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SNP based analysis depicts phenotypic variability in heme oxygenase-1 protein

  • Pratichi Singh ORCID logo EMAIL logo , Syed Habeeb Ahmed , Irfan Ahmad and Mohammad Mahtab Alam
Published/Copyright: February 16, 2023
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Turkish Journal of Biochemistry
From the journal Turkish Journal of Biochemistry Volume 48 Issue 2

Abstract

Objectives

The heme oxygenase-1 (HMOX1) gene is a very critical player in cell homeostasis and takes part in heme catabolism. The HMOX1 gene possesses antioxidant, antiapoptotic anti-inflammatory, and antithrombotic properties. This study aimed to identify the deleterious SNPs which may alter the functional and structural attributes of the HMOX1 protein.

Methods

Deleterious SNPs were predicted using Polyphen-1, SIFT Blink, Polyphen-2, I-MUTANT 2.0, PROVEAN, PANTHER, MAPP, SNAP, and PhD-SNP. The 3D structure of the native protein was modelled using ITASSER and validated using PROCHECK. Mutant structures were created through SWISS PDB Viewer. All structures were energy-minimized using GROMACS. Protein-protein interaction (PPI) network was done using STRING.

Results

Three deleterious SNPs (rs146227657, rs373577583, and rs138349040 corresponding to A88D, A131V, and A206E respectively) in the HMOX1 gene were predicted. The structural analysis revealed notable differences in the structural attributes of wild-type and mutant structures. Furthermore, a PPI network was generated for the HMOX1 gene which predicts its interaction with other crucial cellular signaling molecules.

Conclusions

Three reported SNPs (A88D, A131V, and A206E) were identified as highly deleterious in the HMOX1 coding region that can alter the protein secondary structure, protein stability, and its conservation. This paves a new way to study the phenotype of the HMOX1 gene and its correlating diseases.

Keywords: deleterious; mutation; non-synonymous; protein function; SNPs

Introduction

Heme oxygenase-1 (HMOX1 or HO1, OMIM: 141250), also known as HSP32 (32-kDa heat shock protein) is a key rate-limiting enzyme that mediates the degradation of the heme into three products including biliverdin, free ferrous ion, and CO (carbon monoxide). The generated biliverdin is further reduced to bilirubin with the help of biliverdin reductase [1]. These metabolic products participate in several cellular processes that embrace inflammation, proliferation, apoptosis, and oxidative stress [2], [3], [4], [5], [6]. Heme oxygenase (HO) occurs mainly in three isoforms HO-I (Heme oxygenase-1), HO-2 (Heme oxygenase-2), and HO-3 (Heme oxygenase-3). These isoforms are the products of different genes and are differently regulated. HMOX1 is highly inducible which can be induced by various stresses or stimuli, such as heme or hemoglobin, lipopolysaccharide, heat shock, heavy metals, UV radiation, growth factors, phorbol esters, nitric oxide, hydrogen peroxide, hyperoxia, hypoxia, endotoxins, and cytokines [7], [8], [9], [10], [11]. In contrast, HMOX2 is constitutively articulated in most tissues. HMOX3 has the same protein structure as HMOX2 but with lower enzymatic activity than HMOX2 and is not well-characterized [12]. Despite heme catabolic activity, HMOX1 also engages in several disease progressions that include anti-oxidation and immune-modulatory properties and some skin diseases like psoriasis and vitiligo [12]. The deep-routed significance of HMOX1 in several diseases makes its functional investigation of missense mutation a unique method in establishing an enhanced and better therapeutic or diagnostic approach.

In recent times, a plethora of bioinformatics investigations are performed to screen missense mutation inside the protein-coding gene sequence of any gene via sequence or structural based information. The in-silico approach categorizes these SNPs as deleterious or lethal by considering numerous features like physio-chemical properties of the protein, sequence-conservation among several species, and structural attributes [13]. Structural variants in protein occur due to their biochemical properties, such as acidic, basic, hydrophilic or hydrophobic, etc., amino acid substitutions, and the substitution position [13].

