Comprehensive In Silico Characterization of the Coding and Non-Coding SNPs in Human Dectin-1 Gene with the Potential of High-Risk Pathogenicity Associated with Fungal Infections

The human C-type lectin domain family 7 member A (CLEC7A) gene encodes a Dectin-1 protein that recognizes beta-1,3-linked and beta-1,6-linked glucans, which form the cell walls of pathogenic bacteria and fungi. It plays a role in immunity against fungal infections through pathogen recognition and immune signaling. This study aimed to explore the impact of nsSNPs in the human CLEC7A gene through computational tools (MAPP, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT, SNAP, and PredictSNP) to identify the most deleterious and damaging nsSNPs. Further, their effect on protein stability was checked along with conservation and solvent accessibility analysis by I-Mutant 2.0, ConSurf, and Project HOPE and post-translational modification analysis using MusiteDEEP. Out of the 28 nsSNPs that were found to be deleterious, 25 nsSNPs affected protein stability. Some SNPs were finalized for structural analysis with Missense 3D. Seven nsSNPs affected protein stability. Results from this study predicted that C54R, L64P, C120G, C120S, S135C, W141R, W141S, C148G, L155P, L155V, I158M, I158T, D159G, D159R, I167T, W180R, L183F, W192R, G197E, G197V, C220S, C233Y, I240T, E242G, and Y3D were the most structurally and functionally significant nsSNPs in the human CLEC7A gene. No nsSNPs were found in the predicted sites for post-translational modifications. In the 5′ untranslated region, two SNPs, rs536465890 and rs527258220, showed possible miRNA target sites and DNA binding sites. The present study identified structurally and functionally significant nsSNPs in the CLEC7A gene. These nsSNPs may potentially be used for further evaluation as diagnostic and prognostic biomarkers.


Introduction
Human Dectin-1 is a type II membrane protein expressed in many myeloid cells, including macrophages, monocytes, dendritic cells, neutrophils, and gamma delta T cells from the lymphoid lineage [1][2][3][4]. It plays an essential role in pathogen recognition and signaling in innate immunity [5,6]. Recently, Dectin-1 was found to increase immunological tolerance by binding to the conserved core domain of annexins (annexins A1, A4, and A13) produced on apoptotic cells [6]. It has a cytosolic portion containing an immunoreceptor tyrosine-based activation motif termed ITAM, an extracellular lectin domain, and a transmembrane domain. It has 10 isoforms listed in the UniProt database [7]. It contains a signaling motif known as hemITAM in its intracellular cytoplasmic tail [7], and two conserved amino acids (Trp 221 and His 223) are critical for ligand-Dectin-1 receptor interactions since they contribute to the binding sites and functions of the receptor [7]. The CLEC7A isoform 1 consists of 247 amino acids and serves as a pattern-recognition receptor (PRR) that recognizes β-1,3-linked and β-1,6-linked glucans, which form the cell walls of pathogenic bacteria and fungi [8]. Dectin-1 lacks Ca 2+ -binding sites in its CRD as it does not require Ca 2+ for ligand recognition [9]. However, β-glucan Ca 2+ binding sites have shown that bound calcium ions (Ca 2+ ) are needed to stabilize the domain structure [10]. Dectin-1 binds specifically to β-glucan receptors, requiring at least 10-or 11-mer chain length in Dectin-1's lectin domain [11]. Increasing β-glucan chain length correlates with increasing secondary structure formation, thus increasing the interaction as the helical structures fit into the ligand-binding site of the Dectin-1 lectin domain [12]. Dectin-1 only recognizes the middle part of the β-glucan chain, not the reducing/non-reducing ends [12]. In fungal recognition, on macrophages and DCs, Dectin-1 accomplishes two tasks: it internalizes β-glucan-containing particles and sends signals into the nucleus [12].
Single point mutations (SNPs), including synonymous (sSNPs) and non-synonymous (nsSNP) SNPs, have been shown to contribute to the pathophysiology of fungal infection. In a family with mucocutaneous fungal infections, a recessive mutation that changed an amino acid in Dectin-1 (Y238X) was discovered. Leading to the prematurely terminated translation of the Dectin-1 receptor's tyrosine residue [13]. Even if fungal killing and phagocytosis happen regularly, this mutation has been linked to susceptibility to fungal infections [14]. The final 9 amino acids of the CRD are deleted due to a nonsense mutation brought about by a single nucleotide alteration in this gene. Upon fungal infection or challenge with β-glucan, the mutation exhibits the expected loss of function and decreased cytokine response-changes in the translation termination at the tyrosine residue of the Dectin-1 receptor. The mutations in CLEC7A (SNPs rs3901533, rs7309123, and rs16910527) have been associated with fungal infection susceptibility, and Dectin-1 genetic variation plays a crucial role in fungal infection. Meta-analysis results suggest that CLEC7A SNPs may affect profound fungal infection susceptibility. In contrast, polymorphisms in rs16910526 are unlikely to have a significant effect.
Further investigations are warranted to verify and extend the present results and design novel immunotherapeutic strategies to optimize or replace conventional antifungal treatments. Previous studies used nsSNPs as computational tools to understand the molecular mechanisms of various diseases [15]. These tools are crucial in developing personalized medicine based on genomic variation and structural and functional analysis of nsSNPs. The impact of nsSNPs on the Dectin-1 protein on fungal disease pathogenesis was not fully understood. This study aimed to identify potential coding and non-coding SNPs that may affect Dectin-1 protein function, utilizing various computational approaches and bioinformatics tools.

