M Abdelaziz Elaabid
email: elaabid@telecom-paristech.fr

Sylvain

M Abdelaziz Elaabid

Sylvain Guilley
email: sylvain.guilley@telecom-paristech.fr

Portability of Templates

Keywords: Template attack, Resynchronization, Side-channel attacks. 1. Campaign

published or not. The documents may come

Introduction

Template attacks [START_REF] Chari | Template Attacks[END_REF] are side-channel attacks based on a precharacterization phase. This phase is carried out off-line on an profiling circuit with known key, once for all the subsequent attacks. It aims at preparing templates that characterize the leakage of the circuit, in order to continue with the most powerful attacks, namely Bayesian attacks. These two phases are also referred to as "training" and "matching".

To our best knowledge, most state-of-the-art literature contains only proof-of-concept attacks, where the traces intended for the training and the matching phases are acquired consecutively 1 on the same circuit [START_REF] Chari | Template Attacks[END_REF][START_REF] Rechberger | Practical Template Attacks[END_REF][START_REF] Agrawal | Templates as Master Keys[END_REF][START_REF] Gierlichs | Templates vs. Stochastic Methods[END_REF][START_REF] Archambeau | Template Attacks in Principal Subspaces[END_REF][START_REF] Aabid | Template Attacks with a Power Model[END_REF][START_REF] Standaert | Using Subspace-Based Template Attacks to Compare and Combine Power and Electromagnetic Information Leakages[END_REF][START_REF] Hanley | Unknown Plaintext Template Attacks[END_REF][START_REF] Bär | Verbesserung von Template Attacken auf Chipkarten (Improvements of Template Attacks against Smart Cards)[END_REF][START_REF] Bär | Improved Template Attacks[END_REF]. This case is the most favorable to the attacker, since the templates are built in extremely similar conditions to that of the real attack. In more realistic cases, an attacker does not train and do the attack on the same circuit at the same moment. The effect of time can add new factors that may make the attack more difficult. In fact, the adversary should conduct traces acquisition (a difficult and error-prone experimental process) ideally in the same way for the training and for the matching. Every step involved in an acquisition, from the characteristics of the measurement resistor to the configuration of the oscilloscope, should be as similar as possible. To assess in which respect acquisition discrepancies can impede an adversary, we conduct some experiments that consist of changing conditions to study the induced effects. We identified two major problems: the curves desynchronization in time and in amplitude. We explore some strategies available to an adversary using template attacks to bypass them.

Recent works have formalized the quantity of information that is lost when templates are not portable. Notably, the notation of perceived information is introduced in [START_REF] Renauld | A Formal Study of Power Variability Issues and Side-Channel Attacks for Nanoscale Devices[END_REF] and applied on a protected circuit in [START_REF] Renauld | Information Theoretic and Security Analysis of a 65-Nanometer DDSLL AES S-Box[END_REF]: it is equal to the information leakage an evaluator estimates using a model (templates obtained from another circuit) that differs from the actual leakage of the targeted circuit (on which matching is done). It is shown that this information is always less or equal to that of the hypothetical case where the model exactly describes the targeted circuit. Thus, the mismatch between the templates the targeted circuit underestimates the leakage. In this paper, we do not focus on information theoretic metrics, but rather on attack metrics. This distinction has been introduced in [START_REF] Standaert | A Unified Framework for the Analysis of Side-Channel Key Recovery Attacks[END_REF], to make a difference with the vulnerability assessment (leakage metrics) and the security assessment (attack metrics). Thus, we intend to practically conduct template attacks using the same device at different times. In addition, we target unprotected implementations. Thus, the differences in the templates due to process variation is expected to be much less dominant than in [START_REF] Renauld | Information Theoretic and Security Analysis of a 65-Nanometer DDSLL AES S-Box[END_REF], where the study is conducted on a purportedly power-constant implementation. This motivates the focus of this paper on attack metrics.

The rest of the article is structured as follows. Our methodology, intentionally practiceoriented, is first explained in Sec. 2. Then, we recall in Sec. 3 how principal subspaces can be used to reduce the dimensionality of templates. Resynchronization techniques to temporally realign campaigns are discussed in Sec. 4. Their result is given in Sec. 5. The correction of vertical scaling mismatches is presented in Sec. 6. Eventually, Sec. 7 is the final evaluation of the portability of templates with horizontal and vertical correction. The conclusions and perspectives can be found in Sec. 8.

Methodology

This article aims at investigating the practical effects of possible experimental conditions mismatches between training and matching phases on the attacks' success rate and guessing entropy [START_REF] Standaert | A Unified Framework for the Analysis of Side-Channel Key Recovery Attacks[END_REF]. In this study, we focus on the effect of time in a template attack: the reproducibility of measurement setups is challenged. We choose to consider three sets of measurements acquired on an ASIC implementing DES: 2. Campaign B: 50,000 measurements obtained in year 2010 at nominal voltage (about 1.2 V) on the same ASIC are used for matching, 3. Campaign C: 50,000 measurements obtained in year 2010 at reduced voltage (about 1.0 V) on the same ASIC are also used for matching.

The goal of the campaign C is to provide a comparison of two campaigns (B and C) that were carried out close in time, but with slightly different experimental conditions. Here, the variation comes from the power supply. More precisely, the common features between the A and B/C campaigns are listed below:

The same ASIC is tested.

It is soldered on the same evaluation board (described in Appendix B of [START_REF] Guilley | Improving Side-Channel Attacks by Exploiting Substitution Boxes Properties[END_REF]).

The same differential voltage probe (Agilent 1132A) is used to measure the voltage drop over a resistor placed between the ground of the evaluation board and the ground of the ASIC.

The same oscilloscope (Agilent infiniium 54855A DSO) is used, with exactly the same setup file (refer to Tab. 1 that describes the settings of the several acquisitions).

The differences between the A and B/C campaigns are:

The wiring between the evaluation board and the oscilloscope has been redone (thereby incurring maybe some delay variations, for instance on the trigger line); it is different between A and B, A and C, but equal for B and C. Indeed, the only modification done between campaigns B and C has consisted in turning the power supply button to reduce the voltage from 1.2 to 1.0 volts.

The spying resistor has been changed between campaigns A and B/C. The ASIC has been aging, and has thus undergone hot-carrier-induced degradation [START_REF] Baba | Testing for Transistor Aging[END_REF] (an effect that is hard to quantify on a circuit that was not designed to be tested against aging).

The first and second order statistics on the three campaigns are given in Fig. 1, 2 and 3.

It can be noticed that messages vary randomly, and that the encryption starts around time sample 5,000 and stops at about sample 15,000. The increase of power consumption during these sixteen clock cycles coincides with the sixteen rounds of DES encryption. As can be seen on the same figures (right part), the accompanying decrease of the standard deviation is consistent with the fact the control logic and lines become deterministic during the encryption, thereby subtracting a noise contribution from the encryption process. Indeed, in our architecture, the DES datapath is left enabled in the "idle" state, which makes it produce high and inconsistent activity outside encryption timing windows. From the variance curves, other interesting comments can be done: Regardless of the acquisition campaign (A, B or C), there is a constant noise level (slightly below 1 mV), that represents the intrinsic acquisition noise (incurred by quantization and thermal fluctuations).

