A gene-specific method for predicting hemophilia-causing point mutations

Spectrum of Causative Mutations in Patients with Hemophilia A in Russia

Hemophilia A (HA) is one of the most widespread, X-linked, inherited bleeding disorders, which results from defects in the F8 gene. Nowadays, more than 3500 different pathogenic variants leading to HA have been described. Mutation analysis in HA is essential for accurate genetic counseling of patients and their relatives. We analyzed patients from 273 unrelated families with different forms of HA. The analysis consisted of testing for intron inversion (inv22 and inv1), and then sequencing all functionally important F8 gene fragments. We identified 101 different pathogenic variants in 267 patients, among which 35 variants had never been previously reported in international databases. We found inv22 in 136 cases and inv1 in 12 patients. Large deletions (1-8 exons) were found in 5 patients, and we identified a large insertion in 1 patient. The remaining 113 patients carried point variants involving either single nucleotide or several consecutive nucleotides. We report herein the largest genetic analysis of HA patients issued in Russia.

A Code Within a Code: How Codons Fine-Tune Protein Folding in the Cell

The genetic code sets the correspondence between the sequence of a given nucleotide triplet in an mRNA molecule, called a codon, and the amino acid that is added to the growing polypeptide chain during protein synthesis. With four bases (A, G, U, and C), there are 64 possible triplet codons: 61 sense codons (encoding amino acids) and 3 nonsense codons (so-called, stop codons that define termination of translation). In most organisms, there are 20 common/standard amino acids used in protein synthesis; thus, the genetic code is redundant with most amino acids (with the exception of Met and Trp) are being encoded by more than one (synonymous) codon. Synonymous codons were initially presumed to have entirely equivalent functions, however, the finding that synonymous codons are not present at equal frequencies in mRNA suggested that the specific codon choice might have functional implications beyond coding for amino acid. Observation of nonequivalent use of codons in mRNAs implied a possibility of the existence of auxiliary information in the genetic code. Indeed, it has been found that genetic code contains several layers of such additional information and that synonymous codons are strategically placed within mRNAs to ensure a particular translation kinetics facilitating and fine-tuning co-translational protein folding in the cell via step-wise/sequential structuring of distinct regions of the polypeptide chain emerging from the ribosome at different points in time. This review summarizes key findings in the field that have identified the role of synonymous codons and their usage in protein folding in the cell.

New approaches to predict the effect of co-occurring variants on protein characteristics

Predicting the effect of a mutated gene before the onset of symptoms of genetic diseases would greatly facilitate diagnosis and potentiate early intervention. There have been myriad attempts to predict the effects of single-nucleotide variants. However, the applicability of these efforts does not scale to co-occurring variants. Furthermore, an increasing number of protein therapeutics contain co-occurring nucleotide variations, adding uncertainty during development to the safety and efficiency of these drugs. Co-occurring nucleotide variants may often have synergistic, additive, or antagonistic effects on protein attributes, further complicating the task of outcome prediction. We tested four models based on the cooperative and antagonistic effects of co-occurring variants to predict pathogenicity and effectiveness of protein therapeutics. A total of 30 attributes, including amino acid and nucleotide features, as well as existing single-variant effect prediction tools, were considered on the basis of previous studies on single-nucleotide variants. Importantly, the effects of synonymous variants, often seen in protein therapeutics, were also included in our models. We used 12 datasets of people with monogenic diseases and controls with co-occurring genetic variants to evaluate the accuracy of our models, accomplishing a degree of accuracy comparable to that of prediction tools for single-nucleotide variants. More importantly, our framework is generalizable to new, well-curated datasets of monogenic diseases and new variant scoring tools. This approach successfully assists in addressing the challenging task of predicting the effect of co-occurring variants on pathogenicity and protein effectiveness and is applicable for a wide range of protein therapeutics and genetic diseases.

