Novel Ectodysplasin-A Variants: Structural and Functional Basis of Hypohidrotic Ectodermal Dysplasia

Novel Ectodysplasin-A Variants: Structural and Functional Basis of Hypohidrotic Ectodermal Dysplasia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Novel Ectodysplasin-A Variants: Structural and Functional Basis of Hypohidrotic Ectodermal Dysplasia Prashant Ranjan, Chandra Devi, Rajesh Bansal, Vandita Srivastava, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5743160/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates two novel variants in the EDA , c.680G > A (p.G227E) and c.649_666del (Δ215–220), identified in X-linked ectodermal dysplasia and syndromic tooth agenesis cases. These variants were identified through Sanger sequencing and mapped to highly conserved regions of EDA. Bioinformatics tools consistently classified them as deleterious, with significant disruptions predicted in protein stability, hydrophobicity, and secondary structure. Structural analysis revealed that p.G227E caused a glycine-to-glutamic acid substitution, altering hydrophobicity and secondary structure, while Δ215–220 disrupted a conserved hydrophobic region, leading to increased protein instability Functional studies revealed reduced expression of EDA and WNT4 proteins, alongside increased IκB levels and decreased NF-κB mRNA expression, indicating impaired EDA-NF-κB signaling. Subcellular localization analyses demonstrated diminished cytoplasmic expression of the EDA Variants proteins, corroborated by in silico predictions. Post-translational modifications (PTMs) and gene ontology (GO) analyses revealed alterations in processes critical for ectodermal development, including macromolecule biosynthesis, nitrogen metabolism, and receptor signaling. Molecular dynamics simulations highlighted increased rigidity, compact structure, and reduced flexibility in the EDA variants proteins compared to EDA Wild Type (WT). Interestingly, neither variant significantly impacted calcium or mitochondrial potential under normal experimental conditions, suggesting their pathogenic effects arise primarily from disrupted protein interactions and signaling pathways. This study integrates molecular, bioinformatics, and functional analyses to elucidate the pathogenicity of these novel EDA variants, providing insights into ectodermal dysplasia mechanisms and paving the way for future therapeutic strategies targeting these EDA variants. Molecular Genetics Structural Biology Bioinformatics Medical Genetics Dentistry EDA PTMs MD Simulations X-linked ectodermal dysplasia IκB and NF-κB expression Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction The term "ectodermal dysplasia" (ED) describes a group of inherited disorders characterized by anomalies in the growth of tissues that start in the ectoderma (Wright et al., 2019 ). Hypohidrotic ectodermal dysplasia (HED) is the most common subtype among the more than 200 clinical variants of ED that have been identified, with a frequency of around 1 in 5,000 to 10,000 newborns (Anbouba et al., 2020 ). Among the three characteristics of HED are congenital tooth loss (hypodontia and oligodontia), reduced sweating (hypohidrosis), and poor hair growth (hypotrichosis). According to (Wright et al., 2017 ), secondary symptoms include dry eyes, periorbital hyperpigmentation, and dry, fragile-looking skin conditions. HED may be inherited in an X-linked, autosomal dominant, or autosomal recessive manner. Mutations in the EDA gene on the X chromosome cause the X-linked form of HED (XL-HED), which accounts for more than half of all cases (Cluzeau et al., 2011 ; Kere et al., 1996 ; Nguyen-Nielsen et al., 2013 ). The EDA gene encodes ectodysplasin A (EDA), a critical signaling protein that governs ectoderm-mesoderm interactions throughout development. These interactions are necessary for the development of placodes, which give rise to ectodermal characteristics like as skin, hair, nails, teeth, and sweat glands (Sima et al., 2018 ). The extracellular region of EDA, a type II transmembrane protein, has a collagen domain and a tumor necrosis factor (TNF) homology domain (THD) (Elomaa et al., 2001 ). To create a soluble ligand, the protein is cleaved at a furin protease site. EDA-A1 and EDA-A2, which make up around 80% of the total EDA protein, are the most common isoforms produced via alternative splicing of EDA (Hashimoto et al., 2006 ). Val307-Glu308, a two-amino acid motif unique to EDA-A1, is the difference between these isoforms (Yan et al., 2000 ). Both isoforms structurally produce trimers made of β-sandwich monomers that are jelly-rolls. Although EDA-A1 and EDA-A2 have structural similarities, they have different receptor binding specificities (Hymowitz et al., 2003 ). Through the cysteine-rich domains (CRDs) of the corresponding receptors and the THDs of EDA, EDA-A1 binds to EDAR and EDA-A2 connects with XEDAR. The NF-κB pathway is activated by both receptors, although their intracellular domains, extracellular areas, and signaling pathways differ (Kowalczyk-Quintas & Schneider, 2014 ). Other important pathways, including as BMP, FGF, and SHH, are essential for tooth growth in along with Wnt signalling (Aurrekoetxea et al., 2016 ; Pakvasa et al., 2021 ). Together with EDAR, EDA-A1 is essential for the development, morphogenesis, and differentiating of ectodermal organs. On the other hand, recent research clearly suggests that the EDA-A1/EDAR axis is the principal contribution to HED pathogenesis, raising doubts about the functional significance of EDA-A2 and XEDAR in HED (Lefebvre & Mikkola, 2014 ). In this study, we identified two novel variants in the EDA associated with syndromic congenital tooth agenesis (CTA), observed in one familial case and one sporadic case. These variants were found to result in diverse clinical phenotypes through distinct molecular mechanisms involving exon 4 of the EDA . To evaluate their pathogenicity, we conducted in vitro and in silico functional analyses focusing on EDA signaling pathways. 2. Methods 2.1 Recruitment of participants The Declaration of Helsinki's ethical guidelines were followed in this work. All participants, or in the case of young ones, their parents or legal guardians, provided written informed consent. The Ethics Committee of the Institute of Science at Banaras Hindu University in India examined and approved the study procedure. Participants comprised 20–30-year-olds with full dental records and radiographic imaging who had been diagnosed with CTA. Ectodermal organs including skin, hair, nails, and teeth were evaluated during clinical tests. Interviews were conducted at the Department of Dentistry and Oral Surgery, Institute of Medical Sciences, to gather information on congenital abnormalities such as sweating and heat tolerance. All participants or their legal guardians provided written consent. The affected individuals also had 5 ml peripheral venous blood samples taken in EDTA vials for further examination. 2.2 Identification of Variants Samples of peripheral venous blood were used to isolate genomic DNA. Using polymerase chain reaction (PCR), all coding exons, exon-intron boundaries region of EDA was amplified. Primers, PCR conditions, and the DNA extraction procedure were all carried out as previously mentioned (Ranjan et al., 2024 ). After PCR products were processed with exonuclease I and recombinant Shrimp Alkaline Phosphatase (rSAP) (USB Affimetrix, USA), they were labeled using the ABI Big Dye Terminator V3.1 cycle sequencing kit for Sanger sequencing. Following the manufacturer's instructions, capillary electrophoresis and automated base calling were carried out on an ABI 3130 Genetic Analyzer, and the results were examined using Sequencing Analysis Software V5.2 (Applied Biosystems, USA). Using the NCBI Basic Local Alignment Search Tool (BLAST) (Ye et al., 2006 ), sequences were compared to the GenBank DNA sequence database maintained by the National Center for Biotechnology Information (NCBI). 2.3 Pathogenicity Prediction : Several in silico methods were used to estimate the pathogenicity of the EDA p.G227E and EDA p.Δ215–220 variants (Table 1 ). These instruments examine genetic variations to forecast how they could affect protein function and the correlation between diseases. Revel, Alpha Missense, Eve, MUT Assessor, SIFT, MT, FATHMM, DANN, MetaLR, PrimateAI, BayesDel, and GenoCanyon were among the tools utilized. By integrating characteristics such as evolutionary conservation, biochemical characteristics, sequence context, and functional annotations, these techniques together provide a thorough evaluation of the pathogenic potential of the EDA variations (Garcia et al., 2022 ; Richards et al., 2015 ) ( https://franklin.genoox.com/clinical-db/home ). Table 1 Pathogenicity predictions of EDA variants Variant Tool Prediction Score p.G227E Revel Deleterious (Strong) 0.96 p.G227E Alpha Missense Deleterious (Strong) 0.997 p.G227E Eve Deleterious (Low) 0.66 p.G227E MUT Assessor Deleterious 3.34 p.G227E SIFT Deleterious (Supporting) 0 p.G227E MT Deleterious 1 p.G227E FATHMM Deleterious (Moderate) -6.28 p.G227E DANN Deleterious 0.99 p.G227E MetaLR Deleterious 0.99 p.G227E Primate AI Deleterious (Moderate) 0.93 p.G227E BayesDel Deleterious (Strong) 0.75 p.G227E Geno Canyon Deleterious 1 p.Δ215–220 Revel Deleterious (Strong) 0.94 p.Δ215–220 Alpha Missense Deleterious (Strong) 0.98 p.Δ215–220 Eve Deleterious (Moderate) 0.68 p.Δ215–220 MUT Assessor Deleterious 3.25 p.Δ215–220 SIFT Deleterious (Supporting) 0 p.Δ215–220 MT Deleterious 1 p.Δ215–220 FATHMM Deleterious (Moderate) -6.12 p.Δ215–220 DANN Deleterious 0.98 p.Δ215–220 MetaLR Deleterious 0.97 Note: Higher scores indicate a stronger prediction that the variant is harmful, while lower scores indicate less confidence in its harmful effect. 2.4 Site Directed Mutagenesis (SDM) SDM used to investigate the c.680G > A (p.G227E) and c.649_666del (p.215_220del) mutations in Exon 4 of the EDA . A human EDA cDNA ORF clone, generously gifted by Pascal Schneider of the Department of Biochemistry, University of Lausanne, CH-1066 Epalinges, Switzerland, was modified to include these alterations. PCR3.1 was the plasmid vector that was employed. The DH5α competent cells of E. coli were created using the wild-type (WT) plasmid construct. Following plasmid isolation and double digestion verification, glycerol stocks were made for next research. The Agilent Quick Change® Primer Design Program was used to create SDM primers, which were based on the Homo sapiens EDA mRNA sequence (Gen Bank accession number: NP_001390.1). These primers were designed to include the particular deletion and nonsynonymous mutations. They were utilized exactly as supplied after being commercially produced by Eurofins Scientific. The primer sequences were as follows: Anti-Sense Mutation in G680A: 5'-gaggccagggggttbgaggaccagg-3' 5'-cctggtcctcaagaaccccctggcctc-3' is the sense. c.649_666 Anti-Sense deletion: 5'-tccttgaggaccaggtggtcccataacagttg-3' 5'-caactgttatgggaccacctggtcctcaagga is the sense.-3”. The c.680G > A (p.G227E) and c.649_666del (p.215_220del) mutations were mutagenesis-introduced into the EDA using the Quick Change II Site-Directed Mutagenesis Kit (Agilent, #200524). The kit's instructions were followed to make the reaction mix, and ideal cycling circumstances were used. DpnI digestion was carried out to break down the parental DNA after amplification. The mutated product was converted into competent E. Coli DH5α cells. After screening the colonies using Hind III and EcoRI double digestion, Sanger sequencing was done. Glycerol stocks of validated plasmids with the desired mutations were kept for use in further studies. 2.5 Procurement and Maintenance of Cell Line DMEM (Sigma-Aldrich) with 10% FBS (HiMedia) and an antibiotic mixture (100 µg/mL Streptomycin, 100 U/mL Penicillin) were used to cultivate the COS-7 cell line, which was obtained from the National Centre for Cell Science (NCCS), Pune, India. Before being kept in liquid nitrogen, the cells were cryopreserved in FBS or DMEM with 10% DMSO, routinely passaged, and kept at 37°C with 5% CO₂. 2.6 Transfection Using CaCl₂-Phosphate Method COS-7 cells were transfected at 70–80% confluency after being plated in 6-well plates at a density of 1 × 10⁶ cells per well. Drop by drop, the cells were exposed to the transfection mixture, which contained 2 µg of plasmid DNA, 2.5 M CaCl₂, and 2X HEPES buffer. After 16 hours, a glycerol shock was administered to increase the efficiency of transfection. For further research, cells were extracted 48–72 hours after transfection. 2.7 Western Blotting The CaCl₂-phosphate technique was used to transfect COS-7 cells. Cells were collected after 72 hours, and RIPA buffer was used to extract the proteins. Bradford's test was used to assess the protein content. After being denatured, proteins were separated using SDS-PAGE and then transferred to a PVDF membrane. An ECL reagent kit (Thermofisher catlog: A38555) was used to view the membrane after it had been blocked with 5% non-fat dried milk in TBST, treated with primary antibodies for the whole night, and then incubated with secondary antibodies. As needed, blots were removed and re-probed. EDA rabbit polyclonal antibody (Bioss, Catalog # BS-12347R), Wnt4 rabbit polyclonal antibody (ABclonal, #A7809), IK β (Cat. No. GTX110521), and β-actin mouse monoclonal antibody (Sigma Aldrich: A5441) were the main antibodies utilized in the investigations. The tests were conducted twice. Goat anti-mouse IgG-HRP (GeNei) and goat anti-rabbit IgG-HRP (Santa Cruz Biotechnology) were the primary antibody species' suitable horseradish peroxidase-conjugated secondary antibodies. 2.8 Subcellular Localization of EDA Variants COS-7 cells were grown till 70–80% confluency in DMEM supplemented with 10% FBS. Plasmids encoding the Empty Vector, WT EDA, EDA p.G227E, or EDA Δ215–220 were then used to transfect them. The cells were frozen, permeabilized, and blocked overnight after transfection. Primary EDA antibodies and secondary antibodies conjugated to a fluorescent dye (Alexa floura-568 Thermofisher Cat # A-11011) were used for immunofluorescence staining. DAPI was used to stain the nuclei. Images of the nucleus (blue) and EDA antibody staining (red) were taken using Carl Zeiss confocal microscopy 2012 (63 X), and the merged images showed subcellular localization. All variations showed nuclear localization in enlarged pictures of certain cells. 