# Clinical and genetic spectrum of Ataxia Telangiectasia Tunisian patients: Bioinformatic analysis unveil mechanisms of ATM variants pathogenicity

Rim Jenni, Hedia Klaa, Oussema Khamessi, Asma Chikhaoui, Dorra Najjar, Kais Ghedira, Ichraf Kraoua, Ilhem Turki, Houda Yacoub-Youssef

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## To cite this version:

Rim Jenni, Hedia Klaa, Oussema Khamessi, Asma Chikhaoui, Dorra Najjar, et al.. Clinical and genetic spectrum of Ataxia Telangiectasia Tunisian patients: Bioinformatic analysis unveil mechanisms of ATM variants pathogenicity. International Journal of Biological Macromolecules, 2024, 278, pp.134444. 10.1016/j.ijbiomac.2024.134444. pasteur-05004033

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# Clinical and genetic spectrum of Ataxia Telangiectasia Tunisian patients: Bioinformatic analysis unveil mechanisms of ATM variants pathogenicity

Rim Jenni<sup>a</sup>, Hedia Klaa<sup>b</sup>, Oussema Khamessi<sup>c,d</sup>, Asma Chikhaoui<sup>a</sup>, Dorra Najjar<sup>a</sup>, Kais Ghedira<sup>c</sup>, Ichraf Kraoua<sup>b</sup>, Ilhem Turki<sup>b</sup>, Houda Yacoub-Youssef<sup>a,*</sup>

## ARTICLE INFO

### Keywords:

Ataxia telangiectasia

ATM gene

Structural bioinformatics

## ABSTRACT

Ataxia Telangiectasia (AT) is a rare multisystemic neurodegenerative disease caused by biallelic mutations in the *ATM* gene. Few clinical studies on AT disease have been conducted in Tunisia, however, the mutational landscape is still undefined. Our aim is to determine the clinical and genetic spectrum of AT Tunisian patients and to explore the potential underlying mechanism of variant pathogenicity.

Sanger sequencing was performed for nine AT patients. A comprehensive computational analysis was conducted to evaluate the possible pathogenic effect of ATM identified variants. Genetic screening of *ATM* gene has identified nine different variants from which six have not been previously reported. *In silico* analysis has predicted a pathogenic effect of identified mutations. This was corroborated by a structural bioinformatics study based on molecular modeling and docking for novel missense mutations. Our findings suggest a profound impact of identified mutations not only on the ATM protein stability, but also on the ATM-ligand interactions.

Our study characterizes the mutational landscape of AT Tunisian patients which will allow to set up genetic counseling and prenatal diagnosis for families at risk and expand the spectrum of ATM variants worldwide. Furthermore, understanding the mechanism that underpin variant pathogenicity could provide further insights into disease pathogenesis.

## 1. Introduction

Ataxia Telangiectasia (AT, OMIM #208900) also called Louis-Bar syndrome, is an autosomal recessive cerebellar ataxia (ARCA) [1]. Although AT is a rare neurodegenerative disease, it is the second most common ARCA in children after Freidreich ataxia, and the most common genetic ataxia with early onset [2]. The incidence of AT is estimated between 1:40,000 and 1:100,000 live births world-wide [3]. AT is a multisystemic disease, characterized mainly by defects of the nervous and immune systems [4]. The clinical manifestations of AT include

progressive cerebellar ataxia due to progressive neurodegeneration, variable immunodeficiency, ocular abnormalities notably telangiectasia and oculomotor apraxia, recurrent sinopulmonary infections, radiosensitivity, metabolic disorders and cancer predisposition particularly to lymphoid tumors [5]. Cutaneous telangiectasia and other skin disorders are reported among AT patients [6]. Moreover, a large proportion of patients have extrapyramidal syndrome with movement abnormalities [7]. AT is also characterized by chromosomal instability and sensitivity to irradiation and oxidative stress [8,9]. Molecular advances and ongoing research are being conducted to better understand this disease

<footnote><sup>a</sup> Laboratory of Biomedical Genomics and Oncogenetics (LR16IPT05), Institut Pasteur de Tunis, University Tunis El Manar, Tunis 1002, Tunisia</footnote>

<footnote><sup>b</sup> LR18SP04 and Department of Child and Adolescent Neurology, National Institute Mongi Ben Hmida of Neurology, 1007 Tunis, Tunisia</footnote>

<footnote><sup>c</sup> Laboratory of Bioinformatics, Biomathematics and Biostatistics (BIMS), Institut Pasteur de Tunis (IPT), University of Tunis El Manar, Tunis, Tunisia</footnote>

<footnote><sup>d</sup> Institut de Biotechnologie de Sidi Thabet, Université de la Manouba, Ariana BP-66, Manouba 2010, Tunisia</footnote>

<footnote>* Corresponding author at: Institut Pasteur de Tunis, 13 place pasteur, 1002, Tunisia.</footnote>

<footnote>E-mail addresses: rim.jenni19@gmail.com, rim.jenni@pasteur.utm.tn (R. Jenni), hedia.klaa@fmt.utm.tn (H. Klaa), oussama.khamassi@pasteur.tn (O. Khamessi), asma.chikhaoui@pasteur.utm.tn (A. Chikhaoui), dorra.najjar@pasteur.utm.tn (D. Najjar), kaies.ghedira@pasteur.tn (K. Ghedira), ichraf.kraoua@fmt.utm.tn (I. Kraoua), ilhem.byt@fmt.utm.tn (I. Turki), houda.yacoub@pasteur.utm.tn (H. Yacoub-Youssef).</footnote>
[10]. Unfortunately, to date, no therapeutic strategy exist for this pathology, which presents a high decrease of life expectancy, mainly linked to pulmonary failure following an infection or to cancer development as for leukemia [11,12].

The clinical diagnosis of AT is based mainly on the combination of the clinical hallmarks of the disease (ataxia, telangiectasia, recurrent infections) and specific biological abnormalities, notably an increased level of alpha-fetoprotein (AFP), immunoglobulin deficiency and lymphopenia, specific karyotype abnormalities (translocation between chromosome 7 and 14) and cerebellar atrophy detected by brain Magnetic Resonance Imaging (MRI) [3,13]. Nevertheless, the diagnosis might be challenging. Indeed, AT patients present a significant heterogeneity in clinical features that may overlap with several other neurological and rare disorders, which pose a problem of differential diagnosis [3,14]. In addition, some of the cardinal clinical features of AT may appear late during the disease course or may not be present in all patients, as it is for telangiectasia [15]. These challenges can affect the diagnostic accuracy and therefore patient follow-up. Otherwise, genetic diagnosis is mandatory to confirm the diagnosis.

AT is caused by biallelic mutations in the Ataxia Telangiectasia Mutated (ATM) gene, mapped to the chromosomal region 11q22.3-23.1 [16]. ATM gene mutations result in a clinical heterogeneity and prognostic variability with different degree of disease severity among AT patients, depending mainly on the presence of the ATM protein and its kinase activity [13]. This could define a continuous spectrum of phenotypes [17]. In fact, the majority of ATM mutations result in the absence of the ATM protein and/or its kinase activity, leading to a severe classical AT phenotype with an early-childhood disease onset [18]. Among classical AT form, while biallelic truncated mutations cause the absence of the ATM protein, rare missense mutations may abrogate the kinase activity without affecting the protein expression called “dead-kinase ATM” mutations which are more severe, leading to cancer proneness and lethality [19]. On the other hand, a significant proportion of ATM mutations may result in some retained kinase activity due to missense or leaky splice site mutations leading to a variant AT form characterized by milder phenotype with late AT onset and/or slower progression rate [17,18,20]. Intriguingly, truncating mutations could rarely lead to variant atypical AT phenotype which has been recently reported in some cases, highlighting the possible involvement of other environmental and genetic factors in the AT phenotype expression [3,20]. In fact, the ATM gene encodes for ATM serine/threonine protein kinase, a member of the phosphatidylinositol-3-kinase-related kinases (PIKKs) family. ATM is a master regulator of DNA damage signaling and stress responses, ensuring cytoprotection. It orchestrates cellular responses under different stress conditions, notably DNA damages or ROS accumulation, regulating critical cellular processes including DNA repair, cell cycle, autophagy, oxidative stress, mitochondrial function and proteostasis, through the phosphorylation of several downstream substrates [21].

Different studies have attempted to determine a cryo-electron microscopy (cryo-EM) structure of the ATM protein in order to propose a model of ATM activation. ATM is presented under monomeric and dimeric “open” or “closed” conformation structures. While a dimer-to-monomer transition is required for ATM activation by the MRE11-RAD50-NBS1 (MRN) complex upon DNA damage, a disulfide bound dimeric activated-state has been described in response to H2O2 accumulation independently of double-strand breaks (DSB)-induced activation [22,23]. A basal activity of the ATM dimeric structure has also been reported. It has been documented that the kinase activity of the ATM monomers is 10-fold higher than that of the dimeric structures [24]. However, it has been recently suggested that this difference in catalytic activity could be explained by the activation of a smaller amount of ATM protein by the H2O2, compared to DNA damage. Moreover, it has been suggested that these stimuli may share a common mechanism of ATM

protein activation [23].

In the present study, we aimed to elucidate the clinical characteristics and the molecular landscape of AT Tunisian patients, as well as to assess the pathogenicity of identified mutations *via* advanced computational analysis.

## 2. Material and methods

### 2.1. Patients biospecimens and samples collection

Patients were recruited from the Department of Pediatric Neurology of the National Institute of Neurology Mongi Ben Hamida of Tunis. Clinical diagnosis of AT disease was made by pediatric neurologists based on different examinations; including clinical neurological assessment, brain MRI, Electromyography (EMG), and blood tests. Cytogenetic analyses were performed for some patients to search for eventual chromosomal abnormalities. Blood samples were collected from patients and their parents after obtaining written informed consents from parents and guardians for minors. Clinical, epidemiological and genealogical data were collected from the patients' medical records and after a questionnaire was filled from clinically diagnosed AT patients.

This study was carried out in accordance with the Helsinki principles and was approved by the Institut Pasteur de Tunis (IPT) Biomedical Ethics Committee (Ethical approval reference 2022/4/I/LR16IPT/V2).

### 2.2. Genetic analysis

Genomic DNA extraction was performed using FlexiGen Kit from EDTA peripheral blood samples following the manufacturer's instructions. Isolated DNA concentration and purity were assessed by a Nanodrop Spectrophotometer DeNovix DS-11 (Thermo Fisher Scientific). Coding regions and exon-intron boundaries were amplified by PCR. A set of primers was designed *via* Primer3Plus Software and was listed in Table 1. The PCR was carried out in Qsp of 25 ul, using Master mix, 50 ng of gDNA and 10 uM of each forward and reverse primers. After PCR products were controlled on agarose gel, a Sanger sequencing was performed using the BigDye Terminator v3.1 (Applied Biosystems) on the ABI Prism 3500 sequencer (Applied Biosystems). Sequences were analyzed by UGENE and then blasted against ATM reference sequence (NM_000051.4) to identify genetic variations using UCSC blast, blast Ensembl and blast NCBI. The co-segregation of the variant in probands' parents was examined *via* Sanger sequencing. Genetic pedigrees were designed using Haplopaintor software.