Table 1: Setup of Agilent's files in the three campaigns studied in this article -this data has been extracted after the campaigns from the Agilent ".bin" files (whose format is described in [START_REF]Series Oscilloscopes -User's Guide[END_REF] The height of the variance peaks at the clock rising edges is increasing with that of the average peaks; This confirms that this variance actually models the algorithmic noise.

As a matter of fact, it is indeed expected that the algorithmic noise is an increasing function of the direct power consumption.

The overall shape of these three campaigns looks quite different, especially in amplitude. Now, if we have a closer look at the synchronization of the campaigns between themselves, we observe that they are not in phase. The Fig. 4(a) typically emphasizes the timing mismatch between A, on the one hand, and B & C on the other hand. This figure is zoomed on the first round of encryption.

Template Attack with PCA Preprocessing

Template attacks require to characterize the leakage in an off-line step. This characterization gives all information needed to attack and recover the encryption key. In the usually assumed Gaussian model, the used data consists of averages and covariance of each set of traces categorized according to one model. During the attack phase, the adversary uses the maximum likelihood principle to rank the key hypotheses.

Statistically, the more traces, the better the correct key emerges between the others. Ideally, the attacker uses these templates for a successful attack. However, the large size of covariance matrices makes the calculation infeasible in the case of long traces, since matrices are badly conditioned.

In practice, points of interest (POIs) must be found to carry out all calculations. Many methods are presented in the literature. Among them we find:

1. Manual selection, which requires expertise;

2. Sum Of Squared pairwise (T-)Differences (or sosd [START_REF] Gierlichs | Mutual Information Analysis[END_REF] / sost [START_REF] Gierlichs | Templates vs. Stochastic Methods[END_REF]);

3. Linear Discriminant Analysis (LDA [START_REF] Standaert | Using Subspace-Based Template Attacks to Compare and Combine Power and Electromagnetic Information Leakages[END_REF])

4. Principal Components Analysis (PCA [START_REF] Archambeau | Template Attacks in Principal Subspaces[END_REF]).

To be accurate, the fourth method is a particular case of the so-called principal subspaces template attacks [START_REF] Standaert | Using Subspace-Based Template Attacks to Compare and Combine Power and Electromagnetic Information Leakages[END_REF]. Indeed, the aim of PCA is to reduce the data to a lower-dimensional representation that summarize a large part of (if not all) the variability. In this article, we compare the templates in PCA subspaces, using only one direction for the projections, which will move from one multivariate analysis to a univariate analysis, while keeping the maximum information. For one or more traces acquired on the target circuit, the attack phase consists in guessing the secret key κ used for encryption using Bayes' rule. The attack is successful if and only if:

κ = argmax k 1 (2π) n |Λ k | exp - 1 2 •(τ -µ k ) T Λ -1 k (τ -µ k ) .
Here, τ is the attacked trace, the pairs (µ k , Λ k ) are the templates that correspond to the supposed key k, and n is the number of retained directions.

Traces, averages and covariances are a priori projected into a new database given by the PCA to reduce dimensions. New directions are the eigenvectors of the covariance matrix constructed from averages representing each template. We choose to work with the first eigenvector as unique direction because it concentrates the maximum of variance: the table 2 shows the large difference between the first and the following eigenvalues for all the campaigns.

|(R 0 ⊕ (S(R 0 ⊕ K 1 )) ⊕ P -1 (L 0 ))[1 : 4]| ∈ {0, 1, 2,
3, 4} and detailed in Fig. 5. Consequently we obtain five templates in the profiling phase.

Horizontal

Resynchronization: POC and AOC Misalignment of traces is a problem customarily encountered in side-channel analysis. Several methods have been suggested to recover the proper synchronization between curves. Amongst them, we review amplitude-only and phase-only correlations (abridged AOC and POC [START_REF] Homma | High-Resolution Side-Channel Attack Using Phase-Based Waveform Matching[END_REF]). Those methods consist in estimating the offset between two traces by maximizing their cross-correlation, or the cross-correlation of their phase. A survey of these methods can be found in [START_REF] Guilley | Formal Framework for the Evaluation of Waveform Resynchronization Algorithms[END_REF].

In this section, we compare their efficiency. For this and only for the purpose of comparison, we assume to know the keys and we proceed to a training on the traces attacked using PCA. If the correct partitioning was known (which is true for the templates but not for the traces un-der attack), we could compare the relative ability of AOC and POC to recover the correct offset by applying them on the first eigenvector of PCA. The result, illustrated in Fig. 6(a), shows that AOC is definitely less noisy. However, without any prior information about the secret key of the campaign to attack, only the average of the campaigns or the traces one by one can be used to estimate the timing offset. The performance is respectively illustrated in Fig. 6(b) and (c). In this case, the POC seems more adequate: the maximal peak has a greater contrast for this method.

We thus use the POC to estimate the misalignment of the curves between campaigns A and B on the one hand and A and C on the other. We end up with a global resynchronization of the curves, depicted in Fig. 7(a) and 7(b). After this time shift, the first eigenvectors are also in phase, as depicted in Fig. 8.

The exact figures for resynchronization are given in Tab. 3; one sample represents 0.05 ns, because the sampling rate is 20 Gsample/s. This table shows that the a priori resynchronization on the power traces is slightly different from that using the first eigenvector of the PCA. Nonetheless, we rely in the following on these close values to bring campaigns B and C in synchronization with campaign A.

Successful Attack with Bad Timing Shifts

We tested templates attacks on a large window of offsets: [0..1, 000], which includes the offsets about 170 found previously. Campaign A is used for training with whole set of 80,000 traces, and Campaign B for matching. Currently, we do not use the campaign C, which serves only to see the effect of voltage changing. In the following we focus only on the success rate of the resynchronized campaign B. The success rate on campaign C is similar.

The success rate and the guessing entropy are our comparison metrics. The first-order success rate is, by definition, the percentage of times that the key used during encryption is ranked first among the 64 key assumptions. In most estimates, we have experienced that an increasing number of matching traces generates an increasing success rate. For the sake of representativity, we fix the number of traces to 1,000 to keep enough traces for an accurate estimation of the metrics. The guessing entropy is also important because it illustrates the ranking among the en-cryption key assumptions. In that sense, this metric is less strict and thus more informative than the success rate about the attack trend.

The purpose of this first experiment is to check whether the success rate can reach 100% exactly at the right shift given by the synchronization characterized in Tab. 3. Basically, we want to know if an attacker has a margin of error in the resynchronization process. The first result is that the offsets offering a decent success rate or guessing entropy are not those actually given by the resynchronization. Indeed, figures 9(a) and 9(b) show that we have different peaks corresponding to different shifts.

On the other hand, using the strict guessing entropy definition [START_REF] Standaert | A Unified Framework for the Analysis of Side-Channel Key Recovery Attacks[END_REF]Eqn. (2)], we face a practical problem of calculation: the ex aequo keys. This problem is explained by the fact that more traces added involve that keys probabilities tend to limit values. Thus, it is possible that at some moments, the probability of some guesses keys becomes zero. This set of guesses keys may include the right key, in the case of traces badly processed. Thereby, the right key will have the same probability than other uncertain guessed keys, and therefore, the guessing entropy will be affected.