In this study, the two diverse approaches including support vector-based and empirical-based methods were employed for the screening of non-synonymous SNPs. The objective of this research was to identify the deleterious or harmful SNPs which may tend to alter the functional and structural attributes of the HMOX1 protein.

Materials and methods

Data mining

The variant genomic region of the HMOX1 gene is harbored from the 1,000 genome project and NCBI database on 11th February 2021. Our research was narrowed down to Homo sapiens. A total of 1954 (HMOX1) non-reductant SNPs were curated. Among the 1954 HMOX1 gene variants, the regional SNPs comprise 65 synonymous SNPs (ss-SNP), 161 missenses non-synonymous (ns-SNP), 84 3′ UTR, 21 5′ UTR, and 1,334 intronic SNPs. The amino acid sequence of the HMOX1 gene (UniProt ID P09601) was recovered from the UniProt database. UniProt database is a collective resource of proteins where all the protein sequence data are accessible. HMOX1 gene contains 288 amino acids sequences downloaded in FASTA format for H. sapiens.

Tools for identifying deleterious SNPs

All the nsSNPs of the HMOX1 gene were exposed to various computational tools like SIFT Blink, PolyPhen-2, PROVEAN, I-MUTANT 2.0, PANTHER, MAPP, SNAP, and PhD-SNP to find its deleterious effect [14, 15]. The entire nine tools follow different algorithm-based on the homology of the sequences and evolutionary-based conservation methods. The impact of each variation in amino acid over the native residues was prioritized based on combinatorial scoring by the above-mentioned tools.

SIFT blink

SIFT blink (sorting intolerant from tolerant) predicts whether an amino acid substitution may affect the physical and functional properties of the amino acid sequence and ultimately the encoded protein. SIFT evaluates the pathogenicity of a missense mutation based on the degree of conservation of the residues [13, 14, 17]. SIFT is functional to laboratory-induced missense mutations and naturally occurring non-synonymous polymorphisms. SIFT has developed as one of the standard tools for portraying missense variation [16, 17]. SIFT analyzes a single protein using pre-computed BLAST from NCBI. The mutation can be refereed based on the scores; when the probability score is ≥0.05 or <0.05 it is designated as tolerant or deleterious, respectively.

PolyPhen

PolyPhen (Polymorphism Phenotyping v 1&2) is software that forecasts the possible impact of an amino acid substitution on a protein’s structure and its function, PolyPhen score is the indication of possibly damaging substitution. The FASTA format of the native sequence of a protein and the substitution of native and mutant amino acids is given as an input. The output results as score, sensitivity, specificity, and calculate the PSIC (position-specific independent count) and elucidate the effects as benign, probably or possibly damaging [13, 18].

The PolyPhen-2 score ranges between 0.0 and 1.0 (tolerated to deleterious respectively). The variants having a score between 0.0 and 0.15 are considered to be benign, the scores between 0.15 and 1.0 are considered to be damaging and the scores between 0.85 and 1.0 are considered most probably damaging.

PROVEAN

PROVEAN is another tool that works for both SNPs and indels [19]. PROVEAN (Protein Variation Effect Analyzer) envisages whether an amino acid substitution impacts the biological function of any protein [19]. It requires alignment-based scoring metrics to segregate detrimental mutations from deletions, insertions, point mutations, and multiple amino acid substitutions. The resulting score of ≤ 2.5 specifies a disease-causing or damaging variation, and ≥ 2.5 scores indicate tolerated or neutral variants [19].

I-MUTANT

I-MUTANT 2.0 is a sustain vector machine-based web server that performs the automatic guess of protein stability fluctuates upon single-site mutations [20]. I-MUTANT 2.0 can express the stability change of the mutated protein or mutant structure and the stability of the folded protein. I-MUTANT 2.0 allows the assortment of the prediction of protein stability changes at different series of temperatures and pH. FASTA sequences are provided as an input and output protein stability is obtained in the form of energy calculated as DDG in kcal/Mol [20].

