrane compartment partitioning: Arabidopsis h-type thioredoxins as a case study[END_REF].

MYR and quality control

A MYR, Gly-specific N-degron

Recent data show that two cullin-RING E3 ligases (CRL2 and CRL5) target Gly-starting peptides for degradation [START_REF] Timms | A glycine-specific N-degron pathway mediates the quality control of protein N-myristoylation[END_REF]. Globally, Gly substrates favored by CRL2 included those starting with a Gly followed by Phe, Gly, His, Leu, Met, or Tyr, while those followed by Asp, Glu, Ile, Asn, Pro, Arg, Ser, or Thr were disfavored. Interestingly, metazoan proteomes seem to be depleted of CRL2mediated N-terminal Gly degrons, while CRL2 complexes most likely target protein fragments bearing N-terminal Gly degrons after caspase cleavage during apoptosis. 13% and 17% of known post-translationally MYRed proteins display a residue 3 favored and disfavored by CRL2, respectively. Loss of MYR destabilized Gly-starting polypeptides followed by Cys, Gln, or Lys, so the authors proposed that N-terminal Gly degrons might play a role in MYR quality control, allowing degradation of proteins failing to undergo MYR and avoiding accumulation of non-MYRed "aberrant" species. However, partial stabilization of endogenous N-MYRed proteins after loss of MYR including CRL2 adaptors suggests a requirement for additional E3 ligases and/or other undetermined factors. Finally, it remains to be fully understood which non-MYRed Gly-starting proteins are related to Gly degrons, as there clearly remains a non-negligible number of such species that eventually accumulate in multicellular organisms as either Nα-acetylated or with an unmodified Gly [START_REF] Arnesen | Proteomics analyses reveal the evolutionary conservation and divergence of N-terminal acetyltransferases from yeast and humans[END_REF][START_REF] Castrec | Structural and genomic decoding of human and plant myristoylomes reveals a definitive recognition pattern[END_REF]. The favored CRL2 sequences indeed deal with <14% of the human myristoylome , while the disfavored sequences involve 51% (see SupDataset 2 in [START_REF] Castrec | Structural and genomic decoding of human and plant myristoylomes reveals a definitive recognition pattern[END_REF]).

Competition with other modifications or processes

There is increasing evidence that one unique proteoform may undergo various modifications at the same site within the same cell, including for MYRed proteins, which may be either partially modified or partially N-acetylated [START_REF] Castrec | Structural and genomic decoding of human and plant myristoylomes reveals a definitive recognition pattern[END_REF]. MYR's pronounced effect on membrane binding is likely to have a major effect on protein localization, as observed for mammalian NADH-cytochrome b5 reductase, which is targeted to the ER if Nα acetylated and the mitochondrion when MYRed [START_REF] Colombo | N-myristoylation determines dual targeting of mammalian NADHcytochrome b5 reductase to ER and mitochondrial outer membranes by a mechanism of kinetic partitioning[END_REF].

Roles of sirtuins and HDACs with Lys-MYR

NMTs are thought to exclusively acylate N-terminal Gly residues. Nevertheless, the ε-amino groups of internal lysines of several proteins including tumor necrosis factor-α, interleukin-α1 precursor, and serine hydroxymethyltransferase 2 also undergo MYR [START_REF] Jiang | Protein lipidation: occurrence, mechanisms, biological Functions, and enabling technologies[END_REF][START_REF] Cao | HDAC11 regulates type I interferon signaling through defatty-acylation of SHMT2[END_REF], although the corresponding catalyst(s) still remain unidentified.