Resynchronization by AOC

Resynchronization by POC (a) Synchronization of the first eigenvectors. With this in mind, we refine the concept of guessing entropy by adding the notions of pessimistic and optimistic ranking. The ranking is pessimistic (resp. optimistic) if we consider the worst (resp. best) ranking for ex aequo keys. In our figures, the guessing entropy we represent is the average between the pessimistic and the optimistic guessing entropies. Thus, for instance, when the template attack finds the correct key is not the actual one with probability 1 (which can happen due to the finite resolution of the floating point numbers handled by personal computers), then the pessimistic ranking is 64 whereas the optimistic one is 2. Therefore, we opt for a "tradeoff" guessing entropy of (64 + 2)/2 = 33. Figs 9(a) and 9(b) show a similarity between success rate and guessing entropy. At this level, we can deduce that an attacker may recover the key with a high probability, even if she does not synchronize the training and the matching campaigns with the correct offset. Also, the adversary will notice that, for some shifts, the key is ranked last (i.e. at position 64 out of 64) in terms of guessing entropy. This phenomenon is repeated at different times. This is due to the fact that the number of time offsets is greater than the clock cycle.

We conjecture these errors are caused by the difference in amplitude between the two cam-paigns. Actually, the templates built from campaign A do not have the same scale as traces from campaigns B or C; Hence, they match poorly. As a matter of fact, despite our wish to make the acquisition of campaigns B and C close to that of campaign A, we faced amplitudes mismatches. To validate our hypothesis, we take traces from the same campaign A, and split them into halves: one for training and the other for matching. We shift each target trace in a window of 2,000 samples and examine the success rate and the guessing entropy, represented in figures 10(a) and 10(b). We note that the success rate grows to 100% at near zero offset, and remains stuck at zero otherwise. As we can see in Fig. 4(a), the amplitude is significantly different between the averages. Thus, without resynchronization techniques, it would be difficult to recover the key from traces of campaign B or C using the templates built from campaign A.

Vertical Homothety

Template attacks are only poorly resilient to homotheties (multiplication by a scalar), therefore the vertical variations can definitely hinder the attack. The effect of an homothety in the voltage is sketched in Fig. 11. This figure describes a case with only two templates, where the attacked trace has a voltage less than the nominal value.

To overcome this problem, we perform a vertical homothety on the matching traces to bring their averages as close as possible to that of the templates. We investigate which scaling factor yields the best results. We begin by multiplying each trace by 0.5, and by calculating the success rate and the guessing entropy for different time shifts. The results, illustrated in Fig. 12(a) and 12(b), show that the shifts for a successful attack are entirely different from those obtained without considering the amplitude. Also we get rid off the surprising sharp peaks of Fig. 9(b) and we observe that the success rate is growing to 100% at the offsets predicted in Sec. 4. We test with another factor (namely 0.47) obtained by approaching the first peak on the trace attacked with the first peak on the general mean training traces. This new factor further improves the guessing entropy and the success rate, especially near the right time shift. We could work separately on each point and calculate a scaling coefficient per point. However, to automate this procedure, we suggest to center and normalize all traces: those for profiling and also those for matching. This normalization harmonizes the acquisition campaigns and thereby reduces the scaling deviation between them. This method is customarily used in sidechannel analysis (see for instance [17, §4.1]): it is further investigated in Sec. 7.

Estimation of the Portability

Using the vertical scaling (described in Sec. 6) and the adequate horizontal resynchronization, we compare the efficiency of the templates attacks. The metric is the success rate and the guessing entropy.

We clearly see in Fig. 13(a) and 13(b) that when traces are taken on a setup different from the ones of the templates, the success rate is lower than in the ideal case, where both the matching and training campaigns originate from consecutive measurements. We insist, of course, that the traces for each experiment are well separated: there is indeed no intersection between traces for matching and those of training. Although the attacker could find the key, she nevertheless requires more traces to do so. Actually, the attacker able to globally scale the matching campaign (by a factor of 0.47) does not have the same effectiveness as an attacker who better con- trols the acquisition of traces. The loss can be estimated in terms of number of traces to reach 50% success rate: as can be seen in Fig. 13(b), without precaution, the attack requires about 10 times more queries. Nonetheless, if both campaigns for templates and for matching are normalized (each trace is replaced by its difference with the average trace of the campaign, and this subtraction is itself divided by the overall campaign standard deviation), we observe (also in Fig. 13(a) and 13(b)) that the metrics are almost as good as for the template attacks on the reference campaign (half of A for training versus the other half for matching). Thus, the normalization of campaigns in conjunction with timing resynchronization is a preprocessing that allows for a successful portability of templates from campaign A to B. The the same work can be done on the campaign C. The success rates will be similar to those observed with B. It can therefore be claimed that, according to our experiments, template attacks (with the indicated preprocessing) can indeed be almost 100% efficient even if discrepancies exist in timing or vertical scaling.

It is also interesting to compare template attacks with univariate attacks (typically DPA [START_REF] Bevan | Ways to Enhance Differential Power Analysis[END_REF] and CPA [START_REF] Brier | Correlation Power Analysis with a Leakage Model[END_REF]). In Fig. 14, it can be seen that attacking a campaign B with raw (hence unadapted) precharacterization from campaign A yield results worse than DPA or CPA, but that template attacks conducted with resynchronization and normalization perform better than those attacks.

Conclusion and Perspectives

Despite the favors granted to a template attack adversary, such as unlimited training on a clone device, she can come across difficulties if the traces are neither to scale nor synchronized. Based on real experiments, we indeed note that the vertical amplitude can change between the acquisitions on the clone and the measurements on the targeted circuit.A desynchronization in time might occur, which induces errors especially in the choice of points of interest. We investigate this kind of situation, in the case where an adversary uses PCA to reduce the dimensionality of the side-channel traces. For our case-study, we have made acquisitions at very different dates, and we conclude that despite all our efforts to maintain the same conditions, the traces appearance is not the same.

In this situation, we recommend that the adversary adds a treatment phase between training and the real attacks. We demonstrate how to resynchronize traces in amplitude and time to be able to recover the key. Realignment in time is straightforward; but it is difficult to find a consensual coefficient to change the amplitude of each trace. Thus we introduce the normalization of the traces (both for the templates "training" and for the "matching" traces). This pretreatment appears efficient: it allows to keep a success rate equivalent to an attack that is made on the same acquisition campaign.

Figure 1 :

 1 Figure 1: Mean and standard deviation trace for the campaign A.

Figure 2 :

 2 Figure 2: Mean and standard deviation trace for the campaign B.

Figure 3 :

 3 Figure 3: Mean and standard deviation trace for the campaign C.

Figure 4 :

 4 Figure 4: Comparison of statistics properties of campaigns A, B and C.

Figure 5 :

 5 Figure 5: Datapath of DES involved in the attack of the first round.

  Offset [# of samples](b) Synchronization of the campaigns means.

  of samples] (c) Synchronization of two randomly chosen raw traces. Offset [# of samples]

Figure 6 :

 6 Figure 6: Results of resynchronization between (a) eigenvectors, (b) campaigns means and (c) two samples traces, for AOC and POC.

Figure 7 :

 7 Figure 7: Comparison of statistical properties of resynchronized campaigns A, B and C.

Figure 8 :Table 3 :

 83 Figure 8: Comparison of resynchronized first eigenvectors of campaigns A, B and C passed through PCA.

Figure 11 :

 11 Figure 11: The matching phase can reveal incorrect hypotheses if the observations have gone through an homothety. In this illustration, there are two templates (shown in plain or dashed lines) and the homothety factor is 2/3.

