In Silico Profiling of Non-Synonymous SNPs of Fat Mass and Obesity- Associated Gene: Possible impacts on the treatment of Non-Alcoholic Fatty Liver Disease

doi:10.21203/rs.3.rs-2315245/v1

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In Silico Profiling of Non-Synonymous SNPs of Fat Mass and Obesity- Associated Gene: Possible impacts on the treatment of Non-Alcoholic Fatty Liver Disease

https://doi.org/10.21203/rs.3.rs-2315245/v1

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published 30 Jan, 2023

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Background: Non-alcoholic fatty liver, or NAFLD is the most common chronic liver ailment. It is characterised by excessive fat deposition in hepatocytes of individuals who consume little or no alcohol and are not affected by specific liver damaging factors. It is also associated with extrahepatic manifestations like chronic kidney disease, cardiovascular disease, and sleep apnea. Genome-Wide Association Studies have uncovered SNPs in the Fat Mass and Obesity Associated Gene (FTO) robust association with obesity and higher BMI. Since epidemiological evidence suggests an association of NAFLD with obesity and higher BMI, FTO can be a crucial modulator of NAFLD.

Methods: This study has identified potentially deleterious non-synonymous SNPs (ns-SNPs) employing various in silico tools. The structural changes caused by identified SNPs were studied using molecular modelling approaches. Also, the binding potentials of phytochemicals resveratrol, rosamarinic acid, and capsaicin with different mutant forms of FTO were investigated using molecular dynamics studies.

Results: We identified five potentially deleterious mutations from 383 ns-SNPs in the human FTO gene using various in silico tools. In the present study, we identified 5 nsSNPs of human gene FTO: Gly103Asp, Arg96Pro, Tyr295Cys and Arg322Gln with potential disease association using the Insilco approach. Modulation of demethylation activity by phytomolecule scan explains the hepatoprotective action of molecules. We showed that the binding ability of three polyphenols, rosamarinic acid, resveratrol, and capsaicin, was not affected by predicted mutations.

Conclusion: Since these molecules impart their effects on binding FTO, our data findings may help develop drugs for NAFLD.

Fat mass and obesity-associated gene (FTO)

Non-alcoholic fatty liver disease (NAFLD)

Lipogenesis

Genetic Polymorphism

FTO ns-SNPs

Non-alcoholic steatohepatitis (NASH)

Fat Mass and Obesity Associated Gene/FTO that codes for nucleic acid demethylase have robust association with obesity and higher BMI.
Mutation at Arg96 position to His results in loss of demethylase activity of FTO, therefore reducing catalytic activity of FTO.
Molecular docking and dynamics have indicated that Phytomolecules can potentially bind to the catalytic site of FTO.
Modulation of demethylation activity by phytomolecule scan explains the hepatoprotective action of molecules.
Epidemiological evidence suggests a close association of FTO with the development of NAFLD.

Non-Alcoholic Fatty Liver Disease (NAFLD) is a metabolic disorder identified by excessive fat deposition in the hepatocytes of individuals who consume little or no alcohol and are not affected by other specific liver-damaging factors. Although the etiology of NAFLD is entirely not completely understood, primary risk factors include features of metabolic syndrome like obesity, type 2 diabetes, and dyslipidemia. NAFLD development is attributed to a sedentary lifestyle, a high-calorie diet, genetic and epigenetic influence, and environmental factors like the microbiome [1]. NAFLD can also lead to a progressive liver disease called Non-alcoholic steatohepatitis (NASH), marked by hepatocyte ballooning and inflammation and, in severe conditions, progression to fibrosis, cirrhosis, and hepatocellular carcinoma [2–4]. Genome-Wide Association Studies (GWAS) have uncovered genes robustly associated with NAFLD. These include PNPLA3, Transmembrane 6 superfamily member 2 (TM6SF2), Glucokinase regulator (GCKR), membrane-bound O- acyltransferase domain- containing 7 (MBOAT7), and hydroxysteroid 17β- dehydrogenase (HSD17B13) [5]. With around a quarter of the world’s population affected by NAFLD, it is estimated that it will become the foremost cause of liver-related morbidity and mortality within the next two decades [6].