<table><caption>**Table 1**
PCR primers sequences for ATM gene.</caption><thead><tr><th>Exon</th><th>Primer sequences (5'-3')</th><th>Tm (°C)</th><th>Amplicon size (pb)</th></tr></thead><tbody><tr><td>5</td><td>F: aggccatgtggtcacctc<br>R: actccttgtacgatggagatg</td><td>59.5<br>57.6</td><td>694</td></tr><tr><td>10</td><td>F: agtgctgggaacaccacct<br>R: agtcttctcggccaacaacaag</td><td>58.1<br>59.5</td><td>811</td></tr><tr><td>14</td><td>F: acagtagcacccctcacacct<br>R: tgcagatgacgagttgatg</td><td>56.8<br>56.7</td><td>678</td></tr><tr><td>17</td><td>F: ctcctgcaagaagccatc<br>R: aggttcatgccgttctcc</td><td>56.9<br>58.6</td><td>658</td></tr><tr><td>24</td><td>F: tcatcatggagaatggggt<br>R: cacctatgctgctcttgtctc</td><td>58.7<br>57.7</td><td>931</td></tr><tr><td>39</td><td>F: aggactcttcaecgccatgt<br>R: acagttctaacctagtcagagg</td><td>52.8<br>52.9</td><td>878</td></tr><tr><td>42</td><td>F: agttgtcctgcaacagttca<br>R: gagtataccctgaatgtttagcc</td><td>55.0<br>56.9</td><td>644</td></tr><tr><td>57</td><td>F: ctctcaaacatctaggcagc<br>R: cctctcaagtagctggga</td><td>55.3<br>53.7</td><td>705</td></tr><tr><td>58</td><td>F: ccaatgctgtgatgccac<br>R: acctgccaaacaacaaagtg</td><td>59.6<br>58.7</td><td>554</td></tr></tbody></table>
## 2.3. Computational analysis

### 2.3.1. In silico prediction of pathogenicity of genetic variants

The frequency and novelty of identified variants were examined after searching in Human Gene mutation database (HGMD) (https://www.hgmd.cf.ac.uk/ac/index.php), Genome Aggregation Database (gnomAD) browser (v4.0.0) (https://gnomad.broadinstitute.org/), Exome Aggregation Consortium (ExAC) (https://exac.broadinstitute.org/), and Ensembl (https://www.ensembl.org/Homo_sapiens/Info/Index). The pathogenicity of identified ATM mutations were assessed using different in silico prediction tools, including ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/), Mutation Assessor (http://mutationassessor.org/r3/), SIFT (https://sift.bii.a-star.edu.sg/), PolyPhen2 (http://genetics.bwh.harvard.edu/pph2/), PROVEAN (https://www.jcvi.org/research/provean), LRT, CADD (https://cadd.gs.washington.edu/), FATHMM (http://fathmm.biocompute.org.uk/), Varsome (https://varsome.com/), metaserver PredictSNP (https://loschmidt.chemi.muni.cz/predictsnp/), and based on the criteria introduced by the American College of Medical Genetics and Genomics (ACMG) guideline [25]. To predict the disease-causing potential of mutations, the Mutation Taster tool (https://www.mutationtaster.org/) was used. The effect of mutations on splice site were evaluated using varSEAK (https://varseak.bio/), Human splicing finder (HSF) (http://www.umd.be/HSF/) and SpliceAI (https://spliceailookup.broadinstitute.org/); a deep learning splicing prediction algorithm. The Expasy tool (https://web.expasy.org/translate/) was also used to predict the effect of splice site mutations on the ATM protein.

The ATM transcript (NM_000051.4) was used as a reference sequence.

### 2.3.2. RNA secondary structure and base pairs probabilities analysis

To predict the potential impact of ATM gene missense mutation on local mRNA secondary structure and stability, RNAfold (http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi) and MutaRNA (https://rna.informatik.uni-freiburg.de/MutaRNA/input.jsp) softwares were applied. The ATM wild-type mRNA sequence in FASTA format retrieved from the NCBI database was used as an input for RNA prediction tools. In fact, we performed a local RNA structure analysis, as local folding was reported as more accurate than global folding approaches [26]. The defined mutated sequence and the mutation site were also used as inputs to the RNAfold and MutaRNA web servers, respectively. RNAfold analysis was carried out to generate predicted RNA secondary structures for both wild-type and mutant sequences. Minimum free energy (MFE) was calculated for both the wild-type and mutated structures and the difference of MFE values was compared in order to evaluate the impact of mutations on the stability of the mRNA structure. To further assess the impact of mutations on mRNA structural changes and local folding, the MutaRNA web tool was explored. This tool allows for a comparative estimation of base pairing probabilities, illustrated by circular plots. This reflects the effect of mutations on mRNA intramolecular interaction potential. The intensity of arcs corresponds to the probability of base pairs; darker lines reflect higher base pair probabilities. The mutation is annotated in the boundary and is highlighted by a red arrow. The parameters used for MutaRNA were set as default to predict local folding with a window size of 200 nucleotides and maximal base pair span of 150 nucleotides.

### 2.3.3. miRNA binding site analysis

miRSNP (http://bioinfo.life.hust.edu.cn/miRSNP/) was first used to identify Single Nucleotide Polymorphisms (SNPs) in microRNAs (miRNAs) binding sites within the ATM sequence. Then, to predict the effect of ATM mutations on potential miRNAs binding sites, RNA22 (https://cm.jefferson.edu/rna22/), miRDB (https://mirdb.org/) and RNA-hybrid (https://bibiserv.cebitec.uni-bielefeld.de/rnahybrid) tools were applied. RNA22 was used for miRNA target prediction. Using the “Pre-computed predictions” of RNA22, putative miRNA target sites in the ATM coding domain sequence (CDS) were recovered. To assess the effect

of mutations on miRNAs binding, miRNAs that have been predicted to target the mutation sites were selected and their sequences were used as inputs in the “Interactive prediction” mode using the mutated sequences as targets. Furthermore, using the “Custom Prediction” mode of the miRDB, possible binding sites for specific miRNAs within the CDS of the wild-type and mutant ATM sequences were identified. Thereafter, the thermodynamic stability of the putative miRNA-mRNA duplexes was assessed by estimating the MFE using the RNAhybrid tool. This could identify energetically favorable hybridization between miRNA and its target. The lower the free energy, the higher the binding stability of the miRNA-mRNA duplex, which can be interpreted as the more likely that this interaction will actually occur.

## 2.4. Protein structural analysis

### 2.4.1. Protein stability prediction

I-Mutant2.0 (https://folding.biofold.org/i-mutant/i-mutant2.0.html) and MUpro (https://mupro.proteomics.ics.uci.edu/) web servers were employed to predict the effect of ATM mutations on the protein stability based on the predicted free energy change (ΔΔG) value. While a negative ΔΔG value indicates a decrease in protein stability upon mutation, a positive value indicates an increase in protein stability.

### 2.4.2. Protein structural and functional changes prediction

To further dissect the impact of ATM mutations on structural and functional changes, different computational tools were employed. MutPred (http://mutpredmutdb.org/) a machine learning based tool was first applied using protein sequence to evaluate the pathogenicity of a mutation and to infer the disrupted molecular mechanisms potentially causative of the disease, based on general probability (g score) and p value. A g score >0.5 predicts the pathogenicity of the variant. A prediction with a (g score < 0.5) and (p < 0.05) is referred as actionable hypothesis, (g score > 0.75) and (p < 0.05) is referred as confident hypothesis, whereas (g score > 0.75) and (p < 0.01) is referred as very confident hypothesis. The HOPE (https://www3.cmbi.umcn.nl/hope/input/) online tool was also used to predict the effect of mutation on physical and chemical protein properties, structure and function. In addition, Missense3D (http://missense3d.bc.ic.ac.uk/) and DynaMut2 (https://biosig.lab.uq.edu.au/dynamut2/) computational tools were applied to examine the effect of mutations on protein structural stability. DynaMut2 provides interatomic interactions in both wild-type and mutant structures. The ATM protein structure (PDB: 6K9L, resolution: 4.27 Å) was retrieved from Protein Data Bank (PDB) (https://www.rcsb.org/) and used as reference structure and template for these softwares. Afterwards, PDB 5NP1 (resolution: 5.70 Å) was also used to assess the effect of mutation on the stability of the ATM monomer open promoter structure through DynaMut2 analysis.

### 2.4.3. Structural modeling and molecular docking analysis

To address how the novel identified ATM missense mutations could affect the function of the protein and its interaction with different molecules, a deep molecular modeling and molecular docking analysis were conducted, as depicted in Fig. 1.

#### 2.4.3.1. Protein and ligands preparation.
The whole analysis was carried out using a server with Operating system Linux—Ubuntu 32.04.3 TLS and Memory/RAM 32 GB RAM. The reference ATM protein crystal structure was retrieved from Protein data bank (PDB) (https://www.rcsb.org/) (PDB: 7SIC, resolution: 2.51 Å). This structure (receptor) has been used as a template for molecular modeling. The missing amino acids of this receptor were added using SCWRL (specifically designed to predict side chain conformations from a fixed backbone usually obtained from an experimental structure determined by X-ray crystallography or NMR) (http://dunbrack.fccc.edu/lab/scwrl/) [27]. An energy minimization was performed using Multiscale Modeling Tools for
<figure><img src="image_4.png" /><figcaption>Fig. 1. An overview of the bioinformatic analysis pipeline based on ligand-protein and protein-protein docking.</figcaption></figure>

Structural Biology (MMTSB) ([http://blue11.bch.msu.edu/mmtsb/](http://blue11.bch.msu.edu/mmtsb/)) [28]. In parallel, the protein structure refinement was performed using mdCHARMM.pl with default parameters (recognize explicit water molecules and run a standard molecular dynamics protocol with periodic boundary conditions). Thereafter, mutant models were generated using a mutagenesis wizard (plugin) in PyMol by choosing rotamer with minimum energy and clashes ([https://www.pymol.org/](https://www.pymol.org/)) [29]. Wild-type and mutant proteins were subjected to further analysis. Furthermore, the final simulated structures were then prepared for molecular docking using the HADDOCK 2.4 server (for the peptide-protein docking) ([https://wenmr.science.uu.nl/haddock2.4/](https://wenmr.science.uu.nl/haddock2.4/)) [30] and the Autodock Vina server (for the ligand-protein docking) ([https://vina.scripps.edu/](https://vina.scripps.edu/)) [31].

Adenosine-5'-triphosphate (ATP = C10H16N5O13P3) was the first ligand used in this study extracted from PubChem (largest collection of freely accessible chemical information) ([https://pubchem.ncbi.nlm.nih.gov/](https://pubchem.ncbi.nlm.nih.gov/)). The conversion of the ATP 2D to 3D structure was performed and visualized using PyMol prior to molecular docking analysis. The second ligand was defined using StringDB (version 11.0) ([https://string-db.org/](https://string-db.org/)), corresponding to p53 peptide with 34 amino acids and extracted from the RCSB PDB database under the PDB code "1Q2I". Twenty conformers were available, the best conformational model was chosen with pdb-extract ([https://pdb-extract.wwpdb.org/](https://pdb-extract.wwpdb.org/))

### 2.4.3.2. Molecular docking.
In the first time the upper dimeric interface and the monomeric ATM structures (receptors) were used for receptor-ligand docking with the p53 peptide. The molecular docking was performed using the prediction interface of HADDOCK 2.4 web server ([https://bianca.science.uu.nl/haddock2.4/](https://bianca.science.uu.nl/haddock2.4/)). A guided docking for

protein-protein interaction was performed using residues around 12 Å of the Mg++ ion and the p53 peptide. After selecting the receptor residues, we proceeded to the docking by modifying the HADDOCK parameters. The residues selected belong to the kinase domain of the ATM protein. Although the kinase domain plays a central role in the cascade, emerging largely as an anchor for Mg++ and the p53 peptide, it is also a key element in the recognition of molecular motifs. To the best of our knowledge, this is the first time that a molecular docking study between the p53 peptide and the ATM receptor has been conducted. The patterns of binding and interactions between the active site residues of the protein and the ligand were obtained using PoseEdit, a tool of the Proteins plus structure-based modeling support server ([https://proteins plus/](https://proteins plus/)) [32]. Molecular anchoring was carried out using HADDOCK 2.4, Auto-dock Vina (blind docking) and babel software implemented in pyrex. The principal characteristic of each of these tools is their ability to use interactions and hydrogen bonds to calculate scores representing interaction energy. The Prodigy server was used to compute the binding affinity of the protein-protein complex through estimating the binding free energy score (ΔG) ([https://rascar.science.uu.nl/prodigy/](https://rascar.science.uu.nl/prodigy/)) [33].