Interestingly, it was recently demonstrated that human NMTs may transfer myristate onto the N-terminal epsilon amino groups of lysine side chains, expanding its known substrate range beyond the well-established N-terminal alpha moiety of Gly peptides [START_REF] Dian | High-resolution snapshots of human N-myristoyltransferase in action illuminate a mechanism promoting N-terminal Lys and Gly myristoylation[END_REF]. Furthermore, Lys3 MYR allows ARF6 to remain on membranes during GTPase cycling [START_REF] Kosciuk | NMT1 and NMT2 are lysine myristoyltransferases regulating the ARF6 GTPase cycle[END_REF]. In contrast to the irreversible Gly-MYR, several acyl hydrolases targeting Lys-MYR have recently been described including Sirtuin6, HDAC8, and HDAC11 [START_REF] Jiang | Protein lipidation: occurrence, mechanisms, biological Functions, and enabling technologies[END_REF], making Lys-MYR a reversible modification dependent on these specific hydrolases. This implies that Lys-MYR, as with other reversible modifications (i.e., phosphorylation, N-terminal acetylation), might be intimately related to signal transduction and tightly regulated by as yet unknown biotic or abiotic signals. In the case of ARF6, NMT prefers the GTP-bound form of ARF6 while SIRT2 prefers the GDP-bound form, allowing an NMT/SIRT2-ARF6 regulatory axis of the GTPase cycle [START_REF] Kosciuk | NMT1 and NMT2 are lysine myristoyltransferases regulating the ARF6 GTPase cycle[END_REF]. The N-terminal Lys-MYR might be part of an N-degron, so an important role of Lys-MYR deacylases could be to ensure protein quality control by preventing intracellular Lys-MYR accumulation [START_REF] Dian | High-resolution snapshots of human N-myristoyltransferase in action illuminate a mechanism promoting N-terminal Lys and Gly myristoylation[END_REF].

Concluding remarks

Four decades of seminal contributions have now contributed to knowledge about the key MYR process, with recent studies on MYR and NMT biology leading to revisiting and rethinking old dogmas. Multiscale approaches have proven pivotal in expanding our knowledge of myristoylomes. Recent efforts to precisely annotate all proteoforms in several proteomes have been valuable for comprehensive MYR mapping [START_REF] Castrec | Structural and genomic decoding of human and plant myristoylomes reveals a definitive recognition pattern[END_REF]. Future studies will increase the actual myristoylome with all possible substrates resulting from post-translational MYR. This new pool of post-translational MYRed substrates can be unraveled taking into account protein substrate specificity ensured by the newly identified specific NMT pockets, the amino acid pattern of the identified MYRed proteins, and protease cleavage specificity. This will reveal whether the pools of co-and post-translational Gly-Myr share the same N-terminal features.

Gly-myristoylome characterization has also revealed that a non-negligible number of MYRed substrates are partially modified by MYR and that the same N-terminal Gly can undergo different modifications, completely altering the protein's properties. The factors influencing specific modifications and the impact of quality control involving Gly N-degrons and E3 ligases are still unknown, but future elucidation will prove crucial for understanding both the involved factors and their biological roles.

MYR was long considered an irreversible modification. However, myristoyl-glycine cleavage by Shigella protease IpaJ was recently described [START_REF] Burnaevskiy | Proteolytic elimination of N-myristoyl modifications by the Shigella virulence factor IpaJ[END_REF][START_REF] Burnaevskiy | Myristoylome profiling reveals a concerted mechanism of ARF GTPase deacylation by the bacterial protease IpaJ[END_REF], opening up new directions on the dynamic regulation of MYR. The plant hormone ABA was proposed to inhibit MYR, in turn triggering relocalization of several protein targets [START_REF] Belda-Palazon | ABA inhibits myristoylation and induces shuttling of the RGLG1 E3 ligase to promote nuclear degradation of PP2CA[END_REF]. In this context, we anticipate that NMT activity may be insufficient to modify all protein substrates and MyrCoA donor availability may also be insufficient, while dysregulation of de novo synthesis might accelerate the growth of tumors that express high levels of activated Src kinase [START_REF] Kim | Myristoylation of Src kinase mediates Src-induced and high-fat dietaccelerated prostate tumor progression in mice[END_REF].

Finally, the recent discovery of a novel NMT catalytic mechanism suggesting MYR of Nterminal Lys in addition to Gly takes the field into an exciting era where mechanisms of MYR regulation, biological effects, and pathological consequences become central aspects.