The fat mass and obesity-associated (FTO) gene, discovered during a GWAS study in 2007, was a robust association with obesity and higher BMI [7]. The epidemiological evidence suggesting a close association of NAFLD with obesity propounds the potential role of FTO in developing NAFLD [8]. Upregulated liver FTO expression has been observed in the NAFLD rat model [9]. In addition, increased serum FTO expression was observed in NAFLD patients, with greater prominence in patients with NASH [10]. FTO is described as the "master switch" as it influences epigenetic control over regulatory pathways in energy homeostasis [11]. FTO shares maximum sequence and structure homology with E. coli DNA repair protein AlkB and its mammalian homologs, including ABH 2 and 3. These proteins belong to Fe²⁺ and 2-oxoglutarate (2OG) dependent dioxygenases [12]. FTO encodes for a nucleic acid demethylase in the nucleus, which preferentially demethylates 3-meT in ssDNA while m⁶A and 3meU in ssRNA [13]. RNA modifications affect splicing, intracellular transport, translation, and half-life of RNA [14]; Other than energy homeostasis, FTO shows pleiotropic effects in cell proliferation and brain function [15].

Despite identifying various risk factors and associated genes, no pharmacologically approved medications are available to treat NAFLD, owing to the complex and multifactorial pathophysiology of the disease [1]. Although promising drugs are in the pipeline for treating NAFLD/NASH, lifestyle intervention remains the front-line treatment [16, 17]. It has been shown that the Mediterranean diet can decrease liver fat content and decrease hepatic steatosis [18, 19]. This is primarily attributed to the antioxidant properties of polyphenolic phytochemicals in the Mediterranean diet [20]. Resveratrol in red grapes improves hepatic damage in NAFLD patients [21]. Rosamarinic acid obtained from various plants of the Lamiaceae family has shown potency against NAFLD through an antioxidative mechanism in HepG2 cells [4]. Dietary intake of capsaicin has also been reported to prevent lipid deposition in HepG2 cells and liver tissues through TRPV1 activation-mediated lipolysis (Fig. 1).

In hepatic disorders, several factors are involved, including the creation of reactive oxygen species (ROS), lipid peroxidation, peroxynitrite formation, complement factors, and proinflammatory agents such as chemokines and cytokines [22]. Several biological activities are reported for rosamarinic acid, capsaicin and resveratrol. Rosamarinic acid is a reactive oxygen species scavenger and lipid peroxidation inhibitor [23]. It also possesses anti-inflammatory, angiogenic, and neuroprotective effects. Surprisingly, decreasing the synthesis of IL-6, TNF-alpha, monocyte chemoattractant protein 1, and cyclooxygenase can reduce fat-induced inflammation in the adipose tissue. Adiponectin expression is also increased by capsaicin. The third phytochemical resveratrol may have hepatoprotective effects in NAFLD [24]. It improves lipid and glycemic metabolism by reducing hepatic lipid buildup. In addition, resveratrol effectively reduces insulin resistance and enhances insulin sensitivity. Several phytochemicals are beneficial in obesity and NAFLD; however, the mechanisms are not yet well known. Some compounds bind to FTO protein and thus inhibit the demethylase activity of FTO [25, 26]. Quercetin binds strongly and inhibits FTO activity [27]. These findings open a new avenue toward developing therapeutic interventions through alterations in FTO activity. Based on these findings, it is rational to hypothesise that the selected phytochemicals could be a beneficial therapeutic intervention in NAFLD that targets metabolic abnormalities. FTO gene is associated with the NAFLD patients and animal models [28–30]. Polymorphism of the FTO gene is also associated with hepatic fat content [11]; we, therefore, have identified potentially deleterious non-synonymous SNPs (ns-SNPs) employing various in silico tools in this study. The structural changes caused by the identified SNPs have been studied using molecular modelling approaches. Further, molecular docking and dynamics studies examined the binding potential of phytochemicals resveratrol, rosamarinic acid, and capsaicin with different mutant forms of FTO.

Several studies have identified phytochemicals that inhibit the demethylation activity of FTO and their impact on the wild-type FTO structure [31]. However, the impact of the inhibitor molecules on the mutant FTO structure is not investigated in any of the studies. Therefore, molecular docking was used to study the affinity of the phytocompounds with the native wild-type FTO and to identify if there is any possible variation in the binding affinity of the chosen phytocompounds and mutant protein.