### 2.5. Effect of the identified mutations on carcinogenesis

FATHMM server ([http://fathmm.biocompute.org.uk/cancer.html](http://fathmm.biocompute.org.uk/cancer.html)) was used to predict the oncogenic effects of missense mutations, that we identified, discriminating between cancer-promoting/cancer-driving mutation and other germline variants. The association of identified mutations with different types of cancers was subsequently analyzed by the CBioportal ([https://www.cbioportal.org/](https://www.cbioportal.org/)) and canSAR ([https://cansar.ai/](https://cansar.ai/)) databases.
<table><caption>**Table 2**
Clinical features and biological findings of Ataxia Telangiectasia patients.</caption><thead><tr><th>Parameters/ Patient ID</th><th>Neuro4</th><th>Neuro5</th><th>Neuro7</th><th>Neuro9</th><th>Neuro10</th><th>Neuro11</th><th>Neuro12</th><th>Neuro13</th><th>Neuro14</th></tr></thead><tbody><tr><td>Sex</td><td>F</td><td>M</td><td>M</td><td>M</td><td>M</td><td>F</td><td>F</td><td>M</td><td>F</td></tr><tr><td>Geographical origin</td><td>Bizerte</td><td>Kasserine</td><td>Bizerte</td><td>Kairouan</td><td>Kairouan</td><td>Kairouan</td><td>Kasserine</td><td>Kasserine</td><td>Tunis</td></tr><tr><td>Consanguinity</td><td>—</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td></tr><tr><td>Age (years)</td><td>21</td><td>16</td><td>16</td><td>16</td><td>21</td><td>14</td><td>10</td><td>13</td><td>24</td></tr><tr><td>Age at diagnosis (years)</td><td>8</td><td>7</td><td>3</td><td>6</td><td>7</td><td>2</td><td>9</td><td>7</td><td>10</td></tr><tr><td>Age at disease-onset (months)</td><td>14</td><td>18</td><td>18</td><td>24</td><td>18</td><td>18</td><td>60</td><td>15</td><td>30</td></tr><tr><td>First revealing symptoms</td><td>Gait instability</td><td>Gait instability</td><td>Gait instability</td><td>Gait instability</td><td>Immuno-deficiency</td><td>Gait instability</td><td>Gait instability</td><td>Gait instability</td><td>Abnormal movements concomitant with cancer development</td></tr><tr><td>Cerebellar syndrome</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>- Walking capacity</td><td>Lost</td><td>Lost</td><td>Altered</td><td>Altered</td><td>Altered</td><td>Altered</td><td>Altered</td><td>Lost</td><td>Altered</td></tr><tr><td>- Cerebellar ataxia</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>—</td></tr><tr><td>- Dysmetria</td><td>+</td><td>+</td><td>—</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td></tr><tr><td>- Hypotonia</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>—</td><td>+</td><td>+</td></tr><tr><td>-</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Neurological findings</td><td>Adiadochokinesis</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td></tr><tr><td></td><td>- Dysarthria</td><td>+</td><td>+</td><td>—</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td></tr><tr><td></td><td>- Tremor</td><td>—</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td></tr><tr><td></td><td>- Nystagmus</td><td>+</td><td>—</td><td>+</td><td>—</td><td>+</td><td>—</td><td>—</td><td>+</td></tr><tr><td>Pyramidal syndrome</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Extrapyramidal syndrome</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>- Parkinsonism</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>- Dystonia</td><td>—</td><td>+</td><td>+</td><td>+</td><td>—</td><td>—</td><td>—</td><td>+</td><td>—</td></tr><tr><td>- Chorea</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>—</td><td>—</td><td>+</td><td>+</td></tr><tr><td>- Myoclonus</td><td>—</td><td>—</td><td>+</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Ophthalmological features</td><td>Ocular telangiectasia</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td></tr><tr><td></td><td>Oculomotor apraxia</td><td>+</td><td>+</td><td>—</td><td>+</td><td>+</td><td>—</td><td>+</td><td>+</td></tr><tr><td>Recurrent infections</td><td>—</td><td>—</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>—</td></tr><tr><td>Clinical immunodeficiency</td><td>—</td><td>—</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>—</td></tr><tr><td>Cancer (History of cancer development)</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>+</td></tr><tr><td>Dermatological features</td><td>Cutaneous telangiectasia</td><td>—</td><td>—</td><td>—</td><td>+</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td></td><td>Vitiligo</td><td>—</td><td>—</td><td>+</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td></td><td>Others</td><td>—</td><td>+</td><td>+</td><td>—</td><td>+</td><td>+</td><td>—</td><td>—</td></tr><tr><td>Imaging findings</td><td>Cerebellar atrophy</td><td>—</td><td>+</td><td>+</td><td>+</td><td>+</td><td>—</td><td>+</td><td>+</td></tr><tr><td></td><td>Peripheral neuropathy</td><td>—</td><td>—</td><td>—</td><td>+</td><td>+</td><td>—</td><td>—</td><td>+</td></tr><tr><td>Laboratory findings</td><td>AFP (ng/ml)</td><td>150.8</td><td>58.15</td><td>38.16</td><td>47.71</td><td>184.98</td><td>12.16</td><td>142.67</td><td>33.1</td><td>69</td></tr><tr><td></td><td>Ig (g/l)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>IgA</td><td>0.18</td><td>0.08</td><td>0.14</td><td>0.07</td><td>0.2</td><td>0.165</td><td>0.021</td><td>0.1</td><td>0.38</td></tr><tr><td></td><td>IgG</td><td>8.9</td><td>ND</td><td>ND</td><td>ND</td><td>ND</td><td>9.06</td><td>7.03</td><td>ND</td><td>ND</td></tr><tr><td></td><td>Lymphopenia</td><td>—</td><td>+</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Cytogenetic analysis</td><td>NP</td><td>NP</td><td>NP</td><td>NP</td><td>NP</td><td>NP</td><td>NP</td><td>46, XY inv (7) (p14p35)</td><td>46, XY inv(7)</td></tr></tbody></table>

- Normal range of blood AFP: <10 ng/ml; (-): Absence; (+): Presence; (NP): Not Performed; (ND) No Data available

- Normal range of IgA: 3-6 ans: [0.40-2 g/l] - Normal range of IgA: 6-9 ans: [0.50-2.40 g/l]

- Normal range of IgG: 3-6 ans: [4.90-16.10 g/l] - Normal range of IgG: 6-9 ans: [5.40-16.10 g/l].

# 3. Results

## 3.1. Clinical characterization of the AT patients studied cohort

Demographic, clinical, imaging and immunological features of AT patients are summarized in Table 2.

### 3.1.1. General presentation

The studied cohort is composed of nine patients (Neuro4, Neuro5, Neuro7, Neuro9, Neuro10, Neuro11, Neuro12, Neuro13 and Neuro14) from unrelated families with different geographical origins. It includes five males and four females. Consanguinity was documented among all patients except Neuro4. The pedigree structure for each AT family was

represented in Fig. 2. The mean age of clinical manifestations onset was at 2 years old, while the clinical diagnosis was made at the age of 6.5 years old. Mortality was noted among 7 AT patients from 3 unrelated families, who were excluded from our study.

All reported patients were born at term with no prenatal malformations detected following ultrasound screening and without pregnancy-related complications, while Neuro13 patient has presented cardiac trouble during prenatal development, and two patients were born with unmonitored pregnancy. Birth weight was within the low normal range (mean birth weight: 2575 g, ranging from 1700 g to 5000 g).

Neurological examination showed static and kinetic cerebellar syndrome among all patients, with different clinical manifestations ranging
<figure><img src="image_5.png" /><figcaption>Fig. 2. Pedigrees of Ataxia Telangiectasia patients' families.</figcaption></figure>

from mild to severe phenotype. Gait ataxia associated with postural instability was the most prominent feature of the disease. It was the first revealing clinical symptom except for Neuro10 and Neuro14 patients, who developed immunodeficiency associated with febrile episode or movement disorder concomitant with leukemia, respectively, prior to walking trouble. Progressive worsening of gait was associated with impaired walking capacity with different degree of severity in patients, preventing autonomous ambulation. In fact, the majority of patients require bilateral support to walk, while three of them (Neuro4, Neuro5 and Neuro13) were wheelchair-bound.

Pyramidal syndrome was observed only in Neuro11 patient. However, all patients except Neuro11 and Neuro12 presented extrapyramidal syndrome dominated by chorea and dystonia. Other findings including intellectual disability was observed among six patients

(Neuro4, Neuro7, Neuro9, Neuro10, Neuro12, Neuro13) and language delay was described among three patients (Neuro4, Neuro7, Neuro13). An interactive disturbance was reported in some recruited patients. Psychomotor development was normal except for Neuro5 patient.

Moreover, patients present ocular abnormalities. Ocular telangiectasia was recorded in all patients, seven out of nine present oculo-motor apraxia (Neuro4, Neuro5, Neuro9, Neuro10, Neuro12, Neuro13 and Neuro14), defined as an incoordination between head and eye movements, whereas only three patients showed eye movement abnormalities characterized by impairment of saccadic pursuit (Neuro7, Neuro9, Neuro13). Strabismus was reported among two patients (Neuro5, Neuro13).

While conjunctival telangiectasia was a common feature, cutaneous telangiectasia was documented only among one patient (Neuro9). Other

<figure><img src="image_6.png" /><figcaption>Fig. 3. Sagittal T2-weighted brain MRI scans showing absence (A) and presence of moderate (B) and diffuse (C) atrophy of the cerebellum in Neuro4, Neuro7 and Neuro12 patients, respectively.</figcaption></figure>
dermatological manifestations were observed, characterized by café-au-lait spots and impetigo.

Furthermore, immunodeficiency was reported in six patients (Neuro7, Neuro9, Neuro10, Neuro11, Neuro12, Neuro13), manifested by recurrent sino-pulmonary and respiratory infections.

### 3.1.2. Neuroimaging findings

Brain MRI revealed cerebellar atrophy in seven (Neuro5, Neuro7, Neuro9, Neuro10, Neuro12, Neuro13, Neuro14) out of nine patients (Fig. 3). Electrophysiological studies disclosed an axonal sensory polyneuropathy among four patients (Neuro9, Neuro10, Neuro11, Neuro14). Nevertheless, Neuro4 patient didn't present the imaging features typical of the AT disease. She presents demyelinating lesions related to anorexic lesions that occurred during childbirth.

