In silico Evaluation of Deleterious nsSNPs in SOD1 and TARDBP Genes Associated with Amyotrophic Lateral Sclerosis

In silico Evaluation of Deleterious nsSNPs in SOD1 and TARDBP Genes Associated with Amyotrophic Lateral Sclerosis | 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 Article In silico Evaluation of Deleterious nsSNPs in SOD1 and TARDBP Genes Associated with Amyotrophic Lateral Sclerosis Manjunath B Malshetty, Sandeep Mallya, Sudharshan Prabhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8978267/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder involving mutations in the SOD1 and TARDBP genes that significantly influence its development. Nonsynonymous SNPs in these genes can disrupt protein stability, folding, and regulatory functions, leading to motor neuron loss. This study performed an in-silico approach to assess the functional and structural effects of nsSNPs in SOD1 and TARDBP . A total of 5,964 SOD1 and 10,630 TARDBP variants were retrieved from public databases, filtered for coding region with a MAF ≥ 0.001, and prioritized using CADD. Multiple approaches, including pathogenicity predictions, stability analysis, structural modeling, post-translational modification assessment, and network-based functions, were combined. 12 nsSNPs per gene met the inclusion criteria. Notably, SOD1 variants V15G (rs1202989817) and I19M (rs1182088847) consistently predicted as deleterious, showing decreased stability indicated by negative ΔΔG values and localized structural disruptions without global misfolding. Conversely, TARDBP variants G335D (rs80356729) and I222T (rs1570722030) suggested destabilization but yielded mixed predictions regarding disease association. Network and pathway analyses highlighted SOD1 and TARDBP as key nodes in ALS-related mechanisms such as oxidative stress, RNA metabolism, proteostasis, and mitochondrial impairment. These findings prioritize structurally destabilizing variants with potential pathogenic relevance in ALS and provide a computational framework for downstream experimental validation. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Biological sciences/Genetics Health sciences/Neurology Biological sciences/Neuroscience Amyotrophic lateral sclerosis (ALS) SOD1 TARDBP (TDP-43) nsSNPs protein stability structural modeling. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Amyotrophic lateral sclerosis (ALS) is a progressive and deadly neurodegenerative disorder characterized by the selective loss of upper and lower motor neurons (Vintilescu et al., 2016 ), leading to muscle weakness, spasticity, dysarthria, dysphagia, and eventually respiratory failure. Most patients die within 2–5 years of symptom appearance, although survival varies based on genetic factors, environmental exposures, and molecular influences. (Michael A Van Es, 2024 ; Yuan et al., 2024 ). ALS mainly affects individuals between 50–70 years old and includes both sporadic ALS (sALS), making up nearly 90% of cases, and familial ALS (fALS), accounting for approximately 10% (Goutman et al., 2022 ; Huang et al., 2024 ). Increasing evidence shows that ALS exists within a spectrum of neurodegenerative disorders, sharing key pathological mechanisms with frontotemporal dementia (FTD), primarily through the buildup of TAR DNA-binding protein 43 (TDP-43) inclusions (Balendra et al., 2025 ; Feneberg et al., 2025 ; Zeng et al., 2024 ). More than 40 genes have been linked to ALS, involved in pathways related to proteostasis, oxidative stress responses, RNA metabolism, axonal transport, and mitochondrial maintenance (Goutman et al., 2022 ). Among these, SOD1 (superoxide dismutase 1) and TARDBP (encoding TDP-43) are two of the most significant contributors to ALS pathology. Under normal physiological conditions, SOD1 converts toxic superoxide radicals into hydrogen peroxide and oxygen, thereby protecting neurons from oxidative damage. Meanwhile, TARDBP (TDP-43) plays a role in regulating transcription, alternative splicing, mRNA transport, and microRNA processing. Mutations in SOD1 account for approximately 15–20% of familial ALS (fALS) and 1–2% of sporadic ALS (sALS), making it one of the earliest and most well-studied ALS-related genes (Huang et al., 2024 ; Opie-Martin et al., 2022). Although TARDBP mutations are less common, TDP-43 pathology appears in over 95% of ALS cases, emphasizing its key role in both familial and sporadic forms (Balendra et al., 2025 ; Wang et al., 2024 ). Single-nucleotide polymorphisms (SNPs), particularly nonsynonymous SNPs (nsSNPs), cause amino acid substitutions that alter protein structure and function. In ALS, nsSNPs in SOD1 and TARDBP alter protein stability, aggregation, and toxicity, thereby influencing disease onset and progression (Berdyński et al., 2022 ; Dash et al., 2022 ; Lépine et al., 2024 ). Mutant SOD1 proteins acquire toxic gain-of-function properties, involving abnormal folding, disrupted metallation, defective maturation, and an increased tendency to aggregate (Trist et al., 2021 , 2022). These alterations promote the formation of insoluble aggregates, impair mitochondrial activity, disrupt axonal structure, and induce cellular stress responses that compromise motor neuron survival. Considerable heterogeneity exists among SOD1 mutations, with different variants influencing disease onset, progression, and survival (Berdyński et al., 2022 , 2025 ; Opie-Martin et al., 2022). Specific mutations can alter polypeptide length or structural stability, modify aggregation kinetics, and contribute to variant-specific neurotoxic profiles (Berdyński et al., 2025 ). Pathological studies have further revealed abnormalities in SOD1 maturation, metal imbalance, and oxidation states in ALS spinal cord tissue, highlighting their contribution to the selective vulnerability of motor neurons (Trist et al., 2022). Meanwhile, TDP-43 pathology represents another core hallmark of ALS. In healthy neurons, TDP-43 resides in the nucleus; however, in disease states, it mislocalizes to the cytoplasm and forms phosphorylated, ubiquitinated, and insoluble aggregates that impair RNA metabolism and destabilize neuronal function (Balendra et al., 2025 ; Zeng et al., 2024 ). TDP-43 proteinopathy is also observed in a broader range of neurodegenerative diseases and is characterized by prion-like propagation and progressive accumulation of misprocessed TDP-43 species (de Boer et al., 2021 ). TARDBP mutations further worsen this pathology by disrupting RNA-binding activity and nucleocytoplasmic transport, ultimately impairing neuronal function (Balendra et al., 2025 ; Suk & Rousseaux, 2020 ). Studies using human iPSC-derived motor neurons show that ALS-linked TARDBP variants cause early electrophysiological problems and reduced synaptic activity before significant aggregation occurs (Lépine et al., 2024 ). Additional research demonstrates rapid mislocalization of endogenous TDP-43 in inducible models (Ganssauge et al., 2025 ), and region-specific proteomic analysis reveals alterations in ALS brain and spinal cord tissues (Feneberg et al., 2025 ). Moreover, TDP-43 buildup in peripheral tissues such as the retina may serve as an early biomarker of neurodegeneration (Glashutter et al., 2025 ) Despite their distinct origins, SOD1 and TARDBP -related mechanisms converge on common pathways involving protein misfolding, oxidative damage, mitochondrial dysfunction, impaired RNA metabolism, and deficits in proteostasis (Trist et al., 2021 , 2022; Zeng et al., 2024 ). Understanding how these overlapping processes interact is essential for clarifying ALS pathogenesis and developing effective diagnostic and therapeutic strategies. However, several key questions still need to be answered, including the exact order of disease-causing events, why motor neurons are more vulnerable than other neuron types, and what factors contribute to the clinical differences in SOD1 and TARDBP -related ALS. Solving these issues is crucial for developing a unified model of how ALS progresses based on gene-specific mechanisms. Since SOD1 and TARDBP play significant roles in shaping the molecular structure of ALS, a thorough understanding of their functions is essential for discovering new biomarkers, improving disease models, and developing targeted treatments to slow down neurodegeneration caused by these proteins. 2. Methods 2.1 Ethical considerations This study relied exclusively on computational analyses using publicly available genomic data sets and bioinformatics tools. As no human or animal subjects were involved, ethical approval and informed consent requirements did not apply to this study. 2.2 Sample approach and justification A targeted gene-based computational workflow was employed to analyze SOD1 and TARDBP , two genes closely associated with ALS. The overall workflow is shown in Fig. 1 . Variant data were retrieved from the NCBI dbSNP database, which provides curated and standardized variant annotations suitable for in-silico analyses (Landrum et al., 2018). 2.3 Variant inclusion and exclusion criteria Inclusion criteria SNPs located within coding regions of SOD1 and TARDBP Nonsynonymous single-nucleotide polymorphisms (nsSNPs) Minor allele frequency (MAF) ≥ 0.001 Exclusion criteria Synonymous or intronic SNPs Variants with MAF < 0.001 2.4 Variant retrieval and Dataset Processing Variant data for SOD1 and TARDBP were retrieved from the NCBI dbSNP ( https://www.ncbi.nlm.nih.gov/snp/ ) and UniProt databases. nsSNPs located within the coding regions were filtered and processed in R Studio (version 4.3.2) to remove duplicates, incomplete entries, and variants lacking functional annotations. The refined dataset included the rsID, genomic position, nucleotide substitution, and corresponding amino-acid alterations. Only variants consistently predicted as deleterious by both SIFT and PolyPhen-2 were retained for downstream analyses to ensure high confidence prioritization. 2.5 Functional impact prediction The functional significance of nsSNPs was assessed using the Combined Annotation Dependent Depletion (CADD) scoring system ( https://cadd.gs.washington.edu/ ), which integrates diverse genomic and functional features to estimate variant deleteriousness (Schubach et al., 2024 ). PHRED-like CADD scores were interpreted using standard thresholds, and only variants with scores ≥ 20 were retained for further analysis. 2.6 Pathogenicity assessment Deleterious nsSNPs analysis using SIFT The Sorting Intolerant From Tolerant (SIFT) tool ( https://sift.bii.a-star.edu.sg/ ) was used to evaluate the impact of each amino acid substitutions based on evolutionary conservation (Sim et al., 2012 ). Variants with scores ≤ 0.05 were classified as deleterious, whereas those with scores > 0.05 were predicted to be tolerated. Damaging nsSNPs prediction using PolyPhen-2 Polymorphism Phenotyping v2 (PolyPhen-2) ( http://genetics.bwh.harvard.edu/pph2/ ) was used to predict the structural and functional consequences of amino acid substitutions using evolutionary features, comparative modelling, and physicochemical descriptors (Adzhubei et al., 2013 ). Scores were interpreted based on standard classification thresholds, with values from 0.00 to 0.15 considered benign, 0.15 to 0.85 classified as possibly damaging, and 0.85 to 1.00 classified as probably damaging, respectively. Variants classified as probably or possibly damaging were retained. PhD-SNP analysis The pathogenic potential of nonsynonymous SNPs was evaluated using the Predictor of human deleterious Single Nucleotide Polymorphisms (PhD-SNP) classifier, a Support Vector Machine (SVM) based classifier that predicts whether amino acid substitutions are disease-associated or neutral ( https://snps.biofold.org/phd-snp/phd-snp.html ) (Al-Ayari et al., 2023 ). The tool analyzes sequence-based features, evolutionary conservation, and substitution patterns to classify each variant accurately. Each prediction is accompanied by a Reliability Index (RI; 0–10), indicating the confidence level of each prediction; higher values indicate greater reliability. These Predictions were used to differentiate harmful mutations from functionally tolerated substitutions, thereby providing additional evidence for pathogenicity and aiding in variant prioritization. SNPs&GO analysis The Single Nucleotide Polymorphism & Gene Ontology (SNPs&GO) integrates protein sequence information, evolutionary conservation, and Gene Ontology annotations to predict disease relevance ( https://snps-and-go.biocomp.unibo.it/snps-and-go/ ) (Pavithran & Kumavath, 2021 ).This tool also provides a Reliability Index (RI) ranging from 0 to 10, where a higher value indicates greater confidence in the predictions. Variants predicted as Disease with an RI of 10 were considered highly reliable and likely pathogenic, supported by strong sequence conservation and functional context. SNP&GO predictions were used to complement other pathogenicity tools and strengthen variant prioritization. Clinical annotation using ClinVar Clinical relevance of the prioritized nsSNPs were retrieved from the ClinVar database ( https://www.ncbi.nlm.nih.gov/clinvar/ ), a publicly accessible repository of variant interpretations submitted by clinical laboratories, researchers, and expert groups. ClinVar annotations, including pathogenic, likely pathogenic, conflicting interpretations or not reported, were retrieved for all variants with available rsIDs. ClinVar was used solely for clinical annotation and contextual interpretation and not as an in-silico pathogenicity prediction tool, as the database contains user-submitted clinical classifications rather than standardized algorithmic predictions. 2.7 Protein stability analysis I-Mutant 2.0 analysis Protein stability changes induced by amino acid substitutions were assessed using I-Mutant 2.0, a sequence-based prediction tool that estimates mutation-induced variations in Gibbs free energy (ΔΔG) ( https://folding.biofold.org/i-mutant/i-mutant2.0.html ). It classifies substitutions as stabilizing or destabilizing relative to the wild-type protein, with negative ΔΔG values indicating reduced thermodynamic stability and positive values suggesting stabilization (Al-nakhle & Khateb, 2023 ). Consistent with previously reported in silico variant characterization studies, I-Mutant 2.0 predictions were interpreted with consideration of model-specific performance behaviour across diverse mutation datasets. MUpro analysis Protein stability changes were further evaluated using MUpro, a machine-learning–based method that predicts mutation-induced free-energy differences ( https://mupro.proteomics.ics.uci.edu ). MUpro classifies substitutions as stabilizing or destabilizing on the basis of sequence-derived features and has been widely used in recent structural analyses of deleterious missense variants (Ali et al., 2024 ). Variants predicted to decrease stability by MUpro were considered likely to contribute to structural disruption. DynaMut-2 analysis The effect of missense variants on protein stability and conformational dynamics was evaluated using DynaMut-2 ( https://biosig.lab.uq.edu.au/dynamut2/ ), which integrates normal mode analysis, graph-based signatures, and machine-learning algorithm. The tool predicts mutation-induced changes in Gibbs free energy (ΔΔG) and protein flexibility by assessing alterations in intramolecular interactions and atomic fluctuations. Negative ΔΔG values (ΔΔG 0) indicate stabilizing effects relative to the wild-type protein (Kamal et al., 2024 ). Variants predicted to induce destabilization or marked flexibility changes were prioritized for downstream structural and functional analyses. INPS-MD Analysis The effects of missense variants on protein stability were further evaluated using Impact of Non-synonymous Protein mutations–Molecular Dynamics (INPS-MD), a computational tool which predicts mutation-induced stability changes by combining sequence, structural, and dynamic features ( https://inpsmd.biocomp.unibo.it/ ), which estimates changes in Gibbs free energy (ΔΔG), where positive values (ΔΔG > 0) indicate stabilizing mutations and negative values (ΔΔG < 0) indicate destabilizing effects (Alam et al., 2025 ). The method combines residue environment, solvent accessibility, and interaction networks to evaluate mutation-driven stability alterations. INPS-MD predictions were used to complement other stability tools and support prioritization of variants with potential structural and energetic impacts. 2.8 Post-translational modification analysis Post-translational modification (PTM) alterations were evaluated using the Group-based Prediction System-Methylation sited prediction (GPS-MSP) platform ( http://gps.biocuckoo.cn/online.php ), which predicts kinase-specific phosphorylation sites based on sequence patterns and motif signatures. This machine-learning–based approach follows current PTM prediction practices and enables the identification of mutations that may disrupt regulatory modification sites (Meng et al., 2022 ), thereby providing additional insight into how variants may alter protein regulation, signalling, or stability particularly for proteins involved in neurodegenerative pathways. 