SNP retrieval

The SNPs were retrieved from the SNP database of the National Centre for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/snp) with various limits (Homo-sapiens, missense) [32]. An overview of the methodology is shown in Fig. 2.

Predictions Of Deleterious Effects

In silico tools were employed to distinguish detrimental SNPs from the retrieved SNPs. SIFT (Sorting intolerant from tolerant) uses sequence homology to predict if an amino acid substitution will alter the protein function [17]. If the normalised value is less than the cut-off value (in our case, 0.05), the substitution is likely deleterious. Polymorphism phenotyping (PolyPhen 2.0) is a tool that recognises the possible result of amino acid substitutions based on structure and function. The mutations are categorised as ‘probably damaging,’ ‘possibly damaging,’ and ‘benign’ based on the false-positive rate (FPR) threshold [33]. Single-site mutations can lead to changes in protein stability. The protein stability change can be predicted by I-Mutant [34]. The output provides the difference in Gibbs free energy values (DDG/DDG) between mutated and native types (kcal/mol). Protein analysis through evolutionary relationships (Panther) classifies proteins according to family, subfamily, molecular functions, etc [35]. Apart from these four, we have used Functional Analysis through Hidden Markov Models v2.3 (FATHMM). FATHMM is a high-throughput web-server that can predict the functional consequences of both coding variants, i.e., non-synonymous single nucleotide variants (ns-SNVs), and non-coding variants in the human genome, classifies the substitutions as tolerated or damaging [36].

Structure modelling and root mean square deviation (RMSD) calculations.

The mutation leads to significant changes in the structure and stability of proteins. Three-dimensional structure analyses were performed between the native and mutant to evaluate these changes. SWISS-MODEL, an automated homology modelling software, was used to model the 3D structure of the protein. PROCHECK was used to validate the 3D structures; then, the mutants were generated through Swiss PDB Viewer [37]. The energy of both native and mutants was minimised using NOMAD-Ref, which uses GROMACS by default for minimisation [38]. Finally, the differences in total energy were computed using the minimised structures. Swiss PDB Viewer was used to computing the root mean square value deviation (RMSD) between the Native and mutants.

Trajectory Analysis

The stabilising residues are defined by SRide based on their long-range interactions, hydrophobicity, and amino acid residue conservation [39]. Further investigation was done using PSIPRED, a simple and precise secondary structure prediction method. It analyses the output obtained from PSI-BLAST by including two feed-forward neural networks [40]. The functional regions in proteins were identified by using ConSurf [41]. Apart from these, we have used Missense-3D, which predicts structural changes introduced by an amino acid substitution & is applicable to analyse both PDB coordinates and homolog position-Specifictures [42].

Admet Analysis

SWISS ADME software of the Swiss Institute of Bioinformatics and the pkCSM server were used to estimate individual ADME behaviour of the phytocompounds, i.e. resveratrol, capsaicin, and rosamarinic acid [43, 44]. The toxicity of the phytocompounds was evaluated by using ProTox-II. ProTox-II predicted various toxicity endpoints like the compounds' hepatotoxicity, acute toxicity, carcinogenicity, mutagenicity, cytotoxicity, and immunotoxicity. The similarity between the query molecule’s functional group with those reported and found in the software database forms the basis of prediction.

Molecular Docking

FTO’s crystal structure (PDB ID:4QHO) was retrieved from RCSB, all 5 SNPs were modelled, and energy was minimised using the SWISS-PDB viewer [45]. The drug molecules capsaicin, rosamarinic acid and resveratrol were acquired from PubChem (Compound CID: 1548943, 445154, and 5281792, respectively) in SDF format converted to PDBQT after the addition of hydrogen using Open Babel. The drug molecules were used as ligands, and the energy-minimised native FTO structure and 5 mutants harbouring the solitary SNPs were used as receptors for the docking studies. Protein preparation for the docking was done using AutoDock Tools (version 1.5.6) (ADT). The PDB structure was converted to PDBQT format after removing crystal water molecules and native ligands, assignment of Kollman charges, and adding polar hydrogen. The ligand charges were merged, and non-polar hydrogens were removed in ADT. The search space was defined according to the binding site of FTO, constituted by the residues Arg95, Tyr108, Asn205, His231, Asp233, Tyr295, His307, Thr320, and Arg322 [46]. Docking was performed using AutoDock Vina (version 1.1.2) [47] with exhaustiveness. The interaction of the drug molecules with the active site was analysed using Schrödinger Maestro. Binding pose with the best binding affinity and maximum interactions with the active site residues was selected for MD simulations.