Retrieval of Dectin-1 nsSNPs
SNPs of the Dectin-1 gene were retrieved from the dbSNP database (https://www. ncbi.nlm.nih.gov/snp (accessed on 7 April 2023)). Primarily, 975,620 SNPs were found, but after applying the missense, pathogenic, and frequency filters, 2068 SNPs were retrieved. The 247 nsSNPs in the coding region were selected for further analysis as the change in codon results in different amino acids. To analyze the non-coding region SNPs, the ENSEMBL database was used to collect the dataset for the Dectin-1 protein. An overview of the methodological approaches is summarized in Figure 1

Prediction of Functional Effects of Pathogenicity of nsSNPs
PredictSNP, which combines six software tools, was used to precisely analyze and predict the pathogenicity of Dectin-1 nsSNPs and find the highly risky and deleterious SNPs that can significantly alter the structure or function of Dectin-1 protein. The software includes MAPP, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT, and SNAP. As a result of combining the six best-performing tools into a consensus classifier, PredictSNP, prediction performance improved significantly. At the same time, all mutation results were returned, confirming that consensus prediction offers a more reliable and accurate alternative to individual tool predictions [16].

Determining nsSNPs on the Domains of Dectin-1
InterPro software was used to locate the site of nsSNPs on the conserved domains of Dectin-1 [17] (https://www.ebi.ac.uk/interpro/ (accessed on 7 April 2023)), which can identify motifs and domains of a protein. Consequently, the software was able to determine the functional characterization of a protein using the database consisting of protein families, domains, and functional sites [18].

Analyzing Protein Evolutionary Conservation
The ConSurf server identifies the evolutionary conservation of the amino acids in the protein sequence and analyzes the phylogenetic relationships between homologous sequences [19][20][21]. The conserved nsSNPs of Dectin-1 were considered for further analysis.

Analyzing the Effect of the nsSNPs on Protein Stability
To identify the effect of the damaging nsSNPs on the structure and stability of the Dectin-1 protein, I-Mutant 2.0 was used [22] (https://folding.biofold.org/i-mutant/i-mu-tant2.0.html (accessed on 7 April 2023)). I-Mutant 2.0 is another SVM-based tool that measures the change in free energy (Delta Delta G) and predicts whether it is increasing or decreasing. A Delta Delta G (DDG) (kcal/mole) value of 0 indicates a decrease in protein stability, whereas a DDG (kcal/mole) value > 0 indicates an increase in protein stability. This program predicts the stability of a protein after mutation. A protein stability test was then run on the pathogenic SNPs. Substantially deleterious nsSNPs are those that reduce protein stability.

Prediction of the Structural Effect of High-Risk nsSNPs on Human Dectin-1 Protein
Project HOPE was used to analyze the predicted effect of the nsSNPs or point mutations on the structure of the Dectin-1 protein. It is a web server that identifies the structural effects of point mutations in a protein sequence [23]. We used NP_922938.1 (the NCBI

Prediction of Functional Effects of Pathogenicity of nsSNPs
PredictSNP, which combines six software tools, was used to precisely analyze and predict the pathogenicity of Dectin-1 nsSNPs and find the highly risky and deleterious SNPs that can significantly alter the structure or function of Dectin-1 protein. The software includes MAPP, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT, and SNAP. As a result of combining the six best-performing tools into a consensus classifier, PredictSNP, prediction performance improved significantly. At the same time, all mutation results were returned, confirming that consensus prediction offers a more reliable and accurate alternative to individual tool predictions [16].