2.9 Secondary structure analysis GOR4 (Garnier-Osguthorpe-Robson) Protein secondary structure was predicted using the GOR4 (Garnier–Osguthorpe–Robson) algorithm, which assigns α-helices, β-strands, and random coils based on information-theoretic analysis of amino acid composition and positional context within the sequence ( https://npsa-prabi.ibcp.fr/NPSA/npsa_gor4.html ). GOR4 evaluates residue propensities and neighboring interactions to estimate secondary structure probabilities, enabling assessment of mutation-induced local structural organization (Xia et al., 2011 ). Predicted secondary structure profiles of wild-type and mutant proteins were compared to identify conformational changes associated with nonsynonymous substitutions. SOPMA Secondary structure elements were also predicted using Self-Optimized Prediction Method with Alignment (SOPMA), which estimates α-helices, β-strands, turns, and coils based on residue propensity scores ( https://npsa-pbil.ibcp.fr/NPSA/npsa_sopma.html ). SOPMA enhances prediction accuracy by incorporating multiple sequence alignments and iterative refinement, enabling accurate detection of mutation-induced shifts in secondary structure composition (Mukherjee et al., 2022 ). The secondary structure profiles of wild-type and mutant sequences were compared to identify local conformational changes. 2.10 Three-dimensional structural modeling SWISS-MODEL Three-dimensional protein structures of selected variants were generated using the SWISS-MODEL homology modeling platform ( https://swissmodel.expasy.org/ ) through template identification, alignment, and energy-optimized model construction (Waterhouse et al., 2018 ). Models with the highest Global Model Quality Estimation (GMQE) scores were selected. And structural validation was performed using Ramachandran plot analysis and MolProbity evaluation, confirming acceptable stereochemical value, favorable MolProbity scores, and minimal steric clashes, supporting the reliability of the predicted models for downstream functional analysis. 2.11 Expression, interaction, and functional analysis Human Protein Atlas (HPA) Protein and transcript expression patterns of the target genes were analyzed using the Human Protein Atlas (HPA) database across multiple human brain regions. HPA provides region-specific RNA expression and immunohistochemistry-based protein expression data, enabling evaluation of spatial expression patterns within the central nervous system ( https://www.proteinatlas.org/ ) (Mohamed et al., 2025 ). Expression profiles across different brain regions were analyzed to evaluate regional variability and support the biological relevance of the analyzed genes in neurological contexts. This strategy aligns with established in-silico methodologies that integrate variant analyses with tissue-specific expression data to strengthen functional interpretation. STRING Protein–protein interaction (PPI) networks for SOD1 and TARDBP were constructed using the STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database, which integrates known and predicted interactions from experimental evidence, computational predictions, co-expression, and curated pathway databases ( https://string-db.org/ ) (Crosara et al., 2018 ). STRING was used to identify direct and indirect functional associations and to visualize interaction networks based on confidence scores. Also, it incorporates scoring algorithms and expanded protein–protein association datasets were utilized to support comprehensive interaction mapping relevant to disease mechanisms (Szklarczyk et al., 2023 ). KEGG and ShinyGO Functional pathway and enrichment analyses were performed using the KEGG database ( https://www.kegg.jp ) and the ShinyGO platform ( https://bioinformatics.sdstate.edu/go/ ). KEGG was used to map the identified gene set to curate metabolic and signaling pathways (Kanehisa et al., 2023 ). To complement this analysis, the curated gene list was uploaded to ShinyGO for Gene Ontology (GO) enrichment and pathway interpretation, which identifies significantly enriched biological processes, molecular functions, cellular components, and pathway categories, including KEGG pathways (Karunakara et al., 2024 ) . Cytoscape Cytoscape analysis was done for detailed network visualization and topological analysis of ALS-associated genes. Cytoscape was used to visualize the interaction structure and to calculate key network parameters, including degree centrality, betweenness centrality, closeness centrality, and clustering coefficient, which facilitate the identification of hub nodes and highly influential proteins within the network. These topological metrics enable a systematic assessment of the network's organization and the functional significance of individual proteins (Majeed & Mukhtar, 2023 ). Gene Mania GeneMANIA was used to construct a gene-gene interaction and functional association network for SOD1 and TARDBP with ALS-associated genes ( https://genemania.org/ ). Gene-MANIA combines various data types, including co-expression, physical interactions, genetic interactions, co-localization, pathways, and shared protein domains, to find genes that are functionally related to the query. The tool was applied to expand the interaction map and identify additional genes that may be involved in shared biological processes or disease pathways (Irfan et al., 2022 ). This method enabled the functional contextualization of the genes by highlighting biologically relevant interaction partners. 3. Results 3.1 Identification and prioritization of nsSNPs in SOD1 and TARDBP A comprehensive database mining of the dbSNP repository yielded 5,964 variants in SOD1 and 10,630 variants in TARDBP genes. Among these, 226 SOD1 and 742 TARDBP variants were categorized as missense (nonsynonymous) SNPs, suggesting possible amino acid substitutions with functional significance. After applying stringent inclusion criteria-coding region localization, nonsynonymous effect, and minor allele frequency (MAF ≥ 0.001) refined the dataset to 12 nsSNPs per gene (Table S1 ) . These variants were prioritized using CADD and subsequently assessed using SIFT, PolyPhen-2, PhD-SNP, and SNPs&GO for in-silico functional and pathogenicity analyses. 3.2 CADD functional impact assessment CADD analysis revealed multiple SOD1 and TARDBP variants associated with ALS showing high deleterious potential, characterised by PHRED scores ≥ 20. In SOD1 , three substitution events corresponding to two unique SNPs (rs1202989817 and rs1182088847) were analyzed. The rs1202989817 variant carried two alternate alleles (T > C and T > G), which were analyzed separately due to distinct CADD scores. These substitutions exhibited PHRED scores ranging from 23.0 to 28.0, indicating moderate to strong predicted functional impact. In TARDBP , six substitutions exhibited PHRED scores between 22.6 to 34.0, with the T > G substitution displaying the highest score, suggesting a pronounced functional effect ( Table 1 ) . Overall, the CADD results indicate that prioritized variants in both genes are likely to exert substantial functional constraints, supporting their potential relevance in ALS-associated molecular mechanisms Table 1 High-impact variants with CADD PHRED scores ≥ 20. Gene rsIDs Ref Alt Raw Score PHRED SOD1 rs1202989817 T C 4.504 25.5 rs1202989817 T G 5.010 28.0 rs1182088847 C G 3.601 23.0 TARDBP rs1228733743 T C 4.371 25.1 rs1228733743 T G 6.461 34.0 rs80356715 C G 3.478 22.6 rs80356715 C T 3.654 23.2 rs1570722030 T C 5.125 28.7 rs80356729 G A 3.849 23.6 3.3 In silico evaluation of functional impact and disease association of prioritized variants Functional impact of prioritized SOD1 and TARDBP variants was evaluated using SIFT and PolyPhen-2. Several variants in both genes were predicted as deleterious by SIFT and as probably damaging by PolyPhen-2, indicating potential effects on protein structure and function ( Table 2 ). Disease relevance was further assessed using PhD-SNP and SNPs&GO. In SOD1 , the variants rs1202989817 (V15G) and rs1182088847 (I19M) were consistently classified as deleterious (0.02 and 0.01) by SIFT and probably damaging (1.000 and 0.985) by PolyPhen-2. Both variants were additionally classified as disease-associated by PhD-SNP and by SNPs&GO. In contrast, TARDBP variants rs80356729 (G335D) and rs1570722030 (I222T) were classified as deleterious (0.00) by SIFT and probably damaging (0.999-1.000) by PolyPhen-2 however were predicted as neutral by both PhD-SNP and SNPs&GO ( Table 2 ). Table 2 Consensus pathogenicity predictions for high-confidence variants. Gene RSID Mutation Tool ΔΔG (kcal/mol) Prediction (per tool) SOD1 rs1202989817 V15G SIFT 0.02 Deleterious PolyPhen-2 1.000 Probably damaging PhD-SNP 8 Disease SNPs&GO 10 Disease rs1182088847 I19M SIFT 0.01 Deleterious PolyPhen-2 0.985 Probably damaging PhD-SNP 5 Disease SNPs&GO 10 Disease TARDBP rs80356729 G335D SIFT 0.00 Deleterious PolyPhen-2 0.999 Probably damaging PhD-SNP 3 Neutral SNPs&GO 9 Neutral rs1570722030 I222T SIFT 0.00 Deleterious PolyPhen-2 1.000 Probably damaging PhD-SNP 1 Neutral SNPs&GO 5 Neutral 3.4 ClinVar-based clinical annotation of prioritized variants The clinical annotations for prioritized SOD1 and TARDBP variants were obtained from the ClinVar database, which compiles user-submitted variant interpretations. Among the evaluated SOD1 variants, the T > G substitution of rs1202989817 (p.Val15Gly) was classified as likely pathogenic and associated with ALS, based on a germline submission from a clinical diagnostic laboratory. In contrast, the T > C alternate allele of rs1202989817 and rs1182088847 (C > G) were not reported in ClinVar (Table S2 ) , indicating the absence of current clinical classification for these substitutions. For TARDBP , most prioritized variants, including alternate alleles of rs1228733743, rs80356715, and rs1570722030, were not reported in ClinVar. However, the G > A substitution of rs80356729 exhibited conflicting interpretations of pathogenicity in ClinVar (Table S2 ) , with clinical submissions reporting associations with ALS but lacking a consensus classification. 3.5 Protein stability prediction Protein stability analysis using I-Mutant 2.0, MUpro, DynaMut-2, and INPS-MD consistently demonstrated destabilizing effects for the SOD1 variants (V15G and I19M) ( Table 3 ) . All tools produced negative ΔΔG values for both substitutions, indicating reduced structural stability. For TARDBP , the rs1570722030 (I222T) variant also exhibited consistent destabilization across all prediction tools, returning negative ΔΔG values ( Table 3 ) . In contrast, rs80356729 (G335D) demonstrated mixed predictions; MUpro, DynaMut-2, and INPS-MD predicted destabilization, whereas I-Mutant 2.0 predicted marginal stabilization (ΔΔG = + 0.15 kcal/mol), suggesting a limited or context-dependent stability effect. Overall, the stability predictions indicate a consistent loss of stability for the SOD1 variants and the TARDBP I222T substitution, while the G335D variant exhibits variable stability outcomes across tools ( Table 3 ) . Table 3 Protein stability predictions for consensus variants. Gene RSID Mutation Tool ΔΔG (kcal/mol) Prediction (per tool) Consensus Interpretation SOD1 rs1202989817 V15G MUpro -1.149 Decrease Stability Strong destabilizing effect (all tools consistently predict loss of stability) I-Mutant 2.0 -2.56 Decrease Stability DynaMut-2 -2.7 Destabilizing INPS-MD -3.55 Destabilizing rs1182088847 I19M MUpro -0.866 Decrease Stability Consistently destabilizing across all tools I-Mutant 2.0 -0.38 Decrease Stability DynaMut-2 -0.14 Destabilizing INPS-MD -0.89 Weakly destabilizing TARDBP rs80356729 G335D MUpro -0.951 Decrease Stability Predominantly destabilizing, but I-Mutant 2.0 suggests slight stabilization. I-Mutant 2.0 0.15 Increase Stability DynaMut-2 -0.09 Destabilizing INPS-MD -0.66 Weakly destabilizing rs1570722030 I222T MUpro -2.132 Decrease Stability Strong destabilizing effect (all tools consistently predict loss of stability) I-Mutant 2.0 -3.82 Decrease Stability DynaMut-2 -1.62 Destabilizing INPS-MD -2.66 Destabilizing 3.6 Post-translational modification analysis Post-translational modification analysis using GPS-MSP 6.0 server identified several conserved phosphorylation residues in both SOD1 and TARDBP (Table S3) . In SOD1 , the variants rs1202989817 (V15G) and rs1182088847 (I19M) retained all predicted phosphorylation sites observed in the wild-type protein, including T3, T59, S60, and T89, which were primarily associated with CK1 and TKL kinases. Phosphorylation scores and residue positions were preserved across wild-type and variant sequences. A similar pattern was observed for TARDBP . Both rs80356729 (G335D) and rs1570722030 (I222T) exhibited conserved phosphorylation sites distributed across serine and tyrosine residues. High-confidence tyrosine sites (Y4, Y73, Y123, and Y374) and multiple serine residues (S92, S273, S393, and S403) were consistently predicted across all TARDBP variants. No gain or loss of major phosphorylation sites was observed relative to the wild-type protein (Table S3) . Overall, PTM analysis indicated that the prioritized SOD1 and TARDBP variants do not alter predicted phosphorylation motifs, with conserved kinase recognition patterns maintained across wild-type and mutant sequences. 3.7 Secondary structure impact assessment Secondary structure prediction using GOR4 and SOPMA revealed that prioritized variants in SOD1 and TARDBP introduced only minor, localized alterations without disrupting global secondary-structure organization ( Table 4 ) . In SOD1 , the V15G variant showed a moderate increase in α-helical content with a corresponding decrease in extended strands, while I19M exhibited smaller shifts of similar direction. In TARDBP , G335D showed no detectable changes in secondary-structure elements, whereas I222T demonstrated minor shifts involving slight reductions in α-helix and marginal increases in extended strand and coil regions ( Table 4 ) . Complementary SOPMA analysis supported these findings, confirming that both SOD1 and TARDBP variants largely preserve overall secondary structure profiles (Figure S2 ) . In SOD1 (V15G) variant showed a modest increase in α-helix and extended strand, while (I19M) displayed small increases in α-helix, extended strand, and random coil. Meanwhile TARDBP variants G335D and I222T showed only negligible changes (≤ 0.25%) across structural elements (Figures S1 - S2) Table 4 Predicted impact of nsSNPs on the secondary structure of the SOD1 and TARDBP proteins compared to wild-type. Gene Structure Element GOR4 Wild-Type GOR4 Variant GOR4 Change (%) SOPMA Wild-Type SOPMA Variant SOPMA Change (%) SOD1 (rs1202989817) V15G α-helix 0(0.00%) 6(3.90%) 3.90 0(0.0%) 7(4.55%) 4.55 Extended strand 55(35.71%) 49(31.82%) -3.89 46(29.87%) 47(30.52%) 0.65 Random coil 99(64.29%) 99(64.29%) 0 100(64.94%) 100(64.94%) 0 SOD1 (rs1182088847) I19M α-helix 0(0.00%) 3(1.95%) 1.95 0(0.0% 5(3.25%) 3.25 Extended strand 55(35.71%) 52(33.77%) -1.94 46(29.87%) 47(30.52%) 0.65 Random coil 99(64.29%) 99(64.29%) 0 100(64.94%) 102(66.23%) 1.29 TARDBP (rs80356729) G335D α-helix 77(18.60%) 77(18.60%) 0 54(13.04%) 55(13.29%) 0.25 Extended strand 96(23.19%) 96(23.19%) 0 62(14.98) 63(15.22%) 0.24 Random coil 241(58.21%) 241(58.21%) 0 298(71.98%) 296(71.50%) -0.48 TARDBP (rs1570722030) I222T α-helix 77(18.60%) 72(17.39%) -1.21 54(13.04%) 54(13.04%) 0 Extended strand 96(23.19%) 100(24.15) 0.96 62(14.98) 61(14.73%) -0.25 Random coil 241(58.21%) 242(58.45%) 0.24 298(71.98%) 299(72.22%) 0.24 3.8 Three-dimensional structural modeling SWISS MODEL Homology-based structural modeling using SWISS-MODEL generated high-quality three-dimensional models for all variants ( Fig. 2 ) . The SOD1 (V15G and I19M) variants showed low MolProbity scores (0.62–0.71), zero clash scores, and > 96% residues in favored Ramachandran regions ( Fig. 3 ) , indicating high stereochemical accuracy (Table S4) . In contrast, TARDBP (G335D and I222T) variants exhibited higher MolProbity scores (1.73–1.76) and reduced percentages of favored Ramachandran residues (~ 74%) (Table S4) , with 12% outliers, suggesting localized conformational strain. Despite these differences, the global architecture of all models remained preserved ( Fig. 2 ) . 3.9 Expression profiling and interaction network analysis Analysis of the Human Protein Atlas (HPA) brain RNA-seq dataset showed that SOD1 and TARDBP are broadly expressed across major human brain regions. SOD1 exhibited higher transcript abundance in the cerebral cortex, hippocampus, thalamus, hypothalamus, cerebellum, and pons (Figure S3.A) . Similarly, TARDBP exhibited a similarly widespread expression pattern, with relatively higher levels in the cerebral cortex, hippocampus, cerebellum, and white matter (Figure S3.B) . These expression profiles indicate that both genes are constantly expressed in brain regions associated with motor coordination, synaptic regulation and cognition. 