  Success rate vs different shifts.

Figure 12 :

 12 Figure 12: Comparison of attacks for different vertical scalings as a function of the resynchronization offset. The training is done on campaign A and the matching on campaign B.

  at pages 409-413).

	Campaign A	Campaigns B & C
	### Binary Header Format		### Binary Header Format
	# Version:	10	# Version:	10
	# File Size:	80176	# File Size:	80176
	# Nbr of Waveforms:	1	# Nbr of Waveforms:	1
	### Waveform Header		### Waveform Header
	# Header Size:	140	# Header Size:	140
	# Waveform Type:	HORIZONTAL_HISTOGRAM	# Waveform Type:	HORIZONTAL_HISTOGRAM
	# Nbr of Waveform Buffers:	1	# Nbr of Waveform Buffers:	1
	# Nbr of Hits:	20003	# Nbr of Hits:	20003
	# X Display Range:	7.4228448e-51	# X Display Range:	7.4228448e-51
	# X Display Origin:	2.32682e-06	# X Display Origin:	2.32682e-06
	# X Increment:	5e-11	# X Increment:	5e-11
	# X Origin:	2.32676844e-06	# X Origin:	2.32676844e-06
	# X Unit:	0	# X Unit:	0
	# Y Unit:	0	# Y Unit:	0
	# Date:	8 APR 2006	# Date:	27 MAY 2010
	# Time:	11:31:01	# Time:	20:16:21
	# Model:	54855A:	# Model:	54855A:
	# Label:	channel 3	# Label:	channel 3
	# Tag value:	0	# Tag value:	0
	# Index:	0	# Index:	0
	### Waveform Data Header:		### Waveform Data Header:
	# Waveform Data Header Size: 12	# Waveform Data Header Size: 12
	# Buffer Type:	NORMAL_32	# Buffer Type:	NORMAL_32
	# Bytes Per Point:	4	# Bytes Per Point:	4
	# Buffer size (in bytes):	80012	# Buffer size (in bytes):	80012

Table 2 :

 2 Eigenvalues of PCA on campaigns A, B and C. In this article, we focus on the Hamming distance model on the first round and for the first substitution box, given by

	PCA		Campaign
	eigenvalues	A	B	C
	Eigenvalue 1 0.00062368 0.00482484 0.0033602
	Eigenvalue 2 9.3984e-06 5.0050e-05 3.3966e-05
	Eigenvalue 3 5.3011e-06 3.5375e-05 2.4543e-05
	Eigenvalue 4 2.6232e-06 1.3643e-05 1.0195e-05


Anushree Acharya

Haluk Kavus

Patrick Dunn

Abdul Nasir

Leandra Folk

Kara Withrow

Ingrid M Wentzensen

Maura R Z Ruzhnikov

Camille Fallot

Thomas Smol

Acharya

Anushree

Mélanie Rama

Kathleen Brown

Sandra Whalen

Alban Ziegler

Magalie Barth

Anna Chassevent

Constance Smith-Hicks

Alexandra Afenjar

Thomas Courtin

Solveig Heide

Esperanza Font-Montgomery

Caleb Heid

J Austin Hamm

Donald Love

Farouq Thabet

Vinod K Misra

Mitch Cunningham

Suzanne M Leal

Irma Järvelä

Elizabeth A Normand

Fanggeng Zou

Mayada Helal

Boris Keren

Erin Torti

Wendy K Chung

Isabelle Schrauwen

Delineating the genotypic and phenotypic spectrum of HECW2-related neurodevelopmental disorders

Keywords: HECW2, neurodevelopment, intellectual disability

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

Introduction

Neurodevelopmental disorders (NDDs) affect >3% of children worldwide and can often pose a significant burden on both the individual and their family. NDDs with intellectual disability (ID) are characterized by cognitive impairment and challenges with adaptive behavior [START_REF] Marrus | Intellectual Disability and Language Disorder[END_REF]. Genetic factors have been shown to play an important role in the etiology of NDDs, especially in more severe NDDs [START_REF] Tărlungeanu | Genomics in neurodevelopmental disorders: an avenue to personalized medicine[END_REF][START_REF] Blesson | Genetic Counseling in Neurodevelopmental Disorders[END_REF]. Family-based sequencing approaches have demonstrated that undiagnosed cases of ID are largely caused by de novo variants in genes and/or pathways which genetically overlap with other NDDs [START_REF] Vissers | Genetic studies in intellectual disability and related disorders[END_REF][START_REF] Ku | A new paradigm emerges from the study of de novo mutations in the context of neurodevelopmental disease[END_REF][6][START_REF] Jensen | Mapping a shared genetic basis for neurodevelopmental disorders[END_REF].

HECW2, also known as NEDL2, is a member of the Nedd4 family of mammalian E3 ubiquitin ligases involved in regulation of p73 stability [START_REF] Miyazaki | A novel HECT-type E3 ubiquitin ligase, NEDL2, stabilizes p73 and enhances its transcriptional activity[END_REF][START_REF] Rotin | Physiological functions of the HECT family of ubiquitin ligases[END_REF]. Several members of Nedd4 are involved in cell signaling pathways that directly affect proliferation, migration and differentiation through glial cell line-derived neurotrophic factor (GNDF)/Ret signaling [START_REF] Miyazaki | A novel HECT-type E3 ubiquitin ligase, NEDL2, stabilizes p73 and enhances its transcriptional activity[END_REF][START_REF] Wei | NEDL2 is an essential regulator of enteric neural development and GDNF/Ret signaling[END_REF]. HECW2 combines directly with p73, a member of the p53 family that is predominantly expressed in adult brain, heart and lungs and plays a significant role in both adult and embryonal neurogenesis [START_REF] Miyazaki | A novel HECT-type E3 ubiquitin ligase, NEDL2, stabilizes p73 and enhances its transcriptional activity[END_REF][START_REF] Rotin | Physiological functions of the HECT family of ubiquitin ligases[END_REF][START_REF] Killick | p73: A Multifunctional Protein in Neurobiology[END_REF]. Additionally, p73 knockout mice show developmental defects of the central nervous system, embryonal and adult neurogenesis and display a reduction in cortical thickness due to the loss of mature cortical neurons [START_REF] Killick | p73: A Multifunctional Protein in Neurobiology[END_REF]. Animal models of HECW2 show severe abnormalities in early development. Hecw2 null mice die within two weeks after birth with progressive bowel motility defect and a decreased number of enteric neurons [START_REF] Wei | NEDL2 is an essential regulator of enteric neural development and GDNF/Ret signaling[END_REF]. Knockdown of hecw2a in zebrafish led to early morphological abnormalities of the brain, spinal cord, eyes, the body trunk and tail [START_REF] Lu | Association of HECW2 variants with developmental and epileptic encephalopathy and knockdown of zebrafish hecw2a[END_REF].

Recently, independent studies have identified a total of six de novo heterozygous likely pathogenic and pathogenic missense variants in HECW2 in 13 families with ID/developmental delay, hypotonia, seizures and other variable features [START_REF] Lu | Association of HECW2 variants with developmental and epileptic encephalopathy and knockdown of zebrafish hecw2a[END_REF][START_REF] Berko | De novo missense variants in HECW2 are associated with neurodevelopmental delay and hypotonia[END_REF][14][15][16][17][18]. In this study, we include 22 new cases and expand the genotypic and phenotypic spectrum associated with HECW2-related neurodevelopmental disorders.