Determining nsSNPs on the Domains of Dectin-1
InterPro software was used to locate the site of nsSNPs on the conserved domains of Dectin-1 [17] (https://www.ebi.ac.uk/interpro/ (accessed on 7 April 2023)), which can identify motifs and domains of a protein. Consequently, the software was able to determine the functional characterization of a protein using the database consisting of protein families, domains, and functional sites [18].

Analyzing Protein Evolutionary Conservation
The ConSurf server identifies the evolutionary conservation of the amino acids in the protein sequence and analyzes the phylogenetic relationships between homologous sequences [19][20][21]. The conserved nsSNPs of Dectin-1 were considered for further analysis.

Analyzing the Effect of the nsSNPs on Protein Stability
To identify the effect of the damaging nsSNPs on the structure and stability of the Dectin-1 protein, I-Mutant 2.0 was used [22] (https://folding.biofold.org/i-mutant/i-mutant2.0.html (accessed on 7 April 2023)). I-Mutant 2.0 is another SVM-based tool that measures the change in free energy (Delta Delta G) and predicts whether it is increasing or decreasing. A Delta Delta G (DDG) (kcal/mole) value of 0 indicates a decrease in protein stability, whereas a DDG (kcal/mole) value > 0 indicates an increase in protein stability. This program predicts the stability of a protein after mutation. A protein stability test was then run on the pathogenic SNPs. Substantially deleterious nsSNPs are those that reduce protein stability.

Prediction of the Structural Effect of High-Risk nsSNPs on Human Dectin-1 Protein
Project HOPE was used to analyze the predicted effect of the nsSNPs or point mutations on the structure of the Dectin-1 protein. It is a web server that identifies the structural effects of point mutations in a protein sequence [23]. We used NP_922938.1 (the NCBI Reference Sequence Code of CLEC7A) and the 25 SNPs individually as the input. Missense 3D [24] confirmed the results' stringency and accuracy. Missense 3D predicts the effect of an amino acid substitution on protein structure. This study evaluated structural features used by the Missense3D web server, including disulfide bond breakage and buried H-bond breakage.

Analysis of 5 and 3 UTR Non-Coding SNPs
The ENSEMBL database was used to investigate non-coding regions [26]. We filtered out SNPs from the 5 and 3 regions, and a minor allelic frequency (MAF) value of ≤0.001 was selected. Regulome DB was used to relate SNPs to regulatory elements of the human genome [27]. It also provides chip data, chromatin states, motif information, and ranking based on DNA binding, if available. The PolymiRTS database was utilized to predict if there is DNA variation in miRNA target sites in the 3 UTR region [28]. The results are presented in four classes: D, O, C, and N, along with context and conservation scores, miR IDs, and target miR sites for miRs.

Determining CLEC7A Protein-Protein Interaction (PPI) Network Analysis
Protein-protein interactions are essential for regulating and executing a protein's biological functions. STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) predicts the top ten proteins interacting with the query gene by predicting their interactions with the CLEC7A protein (https://string-db.org (accessed on 7 April 2023)). The STRING algorithm predicts the interaction partners of a protein based on gene fusion, co-expression, function, and experimental data. Each interacting protein is scored from 0 to 1, where 0 indicates the lowest interaction and 1 indicates the highest interaction [29]. The CLEC7A FASTA protein sequence was submitted as input. All predicted interactions associated with confidence scores are included in the output.

Functional and Pathway Enrichment Analysis Using STRING
The functional analysis involved annotating and enriching proteins in the network based on their functional properties. The most common enrichment analysis is based on gene ontology (GO) terms (i.e., biological processes, molecular functions, and cellular components) and pathways. In order to interpret the physical function of a network, functional analysis is necessary. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using STRING [29].