4. Functional enrichment and network analysis Functional enrichment analysis performed using STRING revealed that interaction partners of SOD1 and TARDBP are significantly enriched in biological processes significant to neurodegeneration. The protein–protein interaction (PPI) map formed a dense and highly interconnected cluster, reflecting strong functional coupling among ALS-associated genes ( Fig. 4 A ) . The network enrichment was highly significant (FDR < 1.0 × 10⁻¹⁶), indicating non-random biological associations. Gene Ontology biological process (GO-BP) analysis demonstrated enrichment for pathways involved in intracellular ion regulation, calcium-dependent signaling, and cytoskeletal organization. Highly enriched terms included regulation of calcium ion transport, voltage-gated calcium channel activity, calcineurin-NAFT signaling, and neurofilament organization ( Fig. 4 B ) The strongest enrichments corresponded to processes with large gene representation and low . Gene Ontology molecular function (GO-MF) analysis highlighted enrichment of protein–protein interaction domains and enzymatic activities, including calcium-dependent phosphatase activity, calmodulin binding, and structural components of the intermediate filament cytoskeleton ( Fig. 4 C ) . Gene Ontology cellular component (GO-CC) analysis revealed that interacting proteins localize predominantly to synapses, membrane microdomains, transcriptional regulatory complexes, and Wnt signalosome ( Fig. 4 D ) . 4.1 KEGG pathway integration and disease-level signatures KEGG pathway analysis utilizing ShinyGO extended the STRING-derived functional enrichment into a disease-specific framework. The ALS pathway appeared as the most significantly enriched module, with numerous genes from the SOD1-TARDBP interaction network mapping directly to key pathogenic nodes (Figure S4.A) . Highlighted genes included SOD1, TARDBP, FUS, VCP, OPTN, C9orf72, CHCHD10, and NEK1 , which were spread across diverse ALS-related pathways involving excitotoxicity, mitochondrial dysfunction, proteostasis disruption, axonal transport, and RNA-binding protein aggregation. Complementary KEGG over-representation analysis identified ALS as the top-enriched pathway, exhibiting the highest fold enrichment and statistical significance (-log₁₀P > 20) (Figure S4.B) . Pathways associated with neurodegeneration were also significantly enriched, alongside neuronal signaling processes such as VEGF signaling, calcium signaling, glutamatergic synapse regulation, axon guidance, MAPK signaling, and Wnt signaling. Additional enriched pathways included autophagy-animal, mitophagy, mRNA surveillance, ferroptosis, peroxisome biology, and spliceosome-related processes. Overall, KEGG analyses indicated that the SOD1-TARDBP interaction network aligns with established ALS mechanisms while intersecting with broader neurodegenerative pathways. 4.2 Network construction and topological analysis Network topology analysis using Cytoscape was performed to quantify the structural significance of SOD1 and TARDBP within the ALS-associated interaction network (Fig. 5 ) . The resulting network revealed a highly connected structures centered on two major hubs, TARDBP and SOD1 , with several secondary nodes forming tightly clustered ALS modules. The topology parameters are summarized in ( Table S5) . TARDBP emerged as the most influential node, exhibiting the highest degree (20), lowest average shortest path length (1.048), and highest closeness centrality (0.955). SOD1 ranked as the second major hub, with a degree of 19, high closeness centrality (0.913), and notable betweenness centrality (0.227), indicating its prominent position within the network. A secondary tier of highly connected nodes, including C9orf72, FUS, NEFH, and SETX , demonstrated substantial centrality (degree: 11–14) and high clustering coefficients (0.648–0.891), indicating densely interconnected ALS-associated sub-networks. Additional clustered nodes, such as ALS2, DCTN1 , Fig. 4 , OPTN, and VAPB (degree = 10), exhibited clustering coefficient of 1.0, consistent with tightly linked modules. In contrast, nodes such as TMEM106B, RNF19A, PRNP, SMN1 , and GSC exhibited lower degree values and centrality values, indicating more peripheral network positions. 4.3 Gene-Gene Interaction Network Analysis Gene–gene interaction analysis using GeneMANIA was performed to further characterize the functional landscape surrounding SOD1 and TARDBP . The integrated network comprised 35 nodes connected by 666 functional links, reflecting strong connectivity among ALS-associated genes (Figure S5) ,with most edges contributed by co-expression data, followed by physical interactions, shared pathways, and genetic interactions. The network exhibited a clustered architecture with several coordinated biological modules. Key RNA-binding proteins, including TARDBP, FUS, SETX , and TIA1 , clustered centrally. In parallel autophagy and proteostasis-related genes such as OPTN, TBK1 , VCP , and UBQLN2 formed a second major hub. Additional submodules included genes associated with mitochondrial function ( CHCHD10, NEK1 ) and vesicular trafficking ( C9orf72, VAPB ). Hub gene identification Analysis of node degree and centrality metrics identified several highly connected hubs within the network. Genes with the greatest connectivity included UBB/UBC, OPTN, VCP, TBK1, TARDBP , and SOD1 , indicating their prominent positions within the ALS-associated interaction network (Table S6) . 4.4 Functional enrichment and gene prioritization Functional enrichment of the SOD1-TARDBP network using GeneMANIA revealed several core biological pathways strongly aligned with ALS progression. Dominant functional categories include autophagy and protein degradation ( OPTN, TBK1, VCP, UBQLN2 ), RNA processing and splicing ( TARDBP, FUS, SETX, TIA1 ), oxidative stress response ( SOD1, FTH1 ), vesicular trafficking ( VAPB , GRB7 ), and mitochondrial organization ( CHCHD10, NEK1 ) (Table S7) . Gene prioritization based on weighted GeneMANIA highest scores identified C9orf72 (0.756), NEK1 (0.727), CHCHD10 (0.697), VAPB (0.671), and SETX (0.669) as the highest-ranking genes within the network (Table S8) . Additional centrally positioned genes included TIA1 , VCP, ANG, FUS, OPTN , and TBK1 , while lower-ranked genes such as SNX6 and IRAK2 contributed meaning interactions. Overall, enrichment and prioritization analyses indicate that the SOD1-TARDBP interaction network is organized around interconnected modules related to proteostasis, RNA regulation, mitochondrial biology, and vesicular processes. Discussion This in-silico study systematically evaluated nonsynonymous SNPs in SOD1 and TARDBP , identifying high-confidence deleterious variants with the potential to influence ALS pathogenesis. Where two SOD1 variants (rs1202989817 and rs1182088847) exhibited strong concordance for pathogenic predictions, strong destabilization, and localized secondary structure shift, whereas TARDBP variants showed partial variance across disease-association classifiers, with variant (I222T) demonstrated notable destabilizing potential. Structural modeling indicated preservation of global fold for all variants but revealed localized conformational changes. Network analyses placed SOD1 and TARDBP as central nodes within ALS-associated regulatory hubs, linking RNA metabolism, proteostasis, mitochondrial function, and axonal transport pathways. The SOD1 variants V15G and I19M showed high deleteriousness predictions and understanding among independent computational tools, consistent with evidence that early exon SOD1 variants often carry strong pathogenic signatures and correlate with ALS phenotypes (Ruffo et al., 2022 ). Stability prediction showed consistent thermodynamic destabilization of the SOD1 variants V15G and I19M, consistent with previous reports classifying V15G as likely pathogenic due to recurrent destabilizing signatures, low population frequency, and localization within a structural hotspot (Ruffo et al., 2022 ). Such destabilization aligns with established mechanisms in which mutation-induced instability increases misfolding, impaired metal coordination, and aggregation of SOD1 (Taylor et al., 2016 ; Trist et al., 2021 ). In contrast, TARDBP variants showed stronger deleterious agreement among sequence-based functional predictors than among clinical classification tools. Although G335D and I222T were predicted to exert biochemical impact, only I222T showed consistent destabilization across stability models. Such variance reflects recognized challenges in TARDBP variant interpretation, where biochemical disruption does not always translate into clear clinical classification (Balendra & Isaacs, 2018 ). Structural assessment suggested preserved global architecture but increased local conformational strain, particularly relevant given the intrinsically dynamic nature of TARDBP . Mutations within the low-complexity domain have been shown to alter phase behaviour and aggregation propensity without inducing major structural collapse (Conicella et al., 2016 ; Balendra & Isaacs, 2018 ). The predicted local instability observed here is therefore compatible with mechanisms involving changed liquid–liquid phase separation and aberrant aggregation. Post-translational modification predictions indicated conservation of major phosphorylation motifs throughout variants, suggesting that pathogenic effects are unlikely to arise from direct disruption of phosphorylation sites. This observation aligns with earlier findings that SOD1 mutations typically impair folding stability and metal binding rather than PTM motifs (Ruffo et al., 2022 ), and that TARDBP aggregation is more closely linked to conformational instability and dysregulated phase behaviour than to phosphorylation loss (Balendra & Isaacs, 2018 ). Secondary-structure analysis using GOR4 and SOPMA indicated localized increases in α-helical content for SOD1 V15G and I19M, whereas TARDBP variants G335D and I222T exhibited minimal changes. These modest shifts are consistent with ALS-associated mutations that alter local folding dynamics without disrupting global structure (Trist et al., 2021 ).Variants within the TARDBP low-complexity domain are known to influence aggregation propensity rather than induce major structural rearrangements (Conicella et al., 2016 ).Network-level analyses provided additional context, where STRING and Cytoscape topology analyses identified TARDBP and SOD1 as highly connected hubs within ALS-related pathways. Secondary clusters included genes such as FUS, C9orf72, OPTN, TBK1, VCP, SETX, CHCHD10 , and NEK1 , which participate in RNA metabolism, autophagy, mitochondrial function, and vesicular trafficking. KEGG enrichment confirmed ALS as the most significantly represented pathway and highlighted convergence with calcium signaling, autophagy, mitophagy, and RNA processing. These findings support the view that ALS arises from coordinated dysfunction across interconnected cellular systems rather than a single molecular defect (Goutman et al., 2022 ; Taylor et al., 2016 ). Thus, even modestly destabilizing variants in SOD1 and TARDBP can propagate dysfunction across multiple cellular systems. Several limitations should also be considered. All results are based on computational predictions and homology modeling, which cannot fully replace for experimental validation. Stability predictions represent thermodynamic approximations and may not capture intracellular dynamics. The modeling of TARDBP is constrained by intrinsically disordered regions, reducing confidence in predicted conformational predictions. Also network analyses reflect predicted or literature-derived associations rather than neuronal interactions, and population-genetic integration was not performed. Future studies incorporating molecular dynamics simulations, biochemical aggregation assays, liquid–liquid phase separation experiments, and iPSC-derived motor neuron systems will be required to validate the functional consequences of these variants. Genome-editing approaches such as CRISPR knock-in models could further clarify physiological relevance under endogenous expression conditions. Despite these limitations, the findings have significant clinical implications. The destabilizing SOD1 variants identified here may represent candidates for targeted therapeutic strategies, including antisense approaches aimed at reducing toxic SOD1 protein levels. Likewise, TARDBP variants predicted to influence conformational stability and phase behaviour may inform development of small molecules that modulate TDP-43 aggregation and restore RNA metabolism. Additionally network analysis highlighted hub genes such as OPTN, TBK1, VCP , and C9orf72 , which may serve as biomarkers or therapeutic targets within pathway-oriented intervention strategies. Together, these integrative results provide a framework for prioritizing variants and pathways for experimental validation and therapeutic targets in ALS. Conclusion This in-silico analysis systematically evaluated nonsynonymous variants in SOD1 and TARDBP to assess their potential relevance in amyotrophic lateral sclerosis. Two SOD1 variants, V15G and I19M, demonstrated strong consensus for pathogenicity, consistent thermodynamic destabilization, and structural features aligned with established mechanisms of SOD1 misfolding and aggregation. In contrast, TARDBP variants exhibited variable pathogenicity predictions but showed structural and network-level characteristics consistent with altered conformational stability and RNA-related dysfunction. The Functional enrichment and interaction analyses further underscored SOD1 and TARDBP as central nodes within ALS-related pathways involving proteostasis, autophagy, mitochondrial integrity, and RNA metabolism. Although the findings are based on computational modessling and require experimental validation, this integrative approach prioritizes structurally and network-relevant variants for future investigation. Overall, the study refines the molecular understanding of ALS-associated variation in SOD1 and TARDBP and highlights candidate variants and pathways that may inform mechanistic studies and therapeutic intervention. Declarations Competing interests The authors declare that they have no financial interests or personal conflicts that could have influenced the findings reported in this paper. Funding This work was supported by Indian Council of Medical Research (Sanction number. 54/8/GER/2019-NCD-II), DBT-BUILDER (BT/INF/22/SP43065/2021), Govt. of India, Manipal Research Board (MRB) Grant, and MAHE Seed Money Grant. Author Contribution MBM, SM, performed the analysis, drafted the manuscript and prepared the figures. SP conceptualized, edited, and revised the manuscript. Data Availability All Data are included in the manuscript and supplementary information. References Adzhubei, I., Jordan, D. M. & Sunyaev, S. R. Predicting Functional Effect of Human Missense Mutations Using PolyPhen-2. Curr. Protocols Hum. Genet. 76 (1). https://doi.org/10.1002/0471142905.hg0720s76 (2013). Alam, S. S. M., Samanta, A., Ali, S. & Hoque, M. Structural insights into the impacts of non-synonymous single nucleotide polymorphisms in CD274 gene on the PD-1/PD-L1 interaction: An in silico approach. Biochem. Biophys. Res. Commun. 784 , 152679. https://doi.org/10.1016/j.bbrc.2025.152679 (2025). 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Supplementary Files SupplementaryDataREV2.docx TableS1.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 06 May, 2026 Reviews received at journal 30 Apr, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviews received at journal 10 Apr, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviewers invited by journal 20 Mar, 2026 Editor invited by journal 18 Mar, 2026 Editor assigned by journal 27 Feb, 2026 Submission checks completed at journal 27 Feb, 2026 First submitted to journal 26 Feb, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-8978267","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":611121194,"identity":"6579d518-d3c6-41ca-8c31-3797d443f64e","order_by":0,"name":"Manjunath B Malshetty","email":"","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":false,"prefix":"","firstName":"Manjunath","middleName":"B","lastName":"Malshetty","suffix":""},{"id":611121195,"identity":"6b736d99-d87e-4ab0-abed-8a1c07bc74b2","order_by":1,"name":"Sandeep Mallya","email":"","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":false,"prefix":"","firstName":"Sandeep","middleName":"","lastName":"Mallya","suffix":""},{"id":611121196,"identity":"7fde9797-7822-49e3-bd06-9de35036580c","order_by":2,"name":"Sudharshan Prabhu","email":"data:image/png;base64,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","orcid":"","institution":"Manipal Academy of Higher Education","correspondingAuthor":true,"prefix":"","firstName":"Sudharshan","middleName":"","lastName":"Prabhu","suffix":""}],"badges":[],"createdAt":"2026-02-26 13:52:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8978267/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8978267/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105381081,"identity":"0897cda4-adbf-4a56-9215-dec51ce6ef64","added_by":"auto","created_at":"2026-03-25 11:18:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":184412,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow for SNP retrieval, inclusion, and analysis using \u003cem\u003ein silico\u003c/em\u003e approaches.