In silico analysis of missense mutations in exons 1-5 of the F9 gene that cause hemophilia B

Genotype-phenotype analyses of Iranian patients with hemophilia B (Leyden -) and hemophilia B (Leyden +): A single-center study

BACKGROUND

There is a high prevalence of inherited bleeding disorders in Iran, such as hemophilia A (HA) and hemophilia B (HB). This study aimed to analyze the molecular and clinical profiles of patients with HB.

METHODS

A single-center study was conducted among patients with severe HB between March 20, 2000, and June 31, 2023. The polymerase chain reaction (PCR) amplification was used for all of the major regions, such as the promoter, the exons, the adjacent intronic regions, and the untranslated regions of the F9 gene. Finally, Sanger sequencing was performed on the PCR products.

RESULTS

A total of 111 HB patients (17 with HB [Leyden +] and 94 with HB [Leyden -]) were enrolled in this study. Among 94 patients with HB (Leyden -), 59 (62.8 %) had missense, 21 (22.3 %) had nonsense, and 8 (8.5 %) had frameshift mutations. Moreover, the most frequent pathogenic variant in HB (Leyden +) was c.-17 A>G in this study.

CONCLUSION

The results of this study confirm that HB is caused by a wide range of molecular defects in Iran. Thus, by knowing the genotypes and phenotypes, we would be able to stratify the patients which is important in terms of their management and outcome.

Computational analysis of non-synonymous single nucleotide polymorphism in the bovine PKLR geneComputational analysis of bovine PKLR gene

Pyruvate kinase (PKLR) is a potential candidate gene for milk production traits in cows. The main aim of this work is to investigate the potentially deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in the PKLR gene by using several computational tools. In silico tools including SIFT, Polyphen-2, SNAP2 and Panther indicated only 18 nsSNPs out of 170 were considered deleterious. The analysis of proteins' stability change due to amino acid substitution performed by the use of the I-mutant, MUpro, CUPSTAT, SDM and Dynamut confirmed that 9 nsSNPs decreased protein stability. ConSurf analysis predicted that all 18 nsSNPs were evolutionary moderately or highly conserved. Two different domains of PKLR protein were revealed by the InterPro tool with 12 nsSNPs positioned in the Pyruvate Kinase barrel domain and 6 nsSNP present in the Pyruvate Kinase C Terminal. The PKLR 3D model was predicted by MODELLER software and validated via Ramachandran plot and Prosa which indicated a good quality model. The analysis of energy minimizations for the native and mutated structures was performed by SWISS PDB viewer with GROMOS 96 program and showed that 3 structural and 4 functional residues had total energy higher than the native model. These findings indicate that these mutant structures (rs441424814, rs449326723, rs476805413, rs472263384, rs474320860, rs475521477, rs441633284) were less stable than the native model. Molecular Dynamics simulations were performed to confirm the impact of nsSNPs on the protein structure and function. The present study provides useful information about functional SNPs that have an impact on PKLR protein in cattle.Communicated by Ramaswamy H. Sarma.

Computational approaches for predicting variant impact: An overview from resources, principles to applications

One objective of human genetics is to unveil the variants that contribute to human diseases. With the rapid development and wide use of next-generation sequencing (NGS), massive genomic sequence data have been created, making personal genetic information available. Conventional experimental evidence is critical in establishing the relationship between sequence variants and phenotype but with low efficiency. Due to the lack of comprehensive databases and resources which present clinical and experimental evidence on genotype-phenotype relationship, as well as accumulating variants found from NGS, different computational tools that can predict the impact of the variants on phenotype have been greatly developed to bridge the gap. In this review, we present a brief introduction and discussion about the computational approaches for variant impact prediction. Following an innovative manner, we mainly focus on approaches for non-synonymous variants (nsSNVs) impact prediction and categorize them into six classes. Their underlying rationale and constraints, together with the concerns and remedies raised from comparative studies are discussed. We also present how the predictive approaches employed in different research. Although diverse constraints exist, the computational predictive approaches are indispensable in exploring genotype-phenotype relationship.

Unveiling the influence of factor VIII physicochemical properties on hemophilia A phenotype through an in silico methodology

BACKGROUND AND OBJECTIVES

Hemophilia A (HA) is an X-linked blood disorder. It is caused by pathogenic F8 gene variants, among which missense mutations are the most prevalent. The resulting amino acid substitutions may have different impacts on physicochemical properties and, consequently, on protein functionality. Regular prediction tools do not include structural elements and their physiological significance, which hampers our ability to functionally link variants to disease phenotype, opening an ample field for investigation. The present study aims to elucidate how physicochemical changes generated by substitutions in different protein domains relate to HA, and which of these features are more consequential to protein function and its impact on HA phenotype.