Evolution: all life on Earth has evolved from common ancestors in an unbroken chain since its origin, approximately 3.8 billion years ago. FECA: first eukaryotic common ancestor, a protoeukaryote with endomembranes (approx. 2.4 billion years ago).

GNATs: GCN5-related N-acetyltransferases, a large enzyme superfamily that transfer acyl derivatives from acyl-CoA donors to a variety of acceptors.

GTPases: hydrolyze GTP into GDP, include small proteins like ARFs or large ones such as heterotrimeric G proteins, major molecular switches of signal transduction pathways located at the PM. LACA: last archaeal common ancestor (approx. 2.8 billion years ago), an archaeon featuring ESPs such as cytoskeletal proteins and membrane trafficking systems. LECA: last eukaryotic common ancestor, featuring fully differentiated internal structures (approx.

billion years ago).

Methionine aminopeptidases: enzymes cleaving the first methionine of approx. 60% proteins to unmask small residues such as glycines.

Major histocompatibility complex: cell surface proteins essential for vertebrate acquired immunity. MICOS: mitochondrial contact site complex, maintains crista integrity and mitochondrial function.

MYR: N-α-myristoylation, a lipid acylation of proteins using MyrCoA as a myristate donor; makes amide bonds with free amino groups, usually from N-terminal glycines.

MYRed: N-myristoylated, refers to glycine MYR in large-scale analyses. Myristate, a saturated (i.e., without double bonds) 14 carbon fatty acid (C14:0).

Myristoylome: complete set of MYRed proteins in a proteome.

NAA: enzymes of the GNAT family, which use acetyl CoA as a donor to acetylate the alpha aminotermini of proteins. The catalytic subunit may be associated with auxiliary subunits to form N-acetyltransferase complexes (NATs).

Naa10: catalytic subunit of N--acetyltransferase A (NatA), an NAA operating on small N-terminal residues such as glycine.

NMT: N-myristoyltransferase, the MYR catalyst, part of the GNAT family, uses MyrCoA as donor.

N-terminal acetylation: a modification occurring on the N-termini of proteins. Various complexes have been described, such as NatA and NatB, depending their substrate specificity. Involves 80% of proteins of multicellular eukaryotes.

PAL: S-palmitoylation next to MYR; catalyzed by membrane enzymes, occurs anywhere on cysteine residues provided a membrane-binding motif (transmembrane helix/helices, lipid modification) brings the residue to the interface.

Proteoform: protein derivative from one gene, may differ by one modification.

Proteostasis: the proteome steady-state homeostasis as guaranteed by a network of quality control components ensuring proteins integrity. SFK: Src family kinase, non-receptor Src family of protein tyrosine kinases, nine members, none in plants.

Text Boxes Box 1: Protein modification probability and frequency

Common representations of the substrate selectivity of enzymes involved in protein modifications such as proteases use extremely useful logos and powerful visualizations that integrate massive data into a single, striking image [START_REF] Colaert | Improved visualization of protein consensus sequences by iceLogo[END_REF]. Nevertheless, care must be taken when drawing conclusions given the complexity associated with the recognized pattern, which takes into account: (i) various positive and negative effects of the enzyme; (ii) evolutionary constraints from which the initial non-MYRed target starts; (iii) functional and structural constraints of the Nterminal domain, which may be required for in vivo activity (interaction, folding, catalysis, other modifications); and (iv) the fact that there are other constraints from the genetic message and its interpretation by a specific organism (codon usage, amino acid availability, RNA folding, completion with other enzymes including methionine aminopeptidases and NAAs). The difficulty in drawing decision trees from amino acid patterns explains why artificial intelligence approaches including machine learning strategies are required for prediction [START_REF] Castrec | Structural and genomic decoding of human and plant myristoylomes reveals a definitive recognition pattern[END_REF]. Such tools need not only positive but negative sets to ideally delineate, learn, and distinguish a positive from a negative. It is important to remember that the theoretical diversity with 7 amino acids is huge (10 9 ), and current approaches still deal only with hundreds of data points or two thousand in the case of the macroarray. Obtaining negative sets with MS approaches based on positive selection is only feasible if combined strategies involving diverse selection procedures are used. A strategy involving the identification of proteins with non-modified or Nα-acetylated N-termini was used to obtain information on proteins starting with non-MYRed glycines in vivo [START_REF] Castrec | Structural and genomic decoding of human and plant myristoylomes reveals a definitive recognition pattern[END_REF]. Panel C A data set of a macroarray made of >2,000 octapeptides assayed in vitro with NMT is available in [START_REF] Castrec | Structural and genomic decoding of human and plant myristoylomes reveals a definitive recognition pattern[END_REF]. The panel displays the positive data (836) against the negatives (1,229). Gly2 does not appear as it represents >99% of the entire dataset, but "G" was added for the sake of easier comparison with panel A.