### 3.1.3. Biological and cytogenetic findings

An elevated serum AFP level was reported among all patients, with a mean of 81.85 ng/ml. Immunoglobulin (Ig) analysis was available only for some patients. A reduced level of IgA was observed, contrarily to IgG which was within the normal range among analyzed patients. IgM level was evaluated only for the Neuro12 patient and has revealed an hyper IgM phenotype (IgM = 7.03 g/l; normal range: [0.5-1.80 g/l]). Lymphopenia was recorded solely in one patient (Neuro5).

Cytogenetic analysis was performed only for two patients (Neuro13 and Neuro14) and has revealed a chromosomal rearrangement characterized by inversion of chromosome 7.

### 3.1.4. Medication and clinical course

Vitamino-therapy and prophylactic antibiotics were adopted for all patients. Three patients have received Ig infusion (Ig replacement). During the disease course, all patients exhibited a steady state followed by a decline of the clinical phenotype characterized by neurological and motor deterioration, with variable rates of disease progression.

## 3.2. Mutational analysis of ATM and pathogenicity prediction

The screening of *ATM* gene by direct Sanger sequencing of coding and intronic regions among nine clinically diagnosed AT patients has identified nine different ATM variants (Figs. 4 and 5). Truncating mutations notably non sense were the most frequent type, present among six patients. Three missense variants and one splice site mutation were detected. All patients were homozygous except Neuro4. In fact, Neuro4 patient was compound heterozygous; she presents three different variants. The segregation analysis revealed that she has inherited c.1516G > T and c.8297 T > G variants from her clinically unaffected mother. Additionally, while Neuro5 and Neuro12 patients harbor the same mutation c.2135C > G (p.Ser712*), Neuro9 and Neuro11 patients share c. 5763-2 A > C mutation.

Six ATM mutations; c.3532 A > T(p.Lys1178*), c.8297 T > G(p. Val2766Gly), c.2611G > T (p.Glu871*), c.6098 T > C(p.Leu2033Pro), c.492G > A(p.Trp164*) and c.5763-2 A > C were not reported in gnomAD and ExAC, confirming their novelty. According to gnomAD, the c.8545C > T(p.Arg2849*) mutation was reported (allele frequency = 0.000001859) among African, African American and European populations. While, c.1516G > T(p.Gly506Cys) has been described among different populations (allele frequency = 0.00004585), the c.2135C > G (p.Ser712*) mutation (allele frequency = 6.885e-7) has been only detected among the European (non-finnish) population.

In silico analysis were carried out for newly identified ATM mutations and for variants with conflicting interpretations of pathogenicity (Table 3). They were classified as pathogenic based on ACMG classification guidelines, except for c.1516G > T that was predicted as a likely benign variant. Additionally, c.8297 T > G and c.6098 T > C were classified as variants of uncertain significance. All mutations were predicted as disease-causing by Mutation Taster. Missense mutations were predicted as deleterious according to SIFT, PredictSNP and as probably damaging according to Polyphen2. While c.1516G > T mutation was predicted as neutral based on the PROVEAN prediction tool, the c.8297 T > G and c.6098 T > C were predicted as pathogenic. The list of different variants identified among the AT patients and the in silico

<figure><img src="image_7.png" /><figcaption>Fig. 4. Electropherograms of Ataxia Telangiectasia patients.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Electropherograms showing ATM heterozygous mutations in the Neuro4 patient and their segregation in her mother. Electropherogram analysis showed heterozygous c.3532 A > T (A), c.1516G > T (B) and c.8297 T > G (C) mutations in the proband. The mother carried c.1516G > T (E) and c.8297 T > G (F) mutations.</figcaption></figure>

prediction analysis are presented in Table 3.

To further investigate the effects of the mutations on the splicing process, specific *in silico* prediction tools were used. Notably, Varseak showed no splicing effects, while SpliceAI predicted splice site changes caused by the c.2135C > G, 3532 A > T and 8297 T > G mutations. However, the delta scores were below the recommended cutoff value of 0.2 emphasizing that these variants are potential non-altering variants. Conversely, the intronic variant c.5763-2 A > C was predicted by SpliceAI to strongly affect splicing with a delta score of acceptor loss = 1. The 5763-2 A > C splice disrupting variant was expected to affect the canonical splice site using Varseak and HSF tools. This mutation was predicted to cause an abnormal splicing process by inducing a loss of function of the authentic splice site and a use of cryptic acceptor site 11 nucleotides downstream of 3' splice site (Fig. 6). Importantly, *in silico* analysis using the translate tool of Expasy, has suggested that this variant lead to a frameshift mutation resulting in a premature stop codon ultimately producing a truncated ATM protein (Pro1922 Asnfs*4).

### 3.3. Effects of mutations on ATM mRNA secondary structure and stability

To analyze the impact of ATM mutations, the predicted local RNA secondary structures on 500pb flanking the mutation loci were modeled using RNA fold. The structural changes of mRNA caused by mutations are represented in Fig. 7. The MFE of the RNA structure predicted from the wild-type sequence was compared to its mutated sequence.

The c.1516G > T mutation resulted in a slight change of the local mRNA secondary structure disrupting its stability, associated with a reduction in the MFE from -126.10 kcal/mol vs -124.60 kcal/mol for wild type and mutant RNA, respectively. The mRNA transcribed from

the mutant sequence containing the c.8297 T > G variation exhibited a slightly reduced MFE compared to its wild-type counterpart (-118.00 kcal/mol vs -117.50 kcal/mol) associated with an alteration of the mRNA structure. While the c.1516G > T and c.8297 T > G variants decreased the mRNA stability, the c.6098 T > C mutation resulted in a slightly more thermodynamically stable mRNA structure (-121.10 kcal/mol) compared to the wild-type mRNA (-120.90 kcal/mol).

Next, to investigate the mutation effect on the base pair probabilities of the mRNA local folding, MutaRNA was applied. Circular plots were generated and compared between wild-type and mutated structures to illustrate differences in base pairing patterns around the mutation site, as depicted in Fig. 7. The MutaRNA results of the three missense mutations showed local structural changes. The c.1516G > T mutation decreased base pair potential at the mutated site, disrupting intramolecular interactions compared to the wild-type counterpart. The c.8297 T > G induced some minor difference in base pair potential surrounding the mutation site. It increased Watson-Crick base pair probability at the mutation site and at nearby sites, strengthening existing intramolecular interactions, while reducing the number of base pairs. Furthermore, the plot also demonstrated that the 6098 T > C mutation alters base pair potential. Although it resulted in weak interactions in the flanking regions as reflected by a decrease in the intensity of existing circular plots, the same mutation increased the number of Watson-Crick base pairs at the site and downstream of the mutation, thus creating novel intramolecular interactions. This may contribute to the predicted structural stability caused by this mutation.

Collectively, these mutations induced a local slight disruption of the intramolecular interactions in the ATM mRNA, nevertheless, the overall folding of the predicted structure was not altered.
<table><caption>**Table 3**

List of identified variants and *in silico* prediction of their pathogenicity.</caption><thead><tr><th>Patient ID</th><th>Mutation</th><th>Chr region</th><th>GnomAD</th><th>ExAC</th><th>ClinVar</th><th>ACMG</th><th>Mutation Assesor</th><th>Mutation Tastor</th><th>SIFT</th><th>Polyphen2</th><th>PROVEAN</th><th>PredictSNP</th><th>CADD</th><th>FATHMM</th><th>LRT</th><th>Varseak</th><th>SpliceAI</th></tr></thead><tbody><tr><td></td><td>c.1516G > T (p. Gly506Cys)</td><td>Exon10</td><td>Described</td><td>Not found</td><td>Conflicting interpretations of pathogenicity</td><td>Likely Beginn</td><td>Medium</td><td>Disease-causing</td><td>D</td><td>Possibly D</td><td>Neutral</td><td>D</td><td>D</td><td>D</td><td>D</td><td>No effect</td><td>No effect</td></tr><tr><td rowspan="2">Neuro4</td><td>c.3532 A > T (p.Lys1178*)</td><td>Exon24</td><td>Not found</td><td>Not found</td><td>P/LP</td><td>P</td><td>NA</td><td>Disease-causing</td><td>NA</td><td>NA</td><td>NA</td><td>D</td><td>D</td><td>D</td><td>D</td><td>No effect</td><td>No effect</td></tr><tr><td>c.8297 T > G (p. Val2766Gly)</td><td>Exon57</td><td>Not found</td><td>Not found</td><td>VUS</td><td>VUS</td><td>Medium</td><td>Disease-causing</td><td>D</td><td>Probably D</td><td>D</td><td>D</td><td>D</td><td>D</td><td>D</td><td>No effect</td><td>No effect</td></tr><tr><td>Neuro5</td><td>c.2135C > G (p.Ser712*)</td><td>Exon14</td><td>Described</td><td>Not found</td><td>P/LP</td><td>P</td><td>NA</td><td>Disease-causing</td><td>NA</td><td>NA</td><td>NA</td><td>D</td><td>N</td><td>D</td><td>N</td><td>No effect</td><td>No effect</td></tr><tr><td>Neuro7</td><td>c.8545C > T (p.Arg2849*)</td><td>Exon58</td><td>Described</td><td>Not found</td><td>P/LP</td><td>P</td><td>NA</td><td>Disease-causing</td><td>NA</td><td>NA</td><td>NA</td><td>D</td><td>D</td><td>D</td><td>D</td><td>No effect</td><td>No effect</td></tr><tr><td>Neuro9</td><td>c.5763-2 A > C</td><td>Intron38-39</td><td>Not found</td><td>Not found</td><td>P</td><td>P</td><td>NA</td><td>Disease-causing</td><td>NA</td><td>NA</td><td>NA</td><td>D</td><td>N</td><td>D</td><td>NA</td><td>Yes (Class5)</td><td>Strong impact</td></tr><tr><td>Neuro10</td><td>c.492G > A (p.Trp164*)</td><td>Exon5</td><td>Not found</td><td>Not found</td><td>P</td><td>P</td><td>NA</td><td>Disease-causing</td><td>NA</td><td>NA</td><td>NA</td><td>D</td><td>D</td><td>D</td><td>D</td><td>No effect</td><td>No effect</td></tr><tr><td>Neuro11</td><td>c.5763-2A > C</td><td>Intron38-39</td><td>Not found</td><td>Not found</td><td>P</td><td>P</td><td>NA</td><td>Disease-causing</td><td>NA</td><td>NA</td><td>NA</td><td>D</td><td>N</td><td>D</td><td>NA</td><td>Yes (Class5)</td><td>Strong impact</td></tr><tr><td>Neuro12</td><td>c.2135C > G (p.Ser712*)</td><td>Exon14</td><td>Described</td><td>Not found</td><td>P/LP</td><td>P</td><td>NA</td><td>Disease-causing</td><td>NA</td><td>NA</td><td>NA</td><td>D</td><td>N</td><td>D</td><td>N</td><td>No effect</td><td>No effect</td></tr><tr><td>Neuro13</td><td>c.2611G > T (p.Glu871*)</td><td>Exon17</td><td>Not found</td><td>Not found</td><td>No data</td><td>LP</td><td>NA</td><td>Disease-causing</td><td>NA</td><td>NA</td><td>NA</td><td>D</td><td>N</td><td>D</td><td>N</td><td>No effect</td><td>No effect</td></tr><tr><td>Neuro14</td><td>c.6098 T > C (p. Leu2033Pro)</td><td>Exon42</td><td>Not found</td><td>Not found</td><td>VUS</td><td>VUS</td><td>Low</td><td>Disease-causing</td><td>D</td><td>Possibly D</td><td>D</td><td>D</td><td>D</td><td>Tolerated</td><td>D</td><td>No effect</td><td>No effect</td></tr></tbody></table>