Materials and methods

Patient recruitment and clinical assessment

Written informed consent was obtained from the parents/legal guardian, and all studies were approved by the Ethics Committee or Institutional Review Board of all contributing centers. Patients and their clinical data were gathered from several clinical diagnostic and research laboratories, either through exchange between laboratories or through GeneMatcher [19]. All affected individuals were evaluated by a child neurologist and/or a clinical geneticist. Detailed genetic data were obtained from each participating lab, and clinical information was gathered from clinicians [START_REF] Berko | De novo missense variants in HECW2 are associated with neurodevelopmental delay and hypotonia[END_REF][14][15][16][17][18].

Sequencing analysis

Genomic DNA was obtained from the affected individuals and parents (if possible). In all cases, next-generation sequencing was performed in the affected individuals and available parental samples. Exome sequencing was performed for all individuals in either in a clinical or research setting, with the exception of cases 3 and 4 who had targeted sequencing of a panel of genes for ID and case 12 who had genome sequencing. In short, exonic regions and flanking splice junctions were captured and paired-end sequencing was performed on an Illumina instrument (Illumina Inc, San Diego, CA, USA). For case 12, PCR-free whole genome sequencing libraries were prepared using Kapa Hyper Prep (Roche Holding AG, Basel, Switzerland) followed by sequencing on an Illumina HiSeq4000 with paired-end reads (Illumina Inc, San Diego, CA, USA).

After removing low-quality reads, the filtered reads were aligned to the human reference genome.

Further processing and variant calling of single nucleotide variants (SNVs) and insertion/deletions (InDels) were performed using a "best practices" workflow. Variants were annotated and filtered based on variant minor allele frequency based on databases such as the Genome Aggregation Database (gnomAD) and The Trans-Omics for Precision Medicine (TOPMed) Bravo database [20,21], variant location and predicted effect on protein function. De novo variants identified in patients 3 and 6 were confirmed by Sanger sequencing as they were identified in a research setting. HECW2 variants were classified according to the American College of Medical Genetics and Genomics (ACMG) guidelines [22]. Variants reported in this manuscript were classified as pathogenic/likely pathogenic. In addition, variants of uncertain significance were also included for affected individuals in which this was the primary suspected causal variant, but for which there was not enough evidence to classify the variant as likely pathogenic (such as absence of a parental genotype).

Homology modeling

The amino acid sequence of HECW2 (isoform 1) was retrieved from the UniProt database (identifier: Q9P2P5). Three-dimensional modelling techniques were utilized for the construction of wild-type and mutant HECW2 using the MODELLER program [23]. The crystal structure of WWP1 (PDB ID: 6J1X) [24] was used as a template for the construction of wild-type (WT) and mutant (MT: p.Arg1191Gln, p.Asn1199Lys, p.Phe1327Ser, p.Arg1330Trp) models of HECW2.

PYMOL was used for structural visualization and image processing (The PyMOL Molecular Graphics System, Version 1.8 Schrödinger, LLC).

HECW2 genotype and phenotype analysis

A literature search for HECW2 was performed to retrieve all published cases with likely pathogenic and pathogenic HECW2 variants reported to be implicated in a neurodevelopmental disorder with available clinical details. All variants in HECW2 were annotated using ANNOVAR [25]. Bioinformatic prediction scores were annotated from dbnsfp35a and dbscSNV1.1 to evaluate missense and splice site variants respectively [26,27], including Combined Annotation Dependent Depletion (CADD) and Genomic Evolutionary Rate Profiling (GERP++) scores [28,29]. A Fisher's exact test was done to test differences between groups.

Results

Genetic spectrum of HECW2 variation

We identified 22 novel cases with a neurodevelopmental disorder associated with 15 different heterozygous missense variants in HECW2 (Table 1; Supplementary Table 1). In 18 cases, the variant was confirmed to be de novo, including one case (case 10) with a mosaic variant in the proband (Table 1). Thirteen of the variants identified were novel variants and two variants [p.(Arg1191Gln) and p.(Arg1330Trp)] have previously been published and are thus recurrent [START_REF] Berko | De novo missense variants in HECW2 are associated with neurodevelopmental delay and hypotonia[END_REF][14][15][16][17][18]. When considering all HECW2 variants (N=19) and all cases reported to date (N=35), we identified four variants present in at least two cases [p.(Arg1191Gln), p.(Asn1199Lys), p.(Phe1327Ser) and p.(Arg1330Trp)]. Two missense variants [p.(Arg1191Gln) (N=8/35) and p.(Arg1330Trp), (N=7/35)] were present in 22.9% and 20% of cases respectively [START_REF] Berko | De novo missense variants in HECW2 are associated with neurodevelopmental delay and hypotonia[END_REF][14][15][16][17][18]. In addition, we identified two amino acid (E1445 and R1495) each with two different mutations, and a clustering of mutations at T1503, S1504, and S1505 each in a single patient (Table 1).

All variants identified in this study (N=13) and those previously published were missense variants (Table 1; N=19 total), with one missense variant p.(Tyr863His) which may impact pre-mRNA splicing as well and lead to a loss of the wild-type 3' acceptor of exon 12 (based on Human Splicing Finder and dbscSNV; Table 1), however SpliceAI did not predict a significant change [30]. All variants were absent from gnomAD and TopMed databases (Supplementary table 1) [20].

All variants had a CADD score > 20 and were located at positions conserved between species (Table 1).

The majority of all variants identified in HECW2 so far (N=19) are present in the C-terminal area of the protein (18/19; 94.7%; Figure 1), and particularly in the C-terminal HECT domain of HECW2 (11/19; 57.9%). Nearly all likely pathogenic or pathogenic variants (15/17; 88.2%) are located in or near the C-terminal HECT domain. Three variants were present in the region of interaction with TP73 (3/19; 15.8%) and one in the coiled-coil domain (1/19; 5.3%) (Figure 1).

All variants were deposited into ClinVar under accession numbers: SCV001519450, SCV001519452-SCV001519463, SCV000741462 and SCV001444913.

Phenotypic spectrum of HECW2 variation

We report 22 cases (10 females and 12 males) with variants in HECW2 (median age = 4.8 years [22 months -27 years]) (Table 2; Supplementary Table 2). All our cases presented with developmental delay and mild to severe intellectual disability along with several other features including hypotonia. We analyzed the prevalence of clinical features of our 22 cases together with 13 previously published cases (Table 2; Supplementary table 2) [START_REF] Lu | Association of HECW2 variants with developmental and epileptic encephalopathy and knockdown of zebrafish hecw2a[END_REF][START_REF] Berko | De novo missense variants in HECW2 are associated with neurodevelopmental delay and hypotonia[END_REF][14][15][16][17][18]. Clinical features mentioned below are based on individuals with variants classified as pathogenic and likely pathogenic variants only (N= 33).

Developmental delay and delayed or absent speech were prevalent in all patients assessed (100%). Intellectual disability was present in all individuals old enough to assess (100%). Of all individuals 5 years old or older, 46.2% (6/13) could talk and 58.3% (7/12) could walk, although often with limited capacity. All our patients exhibited hypotonia with or without spastic rigidity (100%). Axial hypotonia was common, with considerable additional problems with movement/motor coordination (75%). Stereotypies such as hand flapping and rocking were common (57.1%), and chorea, choreoathetosis, an ataxic gait, dyspraxia and dystonia were also observed.