Prediction of Pathogenicity of nsSNPs
Results from predictSNP software were compared to predict the pathogenicity of Dectin-1 nsSNPs precisely and label the highly risky deleterious SNPs that can significantly alter the structure or function. Out of 247 nsSNPs, 27 were predicted to be deleterious SNPs in all computational algorithms (Table 1).     We introduced the 28 nsSNPs into the Dectin-1 protein using the I-Mutant 2.0 tool. The outcome revealed that 25 out of 28 deleterious nsSNPs decreased stability ( Table 2). The DDG value was calculated from the mutated protein's unfolding Gibbs free energy value minus the wild type's unfolding Gibbs free energy value (Kcal/mol). DDG/DDG was positive for D13Y, S22F, and S117F and negative for the remaining nsSNPs run by this tool.

Project HOPE Results for Comparing Wild-Type and Mutant Amino Acid Properties
Project HOPE evaluated the differences in wild-type and mutant amino acids in terms of size, charge, hydrophobicity value, and possible interactions that mutated residues might induce. All 25 nsSNPs have a deleterious effect on protein structure (Table S2). Dectin-1 protein amino acid residues were altered in size, charge, and hydrophobicity at their respective positions. Amino acid residue sizes were changed from large to small, and the charge was observed to be lost or gained. The loss of hydrogen bonds caused by nsSNPs also increased or decreased hydrophobicity in the protein. As a result of these changes, the protein cannot fold correctly.

Project HOPE Results for Comparing Wild-Type and Mutant Amino Acid P ties
Project HOPE evaluated the differences in wild-type and mutant amino a terms of size, charge, hydrophobicity value, and possible interactions that mutat dues might induce. All 25 nsSNPs have a deleterious effect on protein structure (Ta Dectin-1 protein amino acid residues were altered in size, charge, and hydrophob

Missense 3D Results
Missense 3D predicted that 7 out of 25 nsSNPs have structural damage for the Dectin-1 protein. C120S, C148G, C233Y, and C220S have disulfide breakage; W141R has buried Hbond breakage; W141S has buried hydrophilic introduced; buried charge introduced; L155P has buried pro introduced; buried H-bond breakage; and W180R has buried hydrophilic introduced; buried charge introduced (Table 3). Table 3. Missense 3D analysis of the structural impact of seven missense nsSNPs in Dectin-1 protein highlighted in gray. The other residues were not determined in our study.

Analysis of 5 and 3 UTR Non-Coding SNPs
After setting the MAF filter to more than or equal to 0.001, 27 SNPs in the 3UTR and 2 SNPs in the 5UTR were found in the Ensemble database. The gene variants of Dectin-1 with transcript ID ENST00000304084.8 (Table 4. In Regulome DB, only the SNPs with a ranking < 4 were considered, and two SNPs, rs536465890 and rs527258220, were chosen for 5UTR. No SNPs were found for 3UTR. The rankings, along with the probability score, are given in Table 4.

PolymiRTS Analysis
SNPs in the PolymiRTS database were classified into four functional groups: (1) D describes the disruption of a conserved miRNA site; (2) n describes the disruption of a non-conserved miRNA site; (3) C denotes the creation of a new miRNA site; and (4) O denotes the absence of a determination of an ancestral allele. The results show that all of the miRNA target sites for miRNA predicted to be disrupted by SNPs in Dectin-1 were obtained from CLASH experimental data (n). No nsSNPs were obtained when filtered, considering the functional classes of C and D and conservation scores of 10 (Table S3).