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8978267/v1/c3bbb97ad79faed4dabc3039.png"},{"id":105752141,"identity":"71e3c9b5-e87d-4771-add0-541ca7e48bbf","added_by":"auto","created_at":"2026-03-30 15:55:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":102625,"visible":true,"origin":"","legend":"\u003cp\u003eHomology-based three-dimensional structural models of pathogenic variants in \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e proteins. The protein structures are displayed as ribbon diagrams in a rainbow gradient (blue to red) from the N-terminal to the C-terminal region. (A) \u003cem\u003eSOD1\u003c/em\u003e variant model illustrates the characteristic β-barrel fold composed of β-sheets and connecting loops, with localized alterations near the mutation site that may influence metal-binding stability. (B) \u003cem\u003eTARDBP\u003c/em\u003e variant model showing the multi-domain \u003cem\u003eTDP-43\u003c/em\u003e architecture, including RNA recognition motifs and flexible coil regions, with the mutation positioned in a structurally dynamic domain that potentially affects RNA binding and protein aggregation behaviour.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8978267/v1/bfd5a3661b7583fdc87d2ecd.png"},{"id":105381086,"identity":"492d40be-59f7-42b1-9a90-4c5687014788","added_by":"auto","created_at":"2026-03-25 11:18:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":105199,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRamachandran plot validation of three-dimensional structural models for \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eSOD1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eand \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eTARDBP \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003evariants\u003c/strong\u003e. The plots illustrate the distribution of backbone dihedral angles phi (φ) and psi (ψ) for all residues, with shaded regions representing sterically allowed conformations. (A) \u003cem\u003eSOD1 \u003c/em\u003evariants rs1182088847 (I19M) and (B) rs1202989817 (V15G) of displayed residues predominantly within favored and allowed regions, indicating well-defined geometries and strongerstereochemical quality. (C) \u003cem\u003eTARDBP \u003c/em\u003evariants rs80356729 (G335D) and (D) rs1570722030 (I222T) showed the most residue distribution within allowed regions but with a slightly higher proportion of outliers, suggesting localized conformational strain. All models demonstrate acceptable stereochemical reliability (MolProbity ≤ 1.76), confirming their suitability for further structural and functional interpretation.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8978267/v1/d064a7569328b414e17dafbb.png"},{"id":105381082,"identity":"205cba6a-7493-47b4-9ae5-ae95803f3a39","added_by":"auto","created_at":"2026-03-25 11:18:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":70589,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment and interaction landscape of the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eSOD1-TARDBP\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e network. \u003c/strong\u003e(A)\u003cstrong\u003e \u003c/strong\u003eSTRING protein-protein interaction (PPI) network showing the functional connectivity between \u003cem\u003eSOD1, TARDBP,\u003c/em\u003e and ALS-related genes. Edge colors correspond to different evidence types used by STRING, including experimental data, curated pathway information, co-expression patterns, and text-mining associations. (B) Gene Ontology- Biological Process (GO-BP) enrichment of the network. Bubble size represents the number of genes contributing to each term, while color indicates statistical significance (FDR). (C) Gene Ontology Molecular Function (GO-MF) enrichment, showing overrepresented functional activities within the network. (D) Gene Ontology-Cellular Component (GO-CC) enrichment highlighting the predominant subcellular localizations associated with \u003cem\u003eSOD1-TARDBP\u003c/em\u003einteraction partners.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8978267/v1/ef7c2df1abaa84acb8a37822.png"},{"id":105381084,"identity":"44eb52f1-b95d-42c0-b47f-4439a803f127","added_by":"auto","created_at":"2026-03-25 11:18:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":101876,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNetwork topology of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eSOD1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eTARDBP\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e within the ALS-associated STRING-Cytoscape interaction map. \u003c/strong\u003eThe Cytoscape visualization shows the central positioning of \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e(highlighted in red) and their strongest interaction partners (yellow nodes) based on STRING-derived functional connections. Edges represent high-confidence associations including co-expression, physical interaction, pathway co-membership, and shared protein domains. Network topology parameters-including average shortest path length, clustering coefficient, closeness centrality, betweenness centrality, and degree-were computed using the Network-Analyzer plugin in Cytoscape. Genes with higher degree and betweenness centrality serve as major hubs and bottleneck regulators within the ALS network.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8978267/v1/5e563afe7227d67b1993e5db.png"},{"id":105752638,"identity":"81e00b50-9b44-4324-8d53-ce2ad08e264f","added_by":"auto","created_at":"2026-03-30 16:03:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2316462,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8978267/v1/6e5a4947-8aba-4341-89c8-57e9b9099562.pdf"},{"id":105381087,"identity":"22eb0ea5-06f8-4671-9db7-69e415d48211","added_by":"auto","created_at":"2026-03-25 11:18:15","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":12473239,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryDataREV2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8978267/v1/ce19889d9aa0061a97cf8604.docx"},{"id":105381085,"identity":"cc51c6b1-e0ee-415d-abe7-0325898dd504","added_by":"auto","created_at":"2026-03-25 11:18:14","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1096282,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8978267/v1/6552e192a3a174d46d6b3931.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"In silico Evaluation of Deleterious nsSNPs in SOD1 and TARDBP Genes Associated with Amyotrophic Lateral Sclerosis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAmyotrophic lateral sclerosis (ALS) is a progressive and deadly neurodegenerative disorder characterized by the selective loss of upper and lower motor neurons (Vintilescu et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), leading to muscle weakness, spasticity, dysarthria, dysphagia, and eventually respiratory failure. Most patients die within 2\u0026ndash;5 years of symptom appearance, although survival varies based on genetic factors, environmental exposures, and molecular influences. (Michael A Van Es, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yuan et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). ALS mainly affects individuals between 50\u0026ndash;70 years old and includes both sporadic ALS (sALS), making up nearly 90% of cases, and familial ALS (fALS), accounting for approximately 10% (Goutman et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Increasing evidence shows that ALS exists within a spectrum of neurodegenerative disorders, sharing key pathological mechanisms with frontotemporal dementia (FTD), primarily through the buildup of TAR DNA-binding protein 43 (TDP-43) inclusions (Balendra et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Feneberg et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zeng et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMore than 40 genes have been linked to ALS, involved in pathways related to proteostasis, oxidative stress responses, RNA metabolism, axonal transport, and mitochondrial maintenance (Goutman et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Among these, \u003cem\u003eSOD1\u003c/em\u003e (superoxide dismutase 1) and \u003cem\u003eTARDBP\u003c/em\u003e (encoding TDP-43) are two of the most significant contributors to ALS pathology. Under normal physiological conditions, \u003cem\u003eSOD1\u003c/em\u003e converts toxic superoxide radicals into hydrogen peroxide and oxygen, thereby protecting neurons from oxidative damage. Meanwhile, \u003cem\u003eTARDBP\u003c/em\u003e (TDP-43) plays a role in regulating transcription, alternative splicing, mRNA transport, and microRNA processing. Mutations in \u003cem\u003eSOD1\u003c/em\u003e account for approximately 15\u0026ndash;20% of familial ALS (fALS) and 1\u0026ndash;2% of sporadic ALS (sALS), making it one of the earliest and most well-studied ALS-related genes (Huang et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Opie-Martin et al., 2022). Although \u003cem\u003eTARDBP\u003c/em\u003e mutations are less common, TDP-43 pathology appears in over 95% of ALS cases, emphasizing its key role in both familial and sporadic forms (Balendra et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Single-nucleotide polymorphisms (SNPs), particularly nonsynonymous SNPs (nsSNPs), cause amino acid substitutions that alter protein structure and function. In ALS, nsSNPs in \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e alter protein stability, aggregation, and toxicity, thereby influencing disease onset and progression (Berdyński et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Dash et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; L\u0026eacute;pine et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMutant \u003cem\u003eSOD1\u003c/em\u003e proteins acquire toxic gain-of-function properties, involving abnormal folding, disrupted metallation, defective maturation, and an increased tendency to aggregate (Trist et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, 2022). These alterations promote the formation of insoluble aggregates, impair mitochondrial activity, disrupt axonal structure, and induce cellular stress responses that compromise motor neuron survival. Considerable heterogeneity exists among \u003cem\u003eSOD1\u003c/em\u003e mutations, with different variants influencing disease onset, progression, and survival (Berdyński et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Opie-Martin et al., 2022). Specific mutations can alter polypeptide length or structural stability, modify aggregation kinetics, and contribute to variant-specific neurotoxic profiles (Berdyński et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Pathological studies have further revealed abnormalities in \u003cem\u003eSOD1\u003c/em\u003e maturation, metal imbalance, and oxidation states in ALS spinal cord tissue, highlighting their contribution to the selective vulnerability of motor neurons (Trist et al., 2022).\u003c/p\u003e \u003cp\u003eMeanwhile, TDP-43 pathology represents another core hallmark of ALS. In healthy neurons, TDP-43 resides in the nucleus; however, in disease states, it mislocalizes to the cytoplasm and forms phosphorylated, ubiquitinated, and insoluble aggregates that impair RNA metabolism and destabilize neuronal function (Balendra et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zeng et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). TDP-43 proteinopathy is also observed in a broader range of neurodegenerative diseases and is characterized by prion-like propagation and progressive accumulation of misprocessed TDP-43 species (de Boer et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eTARDBP\u003c/em\u003e mutations further worsen this pathology by disrupting RNA-binding activity and nucleocytoplasmic transport, ultimately impairing neuronal function (Balendra et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Suk \u0026amp; Rousseaux, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Studies using human iPSC-derived motor neurons show that ALS-linked \u003cem\u003eTARDBP\u003c/em\u003e variants cause early electrophysiological problems and reduced synaptic activity before significant aggregation occurs (L\u0026eacute;pine et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additional research demonstrates rapid mislocalization of endogenous TDP-43 in inducible models (Ganssauge et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and region-specific proteomic analysis reveals alterations in ALS brain and spinal cord tissues (Feneberg et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Moreover, TDP-43 buildup in peripheral tissues such as the retina may serve as an early biomarker of neurodegeneration (Glashutter et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eDespite their distinct origins, \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e-related mechanisms converge on common pathways involving protein misfolding, oxidative damage, mitochondrial dysfunction, impaired RNA metabolism, and deficits in proteostasis (Trist et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, 2022; Zeng et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Understanding how these overlapping processes interact is essential for clarifying ALS pathogenesis and developing effective diagnostic and therapeutic strategies. However, several key questions still need to be answered, including the exact order of disease-causing events, why motor neurons are more vulnerable than other neuron types, and what factors contribute to the clinical differences in \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e-related ALS. Solving these issues is crucial for developing a unified model of how ALS progresses based on gene-specific mechanisms. Since \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e play significant roles in shaping the molecular structure of ALS, a thorough understanding of their functions is essential for discovering new biomarkers, improving disease models, and developing targeted treatments to slow down neurodegeneration caused by these proteins.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Ethical considerations\u003c/h2\u003e \u003cp\u003eThis study relied exclusively on computational analyses using publicly available genomic data sets and bioinformatics tools. As no human or animal subjects were involved, ethical approval and informed consent requirements did not apply to this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sample approach and justification\u003c/h2\u003e \u003cp\u003eA targeted gene-based computational workflow was employed to analyze \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e, two genes closely associated with ALS. The overall workflow is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Variant data were retrieved from the NCBI dbSNP database, which provides curated and standardized variant annotations suitable for in-silico analyses (Landrum et al., 2018).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Variant inclusion and exclusion criteria\u003c/h2\u003e \u003cp\u003e \u003cb\u003eInclusion criteria\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSNPs located within coding regions of \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNonsynonymous single-nucleotide polymorphisms (nsSNPs)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMinor allele frequency (MAF)\u0026thinsp;\u0026ge;\u0026thinsp;0.001\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eExclusion criteria\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSynonymous or intronic SNPs\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eVariants with MAF\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Variant retrieval and Dataset Processing\u003c/h2\u003e \u003cp\u003eVariant data for \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e were retrieved from the NCBI dbSNP (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/snp/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/snp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and UniProt databases. nsSNPs located within the coding regions were filtered and processed in R Studio (version 4.3.2) to remove duplicates, incomplete entries, and variants lacking functional annotations. The refined dataset included the rsID, genomic position, nucleotide substitution, and corresponding amino-acid alterations. Only variants consistently predicted as deleterious by both SIFT and PolyPhen-2 were retained for downstream analyses to ensure high confidence prioritization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Functional impact prediction\u003c/h2\u003e \u003cp\u003eThe functional significance of nsSNPs was assessed using the Combined Annotation Dependent Depletion (CADD) scoring system (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cadd.gs.washington.edu/\u003c/span\u003e\u003cspan address=\"https://cadd.gs.washington.