D: Damaging/Deleterious P: Pathogenic LP: Likely Pathogenic N: Neutral NA: Not Applicable VUS: Variants of Uncertain Significance.
<figure><img src="image_9.png" /><figcaption>Fig. 6. Schematic representation of the effect of splicing mutation, predicted using the Varseak and HSF tools.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Fig. 7. The effect of mutations on mRNA secondary structure predicted using RNAfold and MutaRNA tools. mRNA secondary structure of wild-type and mutant ATM sequences predicted using RNAfold, for 1516G > T (A), 8297 T > G (B) and 6098 T > C (c) compared to their wild-type counterparts. MutaRNA circular plots of base pairing probabilities for both wild type and mutant sequences, for 1516G > T (D), 8297 T > G (E) and 6098 T > C (F). The red arrow is pointing at the site of mutation.</figcaption></figure>

### 3.4. Effect of mutations on miRNA binding site

The effect of the mutations on miRNA binding sites that might alter the miRNA-mediated *ATM* gene regulation, was evaluated using a variety of computational tools. miRSNPdb was firstly used to search for potential SNPs in miRNA-binding sites within the ATM sequence, however, no SNP has been identified in the mutational loci. The putative miRNA binding sites in the CDS of ATM, specifically targeting the identified mutation sites, were first searched using RNA22. In fact, c.8545C, c.6098 T and c.3532 A were not part of target sites by miRNAs. However, the remaining identified mutation sites were putative targets of specific miRNAs. In this regard, the effect of the mutations on miRNA binding was assessed after using the following mutated sequences as inputs to the RNA22 tool. To further examine the effect of the variants on the miRNA-mRNA interactions, the miRDB was used to retrieve

miRNAs targeting the mutation sites by comparing the wild-type and mutant sequences. The thermodynamic properties of the newly miRNA-mRNA duplexes generated upon mutations were then evaluated by estimating the free energies of the predicted interactions using the RNAhybrid tool.

For the majority of variants, mutations induced loss of miRNAs binding sites. miR-1251-5p, miR-1537-5p, miR-1538, miR-3174, miR-302c-3p, miR-2276-3p, miR-1238-5p, miR-4521, miR-4459, miR-4270, miR-3917, miR-3677 and miR-3151-5p binding sites were lost following the c.1516G > T mutation. This was also predicted for c.2611G > T, resulting in the loss of binding sites for miR-193a-5p, miR-2682-3p, miR-3183, miR-4469, miR-597-3p and miR-6878, whereas the binding site for miR-9-5p was preserved. For the 2135C position, while novel binding sites for miR-514b-5p and miR-513c-5p were created following the 2135C > G genetic variation (MFE = -19.4 kcal/mol and
MFE = -16,2 kcal/mol respectively), mutation at this site altered the binding of miR-1254, miR-1287-5p, miR-1298-3p, miR-2277-5p, miR-184, miR-4298, miR-378c, miR-23a-5p and miR-3687. In addition, this mutation preserved the binding sites for miR-6774-5p, miR-6787-5p and miR-378c which were also bound to the wild-type sequence at the same position. According to the miRDB, c.8297 T > G mutation induced a loss of miR-767-5p binding site.

Although c.492G and c.3532 A were not targets for miRNA regula-
tion, mutations at these loci induced the creation of new putative target
sites, specific for miR-1208 following c.492G > A mutation (MFE =
-19.9), and for miR-9985, miR-27b-3p and miR-27a-3p at nearby sites
following c.3532 A > T mutation with MFE of -22.4, -21.2 and -19.9
kcal/mol, respectively.

3.5. Protein domains and stability analysis

We observed a random distribution of identified mutations within
protein regions that affect different ATM protein domains, specifically
the spiral subdomain of the N-terminal region, as illustrated in Fig. 8.

Moreover, I-mutant and MuPro tools showed a decrease of protein
stability upon identified ATM missense mutations. Prediction tools
showed a slight decrease of stability upon Gly506Cys mutation (ΔΔG =
-0.56 and ΔΔG = -0.9 for I-Mutant and Mupro, respectively), while a
more destabilizing effects were predicted for Leu2033Pro (ΔΔG = -1.16,
ΔΔG = -2.7 for I-Mutant and Mupro, respectively) and Val2766Gly
(ΔΔG = -4.84, ΔΔG = -2.04 for I-Mutant and Mupro, respectively)
mutations.

3.6. Impact of identified mutations on the ATM protein function and structure

Mutations were further subjected to MutPred2 tool, which was used
to assess pathogenicity and to infer its possible underlying molecular
mechanisms by predicting structural and functional consequences of
ATM amino acid substitutions. Val2766Gly and Leu2033Pro were pre-
dicted to be pathogenic with a g score of 0.683 and 0.792, respectively.
They were predicted to undergo molecular changes. Val2766Gly was
predicted to alter the protein stability (p = 4.6e-03) and ordered inter-
face (p = 0.05), and to cause gain of allosteric site at Trp2769 (p = 6.8e-
03) and of catalytic site at Glu2768 (p = 0.03). Leu2033Pro was pre-
dicted to induce an alteration of protein stability (p = 3.7e-03) with a
very confident hypothesis of mechanistic disruption.

Project HOPE tool revealed that the mutant residue of Leu2033Pro is smaller than the wild type, probably leading to an empty space in the core of the protein. In fact, Leu2033 is located within an α-helix of the FAT domain; this variation could consequently disrupt this domain and abolish its function. Moreover, for the Val2766Gly mutation, HOPE analysis showed that the wild-type and the mutated residues present different physicochemical properties. Specifically, Glycine is smaller and less hydrophobic than Valine amino acid, hence causing an empty

space and loss of hydrophobic interactions in the core of the protein. In
fact, this variant is located in the PI3K domain. This was predicted to
affect the local protein stability which in turn could affect the ligand-
contacts made by one of the neighboring residues. In addition, the
high flexibility of Glycine may disrupt the protein rigidity at the mutated
position. In the case of Gly506Cys mutation, the HOPE tool indicated
that the mutant residue is more hydrophobic than the wild-type. Indeed,
Glycine is the most flexible amino acid. This substitution may conse-
quently alter the protein's flexibility, which may be required for its
proper function. Moreover, this residue is located on the surface of the
protein, thus mutation at this residue could disturb interactions with
other molecules or other parts of the protein.

To better predict the effect of mutations on the native protein
structure, amino acid substitutions were subjected to DynaMut2 and
Missense3D analysis, using the wild-type PDB ID 6K9L as a template
structure. Missense3D predicted that the Leu2033Pro mutation intro-
duced a buried proline. Indeed, DynaMut2 tool revealed an alteration of
intramolecular interactions. In fact, the structural model obtained by
DynaMut2 showed that Leu2033 displayed several hydrophobic in-
teractions with Ileu2030, Ile2006, Tyr2036 and clash as well as polar
interactions with Tyr2036. However, the mutant Pro2033 displayed two
hydrophobic interactions and clashes with ILeu2030, a polar interaction
with Tyr2036 and no direct interaction was detected with ILeu2006.
Ionic interactions were absent in both wild-type and mutant residues
(Fig. 9). This decline in the overall hydrophobicity explained by the
introduction of a buried Proline with restricted backbone conformation
may disrupt protein stability. The software predicted a destabilizing
effect of this variant on the protein dimer structure (ΔΔG: -0.42 kcal/
mol). In addition, the Missense3D tool predicted structural damage
induced by Val2766Gly mutation. This could be explained by an
expansion of the cavity volume by 75.168 Å3 as a result of Valine to
Glycine substitution. Therefore, the expansion of the cavity may be due
to the loss of amino acid contact predicted by the HOPE project analysis
which is ultimately changing the shape of the protein. Additionally, this
mutation induced a switch from a buried to an exposed state of the
affected residue. Moreover, DynaMut2 demonstrated a profound
destabilizing effect of this mutation which may result from a change of
interatomic molecular interactions with surrounding amino acids con-
sisting of hydrophobic interactions loss (ΔΔG: -1.93 kcal/mol). The
Gly2766 variant disrupted the intramolecular hydrophobic interactions
with neighboring residues, notably Met2667, Val2716, Leu2760,
Tyr2627, as shown in Fig. 9. These in silico findings supported the
deleteriousness of these variants, which can be explained probably by
their effect on the protein structure. Despite that no structural damage
was predicted for Gly506Cys mutation using Missense3D, a decline of
protein stability was predicted by DynaMut2 (ΔΔG: -0.48 kcal/mol). To
better predict the impact of mutations on ATM protein structure,
Gly506Cys was analyzed in combination with Val2766Gly by the
DynaMut2 tool. In fact, Val2766 and Gly506 residues were distant by
91.16 Å. The biallelic mutation profoundly decreased protein stability

<figure><img src="image_11.png" /><figcaption>Fig. 8. Schematic diagram of affected ATM protein domains among AT patients.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Fig. 9.** Effect of ATM mutations on the native protein structure predicted using DynaMut2 server. Interatomic interactions differences of the wild-type L2033 (A), V2766 (B) and G506 (C) compared to the mutant P2033 (D), G2766 (E) and C506 (F) counterparts.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>**Fig. 10.** Comparative binding of the complex ATM-UNK and ATM-ATP.</figcaption></figure>
(ΔΔG: -2.25 kcal/mol).

The destabilizing effect of these mutations predicted using the ATM closed dimer structure as a template for these structural analyses, could suggest that other protein conformations might be stabilized. This may affect the transition between closed dimer and open monomer state of the ATM protein, thereby influencing the protein activation. This prompted us to investigate the impact of these mutations on the open monomer ATM structure. Indeed, Dynamut2 analysis has predicted a destabilizing effect on the ATM open monomer (ΔΔG: -1.91 kcal/mol and - 0.41 kcal/mol upon Val2766Gly and Gly506Cys mutations, respectively). However, the Leu2033Pro has been predicted to induce a slight increase in the stability of the open monomer protein structure (ΔΔG: 0,01 kcal/mol). The destabilizing effect could suggest a disruption of the transition from dimer to monomer protein conformation upon DNA damage stimuli leading to a reduced protein activity.

### 3.7. Molecular docking study

The molecular docking was firstly performed via Autodock Vina to analyze the impact of ATM mutations (Leu2033Pro and Val2766Gly) on protein-ligand interactions, using ATP as a ligand of ATM protein (dimer structure = 6112 amino acids) in the presence of Mg++.

After deep analysis of our results, we have concluded that all the docking solutions generated by Autodock Vina present a ΔG of -6.4 Kcal/mol. The interaction between ATP-ATM stabilized by four H-bonds with Gln2714, Thr2771, Val2774 and Asp2889. As opposed to the complex, UNK-ATM presents only two H-bonds with Lys2717 and Glu2768. Detailed illustrations of these interactions pattern are depicted in Fig. 10.

To assess if the ATM mutations could affect protein activity, we have conducted a molecular docking (protein-protein docking) analysis between ATM (receptor) and the p53 peptide (ligand), one of the most important ATM targets, using ATM upper dimeric and monomeric native and mutant structures. The docking study results revealed significant variations in binding affinity and stability between wild-type and mutant ATM-p53 complexes, using different parameters to evaluate

these properties, as presented in Table 4 and Figs. 11 and 12.