METHODS

An in silico evaluation of 71 F8 variants found in patients with different HA phenotypes (mild, moderate, severe) was performed to understand protein modifications and functional impact. Homology modeling was used for the structural analysis of physicochemical changes including electrostatic potential, hydrophobicity, solvent-accessible/excluded surface areas, disulfide disruptions, and substitutions indexes. These variants and properties were analyzed by hierarchical clustering analysis (HCA) and principal component analysis (PCA), independently and in combination, to investigate their relative contribution.

RESULTS

About 69% of variants show electrostatic changes, and almost all show hydrophobicity and surface area modifications. HCA combining all physicochemical properties analyzed was better in reflecting the impact of different variants in disease severity, more so than the single feature analysis. On the other hand, PCA led to the identification of prominent properties involved in the clustering results for variants of different domains.

CONCLUSIONS

The methodology developed here enables the assessment of structural features not available in other prediction tools (e.g., surface distribution of electrostatic potential), evaluating what kind of physicochemical changes are involved in FVIII functional disruption. HCA results allow distinguishing substitutions according to their properties, and yielded clusters which were more homogeneous in phenotype. All evaluated properties are involved in determining disease severity. The nature, as well as the position of the variants in the protein, were shown to be relevant for physicochemical changes, demonstrating that all these aspects must be collectively considered to fine-tune an approach to predict HA severity.

In silico profiling of non-synonymous SNPs in IDS gene for early diagnosis of Hunter syndrome

Background

Single amino acid substitutions in the Iduronate-2-sulfatase enzyme result in destabilization of the protein and cause a genetic disorder called Hunter syndrome. To gain functional insight into the mutations causing Hunter syndrome, various bioinformatics tools were employed, and special significance is given to molecular docking.

Results

In-silico tools available online for preliminary analysis including SIFT, PolyPhen 2.0, etc., were primarily employed and have identified 51 Non-synonymous Single Nucleotide Polymorphisms (ns-SNPs) as possibly deleterious. Further, modelling and energy minimization followed by Root Mean Square Deviation (RMSD) calculation has labelled 42 mutations as probably deleterious ns-SNPs. Later, trajectory analysis was performed using online tools like PSIPRED, SRide, etc., and has predicted six ns-SNPs as potentially deleterious. Additionally, docking was performed, and three candidate ns-SNPs were identified. Finally, these three ns-SNPs were confirmed to play a significant role in causing syndrome through root mean square fluctuation (RMSF) calculations.

Conclusion

From the observed results, G134E, V503D, and E521D were predicted to be candidate ns-SNPs in comparison with other in-silico tools and confirmed by RMSF calculations. Thus, the identified candidate ns-SNPs can be employed as a potential genetic marker in the early diagnosis of Hunter syndrome after clinical validation.

The Molecular Basis of FIX Deficiency in Hemophilia B

Coagulation factor IX (FIX) is a vitamin K dependent protein and its deficiency causes hemophilia B, an X-linked recessive bleeding disorder. More than 1000 mutations in the F9 gene have been identified in hemophilia B patients. Here, we systematically summarize the structural and functional characteristics of FIX and the pathogenic mechanisms of the mutations that have been identified to date. The mechanisms of FIX deficiency are diverse in these mutations. Deletions, insertions, duplications, and indels generally lead to severe hemophilia B. Those in the exon regions generate either frame shift or inframe mutations, and those in the introns usually cause aberrant splicing. Regarding point mutations, the bleeding phenotypes vary from severe to mild in hemophilia B patients. Generally speaking, point mutations in the F9 promoter region result in hemophilia B Leyden, and those in the introns cause aberrant splicing. Point mutations in the coding sequence can be missense, nonsense, or silent mutations. Nonsense mutations generate truncated FIX that usually loses function, causing severe hemophilia B. Silent mutations may lead to aberrant splicing or affect FIX translation. The mechanisms of missense mutation, however, have not been fully understood. They lead to FIX deficiency, often by affecting FIX's translation, protein folding, protein stability, posttranslational modifications, activation to FIXa, or the ability to form functional Xase complex. Understanding the molecular mechanisms of FIX deficiency will provide significant insight for patient diagnosis and treatment.