Legend to

Panel D. Post-translational MYR recognition pattern. The panel displays residues around the modified glycine (residue 5 before cleavage and MYR). The data are compiled from SupData4 in [START_REF] Thinon | Global profiling of co-and post-translationally N-myristoylated proteomes in human cells[END_REF]. The sequence of the filensin C-domain was added to build the pattern. Panels B-D are independent and were built with iceLogo 1.2 [START_REF] Colaert | Improved visualization of protein consensus sequences by iceLogo[END_REF]. Bottom box, left. Operating principle of a proton wire semiconductor: the protons hop along chains of hydrogen bonds between water molecules making intermediary hydronium ions (adapted from [START_REF] Deng | H+-type and OH--type biological protonic semiconductors and complementary devices[END_REF]). Four steps of proton transfers figured in this cartoon of a wire made of 5 water molecules. A wire can be made of two and more than ten water molecules.

Figure 3: Myristoylome sizes and the categories of MYRed proteins in multicellular organisms

Panel A. Linear relationship between proteome and myristoylome sizes. The data from Table 1 were plotted. The dot in grey corresponds to the yeast data, which is an outlier featuring a reduced myristoylome (see text).

Tables Table 1, Key Table: Myristoylome sizes and conservation of major MYR targets throughout species

Proteomes were extracted from Uniprot (https://www.uniprot.org/); the Gly-ome corresponds to all ORFs displaying a Gly2 residue. Green boxes refer to conserved targets throughout species.

£ data from GT1 strain consolidated and confirmed with data from T. gondii strains ME49 and FOU. All data are from ToxoDB 36 release.

$ library likely cumulating all proteoforms.

£ value calculated from data obtained with the machine learning predictor described in [START_REF] Castrec | Structural and genomic decoding of human and plant myristoylomes reveals a definitive recognition pattern[END_REF]. The values can be compared to those published with another predictor dating to 2002 with far less robust genomic annotation (see Table 6 in Ref. [START_REF] Maurer-Stroh | N-terminal N-myristoylation of proteins: prediction of substrate proteins from amino acid sequence[END_REF]). The performances of various predictors available online are compared in Table 1 of Ref. [START_REF] Traverso | High-throughput profiling of N-myristoylation substrate specificity across species including pathogens[END_REF].

& data from individual or large-scale analysis (with exception of T. gondii, see [START_REF] Alonso | Exploring protein myristoylation in Toxoplasma gondii[END_REF]); data in parentheses refer to those obtained with peptide libraries derived from genuine protein sequences, see references [START_REF] Castrec | Structural and genomic decoding of human and plant myristoylomes reveals a definitive recognition pattern[END_REF][START_REF] Thinon | Global profiling of co-and post-translationally N-myristoylated proteomes in human cells[END_REF][START_REF] Wright | Validation of N-myristoyltransferase as an antimalarial drug target using an integrated chemical biology approach[END_REF][START_REF] Broncel | Multifunctional reagents for quantitative proteome-wide analysis of protein modification in human cells and dynamic profiling of protein lipidation during vertebrate development[END_REF][START_REF] Wright | Global analysis of protein N-myristoylation and exploration of Nmyristoyltransferase as a drug target in the neglected human pathogen Leishmania donovani[END_REF][START_REF] Wright | Global profiling and inhibition of protein lipidation in vector and host stages of the sleeping sickness parasite Trypanosoma brucei[END_REF][START_REF] Roberts | The N-myristoylome of Trypanosoma cruzi[END_REF][START_REF] Majeran | Targeted profiling of A. thaliana sub-proteomes illuminates new coand post-translationally N-terminal Myristoylated proteins[END_REF]. For A. thaliana and H. sapiens, see Supplementary Datasets 1 & 2 in Ref. [START_REF] Castrec | Structural and genomic decoding of human and plant myristoylomes reveals a definitive recognition pattern[END_REF]. # data taking into account all validated proteoforms from NextProt2017 (https://www.nextprot.org/) and AraProt2017 (https://www.arabidopsis.org/index.jsp).