In the wild-type ATM upper dimeric structure, five key p53 peptide residues, including Ser4, Gln5, Glu6, Lys19 and Asn23 form contacts with Glu2975, Asp2974, Thr2978, Leu2780 and Arg2790 at distances of 2.3 to 2.5 Å, as depicted in Fig. 11a. These interactions are characterized by a HADDOCK score of -100.6 ± 9.7 and a ΔG of -10.3 kcal/mol, indicating a moderate binding energy. The van der Waals energy is -108.1 ± 5.7 kcal/mol, and the electrostatic energy is -42.6 ± 8.6 kcal/mol, with a buried surface area of 2975 Å² and a Z-score of -1.9. The Z-score, which measures the statistical significance of the complex's stability, suggests a moderate stability of the wild-type complex. Comparatively, the Leu2033Pro mutation changed interactions, involving Glu6, Thr7, Lys13, Lys19 with residues Arg2688, Asn2586, Glu2975, Asp2974 at distances ranging from 1.9 to 2.5 Å, as presented in Fig. 11c. This resulted in a HADDOCK score of -155.6 ± 5.0, significantly lower than the wild-type, indicating stronger binding affinity despite changes in the specific residues involved in the interaction. The ΔG is -10.3 kcal/mol. The van der Waals energy is -93.2 ± 1.4 kcal/mol, and the electrostatic energy is -101.8 ± 34.5 kcal/mol, highlighting strengthened electrostatic interactions, with a Z-score of -2.1, suggesting better contact between ATM and p53, enhancing the stability of the complex. The Val2766Gly mutation involved interactions of four atoms (Gln5, Glu6, Lys13, Lys19) with residues (Lys2585, Arg2691, Glu2975, Asp2793) at distances of 2.0 to 2.8 Å. This mutation leads to a HADDOCK score of -109.1 ± 5.7 and a ΔG of -10.3 kcal/mol, indicating a relative increase in the binding affinity compared to the wild-type, with potential alterations in the specific residues involved in the interaction. The van der Waals energy is -78.3 ± 6.7 kcal/mol, and the electrostatic energy is -214.2 ± 50.0 kcal/mol, suggesting a substantial contribution from electrostatic interactions, with a Z-score of -2.0, indicating improved stability over the wild-type.

In the monomeric ATM structure, peptide-protein docking analysis between the p53 peptide and the ATM receptor, showed different results in wild-type compared to mutant configurations, as illustrated in Table 4 and Fig. 12. In the wild-type ATM, specific residues such as Ala2321, Ala2325, Ala2364, Lys2363 formed key electrostatic and van der Waals

<table><caption>**Table 4**

Results of docking analysis of the native and mutants ATM-P53 complexes.</caption><thead><tr><th>Receptor (ATM)</th><th>Ligand contact</th><th>Receptor contact</th><th>Distance (Å)</th><th>HADDOCK score</th><th>Binding free energy ΔG (kcal mol<sup>-1</sup>)</th><th>Van der Waals energy (kcal mol<sup>-1</sup>)</th><th>Electrostatic energy (kcal mol<sup>-1</sup>)</th><th>Z-score</th></tr></thead><tbody><tr><td rowspan="10">Upper dimeric interface structure of the ATM</td><td rowspan="6">Wild-type</td><td>Ser4</td><td>Glu2975</td><td>2.3</td><td rowspan="6">-100.6 ± 9.7</td><td rowspan="6">-10.3</td><td rowspan="6">-108.1 ± 5.7</td><td rowspan="6">-42.6 ± 8.6</td><td rowspan="6">-1.9</td></tr><tr><td>Gln5</td><td>Pro2974</td><td>2.3</td></tr><tr><td>Glu6</td><td>Thr2978</td><td>2.5</td></tr><tr><td>Lys19</td><td>Leu2780</td><td>2.3</td></tr><tr><td>Asn23</td><td>Arg2790</td><td>2.4</td></tr><tr><td>Glu6</td><td>Arg2688</td><td>1.9</td></tr><tr><td rowspan="6">Leu2033Pro</td><td>Thr7</td><td>Asn2586</td><td>2.3</td><td rowspan="6">-155.6 ± 5.0</td><td rowspan="6">-10.3</td><td rowspan="6">-93.2 ± 1.4</td><td rowspan="6">-101.8 ± 34.5</td><td rowspan="6">-2.1</td></tr><tr><td>Lys13</td><td>Glu2975</td><td>2.5</td></tr><tr><td>Lys19</td><td>Asp2795</td><td>2.3</td></tr><tr><td>Gln5</td><td>Lys2585</td><td>2.5</td></tr><tr><td>Glu6</td><td>Arg2691</td><td>2.7</td></tr><tr><td>Lys13</td><td>Glu2975</td><td>2</td></tr><tr><td rowspan="6">Val2766Gly</td><td>Lys19</td><td>Asp2795</td><td>2.8</td><td rowspan="6">-109.1 ± 5.7</td><td rowspan="6">-10.3</td><td rowspan="6">-78.3 ± 6.7</td><td rowspan="6">-214.2 ± 50.0</td><td rowspan="6">-2</td></tr><tr><td>Ser4</td><td>Ala2321</td><td>2</td></tr><tr><td>Gln5</td><td>Ala2325</td><td>2.1</td></tr><tr><td>Thr7</td><td>Ala2364</td><td>2.7</td></tr><tr><td>Asp10</td><td>Lys2363</td><td>2.6</td></tr><tr><td>Glu6</td><td>Arg2973</td><td>2.1</td></tr><tr><td rowspan="10">Monomeric structure of the ATM</td><td rowspan="6">Wild-type</td><td>Ser9</td><td>Lys2992</td><td>2.6</td><td rowspan="6">-84.6 ± 7.1</td><td rowspan="6">-8.7</td><td rowspan="6">-76.0 ± 2.4</td><td rowspan="6">-174.4 ± 11.2</td><td rowspan="6">-2.2</td></tr><tr><td>Asp10</td><td>Lys2966</td><td>1.9</td></tr><tr><td>Lys13</td><td>Leu2594</td><td>2.1</td></tr><tr><td>Lys16</td><td>Arg2993</td><td>2.3</td></tr><tr><td>Lys17</td><td>Asp2597</td><td>2.2</td></tr><tr><td>Ser4</td><td>Lys2966</td><td>2</td></tr><tr><td rowspan="6">Leu2033Pro</td><td>Gln5</td><td>Thr2573</td><td>2.2</td><td rowspan="6">-102.8 ± 3.7</td><td rowspan="6">-8.8</td><td rowspan="6">-61.7 ± 3.2</td><td rowspan="6">-336.6 ± 16.7</td><td rowspan="6">-1.8</td></tr><tr><td>Lys13</td><td>Leu2594</td><td>2.1</td></tr><tr><td>Lys16</td><td>Arg2993</td><td>2.3</td></tr><tr><td>Lys17</td><td>Asp2597</td><td>2.2</td></tr><tr><td>Ser4</td><td>Lys2966</td><td>2</td></tr><tr><td>Gln5</td><td>Thr2573</td><td>2.2</td></tr><tr><td rowspan="6">Val2766Gly</td><td>Glu6</td><td>Leu2722</td><td>2.3</td><td rowspan="6">-115.8 ± 5.5</td><td rowspan="6">-11.0</td><td rowspan="6">-79.3 ± 3.9</td><td rowspan="6">-254.0 ± 42.1</td><td rowspan="6">-2.4</td></tr><tr><td>Asp10</td><td>Arg2973</td><td>2.7</td></tr><tr><td>Lys17</td><td>Asp2987</td><td>1.9</td></tr><tr><td>Lys13</td><td>Leu2594</td><td>2.1</td></tr><tr><td>Lys16</td><td>Arg2993</td><td>2.3</td></tr><tr><td>Lys17</td><td>Asp2597</td><td>2.2</td></tr></tbody></table>
<figure><img src="image_14.png" /><figcaption>**Fig. 11.** Comparison of the binding mode of p53 in ATM upper dimeric wild-type and mutant structures. Enlarged view of the interaction between ATM; wild-type (B), Leu2033Pro (C) and Val2033Gly (D), upper dimeric structure with p53 (A).</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>**Fig. 12.** Comparison of the binding mode of p53 in ATM monomer wild-type and mutant structures. Enlarged view of the interaction between ATM; wild-type (B), Leu2033Pro (C) and Val2033Gly (D), monomer structure with p53 (A).</figcaption></figure>

bonds with the p53 peptide at Ser4, Gln5, Thr7 and Asp10 residues at distances of 2.0 to 2.7 Å, contributing to a stable interaction with an average binding energy of −9.2 kcal/mol. The HADDOCK score is of −84.6 ± 7.1, showing moderate binding affinity. The van der Waals energy is −76.0 ± 2.4 kcal/mol, and the electrostatic energy is −174.4 ± 11.2 kcal/mol, with a Z-score of −2.2. Overall, the wild-type ATM-P53 interaction is relatively stable but weaker compared to the mutant

complexes. For the Leu2033Pro mutation, interactions were made between Glu6, Ser9, Asp10, Lys13, Lys16, Lys17 P53 residues and Arg2973, Lys2992, Lys2966, Leu2594, Arg2993, Asp2597 ATM residues at distances of 1.9 to 2.6 Å, resulting in a HADDOCK score of −102.8 ± 3.7, showing higher binding affinity. The van der Waals energy is −61.7 ± 3.2 kcal/mol, and the electrostatic energy is −336.6 ± 16.7 kcal/mol, with a Z-score of −1.8. Indeed, the docking results showed an average
binding energy of -11.5 kcal/mol, indicating a stronger binding. This mutation alters the contact residues forming tighter interactions with the p53 peptide primarily due to increased electrostatic contributions. The visualization in Fig. 12 highlighted a tighter and potentially more stable interaction compared to the wild-type, enhancing the binding affinity. The Val2766Gly mutation's interactions (Ser4, Gln5, Glu6, Asp10, Lys17) with residues (Lys2966, Thr2573, Leu2722, Arg2973, Asp2987) at distances of 1.9 to 2.7 Å yield a HADDOCK score of -115.8 ± 5.5, indicating higher binding affinity. The van der Waals energy is -79.3 ± 3.9 kcal/mol, and the electrostatic energy is -254.0 ± 42.1 kcal/mol, with a Z-score of -2.4, suggesting the most significant stability improvement among the tested mutations. The average binding energy is further improved to -12.8 kcal/mol. Indeed, this mutation created new interactions resulting in stronger electrostatic bonds and emphasizing the mutation's role in enhancing stability, potentially leading to significant functional impacts.

The comparative analysis showed that the Leu2033Pro and Val2766Gly mutations significantly improve the binding affinity and stability of the ATM-p53 complex. The Leu2033Pro mutation exhibited the most substantial increase in interaction strength, potentially altering the biological function of the ATM-p53 complex.

### 3.8. Cancer susceptibility analysis

The oncogenic potential of the variants was assessed through the FATHMM server. While Val2766Gly and Leu2033Pro mutations were predicted to have a cancer-causing/cancer promoting role with a prediction score of -2.73 and -1.47, respectively, the Gly506Cys was predicted as a passenger variant with a score of 0.40. cBioPortal and canSAR databases were also explored. The c.8545C > T(p.Arg2849*) has been described in Uterine Endometrioid Carcinoma, whereas c.2135C > G (p.Ser712*) has been reported to be associated with Uterine Endometrioid Carcinoma and Gastric Cancer.