Protein residue network analysis reveals fundamental properties of the human coagulation factor VIII

Hemophilia A is an X-linked inherited blood coagulation disorder caused by the production and circulation of defective coagulation factor VIII protein. People living with this condition receive either prophylaxis or on-demand treatment, and approximately 30% of patients develop inhibitor antibodies, a serious complication that limits treatment options. Although previous studies performed targeted mutations to identify important residues of FVIII, a detailed understanding of the role of each amino acid and their neighboring residues is still lacking. Here, we addressed this issue by creating a residue interaction network (RIN) where the nodes are the FVIII residues, and two nodes are connected if their corresponding residues are in close proximity in the FVIII protein structure. We studied the characteristics of all residues in this network and found important properties related to disease severity, interaction to other proteins and structural stability. Importantly, we found that the RIN-derived properties were in close agreement with in vitro and clinical reports, corroborating the observation that the patterns derived from this detailed map of the FVIII protein architecture accurately capture the biological properties of FVIII.

Prediction of hemophilia A severity using a small-input machine-learning framework

Hemophilia A is a relatively rare hereditary coagulation disorder caused by a defective F8 gene resulting in a dysfunctional Factor VIII protein (FVIII). This condition impairs the coagulation cascade, and if left untreated, it causes permanent joint damage and poses a risk of fatal intracranial hemorrhage in case of traumatic events. To develop prophylactic therapies with longer half-lives and that do not trigger the development of inhibitory antibodies, it is essential to have a deep understanding of the structure of the FVIII protein. In this study, we explored alternative ways of representing the FVIII protein structure and designed a machine-learning framework to improve the understanding of the relationship between the protein structure and the disease severity. We verified a close agreement between in silico, in vitro and clinical data. Finally, we predicted the severity of all possible mutations in the FVIII structure – including those not yet reported in the medical literature. We identified several hotspots in the FVIII structure where mutations are likely to induce detrimental effects to its activity. The combination of protein structure analysis and machine learning is a powerful approach to predict and understand the effects of mutations on the disease outcome.

Opportunities and challenges for the computational interpretation of rare variation in clinically important genes

Genome sequencing is enabling precision medicine-tailoring treatment to the unique constellation of variants in an individual's genome. The impact of recurrent pathogenic variants is often understood, however there is a long tail of rare genetic variants that are uncharacterized. The problem of uncharacterized rare variation is especially acute when it occurs in genes of known clinical importance with functionally consequential variants and associated mechanisms. Variants of uncertain significance (VUSs) in these genes are discovered at a rate that outpaces current ability to classify them with databases of previous cases, experimental evaluation, and computational predictors. Clinicians are thus left without guidance about the significance of variants that may have actionable consequences. Computational prediction of the impact of rare genetic variation is increasingly becoming an important capability. In this paper, we review the technical and ethical challenges of interpreting the function of rare variants in two settings: inborn errors of metabolism in newborns and pharmacogenomics. We propose a framework for a genomic learning healthcare system with an initial focus on early-onset treatable disease in newborns and actionable pharmacogenomics. We argue that (1) a genomic learning healthcare system must allow for continuous collection and assessment of rare variants, (2) emerging machine learning methods will enable algorithms to predict the clinical impact of rare variants on protein function, and (3) ethical considerations must inform the construction and deployment of all rare-variation triage strategies, particularly with respect to health disparities arising from unbalanced ancestry representation.

Alignment-free method for functional annotation of amino acid substitutions: Application on epigenetic factors involved in hematologic malignancies

For the last couple of decades, there has been a significant growth in sequencing data, leading to an extraordinary increase in the number of gene variants. This places a challenge on the bioinformatics research community to develop and improve computational tools for functional annotation of new variants. Genes coding for epigenetic regulators have important roles in cancer pathogenesis and mutations in these genes show great potential as clinical biomarkers, especially in hematologic malignancies. Therefore, we developed a model that specifically focuses on these genes, with an assumption that it would outperform general models in predicting the functional effects of amino acid substitutions. EpiMut is a standalone software that implements a sequence based alignment-free method. We applied a two-step approach for generating sequence based features, relying on the biophysical and biochemical indices of amino acids and the Fourier Transform as a sequence transformation method. For each gene in the dataset, the machine learning algorithm-Naïve Bayes was used for building a model for prediction of the neutral or disease-related status of variants. EpiMut outperformed state-of-the-art tools used for comparison, PolyPhen-2, SIFT and SNAP2. Additionally, EpiMut showed the highest performance on the subset of variants positioned outside conserved functional domains of analysed proteins, which represents an important group of cancer-related variants. These results imply that EpiMut can be applied as a first choice tool in research of the impact of gene variants in epigenetic regulators, especially in the light of the biomarker role in hematologic malignancies. EpiMut is freely available at https://www.vin.bg.ac.rs/180/tools/epimut.php.