Molecular Dynamics Simulations

The docked complexes of five FTO mutants and the native protein with the three selected drug molecules capsaicin, resveratrol, and rosamarinic acid were subjected to Molecular Dynamics Simulation using GROMACS (version 2018.7) software suite by earlier described methods [48]. GROMOS 54a7 force field was used to build protein topology, while ligand topologies were generated using the PRODRG server [49]. A cubic box was generated extending 10Å from the protein atoms in all directions and solvated using SPC three-point water model. The system was neutralised by adding an appropriate number of sodium atoms. The solvated electro-neutral system was energy minimised to eliminate steric clashes using the steepest descent algorithm with a 1000 kJ/mol/nm convergence criterion. A cut-off of 1.2 nm was used for computing both van der Waals and Coulombic interactions, while long-range electrostatic interactions were handled using Particle-mesh Ewald (PME). The complex's equilibrium was performed first in a canonical ensemble (NVT) by restraining solvent and ions at 300 K for 100 ps. This was followed by NPT equilibration for 100 ps, where the restraint weight on the protein-ligand complex was gradually reduced. LINC algorithm was used as the bond constraint algorithm, while constant temperature and pressure were simulated using Berendsen’s thermostat and Parrinello-Rahman pressure coupling, respectively [50, 51]. A production simulation was performed for the duration of 100 ns with a step size of 2 fs using a leapfrog dynamics integrator. Post-simulation analysis was performed using GROMACS modules and in-house python scripts.

Data mining

A sum of 383 SNPs was obtained from the NCBI database, with functions limited to Homo sapiens retrieved.

Prediction Of Deleterious Ns-snps

27 deleterious ns-SNPs were identified by PolyPhen 2.0, SIFT, Panther, FATHMM, and I-Mutant 3.0. Previous studies have indicated that predicting deleterious ns-SNPs by combined tool analysis leads to more accurate results. It was ascertained that only 27 ns-SNPs are expected to be ‘‘possibly deleterious’’ by all tools named in Table-S1. Variation in alleles showed 10 ns-SNPs for Adenine→Guanine; 6 for Cytosine → Thymine; 1 for Adenine → Cytosine; 3 for Guanine → Thymine; 5 for Cytosine → Guanine; and 2 for Adenine → Thymine. The Adenine → Guanine nucleotide variation was seen maximum times with a SIFT score of zero, followed by Cytosine → Thymine. This verifies the results obtained with the FTO gene analysed in our study and confirms that point mutations may be deleterious possibly. The results are tabulated in Supplementary Table S1.

Modelling Protein Structures And Rmsd Calculation

Point mutations significantly alter the stability of the protein structures. Therefore, it is essential to study the relationship between the structure and function of proteins using three-dimensional structures [52]. The total energy of the native type was − 30, 249 kJ/mol. Considerable variations were found in the total energy of 25 mutants compared to the native type. The structure of the modelled mutant proteins is shown in Supplementary Figure S1.

In silico study on the FTO gene predicted that ns-SNPs, with RMSD > 0.3, play a substantial role during the commencement of disease. Swiss PDB Viewer was used to computing RMSD values. The present study found 12 mutant models with deviations more than 0.3A. A higher RMSD value indicates more deviation between the native and the mutant structure. This variation is damaging as it leads to the deviation in the 3-D space of the binding site of FTO genes. This, in turn, leads to a change in the binding efficiencies with the inhibitors. Hence, these 12 ns-SNPs (Table S2) can be expected to be ‘‘deleterious’’ and were selected for further analysis.

