The gene NNAT in humans consists of two introns and three exons. Its genomic structure (mRNA sequence) specifies its expression as two mRNA species (generated by alternative splicing) i.e., one isoform is α, the other one is β . The α-isoform which we used in our current work contains all three exons, and its mRNA encodes a protein of 81 amino acids by the aid of all three exons, whereas, the β-isoform of NNAT mRNA encodes a protein consisting of 54 amino acids through only first and third exons . NNAT is a paternally expressed gene, and its absence correlates with postnatal growth restriction which is suggestive NNAT’s role as a developmental protein . Therefore, the reported high expression of NNAT in human brain tissues is associated with its role as a specific integral protein for fetal neural cell development, terminal brain differentiation, and pituitary development .
Exploring the tissue specific gene expression mechanism(s) provides the elaborated basis to understand how tissues are distinguished by gene expression patterns and implying their significant regulatory role. The preferred function and expression of particular gene in one or more several tissues (or cells) types set better understanding of genes functionality or tissue - gene relationship, its etiology and discovery of novel tissue-specific drug targets that are especially studied to manage the gene-associated diseases .
The main reason for NNAT gene selection and its further investigation through our in silico study was based on its higher gene expression pattern in brain tissue, in comparison to other tissues [11, 12]. Predominant expression of NNAT α-isoform during an initial stage of brain development was well reported, in comparison to NNAT β-isoform  while the primary adipocytes express only the α-isoform of NNAT gene . Additionally, NNAT has also been expressed in various other tissues which are particularly involved in metabolic energetic homeostasis of body [2, 50]. Especially in human brain, its mRNA is present in the hypothalamus and pituitary regions, whereas, peripheral tissue regions to the brain, NNAT has its expression including in tissues of thyroid and pancreas, with coordinating role in energetic homeostasis [2, 47, 48, 50]. Studies also reported that NNAT is normally expressed in islet of pancreas β - cells [2, 50] and its abnormal expression (NNAT β-isoform) is related with the pancreatic β-cells destruction [2, 47, 48, 50]. The accumulation of great amount of NNAT β isoform was reported as misfolded ubiquitin-positive aggregate protein structures which contributes to the damage of pancreas β - cells and possibly implicated in diabetes mellitus [2, 47]. On the contrary, loss of NNAT caused a decrease in the basal and insulin-stimulated glucose uptake and glycogen synthesis processes . Hence, in AN, it was also suggested that the ratio of NNAT β-isoform to NNAT α-isoform was affected, explaining the unexpected resistance to hypoglycemia in AN patients .
Researchers reported that AN runs in family in relation with genetic factors. Lombardi et al in 2019 and Ceccarrini et al., 2021 sequenced some families (with AN) and found significant association of NNAT (neuronatin) gene variants (single nucleotide polymorphism - SNP) in an identified population with AN [2, 10]. Lombardi et al., suggested that the constitutional reduced level of NNAT-α isoform in brain and in adipocytes from inclusive study subjects may predispose to AN . Previously published findings in humans and in other organisms strengthen the notion that NNAT expression changes or its gene variants may be associated with having susceptibility to EDs like AN . Past researches suggested that NNAT could be considered as a potential therapeutic target for the treatment of human obesity and our data extend this suggestion to AN . The data from tissue specific expression and earlier findings suggested that gene NNAT was involved in adiposity, metabolic homoeostasis and pancreatic mechanism which might dispose to AN [2, 10, 47, 50]. Therefore, our study’s rationale was to investigate transcript level prediction of the rare-coding deleterious nsSNPs of NNAT gene and mutational impact on protein stability. Furthermore, determination of the structural inference of rare-coding nsSNPs of NNAT gene and to estimating the in silico molecular binding energies of 3D homology model of most pathogenic variant(s) with therapeutic compounds of AN was also intended to propose its role in AN.
SNPs are the most common and simplest type to represent genomic variation among individual DNAs, responsible for the diversity in genome evolution and serve as the basis to understand devastating role of disease that have governing impact from gene to phenotype . Many GWAS (Genome wide association studies) have recognized number of SNPs that increase the risk of cancer like breast cancer, colorectal cancer, leukemia among others . Most of the pathogenic SNPs have been reported to modify the secondary structure, influence promoter activity, influentially impact the expression levels, and subcellular localization of mRNAs / affecting the protein regulatory function, thereby contributing to the progression of disease [51, 53]. Generally, mutations (SNPs) are neutral, but few of them have pathogenic predisposition by altering the regular function of protein . In different human diseases, nsSNPs constitute about half of all genetic variation which can impact either neutral or deleterious . To investigate the disease-causing mutation(s) as well as studying their potential role as genetic markers is a popular science and researchers believe that finding these genomic variants may affect the response to treatment [51–54]. Due to increase frequency of SNPs and non-redundant databases, recently it has become challenging to determine each SNPs and its substantial contribution in disease development. From a huge set of mutations, the role of computational analysis to mark off or prioritize pathogenic SNPs for genetic disease screening has its own significance [22–29]. In silico analyses of SNPs is always considered as a cost-effective and feasible option to investigate or to predict the status in terms of pathogenicity or relating its high risk impact as non-synonymous mutation through the available various bioinformatics tools .