* means that an ORF with strong homology, Gly2, and a likely MYR consensus is retrieved in the proteome.

** the protein exists but not with a Gly2; homologs of the apicomplexan phylum (Babesia spp.) display a Gly2 followed by a good MYR consensus.

  Figure I exemplifies this feature. From Figure 1, one can easily identify the main features of the NMT recognition site (for instance C, N, or Q at position 3). If one compares the positives from global analysis in blue and macroarray results in green in Figure I, the overall frequency is clearly identical. Still, considering the most obvious element of selectivity at position 6 (with S being a feature) (Figures 1 & 2), they only represent 60% of the complete set. With the help of the negative microarray set, one can analyze the probability that an extracted genomic sequence is MYRed (orange). The data show that there is no single amino acid which leads to a frequency higher than 82% with N3 (Figure I). Still, such proteins only represent 25% of the entire set. Combination increases the probability to 93% (N3/S6), but those proteins are only 5% of all positives.

Figure I in Box 1 :

 1 relationships between the probability and frequency of MYR Data are derived from the peptide array [7] of 2,066 sequences including 837 positives and all data from click chemistry (365 sequences). % positives click refers to the click chemistry set. % positives macroarray (green bars) refers to the frequency of positives in the positive set of 837 sequences. Probability of being positive or true positives (orange bars) refers to the frequency of positives in the complete array of 2,066, defining the probability of being MYRed.

Figure 1 .

 1 Figure 1. Specific pockets of NMTs ensure protein substrate specificity Panel A. Various structures of human NMT1 crystallized with MyrCoA and one of 5 different peptide substrates (PDB IDs: 5O9S, 5O9V, 5O9U, 6EHJ, 6QRM) superimposed to show how the various side chains fill each pocket. Each pocket is delineated with a full line and labeled (see text). Panel B. Corresponds to 365 octapeptides extracted from N-terminal sequences from MYRed proteins identified by global approaches with a negative set against Swiss-Prot (Homo sapiens was used as the proteome as very similar results were obtained with other proteomes).

Figure 2 :

 2 Figure 2: Pre-organized water molecules in a dedicated conserved channel contribute to the catalytic mechanism Top, the NMT water channel is schematized. Flux directions of water and protons are indicated in blue and red, respectively. The two proton donors are indicated on the right side. Water molecules are numbered from the outer to the inner side of the active site (W12 to W1). Center, superimposition of the water molecules of the channel and active site in HsNMT1:MyrCoA and HsNMT1:MyrCoA:peptide states. The organized water molecules in the peptide-free state shown in purple and those in the peptide-binding, tetrahedral intermediate state in red. The

  Panel B & C. Categories of MYRed proteins in multicellular organisms. Only those proteins for which gene ontology information is available were analyzed. 439 proteins sorted from A. thaliana (Panel B). 221 proteins sorted from H. sapiens (Panel C).