Variation benchmark datasets: update, criteria, quality and applications

Development of new computational methods and testing their performance has to be carried out using experimental data. Only in comparison to existing knowledge can method performance be assessed. For that purpose, benchmark datasets with known and verified outcome are needed. High-quality benchmark datasets are valuable and may be difficult, laborious and time consuming to generate. VariBench and VariSNP are the two existing databases for sharing variation benchmark datasets used mainly for variation interpretation. They have been used for training and benchmarking predictors for various types of variations and their effects. VariBench was updated with 419 new datasets from 109 papers containing altogether 329 014 152 variants; however, there is plenty of redundancy between the datasets. VariBench is freely available at http://structure.bmc.lu.se/VariBench/. The contents of the datasets vary depending on information in the original source. The available datasets have been categorized into 20 groups and subgroups. There are datasets for insertions and deletions, substitutions in coding and non-coding region, structure mapped, synonymous and benign variants. Effect-specific datasets include DNA regulatory elements, RNA splicing, and protein property for aggregation, binding free energy, disorder and stability. Then there are several datasets for molecule-specific and disease-specific applications, as well as one dataset for variation phenotype effects. Variants are often described at three molecular levels (DNA, RNA and protein) and sometimes also at the protein structural level including relevant cross references and variant descriptions. The updated VariBench facilitates development and testing of new methods and comparison of obtained performances to previously published methods. We compared the performance of the pathogenicity/tolerance predictor PON-P2 to several benchmark studies, and show that such comparisons are feasible and useful, however, there may be limitations due to lack of provided details and shared data.

Database URL: http://structure.bmc.lu.se/VariBench

In silico features of ADAMTS13 contributing to plasmatic ADAMTS13 levels in neonates with congenital heart disease

INTRODUCTION

Risk factors contributing to heightened thrombosis in pediatric congenital heart disease (CHD) patients are not fully understood. Among the neonatal CHD population, those presenting with single ventricular physiology are at the highest risk for perioperative thrombosis. The von Willebrand factor and ADAMTS13 interactions have emerged as causative risk factors for pediatric stroke and could contribute to heightened thrombosis in CHD neonates.

METHODS

This study investigates a cohort of children with single ventricle physiology and undergoing cardiac surgery, during which some patients developed thrombosis. In this cohort, we analyzed the relationship of several molecular features of ADAMTS13 with the plasma and activity levels in patients at risk of thrombosis. Additionally, in light of the natural antithrombotic activity of ADAMTS13, we have sequenced the ADAMTS13 gene for each patient and evaluated the role of genetic variants in determining the plasma ADAMTS13 levels using a series of in silico tools including Hidden Markov Models, EVmutation, and Rosetta.

RESULTS

Lower ADAMTS13 levels were found in patients that developed thrombosis. A novel in silico analysis to assess haplotype effect of co-occurring variants identified alterations in relative surface area and solvation energy as important contributors. Our analysis suggested that beneficial or deleterious effect of a variant can be reasonably predicted by comprehensive analysis of in silico assessment and in vitro and/or in vivo data.

CONCLUSION

Findings from this study add to our understanding the role of genetic features of ADAMTS13 in patients at high risk of thrombosis related to an imbalanced relation between VWF and ADAMTS13.

In Silico Study of Correlation between Missense Variations of F8 Gene and Inhibitor Formation in Severe Hemophilia A

Objective:

Deleterious substitutions of the F8 gene are responsible for causing hemophilia A, which is an inherited bleeding disorder resulting from reduced or absent activity of the coagulant protein factor VIII (FVIII). The most important complication in treatment is inhibitor development toward therapeutic factor VIII. In this study, we aimed to analyze the effects of deleterious substitutions in the F8 gene upon protein structure and function.

Materials and Methods:

All tests were conducted by computational methods from the CHAMP (CDC Hemophilia A Mutation Project) database. We performed an in silico analysis of deleterious variations using five software programs, Sift, PolyPhen-2, Align-GVGD, KD4v, and MutationTaster, in order to analyze the correlation between variation and the disease. We also studied the correlation between these variations and inhibitor formation.