Trajectory Analysis

Stabilising residues for deleterious ns-SNPs were identified and compared with native structures. Panther was used to identifying destabilising residues in the ns-SNPs. Destabilising residues were present in five out of the twelve ns-SNPs. The added residues with negative free energy (DDG) values were categorised as “de-stabilised”. For the five ‘‘highly deleterious ns-SNPs,’’ which had RMSD > 0.3 and de-stabilising residues in proteins, solvent accessibility and secondary structure predictions were carried out through SRide, PSIPRED, ConSurf, and Missense 3D (Table 1).

Table 1

Trajectory analysis and stabilizing residues of the deleterious SNPs. Destabilizing residues are shown in bold. (E- Extracellular, C-coil, S-strands)
S.NO	Residual change	SRide(stabilizing residue)	PSIPRED		ConSurf		Missense3D (structural damage)
S.NO	Residual change	SRide(stabilizing residue)	Native (N)	Mutant (M)	Native (N)	Mutant (M)	Missense3D (structural damage)
1	Native	ILE99, VAL116, TRP118,ASN205, ALA243, ALA311
2	R96P	ILE99, VAL116, TRP118, ASN205, ALA243, TRP270, ALA311	E	E	9	8	Damaged
3	G103D	ILE99, VAL116, TRP118, ASN205, ALA243, TRP270, ALA311	S	C	9	7	Damaged
4	Y295C	ILE99, VAL116, TRP118, ASN205, TRP270, ALA311	S	S	9	9	Damaged
5	R316Q	ILE99, VAL116, TRP118, ALA243, TRP270, ALA311	E	E	9	9	Damaged
6	R322Q	ILE99, VAL116, TRP118, ASN205, TRP270, ALA311	C	S	9	9	Damaged

Admet Analysis

The result of the ADMET analysis is tabulated in Tables 2 and 3. All three drug molecules are not substrate for P-glycoprotein (Pgp). The GI absorption is high for Capsaicin and Resveratrol, whereas it is low for rosamarinic acid shown in Table 3. Capsaicin and resveratrol are capable of crossing Brain Blood Barrier (BBB). CYP isoforms (CYP1A2, CYP2C19, CYP2D6, and CYP3A4) and P-gp can synergistically process small molecules [53]. This, in turn, improves tissue and organism protection. If these isoenzymes are inhibited, it may lead to harmful effects like drug accumulation and lower clearance of its metabolites. Capsaicin and resveratrol inhibit CYP1A2. Resveratrol also inhibits CYP2C9, whereas capsaicin inhibits CYP2D6. None of the CYP isoforms is inhibited by Rosamarinic acid. Capsaicin and resveratrol are CYP3A4 inhibitor. K_p or skin permeability coefficient is correlated with molecular size and lipophilicity (Table 3) [42]. The skin permeant of a molecule will be more if the log K_p value is less negative. Resveratrol with log K_pvalue − 5.47 (cm/s) will have high skin permeability, whereas rosamarinic acid will have the least (-6.82 cm/s). The result of ProTox-II showed that resveratrol didn’t have any possible toxic effect.

Table 2

ADME profile of the three phytomolecules
S.No.	Toxicity Type	Result
S.No.	Toxicity Type	Capsaicin	Rosamarinic acid	Resveratrol
1	Hepatotoxicity	Inactive	Inactive	Inactive
2	Carcinogenicity	Active	Inactive	Inactive
3	Immunotoxicity	Active	Active	Inactive
4	Mutagenicity	Active	Inactive	Inactive
5	Cytotoxicity	Inactive	Inactive	Inactive

Table 3

Pharmacokinetic properties of the selected molecules Capsaicin, Rosamarinic Acid and Resveratrol
Molecules	GI absorption	BBB permeant	Pgp substrate	CYP1A2 inhibitor	CYP2C19 inhibitor	CYP2C9 inhibitor	CYP2D6 inhibitor	CYP3A4 inhibitor	log Kp (cm/s)
Capsaicin	High	Yes	No	Yes	No	No	Yes	Yes	-5.62
Rosamarinic acid	Low	No	No	No	No	No	No	No	-6.82
Resveratrol	High	Yes	No	Yes	No	Yes	No	Yes	-5.47

Molecular Docking

Molecular docking of the three selected drugs with FTO was performed using AutoDock Vina to predict their binding poses. Rosamarinic acid showed the best binding affinity among all three drugs against native and predicted deleterious mutants of FTO. MD simulations selected the binding pose with the best affinity for further refinement. The predicted binding affinity of drug molecules with FTO native and mutant forms obtained from AutoDock Vina is represented in Table 4. Detailed interactions of the phytomolecules with different mutant forms of FTO are shown in Fig. 3.