## 4. Discussion

In our study, we aimed to carry out a deep investigation of the clinical particularities, genetic defects, and the impact of certain mutations on the structural stability of the ATM protein in patients suffering from AT disease. To the best of our knowledge, this is the first in depth molecular investigation of AT pathology in Tunisia revealing causative variants and exploring their possible mechanisms of pathogenicity. We have identified six novel mutations among our patient cohort which widens the spectrum of ATM pathogenic variants in AT disease worldwide. Our findings highlight that the mutational landscape of AT in Tunisian patients is diverse and distinct from those in other ethnic areas. Furthermore, our study represents the first comprehensive computational exploration of the ATM mutation impact on different molecular aspects in AT patients. We examined the effect of the mutations on the ATM post-transcriptional regulation; specifically on the mRNA ATM stability and miRNA binding, and post-translational level; notably on the protein structural stability and the ATM-ligand interactions through molecular docking. Our *in silico* analysis suggest a significant impact of mutations, particularly at protein level, affecting the ATM-P53 interaction patterns, which may potentially have a functional effect.

AT is a rare neurodegenerative disease with pleiotropic phenotype. The incidence of AT is estimated between 1:40,000 and 1:100,000 live births world-wide [7]. However, it remains underdiagnosed in Tunisia given the absence of a referral center for the diagnosis of the disease.

The majority of AT patients present a classical or typical phenotype with early childhood onset-disease associated with severe prognosis with premature death (in the second decade of life) due to respiratory failure or cancer development. Nevertheless, some AT patients referred as variant AT form, exhibit a milder phenotype or a late-onset, and are characterized mainly by extrapyramidal manifestations rather than cerebellar ataxia. They are characterized by a less severe prognosis with

extended life span associated with slower neurological progression and lower risk of systemic disorders. However, patients with variant AT form, present a higher risk of cancer development.

### 4.1. Clinical features and mutational spectrum of AT patients

The majority of AT patients enrolled in this study were born from consanguineous families, except Neuro4 patient. Despite that the age at disease diagnosis differs between patients, the disease onset is relatively similar and coincides with the age of ataxia gait disorders onset, which are characterized by frequent falls of the patient. In fact, cerebellar ataxia, due to the progressive neurodegeneration of cerebellum cells mainly Purkinje cells and at a lesser extent granule and basket cells, is the most prominent and debilitating feature of the disease, leading to motor dysfunction which confines most patients at wheelchair at early adolescence [34]. Despite that all AT patients included in our cohort manifest static and kinetic cerebellar syndrome, with a progressive ataxia and a deterioration of the walking ability, only three patients were wheelchair-bound. In addition, all patients except Neuro11 and Neuro12 present extrapyramidal syndrome. In fact, it was reported that while cerebellar ataxia is the first clinical symptom that appears in childhood among classical AT patients, variant AT are characterized mainly by extrapyramidal manifestations and late-onset ataxia associated with milder neuropathological abnormalities [17]. This has been observed among Neuro14 who presents abnormal movements during her childhood that precede gait disturbance manifestation with a less severe clinical phenotype. Furthermore, some patients present intellectual disability and language delay. In fact, it has been reported that cognitive function impairment in AT disease results from cerebellar or extra-cerebellar damage depending on the disease stage [35]. Neuroimaging findings showed cerebellar atrophy and peripheral neuropathy in all patients except Neuro4, who had no notable atrophy at the initial neurological investigation. Given the fact that cerebellar atrophy is a consistent feature of AT disease, new MRI imaging will likely reveal this neurological impairment. In fact, neurological symptoms often coincide with neuroimaging findings characterized by cerebellar atrophy a few years after the first symptoms appear [35]. While the involvement of the central nervous system, mainly of the cerebellum, is a well described hallmark of the AT pathology, the peripheral nervous system alteration is much less characterized [36].

In addition, the majority of patients reported in the present study present recurrent sinopulmonary infections as a consequence of immunodeficiency. It was reported that 3/4 of AT patients are immunodeficient. Indeed, immune system alteration is an important characteristic of the disease, affecting both cellular and humoral response with varying degree of severity [37]. It is reflected mainly by lymphopenia and immunoglobulin deficiency. However, lymphocyte numbers were within the normal range among patients included in the cohort except for one patient (Neuro 5). Moreover, it was reported that some AT patients may represent an hyperIgM phenotype which is associated with a more severe prognosis [38,39]. In fact, AT is a Primary Immunodeficiency Disorder (PID), associated with thymus abnormalities. Despite the scarcity of AT clinical studies in Tunisia, it was described that AT represents one of the predominant groups of PID in the country [40]. Furthermore, a predisposition to cancer development mainly of lymphoid origin is another feature of this multifaceted disease [41]. While immunodeficiency is less severe among variant AT patients, cancer susceptibility especially to solid tumors, is higher [17]. This was noticed in patient Neuro14, who didn't present recurrent infections, whereas she developed acute lymphoblastic leukemia at the age of two and was treated with chemotherapy. The role of ATM as a potent tumor suppressor linked to its role in the DSB repair explain the susceptibility to malignancies in AT patients. In fact, the heterozygous carriers of ATM mutations are prone to cancer development, especially to breast cancer and to digestive cancers, which have been observed among members of AT families enrolled in the present study [38]. Cytogenetic analysis has
been performed for some AT patients and has revealed a chromosomal translocation. It was reported that genetic instability may contribute to the high frequency of malignancies among AT patients. Despite that AT diagnosis is mainly based on the association of specific clinical and biological features, the clinical phenotype may overlap with other disorders, making the diagnosis challenging. Moreover, the absence or the delay of key features of AT phenotype among variant AT group may lead to a misdiagnosis of the disease. Additionally, while the elevation of serum AFP level has been reported as a diagnostic marker for AT disease, patients with other forms of ataxia present also an increased AFP level [42]. Therefore, the clinical diagnosis may be delayed and become inconclusive, highlighting the importance of the genetic investigation to confirm the clinical diagnosis, to provide early patient management and to reduce the risk of life-threatening infections. This will be helpful to establish prenatal diagnosis and genetic counseling for families at risk. Furthermore, identifying healthy carriers would be valuable to set up a planned surveillance program for early detection of cancer in carriers and their AT children.

From a genetic standpoint, AT is due to *ATM* gene mutation, an exceptionally large size gene. In the current study, identified mutations are spread over the entire ATM sequence, highlighting mutational heterogeneity and the lack of a founder-effect mutation in Tunisian patients. In fact, while a number of recurrent variants have been described in the literature among certain ethnic groups and geographically grouped families, such for north African Jews [43], mutations are distributed throughout the *ATM* gene with no hot spots. Indeed, >1400 unique mutations have been identified until now (https://databases.lovd.nl/shared/genes/ATM). Despite that few clinical studies have been carried out in Tunisia, only one molecular study was conducted in the country [44–47]. This retrospective study has been conducted for 17 years to collect 11 AT patients from 8 families, from whom genetic characterization was performed for 6 patients, reporting mutations in different exons, such as exon 42 and 26 [47]. Genetic screening in our cohort has identified a mutation in exon 42 for only one patient. For this, we searched for other mutations described in Moroccan and Algerian patients, ethnically closed populations [48]. In fact, AT is one of the most frequent PID in both countries [49,50]. While no genetic characterization has been performed for Algerian patients, it has been reported that mutations are mainly located in exon 5, 24, 38 and 58 of the *ATM* gene in Moroccan AT patients [48]. In our cohort, we identified nine mutations among which six were not reported previously. The majority are nonsense mutations leading to a premature stop codon which results in a truncated protein, causing classical severe phenotype among homozygous carriers. In fact, while the majority of patients reported in the literature are compound heterozygotes [51], all patients in our cohort, except Neuro4, are homozygotes. In fact, the c.492G > A and c.2611G > T mutations resulted in the formation of a truncated ATM protein with only 164 and 871 amino acids, respectively, compared to the 3056 amino acids in the wild-type protein. Moreover, the c.2135C > G mutation has been described previously among two AT patients at homozygous state, one of them is registered in the French National Reference Center for Primary Immunodeficiencies database and he was classified as class A mutation leading to a classical AT phenotype [13,52]. This was in accordance with our cohort, in which Neuro5 and Neuro12 patients carry this mutation, present a severe phenotype. This variant was also described among a patient with breast cancer as germline mutation [53]. On the other side, the c.8545C > T mutation identified among Neuro7 patient, has been reported in the literature as germline genetic variant strongly associated with hereditary cancer syndrome [54,55]. This variant was reported among Italian, Polish and North American AT families [56–58]. In addition, the c.5763-2 A > C splicing variant was predicted to likely disrupt the original splice acceptor site and activate a cryptic 3' splice site affecting canonical splicing. This results in the deletion of 11 nucleotides at the 5' end of exon 39 leading to a frameshift mutation, resulting in a truncated 1926 amino acids ATM protein (Pro1922 Asn*4). This may account for the AT severe phenotype

observed among the homozygous carriers of this mutation, notably in Neuro9 and Neuro11 patients. In fact, it was described that 85% of *ATM* gene mutations are truncated or null variants resulted from frameshift and nonsense variants, leading to the total absence of the protein or the presence of ATM protein without kinase activity, defining classical AT phenotype [17]. Conversely, variant AT patients present at least one missense or leaky splicing mutation, producing ATM protein with residual activity (1–17% of functional ATM protein) [17]. Three missense mutations were identified in the present cohort. Indeed, the c.6098 T > C resulted in the substitution of a leucine residue by a proline at 2033th position without affecting the remaining amino acids sequence, leading to the formation of a mutant ATM protein with kinase activity. This could explain the late-onset less severe phenotype among Neuro14 patient, that was described above. Moreover, the c.1516G > T mutation detected among Neuro4 patient, has been recently described in a Saudi Arabia female at homozygous status associated with a later-disease onset with a mild phenotype [59]. Intriguingly, the Neuro4 patient, who presents a typical AT phenotype, has inherited c.1516G > T and c.8297 T > G variants from her asymptomatic mother. In fact, the healthy carrier mother presents the two variants on the same allele, whereas she presents a wild-type allele expressing a wild-type ATM protein. The AT patient presents one allele with these variants generating a mutated protein in combination with one allele carrying the c.3532 A > T variant which generates a truncated protein, probably inherited from his father. Unfortunately, we could not analyze the segregation of this variant in the father that was deceased. In fact, while missense mutations outside the kinase domain may result in a residual kinase activity leading to a mild phenotype, as observed in the Saudi Arabia female carrier of the homozygous c.1516G > T variant, missense mutation in this domain resulting from c.8297 T > G probably led to an inactive ATM kinase protein. The c.1516G > T is associated with genetic susceptibility to cutaneous melanoma and breast cancer [60]. Moreover, it was observed that the majority of mutations identified in the current study are mapped to the N-terminal region of the ATM protein. It was documented that frameshifts mutations and truncating mutations in the N-terminal domain of ATM protein (in the spiral or pincer subdomains) are associated with a more prominent oligodendrocytes pathology and consequently myelination defect in the cerebellum [61].