Therefore, we aimed to investigate all rare-coding nsSNPs in the human NNAT gene and predict their transcript level effects on the basis of functional impact analyses, computation of changes in free energies due to nsSNPs (see Table 1–3), PTMs and their structural inference in relation to function, stability and regulation of its respective protein (see Fig. 4, and Table 4). Initially we have 840 exonic mutations from record; the filtration strategies reduced this count to 19 which were only rare-coding mutations. This count was reduced to 10 when segregated on the basis of missense mutation type (see Fig. 3). The distribution of these ten nsSNPs was probed which showed three nsSNPs i.e., rs539681368, rs542858994, rs560845323 as rare-coding nsSNP, distributed in five NNAT transcripts sequences (NNAT201-205), resulting total count of 10 transcript level nsSNP (see Table 1). The transcript based functional impact analyses of nsSNPs by the aid of various in silico mentioned tools such as PROVEAN, PhD-SNP, SNAP2, SNP&GO, PMut, MutPred2 and using consensus classifier for predicting high risk pathogenic mutations computed the nsSNP ‘rs539681368’ (C30Y: NNAT-204) as high risk pathogenic mutation (see Table 1–2). The computation (through MUpro, I-Mutant, iPTree, INPS) of mutation ‘rs539681368’ (C30Y: NNAT-204) in terms of their impact on protein stability showed the change in protein function (see Table 3).
We did ab initio structure prediction of NNAT α-isoform (this isoform sequence is the NNAT-204 transcript sequence) and afterwards we generated an alternate 3D model for rs539681368’ (C30Y: NNAT-204) mutation (see Fig. 5). The identification of active sites or functional interacting residues in protein structure actually described the critical and conserved functional areas of proteins, which can serve as a decisive step in predicting protein activity. When no information of crystal 3D structure of protein is available the selection and identification of active consensus residues for further investigating the role of protein in term of ligand / drug binding, is an important way to proceed. To resolve this, we used CB dock approach which computationally detects the binding sites, and determines the center and size of the druggable cavities in the NNAT protein (see Fig. 6).
In structural biology and computer-assisted drug design, molecular docking is an important tool to identify the best fit orientation of drug / ligand binding with the particular protein targets . The purpose of ligand-protein docking is to anticipate a ligand's most common binding mode(s) with a protein of a known 3D structure. Virtual screening, binding affinity, free energy binding estimates, as well as drawing out and visualizing various forms of bonds and non-bonded interactions between the ligand and amino acid residues of a protein, are all done using molecular docking . To examine our second objective we selected and screened five drugs commonly used for the managing AN (see Table 6). The tricyclic antidepressant ‘amitriptyline’ is used to treat depression, both endogenous and psychotic, as well as anxiety linked with depression. The actual mechanism is not completely understood. The proposed role of amitriptyline is thought to prevent the membrane pump mechanism responsible for the re-uptake of amines like norepinephrine and serotonin, increasing their concentration in synaptic clefts. One of the oldest hypotheses in depression is that the deficiency of serotonin (5-HT) and/or norepinephrine (NE) neurotransmission in the brain causes depressive symptoms hence, amitriptyline inhibits these pathways, which may be the mechanism by which it alleviates depressed symptoms especially in AN . Another tricyclic antidepressant ‘Desipramine’ used to treat depression, as it is referred as a choice of medication that selectively inhibits / blocks the re-uptake of norepinephrine (noradrenaline) neural synapse and inhibits serotonin reuptake . In addition, a tetracyclic antidepressant, ‘Mirtazapine’ is used for treating of major depressive disorder (MDD) and is sometimes prescribed as off-label drug for appetite stimulation. The suggested mechanism of action may be explained by its rapid onset of action, a high degree of responsiveness, a lesser side-effect profile and it also has effects on central adrenergic and serotonergic activity that distinguish it from other antidepressants . The antidepressant ‘Citalopram’ used in AN, belong to the class of drugs which is referred as SSRI (selective serotonin reuptake inhibitor) usually prescribed to treat depression. Citalopram's mechanism of action is based on the suppression of CNS neuronal re-uptake of serotonin (5-HT), and prevents serotonin absorption in the synaptic cleft by blocking the serotonin transporter (solute carrier family 6 member 4, SLC6A4) . Fluoxetine is another SSRI used in managing severe depressive disorder. It decreases the presynaptic re-uptake of the ‘serotonin’, resulting in the rise of the level of 5-hydroxytryptamine (5-HT) levels in different regions of the brain . The binding of these five drugs with the deleterious variant (rs539681368:C30Y) of NNAT gene were estimated in terms of lower free energy values (see Fig. 7, and Table 7). Additionally, in protein-ligand complexes, when hydrogen atom connected to a strongly electronegative atom is in the neighborhood of another electronegative atom with a lone pair of electrons, it creates a specific dipole-dipole attraction called a hydrogen bond [55, 61]. In proteins, hydrogen bonding in secondary structure and in tertiary conformation was observed as a characteristics structural entity which serves as a stabilizing force. Interactions (mostly hydrogen bonds) between surrounding polypeptide backbones that contain Nitrogen-Hydrogen bonded pairs and oxygen atoms make up a protein's secondary structure . Due to the strong electronegative nature of both N and O, hydrogen atoms bound to nitrogen in one polypeptide backbone can hydrogen bond to oxygen atoms in another chain, and vice versa . Docking guided analysis of AN drugs’ binding energies indicated with lowest binding free energy (ΔG), inhibition constant (Ki) and lower intermolecular energies with mutant model, strengthened that NNAT variants (rs539681368:C30Y) significantly interact with AN drugs (see Fig. 7). The all five drugs showed commonest interaction at K59, which was also characterized as the putative PTM site for acetylation; existed in the cytoplasmic domain of gene (see Fig. 4). Therefore, there is a probability that these drugs binds with the identified variant of NNAT significantly which results interference in the binding of these AN therapeutic compounds with their respective target proteins, leads to unmanaged status of AN. However, in vivo studies are still needed to support this propose mechanism.