Results:

Our analysis showed that these in silico tools are coherent and that there are more variations in the A than the C domains. Moreover, we noticed that there are more deleterious variations than neutral variations in each of the A and C domains. We also found that 13.51% of the patients suffered from a severe form of hemophilia A and that carriers of missense variations developed inhibitors. Also, for the first time, we determined that variation nature is not associated with inhibitor formation. Furthermore, this analysis showed that the risk of developing inhibitors increases when the variation causes a change of amino acid class.

Conclusion:

This study will help to correctly associate variations with inhibitor development and aid in early characterization of novel variants.

TissueCoCoPUTs: Novel Human Tissue-Specific Codon and Codon-Pair Usage Tables Based on Differential Tissue Gene Expression

Protein expression in multicellular organisms varies widely across tissues. Codon usage in the transcriptome of each tissue is derived from genomic codon usage and the relative expression level of each gene. We created a comprehensive computational resource that houses tissue-specific codon, codon-pair, and dinucleotide usage data for 51 Homo sapiens tissues (TissueCoCoPUTs: https://hive.biochemistry.gwu.edu/review/tissue_codon), using transcriptome data from the Broad Institute Genotype-Tissue Expression (GTEx) portal. Distances between tissue-specific codon and codon-pair frequencies were used to generate a dendrogram based on the unique patterns of codon and codon-pair usage in each tissue that are clearly distinct from the genomic distribution. This novel resource may be useful in unraveling the relationship between codon usage and tRNA abundance, which could be critical in determining translation kinetics and efficiency across tissues. Areas of investigation such as biotherapeutic development, tissue-specific genetic engineering, and genetic disease prediction will greatly benefit from this resource.

Splicing dysregulation contributes to the pathogenicity of several F9 exonic point variants

BACKGROUND

Pre-mRNA splicing is a complex process requiring the identification of donor site, acceptor site, and branch point site with an adjacent polypyrimidine tract sequence. Splicing is regulated by splicing regulatory elements (SREs) with both enhancer and suppressor functions. Variants located in exonic regions can impact splicing through dysregulation of native splice sites, SREs, and cryptic splice site activation. While splicing dysregulation is considered primary disease-inducing mechanism of synonymous variants, its contribution toward disease phenotype of non-synonymous variants is underappreciated.

METHODS

In this study, we analyzed 415 disease-causing and 120 neutral F9 exonic point variants including both synonymous and non-synonymous for their effect on splicing using a series of in silico splice site prediction tools, SRE prediction tools, and in vitro minigene assays.

RESULTS

The use of splice site and SRE prediction tools in tandem provided better prediction but were not always in agreement with the minigene assays. The net effect of splicing dysregulation caused by variants was context dependent. Minigene assays revealed that perturbed splicing can be found.

CONCLUSION

Synonymous variants primarily cause disease phenotype via splicing dysregulation while additional mechanisms such as translation rate also play an important role. Splicing dysregulation is likely to contribute to the disease phenotype of several non-synonymous variants.

Protein-Engineered Coagulation Factors for Hemophilia Gene Therapy

Hemophilia A (HA) and hemophilia B (HB) are X-linked bleeding disorders due to inheritable deficiencies in either coagulation factor VIII (FVIII) or factor IX (FIX), respectively. Recently, gene therapy clinical trials with adeno-associated virus (AAV) vectors and protein-engineered transgenes, B-domain deleted (BDD) FVIII and FIX-Padua, have reported near-phenotypic cures in subjects with HA and HB, respectively. Here, we review the biology and the clinical development of FVIII-BDD and FIX-Padua as transgenes. We also examine alternative bioengineering strategies for FVIII and FIX, as well as the immunological challenges of these approaches. Other engineered proteins and their potential use in gene therapy for hemophilia with inhibitors are also discussed. Continued advancement of gene therapy for HA and HB using protein-engineered transgenes has the potential to alleviate the substantial medical and psychosocial burdens of the disease.

Analysis of the VSX1 gene in sporadic keratoconus patients from China

Background

Keratoconus normally presents as a sporadic disease. Although different studies have found sequence variants of the visual system homeobox 1 (VSX1) gene associated with keratoconus in humans, no research has detected such variants in sporadic keratoconus patients from China. To investigate the possibility of VSX1 being a candidate susceptibility gene for Chinese patients with sporadic keratoconus, we performed sequence screening of this gene in such patients.