  Figure 3

Table 2 : NMT, its targets, and the major events in eukaryotic evolution 551

 2

	Stage of eukaryogenesis	MYR event
	Protoeukaryote	Early NMT evolution from a prokaryotic GNAT acetyltransferase
	Emergence of endomembranes (ER, Golgi)	ARFs act as early NMT targets
	Early cell division	ESCRTIII Vps20/CHMP6 involved
	Creation of nuclear endomembrane	26S proteasome regulatory subunits are shuttled together with Rpt2
	Aerobic bacterium engulfment, allowing oxygen	AMK/Sip1/SnRK kinase assemble as
	accommodation and energy sensing	heterotrimeric complexes
	Calcium as major signal transducer	Calcium sensing with calcineurin-like proteins
	ER stress, regulation of the unfolded protein response	PP2C phosphatases involved
	Vacuole sorting, macroautophagy	Protein kinase Vps15
	Mitochondria become symbiotic organelles, gene	MICOS complex ensures link between ER and
	transfer to the nucleus	mitochondria
	Early cell differentiation allows cell communication through membrane receptors and allow sex mating in fungi, for instance; leads to multicellularity	Heterotrimeric G alpha subunit, interaction with PI3 kinase/phosphodiesterase activation
	Animal cell differentiation	Tyrosine kinases of the SFK family appear
	Plastids become symbiotic organelles	Fusion of MYRed calcium sensors to kinases to create Ca 2+ -dependent kinases
	552

Acknowledgements

The team was funded by Agence Nationale de la Recherche (ANR-2010-BLAN-1611-01) and Fondation ARC (SFI2011120111203841) and benefitted from the support of the Labex Saclay Plant Sciences-SPS (ANR-10-LABX-0040-SPS).

Glossary

26S proteasome: a 2.5 MDa molecular machine controlling ubiquitin-dependent proteolysis in eukaryotes made of two (19S-regulatory and 20S-core proteolytic) complexes.

ADP-ribosylation factors (ARFs


domain: spi phys

text:
E Terrenoire
email: etienne.terrenoire@onera.fr

& D Hauglustaine
email: didier.hauglustaine@lsce.ipsl.fr

IMPACT OF PRESENT AND FUTURE AIRCRAFT EMISSIONS ON ATMOSPHERIC COMPOSITION AND RADIATIVE FORCING OF CLIMATE

Keywords: present and future aircraft emission, short-term climate impact, global climate model

Depending on the nature and the location of the emitted chemical agents, aircrafts contribute to warm or cool the climate. The chemical perturbation and the associated radiative forcing of carbon dioxide, ozone, methane, soot carbon (BC), sulphates, nitrates and organic carbon (OC) due to aircraft emissions have been calculated using five different global emission inventories using a carbon cycle compact climate change model and the climate-chemistry global model LMDz-ORCH-INCA. We found that the impact of the aviation CO 2 emissions ranges from 45 ± 2 mW/m 2 (2.5 % of the total anthropogenic warming) for an ambitious mitigation strategy scenario (Factor 2) to 78 ± 4 mW/m 2 (2.5 % of the total anthropogenic warming) for the least ambitious mitigation scenario of the study (ICAO based). Apart from CO 2 , the total radiative forcing related to aviation and modifications of gases and aerosols calculated in this work is negative and varies from -2.5mW/m 2 for the present scenario REACT4C (-3.6 mW/m 2 in the case QUANTIFY_2000).

INTRODUCTION

Depending on the nature and the location of the emitted chemical agents, aircrafts contribute to warm or cool the climate. Three other major effect should be distinguished: the short-term effects (a few weeks at maximum), the long-term effects (from one year to more than 100 years) and the cloud type effects. In this paper, we will present global long-term CO 2 and short-term impacts for present and future aircraft emissions that are predominantly related to the injection of nitrogen dioxides (NO x =NO+NO 2 ), water (H 2 O) and non-methane hydrocarbon (NMHC) by planes. They perturb the tropospheric and lower stratospheric repartitions of ozone (O 3 ), methane (CH 4 ,) water (H 2 O), sulphur dioxide (SO 2 ), sulphate (SO 4 ) and nitrate (NO 3 ). Other short-term effects are linked with the direct injection of aerosols such as back carbon (BC), organic carbon (OC) and sulphate (SO 4 ). Other effects that will be evaluated include the long-term effect link to CH 4 concentration decrease due to OH enhancement from the NO x aircraft emissions. This paper aim to bring an updated estimation of the climate impact of those species based on leading-edge carbon cycle compact climate model and climate-chemistry global model LMDz-ORCH-INCA using multi scenarios analysis.