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which integrates diverse genomic and functional features to estimate variant deleteriousness (Schubach et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). PHRED-like CADD scores were interpreted using standard thresholds, and only variants with scores\u0026thinsp;\u0026ge;\u0026thinsp;20 were retained for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Pathogenicity assessment\u003c/h2\u003e \u003cp\u003e \u003cb\u003eDeleterious nsSNPs analysis using SIFT\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe Sorting Intolerant From Tolerant (SIFT) tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sift.bii.a-star.edu.sg/\u003c/span\u003e\u003cspan address=\"https://sift.bii.a-star.edu.sg/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to evaluate the impact of each amino acid substitutions based on evolutionary conservation (Sim et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Variants with scores\u0026thinsp;\u0026le;\u0026thinsp;0.05 were classified as deleterious, whereas those with scores\u0026thinsp;\u003cb\u003e\u0026gt;\u003c/b\u003e\u0026thinsp;0.05 were predicted to be tolerated.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDamaging nsSNPs prediction using PolyPhen-2\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePolymorphism Phenotyping v2 (PolyPhen-2) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://genetics.bwh.harvard.edu/pph2/\u003c/span\u003e\u003cspan address=\"http://genetics.bwh.harvard.edu/pph2/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to predict the structural and functional consequences of amino acid substitutions using evolutionary features, comparative modelling, and physicochemical descriptors (Adzhubei et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Scores were interpreted based on standard classification thresholds, with values from 0.00 to 0.15 considered benign, 0.15 to 0.85 classified as possibly damaging, and 0.85 to 1.00 classified as probably damaging, respectively. Variants classified as probably or possibly damaging were retained.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePhD-SNP analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe pathogenic potential of nonsynonymous SNPs was evaluated using the Predictor of human deleterious Single Nucleotide Polymorphisms (PhD-SNP) classifier, a Support Vector Machine (SVM) based classifier that predicts whether amino acid substitutions are disease-associated or neutral (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://snps.biofold.org/phd-snp/phd-snp.html\u003c/span\u003e\u003cspan address=\"https://snps.biofold.org/phd-snp/phd-snp.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Al-Ayari et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The tool analyzes sequence-based features, evolutionary conservation, and substitution patterns to classify each variant accurately. Each prediction is accompanied by a Reliability Index (RI; 0\u0026ndash;10), indicating the confidence level of each prediction; higher values indicate greater reliability. These Predictions were used to differentiate harmful mutations from functionally tolerated substitutions, thereby providing additional evidence for pathogenicity and aiding in variant prioritization.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSNPs\u0026amp;GO analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe Single Nucleotide Polymorphism \u0026amp; Gene Ontology (SNPs\u0026amp;GO) integrates protein sequence information, evolutionary conservation, and Gene Ontology annotations to predict disease relevance (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://snps-and-go.biocomp.unibo.it/snps-and-go/\u003c/span\u003e\u003cspan address=\"https://snps-and-go.biocomp.unibo.it/snps-and-go/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Pavithran \u0026amp; Kumavath, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).This tool also provides a Reliability Index (RI) ranging from 0 to 10, where a higher value indicates greater confidence in the predictions. Variants predicted as Disease with an RI of 10 were considered highly reliable and likely pathogenic, supported by strong sequence conservation and functional context. SNP\u0026amp;GO predictions were used to complement other pathogenicity tools and strengthen variant prioritization.\u003c/p\u003e \u003cp\u003e \u003cb\u003eClinical annotation using ClinVar\u003c/b\u003e \u003c/p\u003e \u003cp\u003eClinical relevance of the prioritized nsSNPs were retrieved from the ClinVar database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/clinvar/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/clinvar/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a publicly accessible repository of variant interpretations submitted by clinical laboratories, researchers, and expert groups. ClinVar annotations, including pathogenic, likely pathogenic, conflicting interpretations or not reported, were retrieved for all variants with available rsIDs. ClinVar was used solely for clinical annotation and contextual interpretation and not as an \u003cem\u003ein-silico\u003c/em\u003e pathogenicity prediction tool, as the database contains user-submitted clinical classifications rather than standardized algorithmic predictions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Protein stability analysis\u003c/h2\u003e \u003cp\u003e \u003cb\u003eI-Mutant 2.0 analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eProtein stability changes induced by amino acid substitutions were assessed using I-Mutant 2.0, a sequence-based prediction tool that estimates mutation-induced variations in Gibbs free energy (ΔΔG) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://folding.biofold.org/i-mutant/i-mutant2.0.html\u003c/span\u003e\u003cspan address=\"https://folding.biofold.org/i-mutant/i-mutant2.0.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). It classifies substitutions as stabilizing or destabilizing relative to the wild-type protein, with negative ΔΔG values indicating reduced thermodynamic stability and positive values suggesting stabilization (Al-nakhle \u0026amp; Khateb, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consistent with previously reported in silico variant characterization studies, I-Mutant 2.0 predictions were interpreted with consideration of model-specific performance behaviour across diverse mutation datasets.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMUpro analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eProtein stability changes were further evaluated using MUpro, a machine-learning\u0026ndash;based method that predicts mutation-induced free-energy differences (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mupro.proteomics.ics.uci.edu\u003c/span\u003e\u003cspan address=\"https://mupro.proteomics.ics.uci.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). MUpro classifies substitutions as stabilizing or destabilizing on the basis of sequence-derived features and has been widely used in recent structural analyses of deleterious missense variants (Ali et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Variants predicted to decrease stability by MUpro were considered likely to contribute to structural disruption.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDynaMut-2 analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe effect of missense variants on protein stability and conformational dynamics was evaluated using DynaMut-2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biosig.lab.uq.edu.au/dynamut2/\u003c/span\u003e\u003cspan address=\"https://biosig.lab.uq.edu.au/dynamut2/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which integrates normal mode analysis, graph-based signatures, and machine-learning algorithm. The tool predicts mutation-induced changes in Gibbs free energy (ΔΔG) and protein flexibility by assessing alterations in intramolecular interactions and atomic fluctuations. Negative ΔΔG values (ΔΔG\u0026thinsp;\u0026lt;\u0026thinsp;0) indicate destabilizing mutations, whereas positive values (ΔΔG\u0026thinsp;\u0026gt;\u0026thinsp;0) indicate stabilizing effects relative to the wild-type protein (Kamal et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Variants predicted to induce destabilization or marked flexibility changes were prioritized for downstream structural and functional analyses.\u003c/p\u003e \u003cp\u003e \u003cb\u003eINPS-MD Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe effects of missense variants on protein stability were further evaluated using Impact of Non-synonymous Protein mutations\u0026ndash;Molecular Dynamics (INPS-MD), a computational tool which predicts mutation-induced stability changes by combining sequence, structural, and dynamic features (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://inpsmd.biocomp.unibo.it/\u003c/span\u003e\u003cspan address=\"https://inpsmd.biocomp.unibo.it/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which estimates changes in Gibbs free energy (ΔΔG), where positive values (ΔΔG\u0026thinsp;\u0026gt;\u0026thinsp;0) indicate stabilizing mutations and negative values (ΔΔG\u0026thinsp;\u0026lt;\u0026thinsp;0) indicate destabilizing effects (Alam et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The method combines residue environment, solvent accessibility, and interaction networks to evaluate mutation-driven stability alterations. INPS-MD predictions were used to complement other stability tools and support prioritization of variants with potential structural and energetic impacts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Post-translational modification analysis\u003c/h2\u003e \u003cp\u003ePost-translational modification (PTM) alterations were evaluated using the Group-based Prediction System-Methylation sited prediction (GPS-MSP) platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gps.biocuckoo.cn/online.php\u003c/span\u003e\u003cspan address=\"http://gps.biocuckoo.cn/online.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which predicts kinase-specific phosphorylation sites based on sequence patterns and motif signatures. This machine-learning\u0026ndash;based approach follows current PTM prediction practices and enables the identification of mutations that may disrupt regulatory modification sites (Meng et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), thereby providing additional insight into how variants may alter protein regulation, signalling, or stability particularly for proteins involved in neurodegenerative pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Secondary structure analysis\u003c/h2\u003e \u003cp\u003e \u003cb\u003eGOR4 (Garnier-Osguthorpe-Robson)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eProtein secondary structure was predicted using the GOR4 (Garnier\u0026ndash;Osguthorpe\u0026ndash;Robson) algorithm, which assigns α-helices, β-strands, and random coils based on information-theoretic analysis of amino acid composition and positional context within the sequence (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://npsa-prabi.ibcp.fr/NPSA/npsa_gor4.html\u003c/span\u003e\u003cspan address=\"https://npsa-prabi.ibcp.fr/NPSA/npsa_gor4.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). GOR4 evaluates residue propensities and neighboring interactions to estimate secondary structure probabilities, enabling assessment of mutation-induced local structural organization (Xia et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Predicted secondary structure profiles of wild-type and mutant proteins were compared to identify conformational changes associated with nonsynonymous substitutions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSOPMA\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSecondary structure elements were also predicted using Self-Optimized Prediction Method with Alignment (SOPMA), which estimates α-helices, β-strands, turns, and coils based on residue propensity scores (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://npsa-pbil.ibcp.fr/NPSA/npsa_sopma.html\u003c/span\u003e\u003cspan address=\"https://npsa-pbil.ibcp.fr/NPSA/npsa_sopma.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). SOPMA enhances prediction accuracy by incorporating multiple sequence alignments and iterative refinement, enabling accurate detection of mutation-induced shifts in secondary structure composition (Mukherjee et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The secondary structure profiles of wild-type and mutant sequences were compared to identify local conformational changes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Three-dimensional structural modeling\u003c/h2\u003e \u003cp\u003e \u003cb\u003eSWISS-MODEL\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThree-dimensional protein structures of selected variants were generated using the SWISS-MODEL homology modeling platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://swissmodel.expasy.org/\u003c/span\u003e\u003cspan address=\"https://swissmodel.expasy.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) through template identification, alignment, and energy-optimized model construction (Waterhouse et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Models with the highest Global Model Quality Estimation (GMQE) scores were selected. And structural validation was performed using Ramachandran plot analysis and MolProbity evaluation, confirming acceptable stereochemical value, favorable MolProbity scores, and minimal steric clashes, supporting the reliability of the predicted models for downstream functional analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Expression, interaction, and functional analysis\u003c/h2\u003e \u003cp\u003e \u003cb\u003eHuman Protein Atlas (HPA)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eProtein and transcript expression patterns of the target genes were analyzed using the Human Protein Atlas (HPA) database across multiple human brain regions. HPA provides region-specific RNA expression and immunohistochemistry-based protein expression data, enabling evaluation of spatial expression patterns within the central nervous system (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org/\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Mohamed et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Expression profiles across different brain regions were analyzed to evaluate regional variability and support the biological relevance of the analyzed genes in neurological contexts. This strategy aligns with established in-silico methodologies that integrate variant analyses with tissue-specific expression data to strengthen functional interpretation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSTRING\u003c/b\u003e \u003c/p\u003e \u003cp\u003eProtein\u0026ndash;protein interaction (PPI) networks for \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e were constructed using the STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database, which integrates known and predicted interactions from experimental evidence, computational predictions, co-expression, and curated pathway databases (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Crosara et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). STRING was used to identify direct and indirect functional associations and to visualize interaction networks based on confidence scores. Also, it incorporates scoring algorithms and expanded protein\u0026ndash;protein association datasets were utilized to support comprehensive interaction mapping relevant to disease mechanisms (Szklarczyk et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eKEGG and ShinyGO\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFunctional pathway and enrichment analyses were performed using the KEGG database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kegg.jp\u003c/span\u003e\u003cspan address=\"https://www.kegg.jp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the ShinyGO platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinformatics.sdstate.edu/go/\u003c/span\u003e\u003cspan address=\"https://bioinformatics.sdstate.edu/go/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). KEGG was used to map the identified gene set to curate metabolic and signaling pathways (Kanehisa et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To complement this analysis, the curated gene list was uploaded to ShinyGO for Gene Ontology (GO) enrichment and pathway interpretation, which identifies significantly enriched biological processes, molecular functions, cellular components, and pathway categories, including KEGG pathways (Karunakara et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) .