Behavioral abnormalities were described in 88.9% of cases, including autism spectrum disorder (ASD) following DSM-5 criteria in five cases and autistic features in 16 cases. One or more ASDlike features included self-stimulatory behaviors, attachment and social interaction abnormalities, self-injurious behaviors, inappropriate distress when frustrated, sensory aversions and indifference/insensitivity to pain were present a majority of individuals. Hyperactivity, impulse control, episodes of inconsolability and occasional aggressive behaviors were also observed.

Optic neuropathy, cortical visual impairment (CVI) and ocular movement disorders were commonly noted (83.9%). CVI was noted in 53.8% of patients with visual abnormalities, though foveal hypoplasia and optic nerve coloboma and atrophy/hypoplasia were also seen. A range of ocular movement abnormalities were described, including esotropia, exotropia, dissociated vertical deviation, supranuclear gaze palsy, nystagmus and abnormalities of ocular fixation and conjugate movements.

Esophageal-gastrointestinal dysmotility and/or dysphagia (70%) was reported in the form of gastroesophageal reflux disease (GERD), gastroparesis, and constipation with eight cases requiring gastrotomy tube.

Seizures, previously recognized as a main feature of HECW2-related neurodevelopmental disorders, were present in 61.3% of cases. EEG abnormalities were observed in 69.6% of cases when assessed (N=23). Abnormal brain MRI findings were present in 53.1% of available cases, often demonstrating global decreased parenchymal volume. There was considerable variation in additional findings, which included cortical dysplasia of the left temporal lobe, poor development of anterior temporal lobes and a thin corpus callosum. In three out of four cases with serial brain MRIs available, progressive parenchymal volume loss was noted over time. These three cases were all reported to have seizures, with one case described with a normal MRI before seizure onset [17]. Some of patients have microcephaly (20%) and short stature (25%). The median head circumference was -0.51 SD and height was -0.66 SD for available cases. Facial dysmorphisms were observed in 56.7% of the cases, commonly with prominent forehead and nasal bridge, deep-set eyes, midface retrusion, and high-arched palate. Disorders or difficulties of sleep were found in 42.9%, and were mainly represented by insomnia, with one case of sleep apnea and a case of twins exhibiting somnambulism features. Less common manifestations of HECW2-related neurodevelopmental disorder were skeletal abnormalities (32.1%). Decreased bone mineralization was observed most often followed by pectus excavatum, pes planus, brachydactyly of hands and feet, and hindfoot and pronation deformities of the feet.

Joint involvement was comprised of contractures (arthrogryposis) and joint hypermobility. Failure to thrive was reported in 28% of cases.

Cardiac abnormalities were reported in three patients, each with a unique presentation. Case 2, a toddler male, had sinus bradycardia; case 19, middle childhood male, had a small patent foramen ovale with left to right atrial shunting in addition to an incomplete right bundle branch block; and case 1, a middle childhood female, had cardiomyopathy.

Structural analysis of normal and mutant HECW2

The three-dimensional structures of the wild-type (WT) and mutant (MT) HECW2 proteins were built using homology modeling techniques. HECW2 isoform 1 consists of 1572 amino acids (AA) and includes a C2 domain (yellow), a WW1 and WW2 domain (green) and a C-terminal HECT domain (cyan) (Figure 1A). We modeled 1089-1566AA of WT and MT HECW2 to inspect the 3D location of the C-terminal variants (Figure 1B) and the potential effect of these two recurrent variants on HECW2 (p.Arg1191Gln, p.Asn1199Lys, p.Phe1327S and p.Arg1330Trp; Figure 1C-E). Figure 1B shows that all HECT domain variants cluster three-dimensionally with the four variants adjacent to the HECT domain. Homology modelling of HECW2 with variants p.Arg1191Gln, p.Asn1199Lys, p.Phe1327S and p.Arg1330Trp displayed some degree of difference in overall conformation compared to WT with a root mean square deviation (RMSD) value of 0.72 Å, 1.06 Å, 1.12 Å and 0.693 Å, respectively (Figure 1C). Moreover, the surface electrostatic potential displays a difference in charge distribution of these variants as compared to WT as shown in figure 1D-E.

Genotype-phenotype correlation

As mentioned above, the majority of variants identified in HECW2 so far were present in the Cterminal area (Figure 1). One variant however was identified in the N-terminal region [p.(Ile138Val) in case 10]. This case presents with a milder phenotype without seizures (Supplementary Table 1); however, was found to be mosaic for the variant which may explain his milder phenotype. Cases 7 [p.(Ala940Thr)] and 16 [p.(Tyr863His)], with variants that are located mid-protein between the WW domains, also had a milder phenotype without seizures. All patients with a severe phenotype including seizures/epilepsy had a variant in or adjacent to the HECT domain (Figure 1B; Suppl Table 2). Seizures/epilepsy were present in 65.5% (19/29) of individuals with a HECT domain or adjacent HECT variant (Figure 1B) and absent (0/4) from individuals with variants in other locations of HECW2 (p=0.025).

We next assessed 18 individuals with likely pathogenic and pathogenic variants in the HECT domain and 15 individuals with likely pathogenic and pathogenic variants outside the HECT domain. There were a few significant differences noted between these two groups: Individuals with HECT domain variants had a significantly higher frequency of CVI (68.8 % [11/16] HECT versus 20% [3/15] non-HECT; p=0.011). Gastrointestinal issues were also more common in the HECT group (87.5%; 14/16) than the non-HECT group (50%; 7/14) [p=0.046], with a larger percentage being fed via gastrostomy tube in the HECT domain variant group (37.5% vs 21.4%).

Recurrent variant [p.(Arg1330Trp)], which was identified in seven cases (20%), was associated with severe ID, developmental delay, CVI and gastrointestinal (GI) disorders. Seizures and brain MRI abnormalities were reported in five of the seven [p.( Arg1330Trp)] cases with available information, and two cases had reported intractable seizures. EEG was abnormal in all cases assessed (N=4). Six cases were non-verbal or severely limited in their speech, and one was too young to assess. Five of six cases with available information presented with CVI and GI abnormalities. Additionally, four of six of these patients had movement disorders.

Another recurrent variant [p.(Arg1191Gln); 22.9% (8 cases)] revealed considerable phenotypic variability. Four of seven patients had seizures with at least one with severe intractable generalized seizures. Most patients with this variant were non-verbal, with a speech capability of a limited number of words in two individuals who were more than five years old. Five patients demonstrated movement disorders. Six patients with the variant [p.(Arg1191Gln)] reported visual disorders, ranging from strabismus (N=3), to optic neuropathies (N=2) and CVI (N=2).