Protein-Protein Interactions Analysis and Functional Enrichments Analysis
A STRING interaction analysis revealed that the CLEC7A gene is involved in many molecular and biological processes. The CLEC7A has high-confidence interactions with predicted functional partners, including spleen-associated tyrosine kinase (SYK), which mediates signal transduction downstream of a variety of transmembrane receptors, including classical immunoreceptors like the B-cell receptor; Toll-like receptor 2 (TLR2), which cooperates with LY96 to mediate the innate immune response to bacterial lipoproteins and other microbial cell wall components; Galectin-3; Galactose-specific lectin (LGALS3), which binds IgE; and Fc gamma receptor IIb (FCGR2B), aggregated immunoglobulins gamma. It is involved in various effector and regulatory functions, such as phagocytosis of immune complexes and modulation of antibody production by B-cells. Toll-like receptor 4 (TLR4) cooperates with LY96 and CD14 to mediate the innate immune response to bacterial lipopolysaccharide (LPS). Caspase recruitment domain-containing protein 9 (CARD9) is an adapter protein that plays a crucial role in the innate immune response to several intracellular pathogens, such as C. albicans and L. monocytogenes. Proto-oncogene tyrosine-protein kinase (SRC), a non-receptor protein tyrosine kinase that is activated following the engagement of many different classes of cellular receptors, including immune response receptors; 1-phosphatidylinositol 4,5-bisphosphate phosphodiesterase gamma-2 (PLCG2), which is a crucial enzyme in transmembrane signaling; C2 domain containing phospholipases; leukocyte antigen CD37 (CD37); and Galectin 9 (LGALS9), which stimulates bactericidal activity in infected macrophages by causing macrophage activation. All interactions are shown in Figure 4. Functional enrichments of the Dectin-1 network, including KEGG and GO, demonstrate that the Dectin-1 protein is involved in multiple immune signaling pathways [29] (Table S4). LGALS3), which binds IgE; and Fc gamma receptor IIb (FCGR2B), aggregated immunoglobulins gamma. It is involved in various effector and regulatory functions, such as phagocytosis of immune complexes and modulation of antibody production by B-cells. Tolllike receptor 4 (TLR4) cooperates with LY96 and CD14 to mediate the innate immune response to bacterial lipopolysaccharide (LPS). Caspase recruitment domain-containing protein 9 (CARD9) is an adapter protein that plays a crucial role in the innate immune response to several intracellular pathogens, such as C. albicans and L. monocytogenes. Protooncogene tyrosine-protein kinase (SRC), a non-receptor protein tyrosine kinase that is activated following the engagement of many different classes of cellular receptors, including immune response receptors; 1-phosphatidylinositol 4,5-bisphosphate phosphodiesterase gamma-2 (PLCG2), which is a crucial enzyme in transmembrane signaling; C2 domain containing phospholipases; leukocyte antigen CD37 (CD37); and Galectin 9 (LGALS9), which stimulates bactericidal activity in infected macrophages by causing macrophage activation. All interactions are shown in Figure 4. Functional enrichments of the Dectin-1 network, including KEGG and GO, demonstrate that the Dectin-1 protein is involved in multiple immune signaling pathways [29] (Table S4).

Discussion
SNPs are considered among the most significant risk factors associated with many diseases. The presence of SNPs within the protein-coding and non-coding regions can adversely affect the function and conformation of the protein. This study aimed to determine which of the most damaging nsSNP variants may affect the functionality of Dectin-1. To our knowledge, no comprehensive in silico analysis has been performed to predict dele-