Table 4

Predicted binding affinity of drug molecules with FTO native and mutant forms obtained from AutoDock Vina.
S.No.	Target Protein	Predicted Binding Affinity (kcal/mol)
S.No.	Target Protein	Capsaicin	Resveratrol	Rosamarinic acid
1	FTO Native	-7.8	-7.8	-9.2
2	FTO R96P	-7.7	-7.6	-8.7
3	FTO G103D	-7.9	-7.8	-8.9
4	FTO Y295C	-8.3	-7.8	-9.3
5	FTO R316Q	-8.6	-8.4	-9.2
6	FTO R322Q	-7.8	-7.6	-9.7

Molecular Dynamics Simulation Studies

Molecular Dynamics simulations were performed to refine the binding pose and assess the stability of the protein-ligand complex under aqueous conditions. The structural perturbations of drug binding to native and mutant forms of FTO were examined using various parameters obtained from the analysis of the 100 ns trajectory.

RMSD was calculated to quantify the conformational changes in the protein structure. The well-equilibrated backbone RMSD profile (Fig. 4) indicates no significant structural perturbations in the presence of all five mutations and drug binding. The structural integrity of FTO mutants and native protein was also evidenced by the radius of the gyration plot (Fig. S2), which is an indicator of the compactness of protein. The compactness of the protein seemed to be marginally higher in the case of Rosamarinic acid-binding, displaying lower Rg values followed by resveratrol and then Capsaicin. Similarly, more minor changes in SASA with time reflect the global structural stability of the protein since conformational changes are often accompanied by changes in solvent accessibility of the protein. The differences in the SASA profile for different protein complexes were due to differences in solvent substitution upon drug binding (Fig. S3). The plot of RMSF of Cα atoms throughout the simulations is shown in Fig. 5.

The binding potential of FTO with the selected drug molecules shown in docking studies was substantiated in MD simulation studies. The relative stability of the protein-ligand complexes was assessed from the ligand RMSD profiles (Fig. 6). The complexes of drugs rosamarinic acid and resveratrol with different forms of FTO were stable with well-equilibrated ligand RMSD values throughout the 100 ns. In casecapsaicin, the complex with Arg96Pro, Gly103Asp, Arg316Gln, and Arg322Gln mutants of FTO, showed stable binding through interactions with the residues in the jelly-roll motif, while native and Tyr295Cys mutant FTO had comparatively fewer stable complexes with capsaicin.

Despite the elusive molecular mechanism underlying the biology of the FTO gene, it remains a crucial modulator of various human diseases, including NAFLD. We identified FTO genetic variants with potential disease association in the present study using an in silico approach (Table 5). Of the 383 ns-SNPs obtained from dbSNP for the human FTO gene, 27 were predicted deleterious using SIFT, PolyPhen 2.0, I-Mutant, Panther, and FATHMM. Further, 12 out of 27 were shortlisted after RMSD calculations. Five final ns-SNPs of the human FTO gene were identified after trajectory analysis using SRide vis-à-vis Arg96Pro, Gly103Asp, Arg316Gln, Tyr295Cys, and Arg322Gln. The clinical significance of some of these mutations is reported based on ACMG classification obtained from Var Some and ClinVar, Table 5 [54, 55].