PANTHER

PANTHER database defines as Protein Analysis Through Evolutionary Relationship. It uses HMM (hidden markov models) algorithm. The subPSEC scores (Substitution Position Specific Evolutionary Conservation) are calculated by the evolutionary relationship of proteins. Panther requires the protein sequence in FASTA format as input and validates the impact of protein function. The protein sequence with subPSEC score 0 is considered neutral and the score ≤−3 is deemed to be detrimental mutation [21].

MAPP

MAPP defines as a Multivariate analysis of protein polymorphism. Six physicochemical properties are used to evaluate missense variants compromising of charge, polarity, hydropathy, free energy in alpha-helical and beta-sheet conformation, and side-chain volume. The quantification of the physicochemical properties determines the high predictive score. The strong relationship between missense variant and physicochemical characteristics of protein prolongs human disease. It also gives the confidence level of the predicted mutations [22].

SNAP

SNAP defines screening for non-acceptable polymorphisms. This tool can potentially classify the non-synonymous SNPs in all the proteins into neutral and non-neutral (deleterious). It uses the sequence-based algorithm. For each case, SNAP gives a relatability index, which tells the level of confidence of each prediction. SNAP predicts the functional effects of nsSNPs by integrating predicted features of protein structure (including the secondary structure and solvent accessibility), evolutionary information, residue conservation, and other significant information. The sequence of the protein is required as an input. The deleterious mutations with low confidence are reported by SNAP [23].

PhD-SNP

PhD-SNP defines as a Predictor of human Deleterious Single Nucleotide Polymorphisms. It uses a support vector machine (SVM) based algorithm to classify non-synonymous SNPs into human genetic disease-causing (deleterious) or benign mutations [24].

Modeling of HMOX1 protein structure

The presence of SNP can significantly transform protein stability; for better insight on SNPs and their role in protein structural, functional, and stability, it is required to gather information about the 3D structure. The 3D structures allow the comparative analysis of the protein models that checks the structures before and after model refinement and differentiate between the residue geometry and overall structural geometry. The 3D structure of the native protein was modeled using ITASSER [25]. I-TASSER (Iterative Threading Assembly Refinement) is a hierarchical approach to predict protein structure and function. This server provides the most precise structural and functional calculations using state-of-the-art algorithms. After validating the 3D structure using PROCHECK [26] mutants were created through SWISS PDB Viewer [27]. Both native and mutant structures were energy minimized using Gromacs, (GROMOS 43B1 force field) for energy minimization [28].

In this study, the validation of the HMOX1 mutants was done using SAVES (Structure Analysis and Verification Server). This meta-server runs for validating and checking the protein structures throughout and even after model refinement. Moreover, various physical and chemical properties of the HMOX1 protein such as root mean square deviation (RMSD), hydrogen bond, total energy, and conserved residues were calculated using amino acid sequences and its PDB structure.

Protein-protein interaction (PPI) network

STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) is a biological database and a web (universal) resource of known and predicted protein-protein interactions. The STRING database contains information from several sources and computational prediction methods, experimental data, and public text collections. This database provides information of about 9.8 million proteins from >2,000 organisms [29]. STRING has been developed by a consortium of academic institutions, including KU, SIB, CPR, TUD, EMBL, and UZH. STRING imports data resulting from experimentally derived protein interactions through literature review. Connections with proteins in STRING are provided with a confidence score, and with some additional information such as protein domains and their 3D structures, all in a stable and reliable identifier space [29].

Results and discussion

In this study, nsSNPs were selected for HMOX1 using various parameters of coding synonymous (sSNPs) region, coding non-synonymous (nsSNPs) region, mRNA untranslated regions (5′UTR and 3′UTR), and introns. Out of 1954 SNPs, most SNPs were from the intronic region, and there were 161 nsSNPs selected for screening. The functional effect of these 161 sorted nsSNPs was further evaluated by different bioinformatics tools.