2.9 Measurements of Intracellular Ca 2+ Level COS-7 cells were seeded in 6-well plates at a density of 1 × 10⁶ cells per well, and then cultured in DMEM supplemented with 10% FBS (HiMedia) at 37°C with 5% CO₂. The cells were incubated until they reached 70–80% confluence. EDA WT and EDA Variants plasmids were transfected using the CaCl₂-phosphate method. Following transfection, cells were removed, washed with PBS, and treated with 5 µM Fluo-3/AM dye for 30 to 1 hour at 37°C in the dark. Intracellular Ca2 + levels were measured using a flow cytometer (CytoFLEX LX, Beckman Coulter). Every experiment was conducted in three duplicates. CytExpert software was used to analyze fluorescence intensity data in order to compare and quantify calcium levels. 2.10 Mitochondrial membrane potential measurement: COS-7 cells were grown at a density of 1 × 10⁶ cells per well in 6-well plates using DMEM supplemented with 10% FBS. After that, they were maintained at 37°C with 5% CO₂ in an incubator until they reached 70–80% confluency. Following transfection with the EDA WT and EDA Variants plasmids, cells were grown for 48 hours. After being harvested, the cells were washed with 1X PBS and stained with 150 nM TMRM (tetramethylrhodamine, methyl ester) dye for 30 minutes at 37°C in the dark. The mitochondrial membrane potential was ascertained by measuring the orange fluorescence intensity of TMRM using a flow cytometer (FACS Calibur, CytoFLEX LX, Beckman Coulter). Using fluorescence data, mitochondrial membrane potential was compared and measured using the CytExpert program (Beckman Coulter). Every experiment was conducted in three duplicates. 2.11 Evolutionary Conservation Analysis The ConSurf web server was used to perform evolutionary conservation analysis (Glaser et al., 2003 ). The top 150 homologous sequences for the EDA protein were found using PSI-BLAST. ConSurf used Bayesian inference to determine conservation scores, which varied from 1 (variable locations) to 9 (highly conserved sections). The evolutionary conservation of the amino acid residues in the EDA protein was shown by this technique. 2.12 Structural Analysis, Modeling, and Validation AlphaFold was used to obtain the EDA protein's three-dimensional structures (Jumper et al., 2021). Using AlphaFold templates (EDA AF-Q92838-F1-v4) on the Swiss-Model website, mutant models were produced by homology modelling (Kiefer et al., 2009 ). To guarantee structural integrity, energy reduction was carried out using the "steepest descent" approach using GROMACS 2018 (Berendsen et al., 1995 ). PDBsum was used to further evaluate the models' quality (Laskowski et al., 2018 ). 2.13 Prediction of Post-Translational Modifications (PTMs) Several computational methods were used to find possible post-translational modifications (PTMs) in proteins. Serine, threonine, and tyrosine residue phosphorylation sites were predicted using NetPhos 3.1 (Blom et al., 1999 ). N-glycosylation sites were identified using NetNGlyc, whereas O-glycosylation sites were identified using NetOGlyc (Hansen et al., 1998 ; Pugalenthi et al., 2020 ). GPSSNO was used to predict S-nitrosylation (Xue et al., 2010 ), while GPSSUMO was used to find sumoylation sites (Zhao et al., 2014 ). Farnesylation and geranylgeranylation were among the prenylation forms that PrePS examined (Maurer-Stroh & Eisenhaber, 2005 ). The ubiquitination site was predicted using UbPred (Radivojac et al., 2010 )d terminal myristoylation was detected using Myristoylator. Palmitoylation sites were predicted using CKSAAPPalm and eqPalm (Ranjan & Das, 2023 ). Finally, lysine acetylation sites were predicted using GPS PAIL (Deng et al., 2016 ). With the use of protein sequence inputs, these techniques shed light on how PTMs may control the stability and function of proteins, which may have consequences for congenital tooth agenesis. 2.14 Protein Dynamic Simulations GROMACS v.18 was used to perform molecular dynamics (MD) simulations in order to investigate the structural behavior of the EDA proteins in both their WT and EDA Variants forms. The simulations were conducted using the GROMOS96 54a7 force field (Schmid et al., 2011 )(Devi et al., 2024 ). SPC216 water molecules surrounded the proteins in a dodecahedron box, with the proteins and box edges spaced at least 1.0 nm away. In order to replicate physiological conditions, 0.15 M NaCl and Na ions were used to neutralize the system. Steepest descent was employed to reduce energy and settle steric disputes. Subsequently, the system underwent two equilibrium phases: one at 300 K using the NVT ensemble (constant temperature, volume, and particle count) and another at 1 atm for 100 ps using the NPT ensemble (constant temperature, pressure, and particle count). MD simulations were conducted for an extra 50 ns after equilibration. Both the WT and EDA Variants structures reached a stable equilibrium state in less than 10 ns. Key parameters, such as solvent-accessible surface area (SAS), hydrogen bonds (intra-protein and protein-water interactions), radius of gyration (Rg), root mean square deviation (RMSD), and root mean square fluctuation (RMSF), were analyzed using GROMACS tools (e.g., gmx rms, rmsf, gyrate, hbond, and sas). The results were shown using Microsoft Excel (Berk & Carey, 1998 ) and XMGRACE (Cowan & Grosdidier, 2000 ). 2.15 Principal Component Analysis (PCA) of Biophysical Metrics We used Principal Component Analysis (PCA) based on important biophysical metrics, such as solvent-accessible surface area (SAS), hydrogen bond count (H-bonds), radius of gyration (Rg), root mean square deviation (RMSD), root mean square fluctuation (RMSF), and subcellular localization properties, to assess and visualize the variability among various protein variants. Both the WT and EDA Variants protein (EDA_WT, EDA_G227E, and Δ215–220) were subjected to these measurements. PCA was carried out using the scikit-learn toolkit (Kramer, 2016 ), reducing the dataset to two principal components (PC1 and PC2) that captured the majority of the variation after the data were normalized to guarantee that each feature contributed equally. A scatter plot representing the PCA findings was created, with various colors and comments used to identify the variations. Python (v3.12) with pandas for data management and matplotlib (Hunter & Dale, 2007 ) for visualization were used in this investigation. 2.16 Statistical Analysis of t-Test and One-Way ANOVA For statistical analysis, Python's scipy and statsmodels libraries (McKinney, 2015 ) were used. The independent sample t-test was performed using scipy.stats.ttest_ind to compare the means between two groups. For multiple group comparisons, one-way ANOVA was conducted using scipy.stats.f_oneway. 2.17 Miscellaneous analysis Several computational techniques were used to analyze protein variations. The Psipred Workbench was used to undertake secondary structure prediction and Gene Ontology (GO) annotations for both WT and variant versions of EDA (Buchan & Jones, 2019 ). These predictions were based on protein sequences. The seaborn package in Python (Lemenkova, 2020 ) was used to create a heatmap. After loading the dataset into a Pandas DataFrame, it underwent completeness processing. The viridis colormap, 10x8-inch figure size, annotations enabled, and the seaborn heatmap tool were all utilized. DeepLoc 2.0 (Thumuluri et al., 2022 ), which uses a convolutional neural network to predict localization probability for different cellular compartments, was used to provide subcellular localization predictions for WT and EDA variant proteins. A thorough structural analysis was conducted using Discover Studio (Systèmes, 2016 ), which shed light on the biophysical characteristics of every protein variation. Densitometry analysis was performed using ImageJ (Schneider et al., 2012 ), and the scanned picture was imported in TIFF format. To adjust for non-specific signals, we used the rectangle tool to reliably pick the bands and eliminate the backdrop. We then calculated the regions under the peaks and produced a densitometry profile. To guarantee reliable comparisons, we used a loading control to normalize these data. In order to ascertain relevance, we lastly conducted statistical analysis and compared the levels of protein expression. 3. Results 3.1 Identification of Novel Variants in EDA and Pathogenicity prediction In a case presenting oligodontia features along with ectodermal organ involvement, two novel variants in the EDA were identified through Sanger sequencing. The first variant was a transitional change from c.680G > A (Exon4), resulting in a p.G227E variant (Fig. 1 ). This Variant changed glycine (Gly) to glutamic acid (Glu), causing a shift from a non-polar to a polar amino acid. The second variant was a deletion of 18 nucleotides in exon 4, specifically between positions c.649_666del (Fig. 2 ), which led to a deletion in the protein at positions 215–220. These findings highlighted the potential impact of these novel EDA variants on the development and function of ectodermal tissues, including teeth, hair, and sweat glands, and contributed to the observed clinical features. In silico analysis of EDA variants p.G227E and p.Δ215–220 consistently predicted their pathogenicity (Table 1 ). Both variants were classified as deleterious by multiple tools, including Revel, AlphaMissense, and MetaLR, with strong scores (≥ 0.94). p.G227E had a particularly high AlphaMissense score of 0.997, while p.Δ215–220 scored 0.98. Supporting predictions from tools like SIFT, MUT Assessor, and DANN further confirmed their impact. These findings suggest that both variants significantly disrupt EDA function through deleterious effects. 3.2 Effect of EDA Variants on Protein and RNA expression The analysis of the two novel EDA variants (G227E and Δ215–220) revealed significant impacts on both protein and mRNA expression levels (Fig. 3 ). The p.G227E variant involving a glycine-to-glutamic acid substitution, and the c.649_666del deletion variant, resulting in the loss of amino acids 215–220, both led to decreased expression of EDA and WNT4 proteins. Conversely, an increase in IκB expression was observed. Furthermore, the mRNA expression of NF-κB was markedly reduced in these variants, highlighting disruptions in the EDA signaling pathway. These findings suggest that these variants compromise the development and function of ectodermal tissues, including teeth, hair, and sweat glands, by altering critical components of the signaling cascade. 3.3 Effects of EDA Variants on Calcium and Mitochondrial Potential Analysis of calcium potential indicated no significant differences between the EDA-WT and the EDA variants (EDA-G227E and EDA-Δ215–220) (Fig. 4 ). The calcium levels were consistent across all samples, suggesting that these EDA variants do not significantly impact calcium potential (Fig. 4 ). In the analysis of mitochondrial potential, no statistically significant differences were observed between the EDA-WT and the EDA variants (EDA-G227E and EDA-Δ215–220). The mitochondrial potential remained similar among all samples, indicating that these novel EDA variants do not significantly affect mitochondrial function (Fig. 4 ). 3.4 Subcellular Localization of EDA Variants Immunofluorescence staining revealed that the expression of EDA in the cytoplasm was significantly hampered in the EDA variants (G227E and Δ215–220) compared to the WT. However, the nuclear localization of EDA remained consistent across both WT and variant samples (Fig. 5 ). In silico predictions of subcellular localization (Table 2 ) showed changes in the EDA variants when compared to the WT (Fig. 5 ). The PCA plot of subcellular localization data depicted a distinct separation between the WT and variant EDA proteins (Fig. 5 ). This analysis included various subcellular compartments such as the cytoplasm, nucleus, extracellular region, cell membrane, mitochondrion, plastid, endoplasmic reticulum, lysosome/vacuole, Golgi apparatus, and peroxisome. Table 2 Predicted subcellular localization of EDA_WT_CS and EDA_G227E_CS and EDA_Δ215–220_CS EDA proteins across various cellular compartments, highlighting differences in localization probabilities. Subcellular Location EDA_WT_CS EDA_G227E_CS EDA_Δ215–220_CS Cytoplasm 0.1609 0.1556 0.1574 Nucleus 0.161 0.1542 0.1527 Extracellular 0.3458 0.3671 0.3592 Cell Membrane 0.657 0.6641 0.6601 Mitochondrion 0.0346 0.037 0.0376 Plastid 0.0014 0.0013 0.0014 Endoplasmic Reticulum 0.4623 0.4747 0.4733 Lysosome/Vacuole 0.1797 0.1811 0.1829 Golgi Apparatus 0.5661 0.5739 0.5746 Peroxisome 0.0319 0.0332 0.0324 Note: CS: Confidence Score 3.5 Effects of EDA Variants on PTMs and Their Functional Alteration PTMs Analysis EDA variants (G227E and Δ215–220) exhibited changes in several post-translational modifications (PTMs) compared to the WT, affecting ubiquitination, sumoylation, prenylation, N-myristoylation, palmitoylation, N-acetylation, methylation, S-nitrosylation, glycosylation, and phosphorylation. These PTM changes impacted biological processes such as apoptosis, protein stability, membrane association, protein trafficking, cell signalling etc (Fig. 6 ). Principal Component Analysis (PCA) showed distinct clustering of WT and EDA variants, with significant deviations in PTM profiles and functional properties for G227E and Δ215–220 (Fig. 6 ). 3.6 Gene Ontology Prediction on EDA Variants Gene ontology (GO) predictions for EDA variants compared to the WT were visualized using heatmap plots. These predictions covered three main categories: Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). Biological Process (BP): The heatmap indicated alterations in key processes such as cellular macromolecule biosynthetic process, regulation of nitrogen compound metabolic process, and cell surface receptor signaling pathway (Fig. 7 ). The variants exhibited different patterns compared to the WT, suggesting functional changes in these biological processes. Molecular Function (MF): Variations in molecular functions, including protein binding, kinase activity, and nucleic acid binding, were observed between the EDA variants and WT. The heat map showed distinct differences in these functions, highlighting the impact of the variants on protein interactions and enzymatic activities (Fig. 7 ). Cellular Component (CC): The subcellular localization of EDA variants showed significant differences compared to the WT (Fig. 7 ). While nuclear localization remained consistent across WT and variants, cytoplasmic expression was notably hampered in the variants. 