Discussion

In this study, we further delineate the genotypic and phenotypic spectrum of HECW2-related neurodevelopmental disorder. Heterozygous missense variants in HECW2 have been associated with an autosomal dominant neurodevelopmental disorder with hypotonia, seizures and absent language (NDHSAL; OMIM #617268). By comparing published cases (N=13) and novel cases (N=22), we show that developmental delay, intellectual disability, abnormal muscle tone and speech delay are common features of the disorder, and that seizures and other associated features are variable. HECW2 (alias NEDL2) is expressed in all regions of the adult and fetal brain and facilitates neurogenesis and neurodevelopment [START_REF] Berko | De novo missense variants in HECW2 are associated with neurodevelopmental delay and hypotonia[END_REF]14]. We identified 13 novel variants in HECW2, bringing the total number of reported variants to 19. All are missense variants. HEWC2 is intolerant to missense variants (z=3.305) [20] and subRVIS (Residual Variation Intolerance Score) demonstrates this to be true for the majority of the protein (C2 domain and everything C-terminal of this domain, with exception of the WW domains) [START_REF] Gussow | The intolerance to functional genetic variation of protein domains predicts the localization of pathogenic mutations within genes[END_REF]. The C-terminal HECT domain and its vicinity are associated with the highest mutation burden in all cases (31/35 cases). HECW2 is a HECT (homologous to E6-AP carboxy terminus)-type E3 ubiquitin ligase, with the C-terminal HECT domain containing the catalytic cysteine residue (p.1540) required for substrate ubiquitination [START_REF] Krishnamoorthy | E3 ubiquitin ligase HECW2 targets PCNA and lamin B1[END_REF]. As no nonsense or frameshift variants have been described, nor variants in the catalytic cysteine residue (p.1540), this suggests that variants might show reduced function (but not completely abolished) or act through a gain-of-function mechanism. This hypothesis is supported by the fact that HECW2 is highly intolerable of haploinsufficiency and complete loss-

of-function (pLI = 1) [20].

There are 16 large deletions in ClinVar encompassing HECW2 [33]. The majority (N=13) are deletions encompassing several genes in this genomic region, referred to as chr2q32-q33 microdeletion syndrome, of which haploinsufficiency of SATB2 is an established mechanism of disease. Although chr2q32-q33 microdeletion syndrome and HECW2-neurodevelopmental disease have several clinical features in common, these overlapping features are consistent with SATB2 haploinsufficiency [START_REF] Rosenfeld | Small Deletions of SATB2 Cause Some of the Clinical Features of the 2q33.1 Microdeletion Syndrome[END_REF][START_REF] Zarate | SATB2-Associated Syndrome[END_REF]. In addition, two deletions covering HECW2 and other genes (but not SATB2) have been described with limited clinical information. Only one deletion encompassing HECW2 in a single patient with schizophrenia has been described [START_REF] Kushima | Comparative Analyses of Copy-Number Variation in Autism Spectrum Disorder and Schizophrenia Reveal Etiological Overlap and Biological Insights[END_REF]. Therefore, it is unclear whether heterozygous deletions of HECW2 support loss of function as the mechanism of de novo missense variants in HECW2.

A gain-of-function mechanism is however supported by the clustering of recurrent missense variants in genes in neurodevelopmental disorders [START_REF] Geisheker | Hotspots of missense mutation identify novel neurodevelopmental disorder genes and functional domains[END_REF]. We identified specific protein domains implicated in HECW2 neurodevelopmental disease. For example, we identified four recurrent Furthermore, for other neurodevelopmental disorders caused by rare variants in E3 ubiquitin ligases, such as HUWE1 and NEDDL4, the majority of pathogenic variants are also found in the HECT domain and are missense [START_REF] Broix | Mutations in the HECT domain of NEDD4L lead to AKT/mTOR pathway deregulation and cause periventricular nodular heterotopia[END_REF]39]. For HUWE1, both loss-of-function and gain-of-function mechanisms have been implicated in neurodevelopmental disease, each leading to distinct types of ID [40]. The mechanisms of gain-of function effects through missense variants are not fully elucidated; however, increased protein levels or increased auto-ubiquitination have been described for certain missense mutations in and near the HECT domain in vitro [41,42]. Missense variants in the HECT domain of NEDD4L are believed to alter conformation and subsequently trigger auto-ubiquitination followed by proteasomal degradation [START_REF] Broix | Mutations in the HECT domain of NEDD4L lead to AKT/mTOR pathway deregulation and cause periventricular nodular heterotopia[END_REF].

Clinical analysis of HECW2-related neurodevelopmental disorders in this study and existing literature suggests that intellectual disability, developmental delay and hypotonia are consistent features in all cases. Language and/or speech impairments were identified in all patients.

Hypotonia is central with greater involvement of the musculature of the trunk.

Brain MRI was normal in 46.9% of cases when assessed. In affected individuals with an abnormal MRI, abnormalities were regularly consistent with global decreased parenchymal volume. The etiology of this is unclear, but this is likely mainly due to an abnormal neurogenesis, as evidenced by the presence of cortical dysplasia, hypoplasia of the corpus callosum and anterior temporal lobes, and other brain malformations in several cases. Abnormal neurogenesis in HECW2-related neurodevelopmental disorders is also supported by studies in animal models. Prior studies of p73 knockout mice, a direct interaction partner of Hecw2, demonstrated poor differentiation of oligodendrocytes and defects in both embryonal and adult neurogenesis [START_REF] Killick | p73: A Multifunctional Protein in Neurobiology[END_REF]. Data from Hecw2a knock down in zebrafish, which display early brain developmental defects, also suggests a motor neuron migration defect [START_REF] Lu | Association of HECW2 variants with developmental and epileptic encephalopathy and knockdown of zebrafish hecw2a[END_REF]. The latter can cause malformations of cortical development, such as cortical dysplasia, which predisposes patients to seizures [START_REF] Lu | Association of HECW2 variants with developmental and epileptic encephalopathy and knockdown of zebrafish hecw2a[END_REF]43]. Focal cortical dysplasia of unilateral or bilateral temporal lobes has been directly described in our cohort. Seizures were identified in 61.3% of patients with variable presentation.

Behavioral abnormalities in HECW2 neurodevelopmental disorder patients are common (88.9%) and encompass similar manifestations. Compulsive, unusual and repetitive movements are observed in patients with behavioral and movement abnormalities. Hyperactivity, impaired impulse control, and limited tolerance for frustration, inappropriate laughter, self-injurious or selfstimulatory behaviors and/or unusual repetitive movements are all common. We show that five cases with HECW2-related neurodevelopmental disease also qualify for a diagnosis of ASD.

Other cases were too young for evaluation and/or with abnormalities of social interactions and self-stimulatory, restricted, and repetitive behaviors. Currently, the Gene4denovo database, an integrated database of de novo variations in humans, contains 29 de novo HECW2 variants of unknown significance in cases labeled as autism [44], of which 28 are non-coding.

Gastrointestinal issues were seen in 70% of patients. Existing literature has associated reduced expression of HECW2 with congenital aganglionic megacolon (Hirschsprung's disease), that can present with delayed meconium passage, constipation, feeding changes and in severe cases necrotizing enterocolitis [45,46], due to underdevelopment of the nerve cells in the large intestine (colon). Hirschsprung's disease is caused by variants in RET, and its ligand GDNF is regulated by HECW2 [START_REF] Wei | NEDL2 is an essential regulator of enteric neural development and GDNF/Ret signaling[END_REF]. In addition, mice lacking Hecw2 exhibit decreased numbers of enteric neurons, progressive bowel dysmotility and intestinal hypoganglionosis [46]. Given this, a neurologic etiology of enteric system dysfunction may contribute to GI issues in HECW2 neurodevelopmental disease.

Repetitive movements and stereotypies were commonly seen in HECW2-related neurodevelopmental disease (57.1%). Non-specific chorea and choreoathetosis were also observed.