Prediction of deleterious nsSNPs

SNP analysis was executed using various bioinformatic tools like SIFT Blink, Polyphen-1, PolyPhen-2, I-MUTANT 2.0, PROVEAN, PANTHER, MAPP, SNAP, and PhD-SNP. Each tool works on a specific algorithm; therefore, by merging different computational tools, prediction of deleterious nsSNPs can be achieved more precisely [13, 14]. Hence, to ease the false positive predictions, a combinatorial approach was determined. Moreover, only those nsSNPs were noted which were generally predicted to be deleterious by all the above-mentioned tools. By comparing the results of SIFT Blink, Polyphen-1, PolyPhen-2, Provean I-MUTANT 2.0, PANTHER, MAPP, SNAP, and PhD-SNP tools out of 161 SNPs, 17 SNPs were depicted deleterious mentioned in Table 1.

Table 1:

List of deleterious nsSNPs computed by various bioinformatics tools.

Rs-id Nucleotide change Mutation PROVEAN I-MUTANT MAPPa PhD-SNPa PolyPhen-1a PolyPhen-2a SIFTa SNAPa PANTHERa Predict-SNPa
rs150399064 A/T K18M Deleterious 0.16 62% 51% 74% 68% 79% 50% – 61%
rs77672261 G/T E29D Deleterious −0.51 57% 61% 67% 43% 79% 61% – 61%
rs377208959 C/T I65T Deleterious −3.09 66% 58% 67% 47% 79% 61% 57% 63%
rs200856037 C/G N68K Deleterious −0.29 65% 68% 59% 63% 53% 58% 49% 60%
rs369019087 A/G Y78C Deleterious 0.63 75% 68% 74% 63% 79% 62% – 72%
rs146227657 A/C A88D Deleterious −1.39 59% 88% 74% 59% 79% 62% 65% 87%
rs373577583 C/T A131V Deleterious 1.1 75% 88% 74% 81% 79% 72% 65% 87%
rs201596112 A/G R136H Deleterious −0.67 62% 88% 59% 81% 79% 81% – 87%
rs374687228 A/G G139S Deleterious −1.33 57% 88% 74% 81% 79% 72% – 87%
rs368454530 A/G G144D Deleterious −0.76 75% 88% 74% 81% 79% 85% – 87%
rs5755713 C/G Q152H Deleterious −0.32 70% 59% 67% 70% 79% 71% – 63%
rs200966095 A/T K153I Deleterious 0.67 59% 88% 74% 45% 79% 50% – 76%
rs200255845 A/C K153N Deleterious −0.11 64% 68% 67% 47% 45% 67% – 60%
rs202031269 A/C K153Q Deleterious −0.07 73% 55% 67% 40% 53% 61% – 65%
rs138349040 A/C/T A206E Deleterious −1.05 88% 88% 74% 81% 79% 81% 66% 87%
rs202094347 G/T Q212H Deleterious −1.6 70% 78% 59% 43% 79% 58% 48% 60%
rs35980144 C/T P266L Deleterious −0.95 – 59% 74% 65% 79% 50% 66% 76%

  1. Commonly deleterious mutations are highlighted in green by all the computational tools. a% gives the expected accuracy, level of confidence [15].

Among these 17 SNPs, three SNPs with rs-id rs146227657, rs373577583, and rs138349040 were reported damaging/harmful by all the nine computational tools mentioned above. The mutation arises due to change in single nucleotide positions at 88, 131, and 206 from A to D, A to V, and A to E respectively [30, 31] highlighted in Table 1. Prior studies have proposed that identifying detrimental ns-SNPs using various algorithmic tools improves prediction accuracy [32]. Therefore, here these reported deleterious mutations (A88D, A131V, and A206E) may hinder the function and expression of HMOX1 protein.