3.7 Secondary Structure and Protein Disorder Prediction of EDA Variants The EDA WT displayed a mix of structured regions (helices and strands) and disordered segments (Fig. 8 ). High disorder probability was noted around residues 50–100 and 200–250. EDA_G227E variant showed differences in helical structures and additional disorder regions compared to the WT (Fig. 8 ). High disorder was observed around residues 50–150 and 200–250. EDA_Δ215–220 deletion variant significantly altered the secondary structure, increasing disorder around residues 50–100, 150–200, and 250–300. These changes in secondary structure and disorder regions suggest potential impacts on protein stability and function for the EDA variants (Fig. 8 ). 3.8 Effects on Hydrophobicity and Conservation of EDA Variants The hydrophobicity profile of the WT displayed a fluctuating pattern, with both hydrophobic and hydrophilic regions (Fig. 9 ). The G227E variant showed changes in the helical structure elements and an increased hydrophobicity around position 227. The Δ215–220 variant exhibited a significant alteration in hydrophobicity, particularly in the region of the deletion (positions 215–220), indicating increased hydrophilicity in the surrounding areas (Fig. 9 ). The conservation analysis across species, including human, mouse, rat, opossum, chicken, Drosophila, zebrafish, and X.tropicalis, demonstrated that the G227E Variant and the Δ215–220 deletion affected highly conserved regions of the EDA protein (Fig. 9 ). These changes suggest that the identified variants may have significant functional implications due to their impact on conserved domains, potentially disrupting protein stability and function. 3.9 MD Simulation Effects of EDA Variants Compared to WT The structural and dynamic analysis revealed distinct variations among the WT (EDA_WT ) and EDA variants ( EDA_G227E and EDA_Δ215–220 ) EDA proteins (Table 3 ) (Fig. 10 – 11 ). The RMSD was highest for EDA_WT (2.4 nm), indicating greater conformational flexibility, compared to EDA_G227E (2.18 nm) and EDA_Δ215–220 (1.55 nm). Similarly, the RMSF values were reduced in m ( EDA_G227E: 0.52 nm, EDA_Δ215–220: 0.33 nm) compared to the WT (0.68 nm), suggesting decreased atomic fluctuations. The radius of gyration (Rg) showed a reduction from EDA_WT (2.29 nm) to EDA_G227E (2.16 nm) and EDA_Δ215–220 (1.84 nm), reflecting compaction in EDA variants structures. The solvent-accessible surface area (SAS) also decreased in EDA variants, with EDA_Δ215–220 showing the lowest value (131.23 nm²) compared to EDA_WT (144.12 nm²). Interestingly, the number of intra-molecular hydrogen bonds remained relatively consistent across all variants, with minor differences. Table 3 Structural and dynamic parameters of EDA_WT and EDA_G227E and EDA_Δ215–220) EDA proteins. Gene RMSD (nm) RMSF (nm) Rg (nm) SAS (nm2) Intra-H-bond (No.) EDA_WT 2.4 0.68 2.29 144.12 163.33 EDA G227E 2.18 0.52 2.16 135.96 164.26 EDA_Δ215–220 1.55 0.33 1.84 131.23 163.12 Notes : RMSD: Indicates structural stability; RMSF: Reflects residue flexibility; Rg: Measures protein compactness; SAS: Shows surface exposure to solvent; Intra-H-bond: Number of internal hydrogen bonds. 4. Discussion In our study, we identified two novel variants in the EDA : c.680G > A (p.G227E) and c.649_666del (p. GPPGPP Δ215–220) ,; chrX:69247830_69247847delCTGGTCCTCCAGGTCCTC) . These variants were discovered in an Indian family presenting with X-linked hemizygous syndromic tooth agenesis, characterized by oligodontia, sparse hair, dry skin, and nail discoloration. Through a combination of molecular, bioinformatics, and functional analyses, we have provided evidence for the pathogenic nature of these variants and their potential impact on the development and function of ectodermal tissues. In silico analyses consistently classified both variants as deleterious, with high scores from multiple tools, including Alpha Missense and Revel, supporting their impact on EDA function (Li et al., 2008 )(Lee et al., 2024 ). The p.G227E variant introduces a glycine-to-glutamic acid substitution, resulting in a significant shift from a non-polar to a polar amino acid. This substitution occurs in a highly conserved region, as confirmed by conservation analyses across multiple species, suggesting its functional importance (Capra & Singh, 2007 )(Capr. Similarly, the Δ215–220 deletion disrupts a conserved hydrophobic region, leading to increased hydrophilicity and potential destabilization of the protein structure. Secondary structure predictions further demonstrated altered helical content and increased disordered regions in both variants, indicative of their destabilizing effects on EDA . Functional assays demonstrated that both variants led to reduced expression of EDA and associated proteins, including IκB and WNT4, key players in ectodermal development (Abu-Hussein et al., 2015 ). The reduced expression of EDA and WNT4 in both variants suggests impaired function of critical signaling molecules involved in ectodermal organ development. WNT4 plays a pivotal role in processes such as tooth morphogenesis and hair follicle formation, and its reduced levels likely contribute to the clinical features observed in the affected individuals (Xue et al., 2010 ). Interestingly, the increased expression of IκB, an inhibitor of the NF-κB pathway, aligns with the marked reduction in NF-κB mRNA levels. This imbalance highlights the disruption of the EDA - NF-κB axis, a central pathway in ectodermal tissue differentiation. The contrasting changes in IκB and NF-κB suggest that these variants may lead to an aberrant feedback mechanism within the signaling pathway. Elevated IκB levels could inhibit NF-κB activation, thereby dampening downstream gene expression required for ectodermal development. This disruption might explain the observed clinical features, such as oligodontia and ectodermal abnormalities (Gao et al., 2023 ). These findings provide insights into the molecular mechanisms underlying the clinical features observed in affected individuals, including tooth agenesis and ectodermal dysplasia. Immunofluorescence studies revealed that the cytoplasmic localization of EDA was significantly impaired in the p.G227E and Δ215–220 variants, while nuclear localization remained unaffected. In silico analyses corroborated these findings, with subcellular localization patterns distinctly separating WT and variant EDA proteins. Additionally, PTM analysis identified alterations in multiple modifications, including ubiquitination, glycosylation, phosphorylation etc., which may contribute to disrupted protein stability, trafficking, and signaling in the (Audagnotto & Dal Peraro, 2017 ; Ranjan & Das, 2023 ) Gene ontology analysis revealed significant differences in biological processes, molecular functions, and cellular components between WT and EDA variants. Altered processes included macromolecule biosynthesis, nitrogen metabolism, and receptor signaling, which align with the functional disruption observed in ectodermal tissues. Variations in protein binding and kinase activity further emphasize the widespread effects of these EDA variants on molecular pathways critical for ectodermal development. Comparing the EDA variants to the WT, molecular dynamics (MD) simulations provide light on their structural and dynamic characteristics. In contrast to the G227E (2.18 nm) and Δ215–220 (1.55 nm) variants, the WT (2.4 nm) showed a larger structural deviation, according to the Root Mean Square Deviation (RMSD) values, indicating greater stability in the EDA Variants forms. The WT EDA (0.68 nm) exhibited greater flexibility in some places, according to the Root Mean Square Fluctuation (RMSF) study, than the G227E (0.52 nm) and Δ215–220 (0.33 nm) variants, which had less fluctuation and hence more stiffness. Changes in functional dynamics might be the cause of the variations' greater stiffness. In contrast to the G227E (2.16 nm) and Δ215–220 (1.84 nm) variants, which showed more compact structures, the Radius of Gyration (Rg) values for EDA_WT (2.29 nm) were greater, suggesting a more stretched conformation. Changes in the protein's general folding and stability may be reflected in this compaction. In comparison to the G227E (135.96 nm²) and Δ215–220 (131.23 nm²) variants, the EDA_WT (144.12 nm²) variant has a higher solvent-exposed surface area, as shown by the Solvent Accessible Surface Area (SAS). The variations' interactions with other molecules may be impacted by reduced SAS. The number of intramolecular hydrogen bonds (Intra-H bond) was similar across all variants, with EDA_WT (163.33), G227E (164.26), and Δ215–220 (163.12). This suggests that while overall hydrogen bonding remains relatively constant, the specific interactions and stability of the protein could still be impacted by the variants. Molecular dynamics simulations showed that the p.G227E and p.Δ215–220 variants exhibited reduced flexibility and compact structures compared to the WT, indicating increased rigidity and potential functional compromise. These differences highlight the impact of the EDA Variants on the protein's structural and functional properties. Changes in hydrophobicity profiles and the impact on conserved regions further substantiate the deleterious nature of these variants, with likely effects on protein folding and stability (Higgs et al., 2008 ). Interestingly, neither variant significantly affected mitochondrial or calcium potential, suggesting that the pathogenic effects of these variants are more likely mediated through disrupted signaling and protein function rather than alterations in these cellular processes. 5. Conclusion This study provides robust evidence for the pathogenicity of two novel EDA variants, p.G227E and Δ215–220, in a case of X-linked ectodermal dysplasia with syndromic tooth agenesis. The integration of bioinformatics predictions, molecular studies, and functional analyses highlights the diverse mechanisms through which these variants disrupt EDA function, ultimately impairing ectodermal tissue development. Overall, the MD simulation results suggest that the EDA_WT variant has greater structural deviation, flexibility, size, stability, and solvent exposure compared to the G227E and Δ215–220 variants. These findings contribute to our understanding of EDA -associated disorders and underscore the need for further studies to explore potential therapeutic strategies targeting these variants. Abbreviations CTA Congenital Tooth Agenesis Wild Type WT PTMs Protein Translation Modifications MD Molecular Dynamics Rg Radius of Gyration RMSD Root Mean Square Deviation RMSF Root Mean Square Fluctuation PCA Principal Component Analysis GO Gene Ontology WT Wild Type Δ Deletion Declarations 6. Acknowledgement We gratefully acknowledge the Indian Council of Medical Research (ICMR) for providing the Senior Research Fellowship (SRF) to PR and CD. 7. Author Contribution PR conceived the research idea, conducted experiments, performed bioinformatics and data analysis, and wrote the manuscript. CD contributed to data analysis. VS was responsible for identifying CTA patients and conducting OPG analysis. RB and VKS carried out clinical investigations. PD co-conceived the research idea and approval of final draft. 9. Competing interests: The authors declare no conflict of interest. 10. 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PLoS ONE, 5(6), e11290 Yan M, Wang L-C, Hymowitz SG, Schilbach S, Lee J, Goddard A, de Vos AM, Gao W-Q, Dixit VM (2000) Two-amino acid molecular switch in an epithelial morphogen that regulates binding to two distinct receptors. Science 290(5491):523–527 Ye J, McGinnis S, Madden TL (2006) BLAST: improvements for better sequence analysis. Nucleic Acids Res 34(suppl2):W6–W9 Zhao Q, Xie Y, Zheng Y, Jiang S, Liu W, Mu W, Liu Z, Zhao Y, Xue Y, Ren J (2014) GPS-SUMO: a tool for the prediction of sumoylation sites and SUMO-interaction motifs. Nucleic Acids Res 42(W1):W325–W330 Additional Declarations The authors declare no competing interests. Supplementary Files GraphicalAbstract.png Graphical Abstract Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5743160","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":396255649,"identity":"f4699765-0f1f-489e-a7a7-dc9195a28e1e","order_by":0,"name":"Prashant Ranjan","email":"","orcid":"https://orcid.org/0000-0003-4479-4503","institution":"Institute of Sciences., Banaras Hindu University","correspondingAuthor":false,"prefix":"","firstName":"Prashant","middleName":"","lastName":"Ranjan","suffix":""},{"id":396255650,"identity":"1eea305d-0a9f-431b-958e-19e192544313","order_by":1,"name":"Chandra Devi","email":"","orcid":"https://orcid.org/0000-0002-6421-2171","institution":"Institute of Sciences, Banaras Hindu University","correspondingAuthor":false,"prefix":"","firstName":"Chandra","middleName":"","lastName":"Devi","suffix":""},{"id":396256209,"identity":"7e5a48e3-5662-455a-ac42-75c95cd241df","order_by":2,"name":"Rajesh Bansal","email":"","orcid":"","institution":"IMS, Banaras Hindu University","correspondingAuthor":false,"prefix":"","firstName":"Rajesh","middleName":"","lastName":"Bansal","suffix":""},{"id":396256210,"identity":"2b960f77-4c84-4ffa-8df6-31da71245f63","order_by":3,"name":"Vandita Srivastava","email":"","orcid":"","institution":"IMS, Banaras Hindu University","correspondingAuthor":false,"prefix":"","firstName":"Vandita","middleName":"","lastName":"Srivastava","suffix":""},{"id":396256211,"identity":"55c38b30-2267-4ec9-aa23-ab69bbd87e2f","order_by":4,"name":"Vinay Kumar Srivastava","email":"","orcid":"","institution":"IMS, Banaras Hindu University","correspondingAuthor":false,"prefix":"","firstName":"Vinay","middleName":"Kumar","lastName":"Srivastava","suffix":""},{"id":396256212,"identity":"a7289cd3-ee77-4fef-b30a-0cfddc32339d","order_by":5,"name":"Parimal Das","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYJCCAwwMFnIGEhAOD7FaJIxJ0wIEEokbJIhVa85+9uGhGzUS6dulewwYftQwyJgT0mLZk25wOOeYRO7OOWcMGHuOMfBYNhDQYnAgjeFwDptE7oYbOQYMvA0MPAYHCGk5/wyo5Z9EugFQC+NforTcANqS2yaRANLCTJQtljOAtuT2SRjunJFWcFjmmARhLeb8acyfc77ZyJtLJG98+KbGxp6ww5A5QMVExI4BYSWjYBSMglEw4gEAksE8pKYevDoAAAAASUVORK5CYII=","orcid":"","institution":"Institute of Sciences., Banaras Hindu University","correspondingAuthor":true,"prefix":"","firstName":"Parimal","middleName":"","lastName":"Das","suffix":""}],"badges":[],"createdAt":"2024-12-31 17:03:09","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5743160/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5743160/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72909533,"identity":"497fac7f-bc9e-4f71-9b84-5b3c2caf261c","added_by":"auto","created_at":"2025-01-03 14:31:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":611403,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of Novel Variants in the EDA Gene Associated with Dental Anomalies.