Discussion
SNPs are considered among the most significant risk factors associated with many diseases. The presence of SNPs within the protein-coding and non-coding regions can adversely affect the function and conformation of the protein. This study aimed to determine which of the most damaging nsSNP variants may affect the functionality of Dectin-1. To our knowledge, no comprehensive in silico analysis has been performed to predict deleterious nsSNPs in coding and non-coding regions. In 2017, only three mutations of the 91 nsSNPs reported were predicted to be responsible for altering the protein structure of Dectin-1. The three mutations predicted were I223S (rs16910527), I158T 306 (rs138005591), and D159G (rs758623997) [30]. This study also detected the same mutations among other high-risk mutations using two additional in-silico algorithms (PolyPhen-1 and PredictSNP), two structural servers (I-Mutant3.0 and Project Hope), protein-protein interactions analysis, and functional and pathway enrichment analysis using STRING. Raman et al. proposed the consideration of the three non-synonymous SNPs in the Dectin-1 gene as a risk assessment against fungal infections.
InterPro software was used to determine the location of these nsSNPs on different domains of Dectin-1. It revealed 28 nsSNPs in three protein domains, of which two were positioned in the non-cytoplasmic domain. Nine nsSNPs were located in the TMhelix/transmembrane domain, and the rest (16 nsSNPs) were present in the cytoplasmic domain containing the immunoreceptor tyrosine-based activation motif-like sequence (ITAM-like). This ITAM mediates a ligand-induced signaling response's activation by interacting with the SKY protein and producing various immune modulators, suggesting the presence of any of the nsSNPs could disrupt the immune signaling pathways.
According to the ConSurf server, the evolutionary model of macromolecules is interpreted to demonstrate the importance of highly conserved regions in the function and conformation of macromolecules. Furthermore, the functional regions of proteins are often conserved and associated with various functions, including catalysis, interaction, and binding [27,28]. Our analysis of the ConSurf web server found 26 highly deleterious nsS-NPs located in highly conserved regions. Thus, increasing the risk of inactivating Dectin-1 and reducing antifungal effects. This suggests that these variants may alter the function and conformation of the protein. The conformational changes of the Dectin-1 protein during biomolecular interactions are vital for executing its function. In addition, nsSNPs can lead to aberrant conformations, which may inactivate their antifungal properties [31]. Hence, it is crucial to determine the effect of deleterious nsSNPs in Dectin-1 and their association with various diseases.
The stability of proteins is vital to their structural and functional activity [32]. The I-Mutant results showed that 25 nsSNPs decreased the stability of the protein. There are several consequences of changes in protein stability, including misfolding, degradation, or aberrant conglomeration of proteins [33].
There is a high risk of pathology when mutations occur in or near some special amino acids that contribute to functional and spatial conformation. A missense mutation results in amino acid substitutions, resulting in changes in amino acid size, charge, and hydrophobicity, which may disrupt the folding and interaction of proteins. The analysis from HOPE indicates that mutations lead to either loss of interactions or structural changes, particularly in transmembrane domains. In addition, introducing or losing charge or hydrophobicity would result in repulsion, misfolding, or loss of interactions. The Hope project results demonstrated that all 25 nsSNPs have a deleterious effect on the Dectin-1 structure and consequently contribute to the loss of its function and anti-fungal or microbial properties. Furthermore, the Missense 3D tool predicted the consequences of the 28 structural nsSNPs and showed that only seven nsSNPs deleteriously affect the structural conformation of the Dectin-1 protein.
Biological processes and disease development depend on the regulation of PTMs. Among the most harmful nsSNPs are those that alter PTM sites and are associated with the disease. We found none of the PTM sites we identified were affected by our nsSNPs using MusiteDeep software. miRNA binds to mRNA and inhibits mRNA translation through mRNA degradation to regulate protein production. A 3UTR nsSNP in the gene can modify or disrupt mRNA target sites, altering miRNA-mRNA interactions and possibly leading to abnormal gene expression [34]. In this regard, non-coding SNPs can interfere with normal gene expression and protein synthesis regulation. According to the PolymiRTS database, the D and C classes with high conservation and negative context scores have the highest functionally probable effects. Class D refers to disrupting a conserved site, while class C refers to creating a new site [28]. PolymiRTS analysis showed that no non-coding SNPs could alter the miRNA binding sites in the 3UTR of Dectin-1. In Regulome DB, only the SNPs with a ranking < 4 were considered. No nsSNPs were found for 3UTR. The rankings, along with the probability score, are given. According to Regulome DB, among the non-coding SNPs, rs536465890 and rs527258220 showed the best results for 5UTR.
From the STRING protein-protein interaction analysis, Dectin-1 was predicted to have strong interactions with SYK, TLR2, LGALS3, FCGR2B, TLR4, CARD9, SRC, PLCG2, LGALS9, and CD37. The STRING interaction result was further validated by using KEGG pathways for Dectin-1. Interestingly, the same set of proteins was involved in an immune signaling pathway. STRING analysis demonstrated that SYK, TLR4, CARD9, and TLR2 are involved in vulvovaginal candidiasis and infectious disease pathways. Therefore, any of the identified nsSNPs may change the Dectin-1 protein's function.
Studies have shown that a stop codon polymorphism causes inadequate Dectin-1 receptor activity, possibly enhancing vulnerability to invasive aspergillosis (IA) [35]. Recently, it has been discovered that several polymorphisms in genes that encode innate immunity-related proteins enhance susceptibility to Aspergillus infections [31]. The likelihood that a patient has more than one polymorphism and hence has a high vulnerability to developing IA is essential. Chai et al. indicated a strong correlation between the emergence of IA and genetic variations. Although Dectin-1 is one of the main phagocytic receptors in macrophages for fungal infections, other SNPs in other genes have also been reported to affect human-fungal recognition and interaction. These genes include mannose-binding lectin (MBL) 2, toll-like receptors (TLRs 1, 2, 3, 4), CARD9 caspase recruitment domain-containing protein 9, IL-4, and b-Defensin-1 [36].

Conclusions
In this study, we have identified several novel nsSNPs in the Dectin-1 gene that may be considered risk targets against fungal infections. The frequency of such mutations in local populations can be determined in the future through epidemiological research, as can whether these mutations are associated with fungal infections. This study could aid in identifying populations more susceptible to developing fungal infections by identifying genetic variations associated with IA or Candidiasis.