Last, consistent and comprehensive genotype-phenotype correlation is difficult, given the small number of individuals described. HECT domain and closely adjacent HECT variants, including recurrent variant p.(Arg1330Trp), seem to be associated with a more severe phenotype including seizures. HECT domain variants are more highly associated with CVI and severe GI issues compared to any non-HECT variants. Finally, individuals with the recurrent p.(Arg1191Gln) variant show a variable presentation of features, which suggests that environmental factors during fetal development or the genomic background might be important in the determination of disease presentation.

In conclusion, we expand the phenotypic spectrum of HECW2-related neurodevelopmental disease and we show that developmental delay/intellectual disability, hypotonia and speech delay are consistent features. Vision problems, particularly cortical visual impairment, behavioral and GI issues are also common features of the condition. Finally, we report that pathogenic variants are clustered in and near the HECT domain of the protein with the recurrence of several variants.

Funding

WKC is funded by grants from the JPB Foundation and Simons Foundation. IS is supported by a pilot grant from the Columbia University Sergievsky Center.

Figure and Table legends

  variants [p.(Arg1191Gln); p.(Asn1199Lys); p.(Phe1327Ser); p.(Arg1330Trp)]. Two variants [p.(Arg1191Gln) and p.(Arg1330Trp)] were mutated in an exceptionally high percentage of cases (22.9% and 20% respectively). In addition, we found two locations E1445 and R1495, mutated in two cases each, and 3 variants present at positions T1503, S1504, S1505, showing clustered protein locations in the HECT domain. With three-dimensional modeling we show that all HECT domain variants and the variants adjacent to the HECT domain cluster closely together in two areas (Figure 1B), suggesting these specific areas are important in protein function and HECW2neurodevelopmental disease. We also demonstrate that the four recurrent variants, p.(Arg1191Gln); p.(Asn1199Lys); p.(Phe1327Ser); p.(Arg1330Trp), show a difference in protein surface property (electrostatic surface potential) and conformation (RMSD value) compared to WT. This suggests these variants can affect the protein function, stability, enzymatic function or the binding of partner biomolecules.

Figure 1 :

 1 Figure 1: Location, homology modeling and structural analysis of HECW2 variants. (A). Isoform 1 HECW2 C2 domain: AA 167-301 (yellow); WW1 domain: AA 807-840 (green); WW2 domain: AA 985-1018 (green); HECT domain: AA 1237-1572 (cyan). Novel variants identified in this study are indicated above HECW2; variants previously published are indicated in the bottom. Variants highlighted in blue were found in  2 independent cases, with the number of individuals found per variant indicated in parenthesis. *may impact splicing as well. All variants are classified as likely pathogenic or pathogenic, with exception of **two VUS found in two novel cases. (B) Three-dimensional (3D) structures of WT HECW2 predicted using the MODELLER program and visualized with the PyMOL software. The location of the variants here and previously identified are mapped on the WT structure of HECW2 and the residues are shown as a stick model. (C) Superposed protein structures of WT and MT of the 4 recurrent variants. The RMSD (Å) were calculated among the WT and mutant protein structure and are labeled in each model. (D-E) Surface electrostatic potential of WT and MT models generated by the PyMOL APBS plugin. The color scale for surface electrostatic potential was set from -5 kT/e (red) to 5 kT/e (blue). The location of the variant is represented as a circle. The zoom-up views show the difference in electrostatic potential at the site of mutation.

Table 1 : All variants identified in HECW2 in this study and published reports.

 1 *Defined as <35% allelic fraction in next generation sequencing reads ***Heterozygous variant presumed to be de novo, however, one or two parent(s) not available to confirm if de novo ^Case numbers correlate with Table2and Supplementary Table2^^Monozygotic twins

	Variant*	ACMG	Inheritance	Recurrent variant	Known/novel variant	CADD score	GERP++ RS score	dbscSNV ADA score	Subject ID in this study^	Published cases^
	c.412A>G:p.(Ile138Val)	LP	de novo (mosaic [26%])**		novel	23.7	6.04	.	10
	c.2587T>C:p.(Tyr863His)	LP	de novo		novel	28.2	5.39	0.9172	16
	c.2818G>A:p.(Ala940Thr)	VUS	unknown***		novel	33	4.84	.	7
	c.3175C>T:p.(Pro1059Ser)	VUS	unknown***		novel	26.1	5.45	.	4
	c.3572G>A:p.(Arg1191Gln)	P	de novo, unknown***	yes	known	35	4.75	.	5, 12, 13, 14, 22	A, C, D
	c.3577T>G:p.(Phe1193Val)	LP	de novo		known	28.4	4.75	.		E, F^^
	c.3583G>C:p.(Ala1195Pro)	LP	de novo		novel	32	4.75	.	19
	c.3597C>A:p.(Asn1199Lys)	LP	de novo	yes	novel	25.9	3.87	.	11, 15
	c.3980T>C:p.(Phe1327Ser)	LP	de novo, unknown***	yes	novel	33	5.08	.	1, 3
	c.3988C>T:p.(Arg1330Trp)	P	de novo	yes	known	34	3.32	.	9, 17	B, G, H, J, M
	c.4323T>G:p.(Phe1441Leu)	LP	de novo		novel	28.6	1.53	.	6
	c.4333G>C:p.(Glu1445Gln)	P	de novo		novel	33	5.11	.	21
	c.4334A>G:p.(Glu1445Gly)	P	de novo		known	32	5.11	.		I
	c.4355G>T:p.(Gly1452Val)	LP	de novo		novel	34	5.11	.	8
	c.4484G>A:p.(Arg1495Lys)	P	de novo		known	34	5.36	.		K
	c.4485G>T:p.(Arg1495Ser)	P	de novo		known	34	3.45	.		L
	c.4507A>G:p.(Thr1503Ala)	LP	de novo		novel	28.1	5.35	.	20
	c.4511C>A:p.(Ser1504Tyr)	LP	de novo		novel	33	5.35	.	2
	c.4514G>C:p.(Ser1505Thr)	LP	de novo		novel	33	5.35	.	18
	*Based on NM_020760.4

*

Table 2 : Clinical features identified in novel and published cases with HECW2-related neurodevelopmental disease

 2 . NA: Not assessed or unavailable. Individual details are only shown for novel cases.

	Clinical features	Case 1	Case 2	Case 3	Case 4	Case 5	Case 6	Case 7	Case 8	Case 9	Case 10	Case 11	Case 12	Case 13	Case 14	Case 15	Case 16	Case 17	Case 18	Case 19	Case 20	Case 21	Case 22
		Total of
	Total of	clinical
	clinical	features (%)
	features (%)	All reported
	This study	cases (P and
		LP)*

Acknowledgements

None

Data sharing statement

HECW2 variants reported in this study were deposited into ClinVar under accession numbers: SCV001519450, SCV001519452-SCV001519463, SCV000741462 and SCV001444913 (https://www.ncbi.nlm.nih.gov/clinvar/). There are no conditions to reuse the data.

Competing interests

LF, KW, IMW, FZ, EAN, and ET are employees of GeneDx, Inc. All other co-authors have no conflicts of interest related to the work in this manuscript.

Ethics approval statement

The study was approved by the Columbia University IRB (IRB-AAAS3433).

Contributorship statement