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Pedigree chart showing the inheritance pattern of dental anomalies in the family. The proband (III-1) is indicated with an arrow. \u003cstrong\u003e(B)\u003c/strong\u003e Clinical observations of the proband, including multi-missing teeth, sparse hairs, dry skin, and nail discoloration. \u003cstrong\u003e(C)\u003c/strong\u003eOrthopantomogram (OPG) image of the proband showing multiple missing teeth (indicated by asterisks). \u003cstrong\u003e(D)\u003c/strong\u003e Pictorial representation of the missing teeth in the patient, highlighting the specific teeth absent in both the upper and lower jaws. \u003cstrong\u003e(E)\u003c/strong\u003e Forward sequencing of the EDA gene in the patient revealing a novel hemizygous change, c.680G\u0026gt;A, p.G227E, resulting in a nonpolar to polar amino acid change from Glycine to Glutamic acid.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5743160/v1/39345cf0b654d72bba8d212e.jpg"},{"id":72909532,"identity":"50879187-5c86-4395-bfad-b0d2e2329835","added_by":"auto","created_at":"2025-01-03 14:31:22","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":625493,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of a Novel Variant in the EDA Gene Associated with CTA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Pedigree chart displaying the family inheritance pattern of dental anomalies. The proband (III-1) is marked with an arrow. \u003cstrong\u003e(B)\u003c/strong\u003eClinical observations of the proband, including multiple missing teeth, sparse hairs, dry skin, and discolored nails. \u003cstrong\u003e(C)\u003c/strong\u003e Pictorial representation of the missing teeth in the proband, highlighting the absence of multiple teeth in both upper and lower jaws (marked in black). \u003cstrong\u003e(D)\u003c/strong\u003e Orthopantomogram (OPG) image of the proband showing multiple missing teeth (indicated by asterisks). \u003cstrong\u003e(E)\u003c/strong\u003eElectropherogram showing a novel variant in the EDA gene. The variant is a deletion of 18 nucleotides at exon 4 (c.649_666del), resulting in a truncated protein (p.215_220del).\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5743160/v1/e54ae1e1c8606ad7c22aa935.jpg"},{"id":72909535,"identity":"48f45c68-1876-4c36-b536-2e30319294c7","added_by":"auto","created_at":"2025-01-03 14:31:22","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":211951,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. Western Blot Analysis:\u003c/strong\u003e This panel shows the protein expression levels of EDA, IκB, and WNT4 in WT and EDA variants (G227E and Δ215-220). The expression levels were assessed using Western blot analysis. The molecular weight (kDa) of each protein band is indicated on the right side of the image. \u003cstrong\u003eB. Densitometry Analysis:\u003c/strong\u003e This panel presents the densitometry analysis of the Western blot bands shown in panel A. The relative protein expression levels of EDA, IκB, and WNT4 are shown as normalized values, with the WT sample set as the control. Statistical significance between the WT and EDA variant samples is indicated by asterisks (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001). \u003cstrong\u003eC. RT-PCR Analysis of NF-κB:\u003c/strong\u003e This panel shows the relative mRNA expression levels of NF-κB in the WT and EDA variant samples. The expression levels were measured using RT-PCR and normalized to a housekeeping gene (\u003cem\u003e\u003cstrong\u003eB-Actin\u003c/strong\u003e\u003c/em\u003e). Statistical significance between the WT and EDA variant samples is indicated by asterisks (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5743160/v1/2b9e8ba3c9434d1ecf47a2a6.jpg"},{"id":72909537,"identity":"2d6d2160-46a5-4534-9695-fbe26fdbbdff","added_by":"auto","created_at":"2025-01-03 14:31:22","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":198603,"visible":true,"origin":"","legend":"\u003cp\u003eFlow cytometry analysis of calcium and mitochondrial potential in \u003cem\u003eEDA\u003c/em\u003evariants. (A) Calcium potential was measured using B525-FITC-A fluorescence intensity in various cell lines: \u003cem\u003eEDA\u003c/em\u003e WT (green), EDA G227E (pink), and EDAΔ215-220 (orange). (B) Quantification of calcium potential (SD B525-FITC-A) shows no significant difference among the EDA WT, G227E, and Δ215-220 variants. (C) Mitochondrial potential was assessed using Y585-PE-A fluorescence intensity in the COS7 cell lines. (D) Quantification of mitochondrial potential (SD Y585-PE-A) also reveals no significant differences between EDA WT, G227E, and Δ215-220 variants. Data are represented as mean ± SD, and statistical significance is indicated (ns: not significant).\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5743160/v1/7afc6de2f9b32922b57e9a2c.jpg"},{"id":72909538,"identity":"b6b8443b-67bb-47a2-af26-a9ef395e8cd9","added_by":"auto","created_at":"2025-01-03 14:31:22","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":612827,"visible":true,"origin":"","legend":"\u003cp\u003eImmunofluorescence analysis of EDA expression in COS7 cell lines. \u003cstrong\u003e(A)\u003c/strong\u003e \u003cstrong\u003eSubcellular Localization:\u003c/strong\u003eImages showing the subcellular localization of the EDA protein under different conditions. The first column shows the nucleus stained with DAPI (blue), the second column shows EDA antibody staining (red), and the third column presents merged images. The rows represent different conditions: Empty Vector, WT, EDA p.G227E, and EDA Δ215-220. The scale bar represents 20 μm. \u003cstrong\u003e(B)\u003c/strong\u003e \u003cstrong\u003eEnlarged Cell Captured Showing Nuclear Localization:\u003c/strong\u003e Enlarged images of cells highlighting the nuclear localization of the EDA protein in and EDA variants conditions (EDA p.G227E and EDA Δ215-220). White arrows indicate nuclear localization. \u003cstrong\u003e(C)\u003c/strong\u003e \u003cstrong\u003ePCA Plot Based on In Silico Subcellular Localization:\u003c/strong\u003e Principal Component Analysis (PCA) plot representing the subcellular localization data of EDA protein variants. PC1 explains 94.88% of the variance, and PC2 explains 5.12%. The plot includes data points for EDA_WT, EDA_G227E, and EDA_Δ215-220. \u003cstrong\u003eConfocal Microscopy 63X\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5743160/v1/91dc793fb543fc5937017c18.jpg"},{"id":72909542,"identity":"a84fb14c-3eae-4dce-ae1a-41e2f166bd31","added_by":"auto","created_at":"2025-01-03 14:31:22","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":544126,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of Post-Translational Modifications (PTMs) in EDA Variants. \u003c/strong\u003eThe heatmap, \u003cstrong\u003e(A)\u003c/strong\u003e indicates that different PTMs are associated with various functional roles in the EDA protein, and these associations vary among the EDA variants. The bar graph \u003cstrong\u003e(B)\u003c/strong\u003e shows that the EDA WT generally has a higher number of PTMs compared to the EDA Δ215-220 and EDA G227E variants, suggesting that these variants may lead to a reduction in PTM occurrences. The PCA plot \u003cstrong\u003e(C)\u003c/strong\u003e further supports this by showing distinct clustering of the EDA WT from the EDA variants, indicating that the PTM profiles of the EDA Δ215-220 and EDA G227E variants are significantly different from the WT. This suggests that these variants may impact the functional roles of the EDA protein by altering its PTM landscape. These findings highlight the potential functional implications of PTM alterations in the EDA variants, which may contribute to the phenotypic differences observed in congenital tooth agenesis. Further studies are needed to fully understand.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5743160/v1/eddf667519c423ed9ec0e549.jpg"},{"id":72909566,"identity":"9f921789-bc58-41aa-8237-1456ee9e904d","added_by":"auto","created_at":"2025-01-03 14:31:24","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":793699,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene Ontology (GO) Analysis of EDAVariants\u003c/strong\u003e. This figure presents the results of Gene Ontology (GO) enrichment analysis for EDA WT, EDA_G227E_GO, and EDA_Δ215-220_GO variants. The heatmaps display the top enriched GO terms in three categories: Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). The color gradient represents the enrichment score, with darker colors indicating higher scores. The results highlight the varying impacts of the EDA variants on biological processes, molecular functions, and cellular components, suggesting potential alterations in gene regulation mechanisms and protein interactions in congenital tooth agenesis.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5743160/v1/7d1ff25e12bcc7d30e55b38f.jpg"},{"id":72909541,"identity":"ca75177d-e50a-42da-b81b-828989168f31","added_by":"auto","created_at":"2025-01-03 14:31:22","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":832041,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of Secondary Structure and Disorder Prediction for EDA Variants. EDA_WT (Panel A and E):\u003c/strong\u003e The EDA WT shows a mix of structured and disordered regions, with several helices and strands interspersed with coils and disordered segments. The DISOPRED3 plot indicates regions of high disorder probability, particularly around residues 50-100 and 200-250. \u003cstrong\u003eEDA_G227E (Panel B and F):\u003c/strong\u003e The G227E variant shows difference in helical structure elements to the WT and also with some differences in the predicted disordered regions. The DISOPRED3 plot highlights additional regions of disorder compared to the WT, particularly around residues 50-150 and 200-250. \u003cstrong\u003eEDA_Δ215-220 (Panel C and G):\u003c/strong\u003e The Δ215-220 variant shows a notable change in the secondary structure prediction, with the deletion affecting the surrounding regions. The DISOPRED3 plot indicates significant disorder around residues 50-100, 150-200, and 250-300, suggesting that the deletion increases the overall disorder in the protein.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5743160/v1/b46fe6d80a312df9b2e8c312.jpg"},{"id":72910687,"identity":"825c82de-8b3b-4c37-873b-b45cb1990f85","added_by":"auto","created_at":"2025-01-03 14:39:23","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":914232,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHydropathicity and Conservation Analysis of EDA Variants. \u003c/strong\u003eThe image presents the hydropathicity profiles and conservation analysis of different EDA protein variants. Panels A-C display the hydropathicity scores for WT and EDA variantsproteins, highlighting the changes due to specific variants. Panels D-E show the conservation of these variants across different species, indicating the evolutionary importance of the EDA variants regions. This analysis helps understand the structural and functional impacts of these EDA variants on the EDA protein.\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5743160/v1/40f61444a4818e32245e0cae.jpg"},{"id":72909562,"identity":"2ec465cd-d7c7-43fa-9083-90a03fcb8986","added_by":"auto","created_at":"2025-01-03 14:31:23","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":987490,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular Dynamics (MD) Simulation Results for EDA_WT, EDA_G\u0026gt;A (EDA_G227E ), and EDA_DEL Variants (EDA_Δ215-220). (A) Root Mean Square Deviation \u003c/strong\u003e(RMSD) over time (ns). The RMSD values for EDA_WT (blue), EDA_G\u0026gt;A (red), and EDA_DEL (green) are plotted, showing the structural stability of each variant over the simulation period. EDA_DEL exhibits the lowest RMSD, indicating higher stability compared to EDA_WT and EDA_G\u0026gt;A. \u003cstrong\u003e(B) Root Mean Square Fluctuation \u003c/strong\u003e(RMSF) per residue. The RMSF values for EDA_WT (blue), EDA_G\u0026gt;A (red), and EDA_DEL (green) are plotted, indicating the flexibility of residues in each variant. EDA_WT shows higher fluctuations around residues 200-250, suggesting increased flexibility in this region. \u003cstrong\u003e(C) Radius of Gyration \u003c/strong\u003e(Rg) over time (ps). The Rg values for EDA_WT (blue), EDA_G\u0026gt;A (red), and EDA_DEL (green) are plotted, showing the compactness of each variant. EDA_DEL maintains the lowest Rg, indicating a more compact structure. \u003cstrong\u003e(D) Number of Hydrogen Bonds \u003c/strong\u003eover time (ps). The number of hydrogen bonds for EDA_WT (blue), EDA_G\u0026gt;A (red), and EDA_DEL (green) are plotted, showing the stability of hydrogen bonding in each variant. All variants show a similar trend with EDA_DEL having slightly more hydrogen bonds. \u003cstrong\u003e(E) Solvent Accessible Surface Area \u003c/strong\u003e(SAS_Area) over time (ps). The SAS_Area values for EDA_WT (blue), EDA_G\u0026gt;A (red), and EDA_DEL (green) are plotted, indicating the exposure of each variant to the solvent. EDA_DEL has the lowest SAS_Area, suggesting less solvent exposure and a more stable structure.