Modeling of mutant HMOX1 protein structures

Protein stability can be reformed by the presence of SNP, for a better understanding of single nucleotide changes and their role in protein’s functional and structural stability, it is obligatory to gather information about the 3D structure. The 3D structure allows the comparative analysis of the protein models that checks the structures before and after model refinement and differentiates between the residue geometry and overall structural geometry. The 3D structure of the native protein was modeled using automated homology modeling via ITASSER and is validated using PROCHECK and Ramachandran plot. The mutants (A88D, A131V, and A206E) were created through SWISS PDB Viewer, native and mutant structures were energy minimized using GROMACS by default for minimization [28]. The single nucleotide polymorphism that occurred in native and mutant structures of HMOX1 protein is revealed in Figure 1. These mutations disrupt the structure and function of the protein. Physiological parameters for instance total energy, root mean square deviation (RMSD), hydrogen bond, Tm-scores were also calculated for these reported substitutions. The mutant A88D has a total energy of −13,271.421 KJ/Mol, RMSD 0.04 Å, and a hydrogen bond distance of 2.18. Mutant A131V has a total energy of −13,490.983 KJ/Mol, RMSD 0.038 Å, and a hydrogen bond distance of 1.98 whereas, mutant A206E has a total energy of −12,420.129 KJ/Mol, RMSD 0.104 Å, and hydrogen bond distance of 2.09. The evolutionary conservation profile of HMOX1 protein of every single amino acid position was also calculated using ConSurf [33] which gives the Tm-score of 1.0000. Supplementary Material Figure 1 demonstrates the conserved residues of HMOX1 protein. Solvent accessibility was also analyzed for HMOX1 protein using SABLE server representing secondary structure shown in Figure 2.

Figure 1: Native and mutant structure of HMOX1 protein; (A) Native structure displaying alanine (A) at 88th position; (B) mutant structure displaying aspartic acid at 88th position (protein is in yellow color whereas amino acid residue is in magenta color); (C) native structure displaying Alanine (A) at 131th position; (D) mutant structure displaying valine (V) at 131th position (protein is in green color whereas amino acid residue is in yellow color); (E) native structure displaying alanine (A) at 206th position; (F) mutant structure displaying glutamic acid; (E) at 206th position (protein is in cyan color whereas amino acid residue is in orange color).
Figure 1:

Native and mutant structure of HMOX1 protein; (A) Native structure displaying alanine (A) at 88th position; (B) mutant structure displaying aspartic acid at 88th position (protein is in yellow color whereas amino acid residue is in magenta color); (C) native structure displaying Alanine (A) at 131th position; (D) mutant structure displaying valine (V) at 131th position (protein is in green color whereas amino acid residue is in yellow color); (E) native structure displaying alanine (A) at 206th position; (F) mutant structure displaying glutamic acid; (E) at 206th position (protein is in cyan color whereas amino acid residue is in orange color).

Figure 2: Solvent accessibility of every single amino acid of HMOX1 protein.
Figure 2:

Solvent accessibility of every single amino acid of HMOX1 protein.

Network assembly of HMOX1 gene

The protein-protein interaction (PPI) network of the HMOX1 gene was constructed by the STRING database. This includes descriptive statistics such as the number of nodes, expected numbers of edges, average node degree, PPI enrichment p-value, and average clustering coefficient. PPI network of HMOX1 gene in humans was pulled out utilizing predicted functional neighborhood, co-expression, gene-fusion, co-occurrence, experiments, databases, text-mining, homology, and score. The network gained contains 11 nodes and 23 edges, with the average node degree of 4.18, the expected number of edges was 12, and 0.00274 of PPI enrichment p-value as shown in Figure 3.

Figure 3: Network assembly of HMOX1 protein.
Figure 3:

Network assembly of HMOX1 protein.

The 10 interacted partners of Heme Oxygenase −1 (HMOX1) were Biliverdin reductase A (BLVRA), Biliverdin reductase B (BLVRB), Ferrochelatase (FECH), Hephaestin (HEPH), Frataxin (FXN), Ceruloplasmin (CP), Nuclear factor erythroid 2-related factor 2 (NFE2L2), NAD(P)H dehydrogenase [quinone] 1 (NQO1), Transcription factor AP-1 (JUN), and Mitogen-activated protein kinase 14 (MAPK14) showing the highest connectivity in the network. These all reported genes are involved in various activities like heme metabolic process, oxidation-reduction process, response to hydrogen peroxide, response to an antibiotic, heme catabolic process, homeostasis, response to a toxic substance, cellular response to oxidative stress, response to an inorganic substance, drug, aging, apoptotis etc. The molecular, biological and cellular functional enrichment of the network is illustrated in Supplementary Material Figure 2.