AIRCRAFT EMISSIONS INVENTORIES

Emission for present comes from the inventory Reducing Emissions from Aviation by Changing Trajectories for the benefit of Climate project (REACT4C) (Sovde et al., 2014).The inventory had to be adapted to our simulations as some species were not available in the original version of the database. First, for NO x (assimilated as NO 2 ) and BC the data are taken directly from the original inventory. Second, for H 2 O, CO and HC the data comes from the AERO2K project [START_REF] Eyers | AERO2k Global Aviation Emissions Inventories for 2002 and 2025[END_REF]. Third, for OC, SO 2 and SO 4 , we use the mean emission factors that are reported in [START_REF] Lee | Transport impacts on atmosphere and climate: Aviation[END_REF]. In parallel to the REACT4C inventories, another inventory representative of the 2000 emission was used. The inventory is called QUANTIFY in reference to the project of the same name. For this scenario only the fuel burn, NO 2 and BC are available. For the other species of interest (H 2 O, CO, HC, OC, SO 2 , SO 4 ), the emission factors are identical to the ones used for REACT4C. The total emissions of primary species such as NO x , BC, SO 2 and sulphates from the QUANTIFY_2000 scenario are slightly higher than those of REACT4C (ex: 0.77 kgN/year for REACT4C_2006 against 0.84 kgN/year for QUANTIFY_2000). In QUANTIFY_2000, the species available in the original files are fuel burn, NO 2 and BC. For the other species (CO 2 , H 2 O, CO, HC, OC, SO 2 , SO 4 ), the emission factors are identical to those used for the constitution of the basic REACT4C inventory. The spatial distribution is similar for both inventories.

METHODOLOGY

Using the compact Earth System Model (ESM) OSCARv2.2 (Gasser et al., 2017), we quantify the climate impact of present and future (up to 2100) civil aviation carbon dioxide (CO 2 ) emissions using eight aviation scenarios ranging from 386 Mt CO 2 /year (Factor 2 scenario) to 2338 Mt CO 2 /year (ICAO/CAEP scenario) in 2050. This approach allow quantifying the uncertainty due to the difficulty to estimate the future mitigation effort. The influence of other emission sectors is evaluated using two background Representative Concentrations Pathways (RCP2.6 and RCP6.0). For the short-term species, a new version of the LMDz-INCA (LMDz version 5, INCA version 4) have been used. This new version has 39 vertical levels and extends up to 80 km in altitude. The vertical resolution has been improve around the cruise altitude of planes (<1 km). CNRS-LIVE has used the LMDz-INCA global chemistry-aerosol-climate model coupling on-line with the LMDz (Laboratoire de Météorologie Dynamique, version 5) General Circulation Model [START_REF] Hourdin | The LMDZ4 general circulation model: climate performance and sensitivity to parametrized physics with emphasis on tropical convection[END_REF] and the INCA (INteraction with Chemistry and Aerosols, version 4) model [START_REF] Hauglustaine | Interactive chemistry in the Laboratoire de Meteorologie Dynamique general circulation model: Description and background tropospheric chemistry evaluation[END_REF]. INCA initially included a state-of-theart CH 4 -NO x -CO-NMHC-O 3 tropospheric photochemistry [START_REF] Hauglustaine | Interactive chemistry in the Laboratoire de Meteorologie Dynamique general circulation model: Description and background tropospheric chemistry evaluation[END_REF]. The tropospheric photochemistry and aerosols scheme used in this model version is described through a total of 123 tracers including 22 tracers to represent aerosols. The model includes 234 homogeneous chemical reactions, 43 photolytic reactions and 30 heterogeneous reactions. Please refer to [START_REF] Hauglustaine | Interactive chemistry in the Laboratoire de Meteorologie Dynamique general circulation model: Description and background tropospheric chemistry evaluation[END_REF]Folberth et al. (2006) for the list of reactions included in the tropospheric chemistry scheme. The gas-phase version of the model has been extensively compared to observations in the lower-troposphere and in the upper-troposphere. For aerosols, the INCA model simulates the distribution of aerosols with anthropogenic sources such as sulfates, nitrates, black carbon, particulate organic matter, as well as natural aerosols such as sea-salt and dust.