\u003c/p\u003e","description":"","filename":"Figure10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5743160/v1/d469dbc1628314fe0c4e3149.jpg"},{"id":72909545,"identity":"81719e01-d80e-4678-b673-af6fafe2ae03","added_by":"auto","created_at":"2025-01-03 14:31:23","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":366784,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular Dynamics Simulation Analysis of EDA Protein Variants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) Heatmap of RMSD and Rg for Protein Variants:\u003c/strong\u003e This heatmap displays the Root Mean Square Deviation (RMSD) and Radius of Gyration (Rg) values for three EDA protein variants: EDA_WT, EDA_G227E, and EDA_Δ215-220. The RMSD and Rg values are color-coded, with higher values in red and lower values in blue. EDA_WT shows the highest RMSD and Rg values, indicating greater structural deviation and compactness, while EDA_Δ215-220 shows the lowest values, suggesting a more stable and less compact structure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B) RMSF Analysis:\u003c/strong\u003e The Root Mean Square Fluctuation (RMSF) values for the three EDA protein variants are shown in a heatmap. EDA_WT has the highest RMSF value (0.68 nm), indicating greater flexibility, while EDA_Δ215-220 has the lowest RMSF value (0.33 nm), suggesting reduced flexibility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(C) SAS and Intra-Hbond Analysis:\u003c/strong\u003e This heatmap illustrates the Solvent Accessible Surface (SAS) area and the number of intra-hydrogen bonds for the EDA protein variants. EDA_G227E has the highest SAS (164.26 nm²) and intra-hydrogen bonds (164.26), indicating a more exposed and hydrogen-bonded structure. EDA_Δ215-220 has the lowest SAS (131.23 nm²) and intra-hydrogen bonds (163.12), suggesting a more compact and less hydrogen-bonded structure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(D) PCA Analysis of Protein Variants:\u003c/strong\u003e Principal Component Analysis (PCA) plot showing the clustering of the three EDA protein variants based on their structural properties. The first principal component (PC1) accounts for 79.7% of the variance, while the second principal component (PC2) accounts for 20.3% of the variance. EDA_WT and EDA_Δ215-220 are well-separated, indicating distinct structural differences, while EDA_G227E is positioned between them, suggesting intermediate structural properties.\u003c/p\u003e","description":"","filename":"Figure11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5743160/v1/6ca6cbb6989e8f89197056c4.jpg"},{"id":72912085,"identity":"108c8ed2-6ea4-4ef9-9251-bc551602b5d9","added_by":"auto","created_at":"2025-01-03 14:55:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8003444,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5743160/v1/820eb1fa-6371-4f2e-ba0d-85f191502ce5.pdf"},{"id":72909534,"identity":"3fffc4c0-9c67-4076-af59-b3980571e4e5","added_by":"auto","created_at":"2025-01-03 14:31:22","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1251293,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical Abstract\u003c/p\u003e","description":"","filename":"GraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-5743160/v1/02b50aa996c414d434a6a152.png"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eNovel Ectodysplasin-A Variants: Structural and Functional Basis of Hypohidrotic Ectodermal Dysplasia\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe term \"ectodermal dysplasia\" (ED) describes a group of inherited disorders characterized by anomalies in the growth of tissues that start in the ectoderma (Wright et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Hypohidrotic ectodermal dysplasia (HED) is the most common subtype among the more than 200 clinical variants of ED that have been identified, with a frequency of around 1 in 5,000 to 10,000 newborns (Anbouba et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Among the three characteristics of HED are congenital tooth loss (hypodontia and oligodontia), reduced sweating (hypohidrosis), and poor hair growth (hypotrichosis). According to (Wright et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), secondary symptoms include dry eyes, periorbital hyperpigmentation, and dry, fragile-looking skin conditions.\u003c/p\u003e \u003cp\u003eHED may be inherited in an X-linked, autosomal dominant, or autosomal recessive manner. Mutations in the \u003cem\u003eEDA\u003c/em\u003e gene on the X chromosome cause the X-linked form of HED (XL-HED), which accounts for more than half of all cases (Cluzeau et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Kere et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Nguyen-Nielsen et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The \u003cem\u003eEDA\u003c/em\u003e gene encodes ectodysplasin A (EDA), a critical signaling protein that governs ectoderm-mesoderm interactions throughout development. These interactions are necessary for the development of placodes, which give rise to ectodermal characteristics like as skin, hair, nails, teeth, and sweat glands (Sima et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe extracellular region of EDA, a type II transmembrane protein, has a collagen domain and a tumor necrosis factor (TNF) homology domain (THD) (Elomaa et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). To create a soluble ligand, the protein is cleaved at a furin protease site. EDA-A1 and EDA-A2, which make up around 80% of the total EDA protein, are the most common isoforms produced via alternative splicing of EDA (Hashimoto et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVal307-Glu308, a two-amino acid motif unique to EDA-A1, is the difference between these isoforms (Yan et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Both isoforms structurally produce trimers made of β-sandwich monomers that are jelly-rolls. Although EDA-A1 and EDA-A2 have structural similarities, they have different receptor binding specificities (Hymowitz et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Through the cysteine-rich domains (CRDs) of the corresponding receptors and the THDs of EDA, EDA-A1 binds to EDAR and EDA-A2 connects with XEDAR. The NF-κB pathway is activated by both receptors, although their intracellular domains, extracellular areas, and signaling pathways differ (Kowalczyk-Quintas \u0026amp; Schneider, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Other important pathways, including as BMP, FGF, and SHH, are essential for tooth growth in along with Wnt signalling (Aurrekoetxea et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pakvasa et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Together with EDAR, EDA-A1 is essential for the development, morphogenesis, and differentiating of ectodermal organs. On the other hand, recent research clearly suggests that the EDA-A1/EDAR axis is the principal contribution to HED pathogenesis, raising doubts about the functional significance of EDA-A2 and XEDAR in HED (Lefebvre \u0026amp; Mikkola, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we identified two novel variants in the \u003cem\u003eEDA\u003c/em\u003e associated with syndromic congenital tooth agenesis (CTA), observed in one familial case and one sporadic case. These variants were found to result in diverse clinical phenotypes through distinct molecular mechanisms involving exon 4 of the \u003cem\u003eEDA\u003c/em\u003e. To evaluate their pathogenicity, we conducted in vitro and in silico functional analyses focusing on EDA signaling pathways.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Recruitment of participants\u003c/h2\u003e \u003cp\u003e The Declaration of Helsinki's ethical guidelines were followed in this work. All participants, or in the case of young ones, their parents or legal guardians, provided written informed consent. The Ethics Committee of the Institute of Science at Banaras Hindu University in India examined and approved the study procedure. Participants comprised 20\u0026ndash;30-year-olds with full dental records and radiographic imaging who had been diagnosed with CTA. Ectodermal organs including skin, hair, nails, and teeth were evaluated during clinical tests. Interviews were conducted at the Department of Dentistry and Oral Surgery, Institute of Medical Sciences, to gather information on congenital abnormalities such as sweating and heat tolerance. All participants or their legal guardians provided written consent. The affected individuals also had 5 ml peripheral venous blood samples taken in EDTA vials for further examination.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Identification of Variants\u003c/h2\u003e \u003cp\u003eSamples of peripheral venous blood were used to isolate genomic DNA. Using polymerase chain reaction (PCR), all coding exons, exon-intron boundaries region of \u003cem\u003eEDA\u003c/em\u003e was amplified. Primers, PCR conditions, and the DNA extraction procedure were all carried out as previously mentioned (Ranjan et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). After PCR products were processed with exonuclease I and recombinant Shrimp Alkaline Phosphatase (rSAP) (USB Affimetrix, USA), they were labeled using the ABI Big Dye Terminator V3.1 cycle sequencing kit for Sanger sequencing. Following the manufacturer's instructions, capillary electrophoresis and automated base calling were carried out on an ABI 3130 Genetic Analyzer, and the results were examined using Sequencing Analysis Software V5.2 (Applied Biosystems, USA). Using the NCBI Basic Local Alignment Search Tool (BLAST) (Ye et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), sequences were compared to the GenBank DNA sequence database maintained by the National Center for Biotechnology Information (NCBI).\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.3 Pathogenicity Prediction\u003c/b\u003e: Several in silico methods were used to estimate the pathogenicity of the EDA p.G227E and EDA p.Δ215\u0026ndash;220 variants (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These instruments examine genetic variations to forecast how they could affect protein function and the correlation between diseases. Revel, Alpha Missense, Eve, MUT Assessor, SIFT, MT, FATHMM, DANN, MetaLR, PrimateAI, BayesDel, and GenoCanyon were among the tools utilized. By integrating characteristics such as evolutionary conservation, biochemical characteristics, sequence context, and functional annotations, these techniques together provide a thorough evaluation of the pathogenic potential of the EDA variations (Garcia et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Richards et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://franklin.genoox.com/clinical-db/home\u003c/span\u003e\u003cspan address=\"https://franklin.genoox.com/clinical-db/home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePathogenicity predictions of \u003cem\u003eEDA\u003c/em\u003e variants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e Variant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTool\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrediction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep.G227E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRevel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeleterious (Strong)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep.G227E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlpha Missense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeleterious (Strong)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep.G227E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeleterious (Low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep.G227E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMUT Assessor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeleterious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep.G227E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSIFT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeleterious (Supporting)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep.G227E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeleterious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep.G227E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFATHMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeleterious (Moderate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep.G227E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDANN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeleterious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep.G227E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetaLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeleterious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep.G227E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimate AI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeleterious (Moderate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep.G227E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBayesDel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeleterious (Strong)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep.G227E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeno Canyon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeleterious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep.Δ215\u0026ndash;220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRevel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeleterious (Strong)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep.Δ215\u0026ndash;220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlpha Missense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeleterious (Strong)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep.Δ215\u0026ndash;220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeleterious (Moderate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep.Δ215\u0026ndash;220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMUT Assessor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeleterious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep.Δ215\u0026ndash;220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSIFT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeleterious (Supporting)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep.Δ215\u0026ndash;220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeleterious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep.Δ215\u0026ndash;220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFATHMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeleterious (Moderate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep.Δ215\u0026ndash;220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDANN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeleterious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep.Δ215\u0026ndash;220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetaLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeleterious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eHigher scores indicate a stronger prediction that the variant is harmful, while lower scores indicate less confidence in its harmful effect.