This report suggests that heme oxygenase (HMOX1) plays a fundamental role in homeostasis and cellular hormetic stress response. The mutation (A88D, A131V, and A206E) in this gene may affect the gene expression and it may concern the pathophysiology of a wide spectrum of human disorders like diabetes, obesity, cardiovascular disease, pulmonary disease, gastrointestinal ailments, dermatitis, kidney dysfunction, cancer, etc., [34]. Consequently, heme oxygenase has been well-thought-out as one of the most projecting targets for therapeutic involvement in the management of various human diseases [35, 36].

Conclusions

In conclusion, three SNPs A88D, A131V, and A206E were identified as highly deleterious in the HMOX1 coding region via multiple computational platforms. These mutations may be of high concern in the HMOX1 gene and its associated diseases as they can alter the protein secondary structure, protein stability, and its conservation. Additionally, the protein-protein interaction network gives an insight into the functional enrichment of HMOX1 and its allied genes. Hence, this study highlights the functional SNPs that affect the phenotypic variability of the protein. Likewise, it paves a way for researchers associated with wet-lab analysis to characterize the impact of each nsSNPs on protein function is difficult, expensive, and time-consuming to design experiments.

Corresponding author: Pratichi Singh, Department of Biosciences, Galgotias University, Greater Noida, Uttar Pradesh, 203201, India, E-mail:

Funding source: Scientific Research Deanship at King Khalid University, Abha, Saudi Arabia

Award Identifier / Grant number: RGP. 01-48-42

Acknowledgments

The authors are grateful to Scientific Research Deanship at King Khalid University, Abha, Saudi Arabia for their financial support through the Small Research Group Project under grant number (RGP. 01-48-42).

  1. Research funding: No specific funding received.

  2. Author contributions: PS designed and conceived the idea for the study. SHA performed the experiments and write the first manuscript draft. PS, IA and MHA analyzed the data and edited the manuscript final version.

  3. Competing interests: The author declares no conflict of interest.

  4. Informed consent: Not applicable.

  5. Ethical approval: Not applicable.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/tjb-2021-0250).

Received: 2021-10-25
Accepted: 2022-01-07
Published Online: 2023-02-16

© 2023 the author(s), published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/tjb-2021-0250
Keywords for this article
deleterious; mutation; non-synonymous; protein function; SNPs
Creative Commons
BY 4.0

Articles in the same Issue

  1. Frontmatter
  2. Review
  3. A dual role of proton pump inhibition on cancer: a critical review
  4. Opinion Paper
  5. The consumption of vegetable oils and their possible participation as a neuroprotector
  6. Research Articles
  7. Analysis of inappropriate repeated laboratory testing
  8. SNP based analysis depicts phenotypic variability in heme oxygenase-1 protein
  9. The D allele of angiotensin-converting enzyme gene insertion/deletion polymorphism is associated with the lung involvement in COVID-19
  10. The effect of survivin gene in breast cancer risk and prognosis
  11. Increase of Claudin-5, ICAM-1 and eNOS expressions in human brain endothelial cells by ammonium chloride
  12. The effect of different anti-inflammatory treatment strategies on process of atherosclerosis in ankylosing spondylitis patients
  13. Genetic dissection of the Mastl-Arpp19/Ensa-PP2A-B55δ pathway in mammalian cells
  14. Investigation into miRNA profile in patient groups with and without ST elevation
  15. Prognostic value of total thiol and D-dimer in patients hospitalized with COVID-19
  16. Case Report
  17. The effect of heterophilic antibody interference in thyroglobulin measurement on different immunoassay devices
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