## 4.2. Effect of mutations on mRNA structure and miRNA binding site

In the present study, we deciphered the impact of *ATM* gene mutations at different molecular levels using several computational approaches. Hence, we evaluated the effect of the mutations on mRNA secondary structure, stability and local folding, which may profoundly influence the efficiency and the speed of the translation process. Our findings suggest that ATM mutations disrupt locally the mRNA structure implying that the pathogenic effect of these mutations is most likely mediated at the protein level rather than mRNA level. However, an increased structural stability has been predicted upon 6098 T > C mutation. It has been reported that the RNA secondary structure could inversely impact protein translation and folding; a more stable structure could create an impediment for the ribosome speed processing [62]. The connection between the mRNA structure and the rate and efficiency of translation is complex and is far from being fully elucidated, with conflicting data. In fact, the mRNA transcribed from the sequence containing the 6098 T > C mutation is characterized by a higher base pairing potential compared to the wild-type mRNA sequence, which may alter the translation process.

Next, in order to address whether the mutations affect epigenetic regulation, notably through miRNA-mediated *ATM* gene regulation, the impact of genetic changes on miRNA binding site was assessed and the miRNA-mRNA interactions were evaluated. In fact, miRNA are crucial regulators of gene expression. They mediate gene silencing at post-transcriptional level, by targeting 3' untranslated regions (3'UTRs). However, miRNA could also target protein coding sequence (CDS)
potentially inhibiting mRNA translation [63]. In the present study, despite the fact that a loss of binding sites was observed for the majority of mutated mRNA sequences, new putative sites for specific miRNAs were created. Although the estimated MFE values for the predicted miRNA-mRNA duplexes are high, suggesting a less likely interactions, we cannot rule out the hypothesis of a potential interactions and that miRNA could underlie in part the effect of ATM mutation pathogenicity. It is therefore mandatory to validate the miRNAs target prediction data and their functional consequences on *ATM* gene expression level in AT patients. This could be a potential mechanism of ATM downregulation and ultimately contributes to AT pathogenesis.

The absence of a genotype-phenotype correlation has been observed among some AT patients, suggesting the involvement of other factors, besides ATM kinase activity, such as gene modifiers, environmental and epigenetic factors to define the AT phenotype [20]. Although some studies have reported the involvement of epigenetic factors in the AT pathophysiology, the role of miRNAs has not been yet explored [64,65]. The role of miRNA in neurodegenerative diseases has been spotlighted in recent years. Indeed, miRNAs have been reported as disease-related actors highlighting their potential as therapeutic targets in other forms of ataxia [66]. It has been suggested that miRNA-mediated *ATM* gene regulation could contribute to the AT phenotype and to its related clinical variability [67]. Exploring the effect of mutations at post-transcriptional regulation level is therefore an important step towards identifying potential mechanisms that could explain the pathogenicity of the genetic variation and underlie the definition of a particular phenotype.

### 4.3. Effect of mutations on protein structure and function

Protein stability upon amino acid substitution has been assessed and has predicted a destabilizing effect. Further bioinformatic analyses were performed to investigate possible changes at the protein structural level.

Leu2033Pro was predicted to induce a decrease of hydrophobic interactions resulting in a weakening of mechanical properties which may disrupt the overall stability of the protein. In fact, the mutated Leu2033 residue is located within the TRD2 (Tetratricopeptide Repeat Domain) subdomain of FAT domain (α4 to α12). FAT domain plays a crucial role in the protein dimerization and stabilization of the dimeric form. Mutations in this domain may interfere with ATM dimerization which may lead to an improper dimerization or dissociation of the dimer affecting consequently conformational stability. TRD2 is a critical region to stabilize the homodimer complex by assuring interactions with TRD2/TRD3 of another ATM monomer [68]. TRD2-TRD3 interactions represent the lower dimeric interface of the ATM dimer, which remains intact in the H2O2-activated state. These interactions are mediated by hydrophobic residues through Van der Waals interactions and hydrogen bonds. In fact, it was reported that the addition of H2O2 could capture the active dimeric state of the ATM protein, stabilizing this confirmation through addition of disulfide bond [23]. Mutations in the TRD2 that affect protein dimerization could therefore abolish protein response to H2O2. An ATM mutation that leads to a protein which retains normal activity in response to DNA damage, but which is unable to sense oxidative stress could lead to a variant AT phenotype [69]. This could represent the most probable hypothesis underlying Leu2033Pro variant pathogenicity and that could explain the milder phenotype of the Neuro14 patient. On the other hand, the detrimental effect of a proline introduction could be explained by the alterations of the intramolecular interactions due to its restricted backbone structure. The exceptional backbone of proline imposes a local structure rigidity and a conformational restriction to the protein's 3D structure [70]. Otherwise, the unique stereochemical characteristic of proline amino acid may lead to another possible probable mechanism that could explain the pathogenicity of this variant. Indeed, the structural rigidity conferred upon proline introduction may affect the motion of the FAT domain that is required for crucial rotation of the kinase-binding domain. This may

abolish the transition from an inactive to an active dimeric or monomeric state of the ATM protein or leading to a less active form of the protein, crippling protein flexibility.

The Val2766Gly mutation is located at the N-terminal lobe of the Kinase domain (KD-N lobe). While the active site is located within the C-terminal lobe, which contains activation (2866-2875aa) and catalytic (2888-2911) loops, the two lobes are essential for ATP binding [68]. In fact, the vicinity of Val2766 residue to the catalytic cleft, which binds the ATP cofactor, localized between the two lobes, and to critical residues of the N-lobe, notably Leu2715, Leu2767 and Trp2769, likely supports the functional relevance of this mutation. It was reported that the KD-N lobe represents a crucial regulatory subdomain for the kinase activity [71]. Mutations at this site may affect the intrinsic kinase activity through disrupting regulation, rather than the catalysis processus *per se*. This pathogenic mechanism could be supported by computational analysis that predicted an expansion of the pocket volume. Indeed, The ATP-binding pocket is located between the N and C lobes of the kinase domain. Mutations that could affect the way that ATP is captured may abolish kinase activity. Another possible explanation is that this substitution alters the position of the N-lobe and ATP-interacting residues relative to the C-lobe, disrupting correct N and C-lobes alignment in response to stress-signals. This is necessary to relieve the ATMs' catalytically competent conformation, enabling effective catalysis. Furthermore, *in vitro* protein modeling has reported that Gly2765Ser mutation results in ATM kinase inactive mutants (ATM without kinase activity), while Glu2039Lys is an ATM kinase reduced mutants (reduced ATM kinase activity) [72].

In the present study, conflicting data concerning the pathogenicity of Gly506Cys variant at structural level has been predicted. While Misp sense3D showed no structural alteration induced by this mutation, DynaMut2 tool has revealed a destabilizing impact. In fact, the mutated residue is located at the spiral domain of the N-terminal region of the ATM protein. Despite that the N-solenoid subdomain is not involved in the ATM protein dimerization, it binds proteins; notably substrates, regulators and adaptors [73]. A mutation within this subdomain could potentially affect the binding affinity of an ATM regulator impairing protein-protein interaction and probably protein function. The milder phenotype of the Saudi Arabia female reported in the literature, carriers of this mutation [59], could support the hypothesis of the alteration of an activator binding site resulting in a mutant protein with a residual kinase activity.

Since a proper structure of a protein is essential for its activity, changing its conformational geometry can influence the protein's interactions with its targets, which in turn can affect protein functional activity. To further gain insights into molecular mechanisms that may underlie variant pathogenicity, we have performed a molecular docking analysis using ATP and p53 as ligands. Molecular docking analysis using ATP has revealed no difference between wild-type and mutant complexes, suggesting that Leu2033Pro and Val2766Gly mutant structures did not affect ATP binding to ATM protein. Nonetheless, our results show that the Leu2033Pro and Val2766Gly mutations improve binding affinity and stability of the ATM-p53 complex, and that the Leu2033Pro mutation has the most significant impact. These findings illustrate how mutations can markedly influence the binding affinity and stability of the ATM receptor-ligand interactions affecting receptor function, which could consequently have an impact on cellular processes.

The mutations alter the contact points and interaction energies between different residues, suggesting that they may significantly affect the function and stability of the protein complex. This may suggest a disruption of the ATM-p53 complex dissociation which may have an impact on the active-to-inactive conformational state transition, upon different stimuli, affecting the kinase activity of the protein. Indeed, a dynamic equilibrium between active and inactive states is required for an optimal function of a kinase protein. The effect of Leu2033Pro and Val2766Gly on the ATM kinase activity may be explained by a restriction in the transition from active to inactive conformation resulting in
one conformational state being favored. In fact, Leu2033Pro mutation results in a more stable upper dimeric ATM-p53 complex which may affect the transition between different states upon H2O2 stimulus, this could consequently impact cellular response to oxidative stress. While the Val2766Gly mainly induces a stable monomeric ATM-p53 complex which may impair the active-to-inactive state transition in response to DNA damage signals. Indeed, several studies have attempted to understand the model of ATM protein activation, nevertheless, mechanisms involved in the active-to-inactive state transition remain yet unexplored. Furthermore, the increase of ligand binding affinity may alter the internal motions of the kinase protein interfering with the role of dynamics in kinase regulation.

Obviously, one limitation of our study is that the *in silico* prediction tools are based on different algorithms which may underpin the conflicting results of variant pathogenicity. However, our approach based on combining computational tools may potentially mitigate this issue, improving the prediction performance, thereby providing strong evidence arguing variant pathogenicity.

Our study demonstrates the importance of the exploration of the molecular mechanism of variant pathogenicity at different molecular levels which may extend our understanding of ATM mutational impact to a more complex landscape and provide insights into disease pathogenesis. This could lead to a better understanding of the genetic basis of AT disease. Further studies based on molecular dynamic simulation and functional analyses will be of valuable interest to complement *in silico* prediction tools to further decipher the underlying mechanisms of variant pathogenicity.

## 5. Conclusion

Our results highlight the heterogeneity of the clinical and genetic landscapes of AT Tunisian patients, extending the mutational spectrum of AT disease worldwide. Computational analyses based on *in silico* predictions and structural bioinformatic analysis suggest a profound impact of identified missense mutations disrupting the intricate network of intramolecular interactions affecting protein structural stability, and also altering the binding affinity and the stability of the ATM receptor-ligand interactions. This may have important functional consequences, which could be included in the pathogenicity of the AT disease. Our findings point out the complexity of *ATM* gene regulation at different levels, underscoring the importance of functional analysis to further characterize and explore the mechanism of variant pathogenicity.

## Institutional review statement

The study was approved by the Institut Pasteur Ethics Committee in Tunisia (reference 2022/4/I/LR16IPT/V2), in accordance with the Declaration of Helsinki Principles.

## Funding

The study was supported by the Tunisian Ministry of Public Health, the Ministry of Higher Education and Scientific Research (LR16IPT05).

## CRediT authorship contribution statement

**Rim Jenni:** Writing – original draft, Methodology, Investigation, Formal analysis, Conceptualization.
**Hedia Klaa:** Investigation. **Ousema Khamessi:** Writing – review & editing, Software, Methodology, Formal analysis, Conceptualization.
**Asma Chikhaoui:** Methodology.
**Dorra Najjar:** Methodology.
**Kais Ghedira:** Writing – review & editing.
**Ichraf Kraoua:** Investigation.
**Ilhem Turki:** Investigation.
**Houda Yacoub-Youssef:** Writing – review & editing, Validation, Supervision,

Funding acquisition, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare no conflict of interest.

## Data availability

All data have been provided in the manuscript. The data generated in this study can be provided by the corresponding author upon reasonable request.

## Acknowledgments

We would like to thank the patients for their collaboration and trust.

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