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Site Directed Mutagenesis (SDM)\u003c/h2\u003e \u003cp\u003eSDM used to investigate the c.680G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.G227E) and c.649_666del (p.215_220del) mutations in Exon 4 of the \u003cem\u003eEDA\u003c/em\u003e. A human \u003cem\u003eEDA\u003c/em\u003e cDNA ORF clone, generously gifted by Pascal Schneider of the Department of Biochemistry, University of Lausanne, CH-1066 Epalinges, Switzerland, was modified to include these alterations. PCR3.1 was the plasmid vector that was employed. The DH5α competent cells of E. coli were created using the wild-type (WT) plasmid construct. Following plasmid isolation and double digestion verification, glycerol stocks were made for next research. The Agilent Quick Change\u0026reg; Primer Design Program was used to create SDM primers, which were based on the Homo sapiens \u003cem\u003eEDA\u003c/em\u003e mRNA sequence (Gen Bank accession number: NP_001390.1). These primers were designed to include the particular deletion and nonsynonymous mutations. They were utilized exactly as supplied after being commercially produced by Eurofins Scientific. The primer sequences were as follows: Anti-Sense Mutation in G680A: 5'-gaggccagggggttbgaggaccagg-3' 5'-cctggtcctcaagaaccccctggcctc-3' is the sense. c.649_666 Anti-Sense deletion: 5'-tccttgaggaccaggtggtcccataacagttg-3' 5'-caactgttatgggaccacctggtcctcaagga is the sense.-3\u0026rdquo;. The c.680G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.G227E) and c.649_666del (p.215_220del) mutations were mutagenesis-introduced into the \u003cem\u003eEDA\u003c/em\u003e using the Quick Change II Site-Directed Mutagenesis Kit (Agilent, #200524). The kit's instructions were followed to make the reaction mix, and ideal cycling circumstances were used. DpnI digestion was carried out to break down the parental DNA after amplification. The mutated product was converted into competent E. Coli DH5α cells. After screening the colonies using Hind III and EcoRI double digestion, Sanger sequencing was done. Glycerol stocks of validated plasmids with the desired mutations were kept for use in further studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Procurement and Maintenance of Cell Line\u003c/h2\u003e \u003cp\u003e DMEM (Sigma-Aldrich) with 10% FBS (HiMedia) and an antibiotic mixture (100 \u0026micro;g/mL Streptomycin, 100 U/mL Penicillin) were used to cultivate the COS-7 cell line, which was obtained from the National Centre for Cell Science (NCCS), Pune, India. Before being kept in liquid nitrogen, the cells were cryopreserved in FBS or DMEM with 10% DMSO, routinely passaged, and kept at 37\u0026deg;C with 5% CO₂.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Transfection Using CaCl₂-Phosphate Method\u003c/h2\u003e \u003cp\u003eCOS-7 cells were transfected at 70\u0026ndash;80% confluency after being plated in 6-well plates at a density of 1 \u0026times; 10⁶ cells per well. Drop by drop, the cells were exposed to the transfection mixture, which contained 2 \u0026micro;g of plasmid DNA, 2.5 M CaCl₂, and 2X HEPES buffer. After 16 hours, a glycerol shock was administered to increase the efficiency of transfection. For further research, cells were extracted 48\u0026ndash;72 hours after transfection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Western Blotting\u003c/h2\u003e \u003cp\u003eThe CaCl₂-phosphate technique was used to transfect COS-7 cells. Cells were collected after 72 hours, and RIPA buffer was used to extract the proteins. Bradford's test was used to assess the protein content. After being denatured, proteins were separated using SDS-PAGE and then transferred to a PVDF membrane. An ECL reagent kit (Thermofisher catlog: A38555) was used to view the membrane after it had been blocked with 5% non-fat dried milk in TBST, treated with primary antibodies for the whole night, and then incubated with secondary antibodies. As needed, blots were removed and re-probed. EDA rabbit polyclonal antibody (Bioss, Catalog # BS-12347R), Wnt4 rabbit polyclonal antibody (ABclonal, #A7809), IK β (Cat. No. GTX110521), and β-actin mouse monoclonal antibody (Sigma Aldrich: A5441) were the main antibodies utilized in the investigations. The tests were conducted twice. Goat anti-mouse IgG-HRP (GeNei) and goat anti-rabbit IgG-HRP (Santa Cruz Biotechnology) were the primary antibody species' suitable horseradish peroxidase-conjugated secondary antibodies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Subcellular Localization of EDA Variants\u003c/h2\u003e \u003cp\u003eCOS-7 cells were grown till 70\u0026ndash;80% confluency in DMEM supplemented with 10% FBS. Plasmids encoding the Empty Vector, WT EDA, EDA p.G227E, or EDA Δ215\u0026ndash;220 were then used to transfect them. The cells were frozen, permeabilized, and blocked overnight after transfection. Primary EDA antibodies and secondary antibodies conjugated to a fluorescent dye (Alexa floura-568 Thermofisher Cat # A-11011) were used for immunofluorescence staining. DAPI was used to stain the nuclei. Images of the nucleus (blue) and EDA antibody staining (red) were taken using Carl Zeiss confocal microscopy 2012 (63 X), and the merged images showed subcellular localization. All variations showed nuclear localization in enlarged pictures of certain cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Measurements of Intracellular Ca\u003csup\u003e2+\u003c/sup\u003e Level\u003c/h2\u003e \u003cp\u003eCOS-7 cells were seeded in 6-well plates at a density of 1 \u0026times; 10⁶ cells per well, and then cultured in DMEM supplemented with 10% FBS (HiMedia) at 37\u0026deg;C with 5% CO₂. The cells were incubated until they reached 70\u0026ndash;80% confluence. EDA WT and EDA Variants plasmids were transfected using the CaCl₂-phosphate method. Following transfection, cells were removed, washed with PBS, and treated with 5 \u0026micro;M Fluo-3/AM dye for 30 to 1 hour at 37\u0026deg;C in the dark. Intracellular Ca2\u0026thinsp;+\u0026thinsp;levels were measured using a flow cytometer (CytoFLEX LX, Beckman Coulter). Every experiment was conducted in three duplicates. CytExpert software was used to analyze fluorescence intensity data in order to compare and quantify calcium levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Mitochondrial membrane potential measurement:\u003c/h2\u003e \u003cp\u003eCOS-7 cells were grown at a density of 1 \u0026times; 10⁶ cells per well in 6-well plates using DMEM supplemented with 10% FBS. After that, they were maintained at 37\u0026deg;C with 5% CO₂ in an incubator until they reached 70\u0026ndash;80% confluency. Following transfection with the EDA WT and EDA Variants plasmids, cells were grown for 48 hours. After being harvested, the cells were washed with 1X PBS and stained with 150 nM TMRM (tetramethylrhodamine, methyl ester) dye for 30 minutes at 37\u0026deg;C in the dark. The mitochondrial membrane potential was ascertained by measuring the orange fluorescence intensity of TMRM using a flow cytometer (FACS Calibur, CytoFLEX LX, Beckman Coulter). Using fluorescence data, mitochondrial membrane potential was compared and measured using the CytExpert program (Beckman Coulter). Every experiment was conducted in three duplicates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Evolutionary Conservation Analysis\u003c/h2\u003e \u003cp\u003eThe ConSurf web server was used to perform evolutionary conservation analysis (Glaser et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The top 150 homologous sequences for the EDA protein were found using PSI-BLAST. ConSurf used Bayesian inference to determine conservation scores, which varied from 1 (variable locations) to 9 (highly conserved sections). The evolutionary conservation of the amino acid residues in the EDA protein was shown by this technique.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.12 Structural Analysis, Modeling, and Validation\u003c/h2\u003e \u003cp\u003eAlphaFold was used to obtain the EDA protein's three-dimensional structures (Jumper et al., 2021). Using AlphaFold templates (EDA AF-Q92838-F1-v4) on the Swiss-Model website, mutant models were produced by homology modelling (Kiefer et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). To guarantee structural integrity, energy reduction was carried out using the \"steepest descent\" approach using GROMACS 2018 (Berendsen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). PDBsum was used to further evaluate the models' quality (Laskowski et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.13 Prediction of Post-Translational Modifications (PTMs)\u003c/h2\u003e \u003cp\u003eSeveral computational methods were used to find possible post-translational modifications (PTMs) in proteins. Serine, threonine, and tyrosine residue phosphorylation sites were predicted using NetPhos 3.1 (Blom et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). N-glycosylation sites were identified using NetNGlyc, whereas O-glycosylation sites were identified using NetOGlyc (Hansen et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Pugalenthi et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). GPSSNO was used to predict S-nitrosylation (Xue et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), while GPSSUMO was used to find sumoylation sites (Zhao et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Farnesylation and geranylgeranylation were among the prenylation forms that PrePS examined (Maurer-Stroh \u0026amp; Eisenhaber, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The ubiquitination site was predicted using UbPred (Radivojac et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)d terminal myristoylation was detected using Myristoylator. Palmitoylation sites were predicted using CKSAAPPalm and eqPalm (Ranjan \u0026amp; Das, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Finally, lysine acetylation sites were predicted using GPS PAIL (Deng et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). With the use of protein sequence inputs, these techniques shed light on how PTMs may control the stability and function of proteins, which may have consequences for congenital tooth agenesis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.14 Protein Dynamic Simulations\u003c/h2\u003e \u003cp\u003eGROMACS v.18 was used to perform molecular dynamics (MD) simulations in order to investigate the structural behavior of the EDA proteins in both their WT and EDA Variants forms. The simulations were conducted using the GROMOS96 54a7 force field (Schmid et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)(Devi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). SPC216 water molecules surrounded the proteins in a dodecahedron box, with the proteins and box edges spaced at least 1.0 nm away. In order to replicate physiological conditions, 0.15 M NaCl and Na ions were used to neutralize the system. Steepest descent was employed to reduce energy and settle steric disputes. Subsequently, the system underwent two equilibrium phases: one at 300 K using the NVT ensemble (constant temperature, volume, and particle count) and another at 1 atm for 100 ps using the NPT ensemble (constant temperature, pressure, and particle count). MD simulations were conducted for an extra 50 ns after equilibration. Both the WT and EDA Variants structures reached a stable equilibrium state in less than 10 ns. Key parameters, such as solvent-accessible surface area (SAS), hydrogen bonds (intra-protein and protein-water interactions), radius of gyration (Rg), root mean square deviation (RMSD), and root mean square fluctuation (RMSF), were analyzed using GROMACS tools (e.g., gmx rms, rmsf, gyrate, hbond, and sas). The results were shown using Microsoft Excel (Berk \u0026amp; Carey, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and XMGRACE (Cowan \u0026amp; Grosdidier, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.15 Principal Component Analysis (PCA) of Biophysical Metrics\u003c/h2\u003e \u003cp\u003eWe used Principal Component Analysis (PCA) based on important biophysical metrics, such as solvent-accessible surface area (SAS), hydrogen bond count (H-bonds), radius of gyration (Rg), root mean square deviation (RMSD), root mean square fluctuation (RMSF), and subcellular localization properties, to assess and visualize the variability among various protein variants. Both the WT and EDA Variants protein (EDA_WT, EDA_G227E, and Δ215\u0026ndash;220) were subjected to these measurements. PCA was carried out using the scikit-learn toolkit (Kramer, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), reducing the dataset to two principal components (PC1 and PC2) that captured the majority of the variation after the data were normalized to guarantee that each feature contributed equally. A scatter plot representing the PCA findings was created, with various colors and comments used to identify the variations. Python (v3.12) with pandas for data management and matplotlib (Hunter \u0026amp; Dale, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) for visualization were used in this investigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.16 Statistical Analysis of t-Test and One-Way ANOVA\u003c/h2\u003e \u003cp\u003eFor statistical analysis, Python's scipy and statsmodels libraries (McKinney, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) were used. The independent sample t-test was performed using scipy.stats.ttest_ind to compare the means between two groups. For multiple group comparisons, one-way ANOVA was conducted using scipy.stats.f_oneway.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e2.17 Miscellaneous analysis\u003c/h2\u003e \u003cp\u003eSeveral computational techniques were used to analyze protein variations. The Psipred Workbench was used to undertake secondary structure prediction and Gene Ontology (GO) annotations for both WT and variant versions of EDA (Buchan \u0026amp; Jones, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These predictions were based on protein sequences. The seaborn package in Python (Lemenkova, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) was used to create a heatmap. After loading the dataset into a Pandas DataFrame, it underwent completeness processing. The viridis colormap, 10x8-inch figure size, annotations enabled, and the seaborn heatmap tool were all utilized. DeepLoc 2.0 (Thumuluri et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which uses a convolutional neural network to predict localization probability for different cellular compartments, was used to provide subcellular localization predictions for WT and EDA variant proteins. A thorough structural analysis was conducted using Discover Studio (Syst\u0026egrave;mes, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which shed light on the biophysical characteristics of every protein variation.