RESULTS

In 2050, on a climate trajectory in line with the Paris Agreement limiting the global warming below 2 °C (RCP2.6), we found that the impact of the aviation CO 2 emissions ranges from 45 ± 2 mW/m 2 (2.5 % of the total anthropogenic warming) for an ambitious mitigation strategy scenario (Factor 2) to 78 ± 4 mW/m 2 (2.5 % of the total anthropogenic warming) for the least ambitious mitigation scenario of the study (ICAO based) (Terrenoire et al., 2019). Figure 1 summarizes the radiative forcing calculated for the different scenarios for the short-term species (Terrenoire et al., in preparation). The increase of ozone in the troposphere is responsible for a positive radiative forcing of 15.8 mW/m 2 for the present reference scenario REACT4C_2006. In the case of methane we calculate a negative forcing of about -15 mW/m 2 for different simulations by aviation. These two steady-state forcings associated with nitrogen oxides (ozone and methane) largely offset each other. The forcing of methane can in fact be broken down into four distinct forcings: a direct forcing and three indirect forcing. Indirect forcings were recalculated with the LMDZ-OR-INCA model. On the one hand, the increase in OH directly disrupts the methane well in the atmosphere. The increase in this photochemical loss results in a decrease in methane and a direct radiative forcing at equilibrium of -10.7 mW/m 2 for the present. Concentrations of several types of particles will also be disrupted by aviation-related emissions. Direct emissions of soot carbon (BC) by aviation induce a positive radiative forcing of 0.46 mW/m 2 in the present case for the REACT4C inventory. In the future, this forcing reaches 1.88 mW/m 2 . Sulfates that are reflective particles will be influenced by SO 2 emissions but also by direct SO4 emissions. The negative forcing associated with these particles dominates the effect of the other particles (-3.9 mW/m 2 ). The distribution of nitrates is indirectly affected by aviation by the variation of the content in sulphates and the NO x disturbance. These particles are responsible for a positive forcing (0.07 mW /m 2 ). A slight negative forcing is associated with the emission of organic carbon by aviation. In total the particles are responsible for a negative forcing largely dominated by sulphates ranging from -3.4 mW/m 2 in this case REACT4C. Apart from CO 2 and streaks, the total radiative forcing related to aviation and modifications of gases and aerosols calculated in this work is negative and varies from -2.5 mW/m 2 for the present scenario REACT4C (-3.6 mW/m 2 in the case QUANTIFY_2000).

CONCLUSION

The chemical perturbation and the associated radiative forcing of carbon dioxide, ozone, methane, soot carbon (BC), sulphates, nitrates and organic carbon (OC) due to aircraft emissions have been calculated using five different global emission inventories using a carbon cycle compact climate change model and the climate-chemistry global model LMDz-ORCH-INCA. We found that the impact of the aviation CO 2 emissions ranges from 45 ± 2 mW/m 2 (2.5 % of the total anthropogenic warming) for an ambitious mitigation strategy scenario (Factor 2) to 78 ± 4 mW/m 2 (2.5 % of the total anthropogenic warming) for the least ambitious mitigation scenario of the study (ICAO based). The increase of ozone in the troposphere is responsible for a positive radiative forcing of 15.8 mW/m 2 for the present reference scenario REACT4C_2006. In the case of methane, we calculate a negative forcing of about -15 mW/m 2 . In total, the particles are responsible for a negative forcing largely dominated by sulphates ranging from -3.4 mW/m 2 in this case REACT4C to -10.6 mW/m 2 in 2050. Apart from CO 2 and streaks, the total radiative forcing related to aviation and modifications of gases and aerosols calculated in this work is negative and varies from -2.5mW/m 2 for the present scenario REACT4C (-3.6 mW/m 2 in the case QUANTIFY_2000).

Figure 1 :

 1 Figure 1: Radiative forcings of climate associated with aircraft emissions REACT4C emission inventory (2006).