Table 5

Genetic variants of FTO with potential disease association
dbSNP ID	Induced Mutation	Clinical Significance
rs121918214	Arg316Gln	Pathogenic
rs139577103	Arg96Pro	Uncertain Significance
rs745616565	Arg322Gln	Likely Pathogenic
rs1203776934	Gly103Asp	Uncertain Significance
rs1259762053	Tyr295Cys	-

The FTO structure comprises two domains: an N-terminal domain with β-helices in a jelly roll motif that harbours the catalytic core and an α-helical C-terminal domain with unknown structural homology [13]. The residues at positions 96, 103, 295, 316, and 322 are well conserved among all FTO homologs [56]. Tyr295, Arg316, and Arg322 are known to form crucial interaction with the carboxylate of the co-substrate 2OG, including salt bridges and hydrogen bonding. Arg96 is located on the substrate recognition lid and is responsible for specific substrate base recognition through hydrogen bond interactions. The residue Gly103 is present at the interface of the N-terminal and C-terminal domains. Despite having the catalytic core, N-terminal domain (NTD) is non-functional when expressed in the absence of C-terminal domain (CTD), or the interface between NTD and CTD is perturbed [57].

The mutation Arg316Gln is known to cause the inactivation of FTO enzymatic activity and cause the autosomal-recessive lethal disorder. Studies using fibroblasts harbouring FTO protein with Arg316Gln mutation showed enlarged cell size, altered cell morphology, increased senescence, and decreased cell viability [58]. Since the SNP pertaining to Arg316Gln mutation has been classified as pathogenic, it endorses the pipeline for identifying potentially deleterious ns-SNPs. Similarly, the mutation Arg322Gln also renders FTO inactive and is known to be associated with malformation syndrome, an autosomal recessive disorder. Mutation at Arg96 position to His results in loss of demethylase activity, and a thus similar effect is expected for the mutation to Proline [57]. Gly103Asp mutations have shown reduced demethylase activity in AlkB and ABH homologs of FTO. Since aspartic acid is a charged and large amino acid compared to glycine, the structural changes accompanying the mutation can drastically alter the interactions at the interface of NTD and CTD of FTO. The effect of Tyr295Cys mutation in FTO is not studied in detail. The change from tyrosine to cysteine at the 295th position will hamper the hydrogen bond between Tyr295 and 2-OG, reducing FTO activity.

Molecular docking and dynamics studies have shown that the phytochemicals such as rosamarinic acid resveratrol and capsaicin, showing promising effects against NAFLD, can potentially bind to the catalytic site of FTO. In addition, polyphenolic compounds like flavonoids have interacted with FTO. Modulating the demethylation activity by these phytochemicals can explain the hepatoprotective action of these molecules, as, the binding of the molecules was not affected by the mutations in the FTO gene caused by the predicted deleterious ns-SNPs.

The global burden of NAFLD is increasing at an alarming rate. However, no pharmacologically approved drugs against NAFLD are available owing to their complex pathophysiology. Since FTO might play a crucial role in NAFLD development, the current study identified five potentially deleterious mutations from 383 ns-SNPs in the human FTO gene using various in silico tools. The structural effect of the mutations was studied using molecular modelling. The mutations Arg316 and Arg322 have previously been validated through in vitro assays; further validation is required for Arg96Pro, Gly103Asp, and Tyr295Cys.

Additionally, molecular docking and simulations analysed the potential binding ability of three natural polyphenols, rosamarinic acid, resveratrol, and capsaicinicin. The binding of the three molecules was not affected by the predicted mutations. Thus these molecules can significantly aid in drug development against FTO and NAFLD.

Author contribution

DP and ADR participated in the design of the project. DP and AJ participated in data extraction and analysis, prepared figures, and participated in the writing of the manuscript. All the authors read and approved the final manuscript. The authors declare that all data were generated in-house and that no paper mill was used.

Ethical Approval

Not applicable

Consent for publication

Not applicable.

Conflict of interest

The authors declare no conflict of interest.

Availability of data and materials

The datasets and analysis of the current study can be availed upon request.

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In Silico Profiling of Non-Synonymous SNPs of Fat Mass and Obesity- Associated Gene: Possible impacts on the treatment of Non-Alcoholic Fatty Liver Disease

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Journal Publication

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Abstract

Figures

Highlights

Introduction

Materials& Methods

SNP retrieval

Predictions Of Deleterious Effects

Trajectory Analysis

Admet Analysis

Molecular Docking

Molecular Dynamics Simulations

Results

Data mining

Prediction Of Deleterious Ns-snps

Modelling Protein Structures And Rmsd Calculation

Trajectory Analysis

Admet Analysis

Molecular Docking

Molecular Dynamics Simulation Studies

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1