\u003c/p\u003e \u003cp\u003eDensitometry analysis was performed using ImageJ (Schneider et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and the scanned picture was imported in TIFF format. To adjust for non-specific signals, we used the rectangle tool to reliably pick the bands and eliminate the backdrop. We then calculated the regions under the peaks and produced a densitometry profile. To guarantee reliable comparisons, we used a loading control to normalize these data. In order to ascertain relevance, we lastly conducted statistical analysis and compared the levels of protein expression.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Identification of Novel Variants in \u003cem\u003eEDA\u003c/em\u003e and Pathogenicity prediction\u003c/h2\u003e \u003cp\u003eIn a case presenting oligodontia features along with ectodermal organ involvement, two novel variants in the \u003cem\u003eEDA\u003c/em\u003e were identified through Sanger sequencing. The first variant was a transitional change from c.680G\u0026thinsp;\u0026gt;\u0026thinsp;A (Exon4), resulting in a p.G227E variant (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This Variant changed glycine (Gly) to glutamic acid (Glu), causing a shift from a non-polar to a polar amino acid. The second variant was a deletion of 18 nucleotides in exon 4, specifically between positions c.649_666del (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which led to a deletion in the protein at positions 215\u0026ndash;220. These findings highlighted the potential impact of these novel \u003cem\u003eEDA\u003c/em\u003e variants on the development and function of ectodermal tissues, including teeth, hair, and sweat glands, and contributed to the observed clinical features.\u003c/p\u003e \u003cp\u003eIn silico analysis of \u003cem\u003eEDA\u003c/em\u003e variants p.G227E and p.Δ215\u0026ndash;220 consistently predicted their pathogenicity (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Both variants were classified as deleterious by multiple tools, including Revel, AlphaMissense, and MetaLR, with strong scores (\u0026ge;\u0026thinsp;0.94). p.G227E had a particularly high AlphaMissense score of 0.997, while p.Δ215\u0026ndash;220 scored 0.98. Supporting predictions from tools like SIFT, MUT Assessor, and DANN further confirmed their impact. These findings suggest that both variants significantly disrupt \u003cem\u003eEDA\u003c/em\u003e function through deleterious effects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Effect of EDA Variants on Protein and RNA expression\u003c/h2\u003e \u003cp\u003eThe analysis of the two novel \u003cem\u003eEDA\u003c/em\u003e variants (G227E and Δ215\u0026ndash;220) revealed significant impacts on both protein and mRNA expression levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The p.G227E variant involving a glycine-to-glutamic acid substitution, and the c.649_666del deletion variant, resulting in the loss of amino acids 215\u0026ndash;220, both led to decreased expression of \u003cem\u003eEDA\u003c/em\u003e and WNT4 proteins. Conversely, an increase in IκB expression was observed. Furthermore, the mRNA expression of NF-κB was markedly reduced in these variants, highlighting disruptions in the EDA signaling pathway. These findings suggest that these variants compromise the development and function of ectodermal tissues, including teeth, hair, and sweat glands, by altering critical components of the signaling cascade.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Effects of EDA Variants on Calcium and Mitochondrial Potential\u003c/h2\u003e \u003cp\u003eAnalysis of calcium potential indicated no significant differences between the EDA-WT and the EDA variants (EDA-G227E and EDA-Δ215\u0026ndash;220) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The calcium levels were consistent across all samples, suggesting that these EDA variants do not significantly impact calcium potential (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the analysis of mitochondrial potential, no statistically significant differences were observed between the EDA-WT and the EDA variants (EDA-G227E and EDA-Δ215\u0026ndash;220). The mitochondrial potential remained similar among all samples, indicating that these novel EDA variants do not significantly affect mitochondrial function (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Subcellular Localization of EDA Variants\u003c/h2\u003e \u003cp\u003eImmunofluorescence staining revealed that the expression of EDA in the cytoplasm was significantly hampered in the EDA variants (G227E and Δ215\u0026ndash;220) compared to the WT. However, the nuclear localization of EDA remained consistent across both WT and variant samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn silico predictions of subcellular localization (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) showed changes in the EDA variants when compared to the WT (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The PCA plot of subcellular localization data depicted a distinct separation between the WT and variant EDA proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This analysis included various subcellular compartments such as the cytoplasm, nucleus, extracellular region, cell membrane, mitochondrion, plastid, endoplasmic reticulum, lysosome/vacuole, Golgi apparatus, and peroxisome.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredicted subcellular localization of EDA_WT_CS and EDA_G227E_CS and EDA_Δ215\u0026ndash;220_CS EDA proteins across various cellular compartments, highlighting differences in localization probabilities.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubcellular Location\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEDA_WT_CS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEDA_G227E_CS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEDA_Δ215\u0026ndash;220_CS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCytoplasm\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1574\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNucleus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1527\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExtracellular\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3592\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCell Membrane\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6601\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMitochondrion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0376\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlastid\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEndoplasmic Reticulum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.4623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4733\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLysosome/Vacuole\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1829\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGolgi Apparatus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePeroxisome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eNote: CS: Confidence Score\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Effects of EDA Variants on PTMs and Their Functional Alteration\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003ePTMs Analysis\u003c/strong\u003e \u003cp\u003eEDA variants (G227E and Δ215\u0026ndash;220) exhibited changes in several post-translational modifications (PTMs) compared to the WT, affecting ubiquitination, sumoylation, prenylation, N-myristoylation, palmitoylation, N-acetylation, methylation, S-nitrosylation, glycosylation, and phosphorylation. These PTM changes impacted biological processes such as apoptosis, protein stability, membrane association, protein trafficking, cell signalling etc (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003ePrincipal Component Analysis (PCA) showed distinct clustering of WT and EDA variants, with significant deviations in PTM profiles and functional properties for G227E and Δ215\u0026ndash;220 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Gene Ontology Prediction on EDA Variants\u003c/h2\u003e \u003cp\u003eGene ontology (GO) predictions for EDA variants compared to the WT were visualized using heatmap plots. These predictions covered three main categories: Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). Biological Process (BP): The heatmap indicated alterations in key processes such as cellular macromolecule biosynthetic process, regulation of nitrogen compound metabolic process, and cell surface receptor signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The variants exhibited different patterns compared to the WT, suggesting functional changes in these biological processes. Molecular Function (MF): Variations in molecular functions, including protein binding, kinase activity, and nucleic acid binding, were observed between the EDA variants and WT. The heat map showed distinct differences in these functions, highlighting the impact of the variants on protein interactions and enzymatic activities (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Cellular Component (CC): The subcellular localization of EDA variants showed significant differences compared to the WT (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). While nuclear localization remained consistent across WT and variants, cytoplasmic expression was notably hampered in the variants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Secondary Structure and Protein Disorder Prediction of EDA Variants\u003c/h2\u003e \u003cp\u003eThe EDA WT displayed a mix of structured regions (helices and strands) and disordered segments (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). High disorder probability was noted around residues 50\u0026ndash;100 and 200\u0026ndash;250. EDA_G227E variant showed differences in helical structures and additional disorder regions compared to the WT (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). High disorder was observed around residues 50\u0026ndash;150 and 200\u0026ndash;250. EDA_Δ215\u0026ndash;220 deletion variant significantly altered the secondary structure, increasing disorder around residues 50\u0026ndash;100, 150\u0026ndash;200, and 250\u0026ndash;300. These changes in secondary structure and disorder regions suggest potential impacts on protein stability and function for the EDA variants (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Effects on Hydrophobicity and Conservation of EDA Variants\u003c/h2\u003e \u003cp\u003eThe hydrophobicity profile of the WT displayed a fluctuating pattern, with both hydrophobic and hydrophilic regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The G227E variant showed changes in the helical structure elements and an increased hydrophobicity around position 227. The Δ215\u0026ndash;220 variant exhibited a significant alteration in hydrophobicity, particularly in the region of the deletion (positions 215\u0026ndash;220), indicating increased hydrophilicity in the surrounding areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe conservation analysis across species, including human, mouse, rat, opossum, chicken, Drosophila, zebrafish, and X.tropicalis, demonstrated that the G227E Variant and the Δ215\u0026ndash;220 deletion affected highly conserved regions of the EDA protein (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). These changes suggest that the identified variants may have significant functional implications due to their impact on conserved domains, potentially disrupting protein stability and function.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e3.9 MD Simulation Effects of EDA Variants Compared to WT\u003c/h2\u003e \u003cp\u003eThe structural and dynamic analysis revealed distinct variations among the WT (EDA_WT\u003cem\u003e)\u003c/em\u003e and EDA variants \u003cem\u003e(\u003c/em\u003eEDA_G227E and EDA_Δ215\u0026ndash;220\u003cem\u003e)\u003c/em\u003e EDA proteins (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). The RMSD was highest for EDA_WT (2.4 nm), indicating greater conformational flexibility, compared to EDA_G227E (2.18 nm) and EDA_Δ215\u0026ndash;220 (1.55 nm). Similarly, the RMSF values were reduced in m \u003cem\u003e(\u003c/em\u003eEDA_G227E: 0.52 nm, EDA_Δ215\u0026ndash;220: 0.33 nm) compared to the WT (0.68 nm), suggesting decreased atomic fluctuations. The radius of gyration (Rg) showed a reduction from EDA_WT (2.29 nm) to EDA_G227E (2.16 nm) and EDA_Δ215\u0026ndash;220 (1.84 nm), reflecting compaction in EDA variants structures. The solvent-accessible surface area (SAS) also decreased in EDA variants, with EDA_Δ215\u0026ndash;220 showing the lowest value (131.23 nm\u0026sup2;) compared to EDA_WT (144.12 nm\u0026sup2;). Interestingly, the number of intra-molecular hydrogen bonds remained relatively consistent across all variants, with minor differences.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStructural and dynamic parameters of EDA_WT and EDA_G227E and EDA_Δ215\u0026ndash;220) EDA proteins.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRMSD (nm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRMSF (nm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRg (nm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSAS (nm2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIntra-H-bond (No.)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEDA_WT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e144.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e163.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEDA G227E\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e135.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e164.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEDA_Δ215\u0026ndash;220\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e131.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e163.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eNotes\u003c/b\u003e: RMSD: Indicates structural stability; RMSF: Reflects residue flexibility; Rg: Measures protein compactness; SAS: Shows surface exposure to solvent; Intra-H-bond: Number of internal hydrogen bonds.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn our study, we identified two novel variants in the \u003cb\u003eEDA\u003c/b\u003e: \u003cb\u003ec.680G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.G227E)\u003c/b\u003e and \u003cb\u003ec.649_666del (p. GPPGPP Δ215\u0026ndash;220) ,; chrX:69247830_69247847delCTGGTCCTCCAGGTCCTC)\u003c/b\u003e. These variants were discovered in an Indian family presenting with X-linked hemizygous syndromic tooth agenesis, characterized by oligodontia, sparse hair, dry skin, and nail discoloration. Through a combination of molecular, bioinformatics, and functional analyses, we have provided evidence for the pathogenic nature of these variants and their potential impact on the development and function of ectodermal tissues.