\u003c/p\u003e \u003cp\u003e \u003cb\u003eCytoscape\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCytoscape analysis was done for detailed network visualization and topological analysis of ALS-associated genes. Cytoscape was used to visualize the interaction structure and to calculate key network parameters, including degree centrality, betweenness centrality, closeness centrality, and clustering coefficient, which facilitate the identification of hub nodes and highly influential proteins within the network. These topological metrics enable a systematic assessment of the network's organization and the functional significance of individual proteins (Majeed \u0026amp; Mukhtar, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eGene Mania\u003c/b\u003e \u003c/p\u003e \u003cp\u003eGeneMANIA was used to construct a gene-gene interaction and functional association network for \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e with ALS-associated genes (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genemania.org/\u003c/span\u003e\u003cspan address=\"https://genemania.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Gene-MANIA combines various data types, including co-expression, physical interactions, genetic interactions, co-localization, pathways, and shared protein domains, to find genes that are functionally related to the query. The tool was applied to expand the interaction map and identify additional genes that may be involved in shared biological processes or disease pathways (Irfan et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This method enabled the functional contextualization of the genes by highlighting biologically relevant interaction partners.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Identification and prioritization of nsSNPs in \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eA comprehensive database mining of the dbSNP repository yielded 5,964 variants in \u003cem\u003eSOD1\u003c/em\u003e and 10,630 variants in \u003cem\u003eTARDBP\u003c/em\u003e genes. Among these, 226 \u003cem\u003eSOD1\u003c/em\u003e and 742 \u003cem\u003eTARDBP\u003c/em\u003e variants were categorized as missense (nonsynonymous) SNPs, suggesting possible amino acid substitutions with functional significance. After applying stringent inclusion criteria-coding region localization, nonsynonymous effect, and minor allele frequency (MAF\u0026thinsp;\u0026ge;\u0026thinsp;0.001) refined the dataset to 12 nsSNPs per gene \u003cb\u003e(Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/b\u003e. These variants were prioritized using CADD and subsequently assessed using SIFT, PolyPhen-2, PhD-SNP, and SNPs\u0026amp;GO for in-silico functional and pathogenicity analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2 CADD functional impact assessment\u003c/h2\u003e \u003cp\u003eCADD analysis revealed multiple \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e variants associated with ALS showing high deleterious potential, characterised by PHRED scores\u0026thinsp;\u0026ge;\u0026thinsp;20. In \u003cem\u003eSOD1\u003c/em\u003e, three substitution events corresponding to two unique SNPs (rs1202989817 and rs1182088847) were analyzed. The rs1202989817 variant carried two alternate alleles (T\u0026thinsp;\u0026gt;\u0026thinsp;C and T\u0026thinsp;\u0026gt;\u0026thinsp;G), which were analyzed separately due to distinct CADD scores. These substitutions exhibited PHRED scores ranging from 23.0 to 28.0, indicating moderate to strong predicted functional impact. In \u003cem\u003eTARDBP\u003c/em\u003e, six substitutions exhibited PHRED scores between 22.6 to 34.0, with the \u003cem\u003eT\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e substitution displaying the highest score, suggesting a pronounced functional effect \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Overall, the CADD results indicate that prioritized variants in both genes are likely to exert substantial functional constraints, supporting their potential relevance in ALS-associated molecular mechanisms\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\u003eHigh-impact variants with CADD PHRED scores\u0026thinsp;\u0026ge;\u0026thinsp;20.\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=\"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 \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\u003ersIDs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlt\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRaw Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePHRED\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eSOD1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers1202989817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers1202989817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers1182088847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cem\u003eTARDBP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers1228733743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers1228733743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers80356715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers80356715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers1570722030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers80356729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.6\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 \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3 \u003cem\u003eIn silico\u003c/em\u003e evaluation of functional impact and disease association of prioritized variants\u003c/h2\u003e \u003cp\u003eFunctional impact of prioritized \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e variants was evaluated using SIFT and PolyPhen-2. Several variants in both genes were predicted as deleterious by SIFT and as probably damaging by PolyPhen-2, indicating potential effects on protein structure and function \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDisease relevance was further assessed using PhD-SNP and SNPs\u0026amp;GO. In \u003cem\u003eSOD1\u003c/em\u003e, the variants rs1202989817 (V15G) and rs1182088847 (I19M) were consistently classified as deleterious (0.02 and 0.01) by SIFT and probably damaging (1.000 and 0.985) by PolyPhen-2. Both variants were additionally classified as disease-associated by PhD-SNP and by SNPs\u0026amp;GO. In contrast, \u003cem\u003eTARDBP\u003c/em\u003e variants rs80356729 (G335D) and rs1570722030 (I222T) were classified as deleterious (0.00) by SIFT and probably damaging (0.999-1.000) by PolyPhen-2 however were predicted as neutral by both PhD-SNP and SNPs\u0026amp;GO \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\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\u003eConsensus pathogenicity predictions for high-confidence variants.\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=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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\u003eRSID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMutation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTool\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eΔΔG (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePrediction (per tool)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cem\u003eSOD1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ers1202989817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eV15G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSIFT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDeleterious\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolyPhen-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProbably damaging\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhD-SNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDisease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSNPs\u0026amp;GO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDisease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ers1182088847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eI19M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSIFT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDeleterious\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolyPhen-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProbably damaging\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhD-SNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDisease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSNPs\u0026amp;GO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDisease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cem\u003eTARDBP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ers80356729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eG335D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSIFT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDeleterious\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolyPhen-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProbably damaging\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhD-SNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSNPs\u0026amp;GO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ers1570722030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eI222T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSIFT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDeleterious\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolyPhen-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProbably damaging\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhD-SNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSNPs\u0026amp;GO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNeutral\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 \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4 ClinVar-based clinical annotation of prioritized variants\u003c/h2\u003e \u003cp\u003eThe clinical annotations for prioritized \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e variants were obtained from the ClinVar database, which compiles user-submitted variant interpretations. Among the evaluated \u003cem\u003eSOD1\u003c/em\u003e variants, the T\u0026thinsp;\u0026gt;\u0026thinsp;G substitution of rs1202989817 (p.Val15Gly) was classified as likely pathogenic and associated with ALS, based on a germline submission from a clinical diagnostic laboratory. In contrast, the T\u0026thinsp;\u0026gt;\u0026thinsp;C alternate allele of rs1202989817 and rs1182088847 (C\u0026thinsp;\u0026gt;\u0026thinsp;G) were not reported in ClinVar \u003cb\u003e(Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e)\u003c/b\u003e, indicating the absence of current clinical classification for these substitutions.\u003c/p\u003e \u003cp\u003eFor \u003cem\u003eTARDBP\u003c/em\u003e, most prioritized variants, including alternate alleles of rs1228733743, rs80356715, and rs1570722030, were not reported in ClinVar. However, the G\u0026thinsp;\u0026gt;\u0026thinsp;A substitution of rs80356729 exhibited conflicting interpretations of pathogenicity in ClinVar \u003cb\u003e(Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e)\u003c/b\u003e, with clinical submissions reporting associations with ALS but lacking a consensus classification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Protein stability prediction\u003c/h2\u003e \u003cp\u003eProtein stability analysis using I-Mutant 2.0, MUpro, DynaMut-2, and INPS-MD consistently demonstrated destabilizing effects for the \u003cem\u003eSOD1\u003c/em\u003e variants (V15G and I19M) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. All tools produced negative ΔΔG values for both substitutions, indicating reduced structural stability. For \u003cem\u003eTARDBP\u003c/em\u003e, the rs1570722030 (I222T) variant also exhibited consistent destabilization across all prediction tools, returning negative ΔΔG values \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. In contrast, rs80356729 (G335D) demonstrated mixed predictions; MUpro, DynaMut-2, and INPS-MD predicted destabilization, whereas I-Mutant 2.0 predicted marginal stabilization (ΔΔG\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.15 kcal/mol), suggesting a limited or context-dependent stability effect.\u003c/p\u003e \u003cp\u003eOverall, the stability predictions indicate a consistent loss of stability for the \u003cem\u003eSOD1\u003c/em\u003e variants and the \u003cem\u003eTARDBP\u003c/em\u003e I222T substitution, while the G335D variant exhibits variable stability outcomes across tools \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\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\u003eProtein stability predictions for consensus variants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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\u003eRSID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMutation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTool\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eΔΔG (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePrediction (per tool)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eConsensus Interpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cem\u003eSOD1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ers1202989817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eV15G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMUpro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDecrease Stability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eStrong destabilizing effect (all tools consistently predict loss of stability)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI-Mutant 2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDecrease Stability\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDynaMut-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDestabilizing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eINPS-MD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDestabilizing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ers1182088847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eI19M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMUpro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDecrease Stability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eConsistently destabilizing across all tools\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI-Mutant 2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDecrease Stability\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDynaMut-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDestabilizing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eINPS-MD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWeakly destabilizing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cem\u003eTARDBP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ers80356729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eG335D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMUpro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDecrease Stability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePredominantly destabilizing, but I-Mutant 2.0 suggests slight stabilization.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI-Mutant 2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIncrease Stability\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDynaMut-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDestabilizing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eINPS-MD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWeakly destabilizing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ers1570722030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eI222T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMUpro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDecrease Stability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eStrong destabilizing effect (all tools consistently predict loss of stability)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI-Mutant 2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDecrease Stability\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDynaMut-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDestabilizing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eINPS-MD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDestabilizing\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 \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Post-translational modification analysis\u003c/h2\u003e \u003cp\u003ePost-translational modification analysis using GPS-MSP 6.0 server identified several conserved phosphorylation residues in both \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e \u003cb\u003e(Table S3)\u003c/b\u003e. In \u003cem\u003eSOD1\u003c/em\u003e, the variants rs1202989817 (V15G) and rs1182088847 (I19M) retained all predicted phosphorylation sites observed in the wild-type protein, including T3, T59, S60, and T89, which were primarily associated with CK1 and TKL kinases. Phosphorylation scores and residue positions were preserved across wild-type and variant sequences. A similar pattern was observed for \u003cem\u003eTARDBP\u003c/em\u003e. Both rs80356729 (G335D) and rs1570722030 (I222T) exhibited conserved phosphorylation sites distributed across serine and tyrosine residues. High-confidence tyrosine sites (Y4, Y73, Y123, and Y374) and multiple serine residues (S92, S273, S393, and S403) were consistently predicted across all \u003cem\u003eTARDBP\u003c/em\u003e variants. No gain or loss of major phosphorylation sites was observed relative to the wild-type protein \u003cb\u003e(Table S3)\u003c/b\u003e. Overall, PTM analysis indicated that the prioritized \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e variants do not alter predicted phosphorylation motifs, with conserved kinase recognition patterns maintained across wild-type and mutant sequences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Secondary structure impact assessment\u003c/h2\u003e \u003cp\u003eSecondary structure prediction using GOR4 and SOPMA revealed that prioritized variants in \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e introduced only minor, localized alterations without disrupting global secondary-structure organization \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. In \u003cem\u003eSOD1\u003c/em\u003e, the V15G variant showed a moderate increase in α-helical content with a corresponding decrease in extended strands, while I19M exhibited smaller shifts of similar direction.\u003c/p\u003e \u003cp\u003eIn \u003cem\u003eTARDBP\u003c/em\u003e, G335D showed no detectable changes in secondary-structure elements, whereas I222T demonstrated minor shifts involving slight reductions in α-helix and marginal increases in extended strand and coil regions \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Complementary SOPMA analysis supported these findings, confirming that both \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e variants largely preserve overall secondary structure profiles \u003cb\u003e(Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e)\u003c/b\u003e. In \u003cem\u003eSOD1\u003c/em\u003e (V15G) variant showed a modest increase in α-helix and extended strand, while (I19M) displayed small increases in α-helix, extended strand, and random coil. Meanwhile \u003cem\u003eTARDBP\u003c/em\u003e variants G335D and I222T showed only negligible changes (\u0026le;\u0026thinsp;0.25%) across structural elements \u003cb\u003e(Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e- S2)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredicted impact of nsSNPs on the secondary structure of the \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e proteins compared to wild-type.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\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\u003eStructure Element\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGOR4\u003c/p\u003e \u003cp\u003eWild-Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGOR4\u003c/p\u003e \u003cp\u003eVariant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGOR4\u003c/p\u003e \u003cp\u003eChange (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSOPMA\u003c/p\u003e \u003cp\u003eWild-Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSOPMA\u003c/p\u003e \u003cp\u003eVariant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSOPMA\u003c/p\u003e \u003cp\u003eChange (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eSOD1\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(rs1202989817)\u003c/p\u003e \u003cp\u003eV15G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eα-helix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0(0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6(3.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7(4.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtended strand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55(35.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49(31.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46(29.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e47(30.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRandom coil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99(64.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99(64.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e100(64.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e100(64.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eSOD1\u003c/em\u003e (rs1182088847)\u003c/p\u003e \u003cp\u003eI19M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eα-helix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0(0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3(1.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0(0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5(3.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtended strand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55(35.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52(33.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46(29.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e47(30.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRandom coil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99(64.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99(64.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e100(64.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e102(66.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eTARDBP\u003c/em\u003e (rs80356729)\u003c/p\u003e \u003cp\u003eG335D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eα-helix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77(18.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77(18.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e54(13.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e55(13.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtended strand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96(23.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96(23.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e62(14.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e63(15.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRandom coil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e241(58.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e241(58.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e298(71.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e296(71.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eTARDBP\u003c/em\u003e (rs1570722030)\u003c/p\u003e \u003cp\u003eI222T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eα-helix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77(18.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72(17.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e54(13.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e54(13.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtended strand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96(23.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100(24.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e62(14.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e61(14.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRandom coil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e241(58.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e242(58.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e298(71.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e299(72.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.24\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 \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Three-dimensional structural modeling\u003c/h2\u003e \u003cp\u003e \u003cb\u003eSWISS MODEL\u003c/b\u003e \u003c/p\u003e \u003cp\u003eHomology-based structural modeling using SWISS-MODEL generated high-quality three-dimensional models for all variants \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The \u003cem\u003eSOD1\u003c/em\u003e (V15G and I19M) variants showed low MolProbity scores (0.62\u0026ndash;0.71), zero clash scores, and \u0026gt;\u0026thinsp;96% residues in favored Ramachandran regions \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, indicating high stereochemical accuracy \u003cb\u003e(Table S4)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eIn contrast, \u003cem\u003eTARDBP\u003c/em\u003e (G335D and I222T) variants exhibited higher MolProbity scores (1.73\u0026ndash;1.76) and reduced percentages of favored Ramachandran residues (~\u0026thinsp;74%) \u003cb\u003e(Table S4)\u003c/b\u003e, with 12% outliers, suggesting localized conformational strain. Despite these differences, the global architecture of all models remained preserved \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Expression profiling and interaction network analysis\u003c/h2\u003e \u003cp\u003eAnalysis of the Human Protein Atlas (HPA) brain RNA-seq dataset showed that \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e are broadly expressed across major human brain regions. \u003cem\u003eSOD1\u003c/em\u003e exhibited higher transcript abundance in the cerebral cortex, hippocampus, thalamus, hypothalamus, cerebellum, and pons \u003cb\u003e(Figure S3.A)\u003c/b\u003e. Similarly, \u003cem\u003eTARDBP\u003c/em\u003e exhibited a similarly widespread expression pattern, with relatively higher levels in the cerebral cortex, hippocampus, cerebellum, and white matter \u003cb\u003e(Figure S3.B)\u003c/b\u003e. These expression profiles indicate that both genes are constantly expressed in brain regions associated with motor coordination, synaptic regulation and cognition.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Functional enrichment and network analysis","content":"\u003cp\u003eFunctional enrichment analysis performed using STRING revealed that interaction partners of \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e are significantly enriched in biological processes significant to neurodegeneration. The protein\u0026ndash;protein interaction (PPI) map formed a dense and highly interconnected cluster, reflecting strong functional coupling among ALS-associated genes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. The network enrichment was highly significant (FDR\u0026thinsp;\u0026lt;\u0026thinsp;1.0 \u0026times; 10⁻\u0026sup1;⁶), indicating non-random biological associations.\u003c/p\u003e \u003cp\u003eGene Ontology biological process (GO-BP) analysis demonstrated enrichment for pathways involved in intracellular ion regulation, calcium-dependent signaling, and cytoskeletal organization. Highly enriched terms included regulation of calcium ion transport, voltage-gated calcium channel activity, calcineurin-NAFT signaling, and neurofilament organization \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e The strongest enrichments corresponded to processes with large gene representation and low .\u003c/p\u003e \u003cp\u003eGene Ontology molecular function (GO-MF) analysis highlighted enrichment of protein\u0026ndash;protein interaction domains and enzymatic activities, including calcium-dependent phosphatase activity, calmodulin binding, and structural components of the intermediate filament cytoskeleton \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eGene Ontology cellular component (GO-CC) analysis revealed that interacting proteins localize predominantly to synapses, membrane microdomains, transcriptional regulatory complexes, and Wnt signalosome \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.1 KEGG pathway integration and disease-level signatures\u003c/h2\u003e \u003cp\u003eKEGG pathway analysis utilizing ShinyGO extended the STRING-derived functional enrichment into a disease-specific framework. The ALS pathway appeared as the most significantly enriched module, with numerous genes from the \u003cem\u003eSOD1-TARDBP\u003c/em\u003e interaction network mapping directly to key pathogenic nodes \u003cb\u003e(Figure S4.A)\u003c/b\u003e. Highlighted genes included \u003cem\u003eSOD1, TARDBP, FUS, VCP, OPTN, C9orf72, CHCHD10, and NEK1\u003c/em\u003e, which were spread across diverse ALS-related pathways involving excitotoxicity, mitochondrial dysfunction, proteostasis disruption, axonal transport, and RNA-binding protein aggregation.\u003c/p\u003e \u003cp\u003eComplementary KEGG over-representation analysis identified ALS as the top-enriched pathway, exhibiting the highest fold enrichment and statistical significance (-log₁₀P\u0026thinsp;\u0026gt;\u0026thinsp;20) \u003cb\u003e(Figure S4.B)\u003c/b\u003e. Pathways associated with neurodegeneration were also significantly enriched, alongside neuronal signaling processes such as VEGF signaling, calcium signaling, glutamatergic synapse regulation, axon guidance, MAPK signaling, and Wnt signaling. Additional enriched pathways included autophagy-animal, mitophagy, mRNA surveillance, ferroptosis, peroxisome biology, and spliceosome-related processes.\u003c/p\u003e \u003cp\u003eOverall, KEGG analyses indicated that the \u003cem\u003eSOD1-TARDBP\u003c/em\u003e interaction network aligns with established ALS mechanisms while intersecting with broader neurodegenerative pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Network construction and topological analysis\u003c/h2\u003e \u003cp\u003eNetwork topology analysis using Cytoscape was performed to quantify the structural significance of \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e within the ALS-associated interaction network (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The resulting network revealed a highly connected structures centered on two major hubs, \u003cem\u003eTARDBP\u003c/em\u003e and \u003cem\u003eSOD1\u003c/em\u003e, with several secondary nodes forming tightly clustered ALS modules. The topology parameters are summarized in (\u003cb\u003eTable S5)\u003c/b\u003e. \u003cem\u003eTARDBP\u003c/em\u003e emerged as the most influential node, exhibiting the highest degree (20), lowest average shortest path length (1.048), and highest closeness centrality (0.955). \u003cem\u003eSOD1\u003c/em\u003e ranked as the second major hub, with a degree of 19, high closeness centrality (0.913), and notable betweenness centrality (0.227), indicating its prominent position within the network.\u003c/p\u003e \u003cp\u003eA secondary tier of highly connected nodes, including \u003cem\u003eC9orf72, FUS, NEFH, and SETX\u003c/em\u003e, demonstrated substantial centrality (degree: 11\u0026ndash;14) and high clustering coefficients (0.648\u0026ndash;0.891), indicating densely interconnected ALS-associated sub-networks. Additional clustered nodes, such as \u003cem\u003eALS2, DCTN1\u003c/em\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cem\u003eOPTN, and VAPB\u003c/em\u003e (degree\u0026thinsp;=\u0026thinsp;10), exhibited clustering coefficient of 1.0, consistent with tightly linked modules. In contrast, nodes such as \u003cem\u003eTMEM106B, RNF19A, PRNP, SMN1\u003c/em\u003e, and \u003cem\u003eGSC\u003c/em\u003e exhibited lower degree values and centrality values, indicating more peripheral network positions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Gene-Gene Interaction Network Analysis\u003c/h2\u003e \u003cp\u003eGene\u0026ndash;gene interaction analysis using GeneMANIA was performed to further characterize the functional landscape surrounding \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e. The integrated network comprised 35 nodes connected by 666 functional links, reflecting strong connectivity among ALS-associated genes \u003cb\u003e(Figure S5)\u003c/b\u003e,with most edges contributed by co-expression data, followed by physical interactions, shared pathways, and genetic interactions.