\u003c/p\u003e \u003cp\u003eIn silico analyses consistently classified both variants as deleterious, with high scores from multiple tools, including Alpha Missense and Revel, supporting their impact on \u003cem\u003eEDA\u003c/em\u003e function (Li et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)(Lee et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The p.G227E variant introduces a glycine-to-glutamic acid substitution, resulting in a significant shift from a non-polar to a polar amino acid. This substitution occurs in a highly conserved region, as confirmed by conservation analyses across multiple species, suggesting its functional importance (Capra \u0026amp; Singh, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2007\u003c/span\u003e)(Capr. Similarly, the Δ215\u0026ndash;220 deletion disrupts a conserved hydrophobic region, leading to increased hydrophilicity and potential destabilization of the protein structure. Secondary structure predictions further demonstrated altered helical content and increased disordered regions in both variants, indicative of their destabilizing effects on \u003cem\u003eEDA\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eFunctional assays demonstrated that both variants led to reduced expression of \u003cem\u003eEDA\u003c/em\u003e and associated proteins, including IκB and WNT4, key players in ectodermal development (Abu-Hussein et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The reduced expression of EDA and WNT4 in both variants suggests impaired function of critical signaling molecules involved in ectodermal organ development. WNT4 plays a pivotal role in processes such as tooth morphogenesis and hair follicle formation, and its reduced levels likely contribute to the clinical features observed in the affected individuals (Xue et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Interestingly, the increased expression of IκB, an inhibitor of the NF-κB pathway, aligns with the marked reduction in NF-κB mRNA levels. This imbalance highlights the disruption of the \u003cem\u003eEDA\u003c/em\u003e-\u003cem\u003eNF-κB\u003c/em\u003e axis, a central pathway in ectodermal tissue differentiation.\u003c/p\u003e \u003cp\u003eThe contrasting changes in IκB and NF-κB suggest that these variants may lead to an aberrant feedback mechanism within the signaling pathway. Elevated IκB levels could inhibit NF-κB activation, thereby dampening downstream gene expression required for ectodermal development. This disruption might explain the observed clinical features, such as oligodontia and ectodermal abnormalities (Gao et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These findings provide insights into the molecular mechanisms underlying the clinical features observed in affected individuals, including tooth agenesis and ectodermal dysplasia.\u003c/p\u003e \u003cp\u003eImmunofluorescence studies revealed that the cytoplasmic localization of EDA was significantly impaired in the p.G227E and Δ215\u0026ndash;220 variants, while nuclear localization remained unaffected. In silico analyses corroborated these findings, with subcellular localization patterns distinctly separating WT and variant EDA proteins. Additionally, PTM analysis identified alterations in multiple modifications, including ubiquitination, glycosylation, phosphorylation etc., which may contribute to disrupted protein stability, trafficking, and signaling in the (Audagnotto \u0026amp; Dal Peraro, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ranjan \u0026amp; Das, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) Gene ontology analysis revealed significant differences in biological processes, molecular functions, and cellular components between WT and \u003cem\u003eEDA\u003c/em\u003e variants. Altered processes included macromolecule biosynthesis, nitrogen metabolism, and receptor signaling, which align with the functional disruption observed in ectodermal tissues. Variations in protein binding and kinase activity further emphasize the widespread effects of these EDA variants on molecular pathways critical for ectodermal development.\u003c/p\u003e \u003cp\u003eComparing the EDA variants to the WT, molecular dynamics (MD) simulations provide light on their structural and dynamic characteristics. In contrast to the G227E (2.18 nm) and Δ215\u0026ndash;220 (1.55 nm) variants, the WT (2.4 nm) showed a larger structural deviation, according to the Root Mean Square Deviation (RMSD) values, indicating greater stability in the EDA Variants forms. The WT EDA (0.68 nm) exhibited greater flexibility in some places, according to the Root Mean Square Fluctuation (RMSF) study, than the G227E (0.52 nm) and Δ215\u0026ndash;220 (0.33 nm) variants, which had less fluctuation and hence more stiffness. Changes in functional dynamics might be the cause of the variations' greater stiffness.\u003c/p\u003e \u003cp\u003eIn contrast to the G227E (2.16 nm) and Δ215\u0026ndash;220 (1.84 nm) variants, which showed more compact structures, the Radius of Gyration (Rg) values for EDA_WT (2.29 nm) were greater, suggesting a more stretched conformation. Changes in the protein's general folding and stability may be reflected in this compaction. In comparison to the G227E (135.96 nm\u0026sup2;) and Δ215\u0026ndash;220 (131.23 nm\u0026sup2;) variants, the EDA_WT (144.12 nm\u0026sup2;) variant has a higher solvent-exposed surface area, as shown by the Solvent Accessible Surface Area (SAS). The variations' interactions with other molecules may be impacted by reduced SAS.\u003c/p\u003e \u003cp\u003eThe number of intramolecular hydrogen bonds (Intra-H bond) was similar across all variants, with EDA_WT (163.33), G227E (164.26), and Δ215\u0026ndash;220 (163.12). This suggests that while overall hydrogen bonding remains relatively constant, the specific interactions and stability of the protein could still be impacted by the variants.\u003c/p\u003e \u003cp\u003eMolecular dynamics simulations showed that the p.G227E and p.Δ215\u0026ndash;220 variants exhibited reduced flexibility and compact structures compared to the WT, indicating increased rigidity and potential functional compromise. These differences highlight the impact of the EDA Variants on the protein's structural and functional properties. Changes in hydrophobicity profiles and the impact on conserved regions further substantiate the deleterious nature of these variants, with likely effects on protein folding and stability (Higgs et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Interestingly, neither variant significantly affected mitochondrial or calcium potential, suggesting that the pathogenic effects of these variants are more likely mediated through disrupted signaling and protein function rather than alterations in these cellular processes.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study provides robust evidence for the pathogenicity of two novel EDA variants, p.G227E and Δ215\u0026ndash;220, in a case of X-linked ectodermal dysplasia with syndromic tooth agenesis. The integration of bioinformatics predictions, molecular studies, and functional analyses highlights the diverse mechanisms through which these variants disrupt \u003cem\u003eEDA\u003c/em\u003e function, ultimately impairing ectodermal tissue development. Overall, the MD simulation results suggest that the EDA_WT variant has greater structural deviation, flexibility, size, stability, and solvent exposure compared to the G227E and Δ215\u0026ndash;220 variants. These findings contribute to our understanding of \u003cem\u003eEDA\u003c/em\u003e-associated disorders and underscore the need for further studies to explore potential therapeutic strategies targeting these variants.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCTA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCongenital Tooth Agenesis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWild Type\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWT\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePTMs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProtein Translation Modifications\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMolecular Dynamics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRg\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRadius of Gyration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRMSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRoot Mean Square Deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRMSF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRoot Mean Square Fluctuation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrincipal Component Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWild Type\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eΔ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDeletion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e6. Acknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the Indian Council of Medical Research (ICMR) for providing the Senior Research Fellowship (SRF) to PR and CD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7. Author Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePR\u003c/strong\u003e conceived the research idea, conducted experiments, performed bioinformatics and data analysis, and wrote the manuscript. \u003cstrong\u003eCD\u003c/strong\u003e contributed to data analysis. \u003cstrong\u003eVS\u003c/strong\u003e was responsible for identifying CTA patients and conducting OPG analysis. \u003cstrong\u003eRB\u003c/strong\u003e and \u003cstrong\u003eVKS\u003c/strong\u003e carried out clinical investigations. \u003cstrong\u003ePD\u0026nbsp;\u003c/strong\u003eco-conceived the research idea and approval of final draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e9. Competing interests:\u003c/strong\u003e The authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e10. Consent to participate:\u003c/strong\u003e All subjects participating in this study were fully informed about the purpose, process, potential risks and benefits of the study and voluntarily signed a written informed consent form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e11. Funding:\u0026nbsp;\u003c/strong\u003eThere was no funding for this project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbu-Hussein M, Watted N, Yehia M, Proff P, Iraqi F (2015) Clinical genetic basis of tooth agenesis. J Dent Med Sci 14(12):68\u0026ndash;77\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnbouba GM, Carmany EP, Natoli JL (2020) The characterization of hypodontia, hypohidrosis, and hypotrichosis associated with X-linked hypohidrotic ectodermal dysplasia: A systematic review. 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Nucleic Acids Res 42(W1):W325\u0026ndash;W330\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Banaras Hindu University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"EDA, PTMs, MD Simulations, X-linked ectodermal dysplasia, IκB and NF-κB expression","lastPublishedDoi":"10.21203/rs.3.rs-5743160/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5743160/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates two novel variants in the \u003cem\u003eEDA\u003c/em\u003e, c.680G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.G227E) and c.649_666del (Δ215\u0026ndash;220), identified in X-linked ectodermal dysplasia and syndromic tooth agenesis cases. These variants were identified through Sanger sequencing and mapped to highly conserved regions of EDA. Bioinformatics tools consistently classified them as deleterious, with significant disruptions predicted in protein stability, hydrophobicity, and secondary structure. Structural analysis revealed that p.G227E caused a glycine-to-glutamic acid substitution, altering hydrophobicity and secondary structure, while Δ215\u0026ndash;220 disrupted a conserved hydrophobic region, leading to increased protein instability\u003c/p\u003e \u003cp\u003eFunctional studies revealed reduced expression of EDA and WNT4 proteins, alongside increased IκB levels and decreased \u003cem\u003eNF-κB\u003c/em\u003e mRNA expression, indicating impaired EDA-NF-κB signaling. Subcellular localization analyses demonstrated diminished cytoplasmic expression of the EDA Variants proteins, corroborated by in silico predictions. Post-translational modifications (PTMs) and gene ontology (GO) analyses revealed alterations in processes critical for ectodermal development, including macromolecule biosynthesis, nitrogen metabolism, and receptor signaling.\u003c/p\u003e \u003cp\u003eMolecular dynamics simulations highlighted increased rigidity, compact structure, and reduced flexibility in the EDA variants proteins compared to EDA Wild Type (WT). Interestingly, neither variant significantly impacted calcium or mitochondrial potential under normal experimental conditions, suggesting their pathogenic effects arise primarily from disrupted protein interactions and signaling pathways.\u003c/p\u003e \u003cp\u003eThis study integrates molecular, bioinformatics, and functional analyses to elucidate the pathogenicity of these novel \u003cem\u003eEDA\u003c/em\u003e variants, providing insights into ectodermal dysplasia mechanisms and paving the way for future therapeutic strategies targeting these EDA variants.\u003c/p\u003e","manuscriptTitle":"Novel Ectodysplasin-A Variants: Structural and Functional Basis of Hypohidrotic Ectodermal Dysplasia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-03 14:31:17","doi":"10.21203/rs.3.rs-5743160/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f0437f9b-e76b-4c12-80d7-00563a75829d","owner":[],"postedDate":"January 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":42219910,"name":"Molecular Genetics"},{"id":42219911,"name":"Structural Biology"},{"id":42219912,"name":"Bioinformatics"},{"id":42219913,"name":"Medical Genetics"},{"id":42219914,"name":"Dentistry"}],"tags":[],"updatedAt":"2025-01-03T14:31:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-03 14:31:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5743160","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5743160","identity":"rs-5743160","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}