\u003c/p\u003e \u003cp\u003eThe network exhibited a clustered architecture with several coordinated biological modules. Key RNA-binding proteins, including \u003cem\u003eTARDBP, FUS, SETX\u003c/em\u003e, and \u003cem\u003eTIA1\u003c/em\u003e, clustered centrally. In parallel autophagy and proteostasis-related genes such as \u003cem\u003eOPTN, TBK1\u003c/em\u003e, \u003cem\u003eVCP\u003c/em\u003e, and \u003cem\u003eUBQLN2\u003c/em\u003e formed a second major hub. Additional submodules included genes associated with mitochondrial function (\u003cem\u003eCHCHD10, NEK1\u003c/em\u003e) and vesicular trafficking (\u003cem\u003eC9orf72, VAPB\u003c/em\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eHub gene identification\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAnalysis of node degree and centrality metrics identified several highly connected hubs within the network. Genes with the greatest connectivity included \u003cem\u003eUBB/UBC, OPTN, VCP, TBK1, TARDBP\u003c/em\u003e, and \u003cem\u003eSOD1\u003c/em\u003e, indicating their prominent positions within the ALS-associated interaction network \u003cb\u003e(Table S6)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Functional enrichment and gene prioritization\u003c/h2\u003e \u003cp\u003eFunctional enrichment of the \u003cem\u003eSOD1-TARDBP\u003c/em\u003e network using GeneMANIA revealed several core biological pathways strongly aligned with ALS progression. Dominant functional categories include autophagy and protein degradation (\u003cem\u003eOPTN, TBK1, VCP, UBQLN2\u003c/em\u003e), RNA processing and splicing (\u003cem\u003eTARDBP, FUS, SETX, TIA1\u003c/em\u003e), oxidative stress response (\u003cem\u003eSOD1, FTH1\u003c/em\u003e), vesicular trafficking (\u003cem\u003eVAPB\u003c/em\u003e, \u003cem\u003eGRB7\u003c/em\u003e), and mitochondrial organization (\u003cem\u003eCHCHD10, NEK1\u003c/em\u003e) \u003cb\u003e(Table S7)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eGene prioritization based on weighted GeneMANIA highest scores identified \u003cem\u003eC9orf72\u003c/em\u003e (0.756), \u003cem\u003eNEK1\u003c/em\u003e (0.727), \u003cem\u003eCHCHD10\u003c/em\u003e (0.697), \u003cem\u003eVAPB\u003c/em\u003e (0.671), and \u003cem\u003eSETX\u003c/em\u003e (0.669) as the highest-ranking genes within the network \u003cb\u003e(Table S8)\u003c/b\u003e. Additional centrally positioned genes included \u003cem\u003eTIA1\u003c/em\u003e, \u003cem\u003eVCP, ANG, FUS, OPTN\u003c/em\u003e, and \u003cem\u003eTBK1\u003c/em\u003e, while lower-ranked genes such as \u003cem\u003eSNX6\u003c/em\u003e and \u003cem\u003eIRAK2\u003c/em\u003e contributed meaning interactions. Overall, enrichment and prioritization analyses indicate that the \u003cem\u003eSOD1-TARDBP\u003c/em\u003e interaction network is organized around interconnected modules related to proteostasis, RNA regulation, mitochondrial biology, and vesicular processes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis in-silico study systematically evaluated nonsynonymous SNPs in \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e, identifying high-confidence deleterious variants with the potential to influence ALS pathogenesis. Where two \u003cem\u003eSOD1\u003c/em\u003e variants (rs1202989817 and rs1182088847) exhibited strong concordance for pathogenic predictions, strong destabilization, and localized secondary structure shift, whereas \u003cem\u003eTARDBP\u003c/em\u003e variants showed partial variance across disease-association classifiers, with variant (I222T) demonstrated notable destabilizing potential. Structural modeling indicated preservation of global fold for all variants but revealed localized conformational changes. Network analyses placed \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e as central nodes within ALS-associated regulatory hubs, linking RNA metabolism, proteostasis, mitochondrial function, and axonal transport pathways.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eSOD1\u003c/em\u003e variants V15G and I19M showed high deleteriousness predictions and understanding among independent computational tools, consistent with evidence that early exon \u003cem\u003eSOD1\u003c/em\u003e variants often carry strong pathogenic signatures and correlate with ALS phenotypes (Ruffo et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Stability prediction showed consistent thermodynamic destabilization of the \u003cem\u003eSOD1\u003c/em\u003e variants V15G and I19M, consistent with previous reports classifying V15G as likely pathogenic due to recurrent destabilizing signatures, low population frequency, and localization within a structural hotspot (Ruffo et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Such destabilization aligns with established mechanisms in which mutation-induced instability increases misfolding, impaired metal coordination, and aggregation of \u003cem\u003eSOD1\u003c/em\u003e (Taylor et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Trist et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In contrast, \u003cem\u003eTARDBP\u003c/em\u003e variants showed stronger deleterious agreement among sequence-based functional predictors than among clinical classification tools. Although G335D and I222T were predicted to exert biochemical impact, only I222T showed consistent destabilization across stability models. Such variance reflects recognized challenges in \u003cem\u003eTARDBP\u003c/em\u003e variant interpretation, where biochemical disruption does not always translate into clear clinical classification (Balendra \u0026amp; Isaacs, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Structural assessment suggested preserved global architecture but increased local conformational strain, particularly relevant given the intrinsically dynamic nature of \u003cem\u003eTARDBP\u003c/em\u003e. Mutations within the low-complexity domain have been shown to alter phase behaviour and aggregation propensity without inducing major structural collapse (Conicella et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Balendra \u0026amp; Isaacs, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The predicted local instability observed here is therefore compatible with mechanisms involving changed liquid\u0026ndash;liquid phase separation and aberrant aggregation.\u003c/p\u003e \u003cp\u003ePost-translational modification predictions indicated conservation of major phosphorylation motifs throughout variants, suggesting that pathogenic effects are unlikely to arise from direct disruption of phosphorylation sites. This observation aligns with earlier findings that \u003cem\u003eSOD1\u003c/em\u003e mutations typically impair folding stability and metal binding rather than PTM motifs (Ruffo et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and that \u003cem\u003eTARDBP\u003c/em\u003e aggregation is more closely linked to conformational instability and dysregulated phase behaviour than to phosphorylation loss (Balendra \u0026amp; Isaacs, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Secondary-structure analysis using GOR4 and SOPMA indicated localized increases in α-helical content for \u003cem\u003eSOD1\u003c/em\u003e V15G and I19M, whereas \u003cem\u003eTARDBP\u003c/em\u003e variants G335D and I222T exhibited minimal changes. These modest shifts are consistent with ALS-associated mutations that alter local folding dynamics without disrupting global structure (Trist et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).Variants within the \u003cem\u003eTARDBP\u003c/em\u003e low-complexity domain are known to influence aggregation propensity rather than induce major structural rearrangements (Conicella et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).Network-level analyses provided additional context, where STRING and Cytoscape topology analyses identified \u003cem\u003eTARDBP\u003c/em\u003e and \u003cem\u003eSOD1\u003c/em\u003e as highly connected hubs within ALS-related pathways. Secondary clusters included genes such as \u003cem\u003eFUS, C9orf72, OPTN, TBK1, VCP, SETX, CHCHD10\u003c/em\u003e, and \u003cem\u003eNEK1\u003c/em\u003e, which participate in RNA metabolism, autophagy, mitochondrial function, and vesicular trafficking. KEGG enrichment confirmed ALS as the most significantly represented pathway and highlighted convergence with calcium signaling, autophagy, mitophagy, and RNA processing. These findings support the view that ALS arises from coordinated dysfunction across interconnected cellular systems rather than a single molecular defect (Goutman et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Taylor et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Thus, even modestly destabilizing variants in \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e can propagate dysfunction across multiple cellular systems.\u003c/p\u003e \u003cp\u003eSeveral limitations should also be considered. All results are based on computational predictions and homology modeling, which cannot fully replace for experimental validation. Stability predictions represent thermodynamic approximations and may not capture intracellular dynamics. The modeling of \u003cem\u003eTARDBP\u003c/em\u003e is constrained by intrinsically disordered regions, reducing confidence in predicted conformational predictions. Also network analyses reflect predicted or literature-derived associations rather than neuronal interactions, and population-genetic integration was not performed. Future studies incorporating molecular dynamics simulations, biochemical aggregation assays, liquid\u0026ndash;liquid phase separation experiments, and iPSC-derived motor neuron systems will be required to validate the functional consequences of these variants. Genome-editing approaches such as CRISPR knock-in models could further clarify physiological relevance under endogenous expression conditions.\u003c/p\u003e \u003cp\u003eDespite these limitations, the findings have significant clinical implications. The destabilizing \u003cem\u003eSOD1\u003c/em\u003e variants identified here may represent candidates for targeted therapeutic strategies, including antisense approaches aimed at reducing toxic SOD1 protein levels. Likewise, \u003cem\u003eTARDBP\u003c/em\u003e variants predicted to influence conformational stability and phase behaviour may inform development of small molecules that modulate \u003cem\u003eTDP-43\u003c/em\u003e aggregation and restore RNA metabolism. Additionally network analysis highlighted hub genes such as \u003cem\u003eOPTN, TBK1, VCP\u003c/em\u003e, and \u003cem\u003eC9orf72\u003c/em\u003e, which may serve as biomarkers or therapeutic targets within pathway-oriented intervention strategies. Together, these integrative results provide a framework for prioritizing variants and pathways for experimental validation and therapeutic targets in ALS.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis in-silico analysis systematically evaluated nonsynonymous variants in \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e to assess their potential relevance in amyotrophic lateral sclerosis. Two \u003cem\u003eSOD1\u003c/em\u003e variants, V15G and I19M, demonstrated strong consensus for pathogenicity, consistent thermodynamic destabilization, and structural features aligned with established mechanisms of \u003cem\u003eSOD1\u003c/em\u003e misfolding and aggregation. In contrast, \u003cem\u003eTARDBP\u003c/em\u003e variants exhibited variable pathogenicity predictions but showed structural and network-level characteristics consistent with altered conformational stability and RNA-related dysfunction. The Functional enrichment and interaction analyses further underscored \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e as central nodes within ALS-related pathways involving proteostasis, autophagy, mitochondrial integrity, and RNA metabolism. Although the findings are based on computational modessling and require experimental validation, this integrative approach prioritizes structurally and network-relevant variants for future investigation. Overall, the study refines the molecular understanding of ALS-associated variation in \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e and highlights candidate variants and pathways that may inform mechanistic studies and therapeutic intervention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no financial interests or personal conflicts that could have influenced the findings reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by Indian Council of Medical Research (Sanction number. 54/8/GER/2019-NCD-II), DBT-BUILDER (BT/INF/22/SP43065/2021), Govt. of India, Manipal Research Board (MRB) Grant, and MAHE Seed Money Grant.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMBM, SM, performed the analysis, drafted the manuscript and prepared the figures. SP conceptualized, edited, and revised the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll Data are included in the manuscript and supplementary information.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdzhubei, I., Jordan, D. M. \u0026amp; Sunyaev, S. R. Predicting Functional Effect of Human Missense Mutations Using PolyPhen-2. \u003cem\u003eCurr. Protocols Hum. Genet.\u003c/em\u003e \u003cb\u003e76\u003c/b\u003e (1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/0471142905.hg0720s76\u003c/span\u003e\u003cspan address=\"10.1002/0471142905.hg0720s76\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlam, S. S. M., Samanta, A., Ali, S. \u0026amp; Hoque, M. 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Commun.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (1), 205. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40478-024-01914-9\u003c/span\u003e\u003cspan address=\"10.1186/s40478-024-01914-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Amyotrophic lateral sclerosis (ALS), SOD1, TARDBP (TDP-43), nsSNPs, protein stability, structural modeling.","lastPublishedDoi":"10.21203/rs.3.rs-8978267/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8978267/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAmyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder involving mutations in the \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e genes that significantly influence its development. Nonsynonymous SNPs in these genes can disrupt protein stability, folding, and regulatory functions, leading to motor neuron loss. This study performed an in-silico approach to assess the functional and structural effects of nsSNPs in \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e. A total of 5,964 \u003cem\u003eSOD1\u003c/em\u003e and 10,630 \u003cem\u003eTARDBP\u003c/em\u003e variants were retrieved from public databases, filtered for coding region with a MAF\u0026thinsp;\u0026ge;\u0026thinsp;0.001, and prioritized using CADD. Multiple approaches, including pathogenicity predictions, stability analysis, structural modeling, post-translational modification assessment, and network-based functions, were combined. 12 nsSNPs per gene met the inclusion criteria. Notably, \u003cem\u003eSOD1\u003c/em\u003e variants V15G (rs1202989817) and I19M (rs1182088847) consistently predicted as deleterious, showing decreased stability indicated by negative ΔΔG values and localized structural disruptions without global misfolding. Conversely, \u003cem\u003eTARDBP\u003c/em\u003e variants G335D (rs80356729) and I222T (rs1570722030) suggested destabilization but yielded mixed predictions regarding disease association. Network and pathway analyses highlighted \u003cem\u003eSOD1\u003c/em\u003e and \u003cem\u003eTARDBP\u003c/em\u003e as key nodes in ALS-related mechanisms such as oxidative stress, RNA metabolism, proteostasis, and mitochondrial impairment. These findings prioritize structurally destabilizing variants with potential pathogenic relevance in ALS and provide a computational framework for downstream experimental validation.\u003c/p\u003e","manuscriptTitle":"In silico Evaluation of Deleterious nsSNPs in SOD1 and TARDBP Genes Associated with Amyotrophic Lateral Sclerosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-25 11:18:09","doi":"10.21203/rs.3.rs-8978267/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"133129405421593270722190624252596136006","date":"2026-05-06T06:45:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T05:08:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285807065025357188899998992838863164193","date":"2026-04-30T04:53:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-10T13:53:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"139097395710420892789146807769263934804","date":"2026-03-20T16:03:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"181089612174653407532482817220687280328","date":"2026-03-20T14:15:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-20T13:57:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-18T12:26:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-27T12:01:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-27T11:55:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-26T13:32:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ed04dab9-6bd8-42d8-a846-a2bb066cbf99","owner":[],"postedDate":"March 25th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"133129405421593270722190624252596136006","date":"2026-05-06T06:45:47+00:00","index":81,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T05:08:39+00:00","index":66,"fulltext":""},{"type":"reviewerAgreed","content":"285807065025357188899998992838863164193","date":"2026-04-30T04:53:13+00:00","index":65,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65021105,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":65021106,"name":"Health sciences/Diseases"},{"id":65021107,"name":"Biological sciences/Genetics"},{"id":65021108,"name":"Health sciences/Neurology"},{"id":65021109,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-03-25T11:18:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-25 11:18:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8978267","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8978267","identity":"rs-8978267","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}