Methods

Whole DNA was obtained from the leukocytes in the peripheral venous blood of 50 patients with sporadic keratoconus and 50 control subjects without this ocular disorder. Polymerase chain reaction single-strand conformation polymorphism analysis and direct DNA sequencing technology were used to detect sequence variation in the five exons and splicing regions of the introns of the VSX1 gene. The sequencing results were analyzed using DNAstar software.

Results

One novel missense heterozygous sequence variant (p.Arg131Pro) was found in the first exon of the VSX1 gene in one keratoconus patient. Another heterozygous sequence variant (p.Gly160Val) in the second exon was found in two keratoconus patients. These variants were not detected in the control subjects. In the third intron of the VSX1 gene, c.8326G > A nucleotide substitution (including heterozygous and homozygous change) was also discovered. The frequency of this variation did not differ significantly between patients and controls, it should belong to single-nucleotide polymorphism of the VSX1 gene. Bioinformatic analysis also predicted that one missense sequence variation (p.Arg131Pro) may not cause a pathogenic change.

Conclusions

In this study, we added one novel missense sequence variation (p.Arg131Pro) in the coding region of the VSX1 gene to the range of VSX1 coding region variations observed in patients with sporadic keratoconus from China. Our work suggests that VSX1 sequence variants might be involved in the pathogenesis of sporadic keratoconus, but their precise role in disease causation requires further investigation.

The importance of mRNA structure in determining the pathogenicity of synonymous and non-synonymous mutations in haemophilia

INTRODUCTION

Mutational analysis is commonly used to support the diagnosis and management of haemophilia. This has allowed for the generation of large mutation databases which provide unparalleled insight into genotype-phenotype relationships. Haemophilia is associated with inversions, deletions, insertions, nonsense and missense mutations. Both synonymous and non-synonymous mutations influence the base pairing of messenger RNA (mRNA), which can alter mRNA structure, cellular half-life and ribosome processivity/elongation. However, the role of mRNA structure in determining the pathogenicity of point mutations in haemophilia has not been evaluated.

AIM

To evaluate mRNA thermodynamic stability and associated RNA prediction software as a means to distinguish between neutral and disease-associated mutations in haemophilia.

METHODS

Five mRNA structure prediction software programs were used to assess the thermodynamic stability of mRNA fragments carrying neutral vs. disease-associated and synonymous vs. non-synonymous point mutations in F8, F9 and a third X-linked gene, DMD (dystrophin).

RESULTS

In F8 and DMD, disease-associated mutations tend to occur in more structurally stable mRNA regions, represented by lower MFE (minimum free energy) levels. In comparing multiple software packages for mRNA structure prediction, a 101-151 nucleotide fragment length appears to be a feasible range for structuring future studies.

CONCLUSION

mRNA thermodynamic stability is one predictive characteristic, which when combined with other RNA and protein features, may offer significant insight when screening sequencing data for novel disease-associated mutations. Our results also suggest potential utility in evaluating the mRNA thermodynamic stability profile of a gene when determining the viability of interchanging codons for biological and therapeutic applications.

ITPR1 gene p.Val1553Met mutation in Russian family with mild Spinocerebellar ataxia

Background

Spinocerebellar ataxias (SСAs) are a highly heterogeneous group of inherited neurological disorders. The symptoms of ataxia vary in individual patients and even within the same SCA subtype. A study of a four-generation family with autosomal dominant (AD) non-progressive SCA with mild symptoms was conducted. The genotyping of this family revealed no frequent pathogenic mutations. So the objective of this study was to identify the genetic causes of the disease in this family with the technology of whole-exome sequencing (WES).

Methods and results

WES, candidate variant analysis with further Sanger sequencing, mRNA secondary structure prediction, and RSCU analysis were performed; a heterozygous missense mutation in ITPR1 was identified.

Conclusion

Our study confirms the fact that ITPR1 gene plays a certain role in the pathogenesis of SCAs, and, therefore, we suggest that c.4657G>A p.Val1553Met) is a disease-causing mutation in the family studied.

Conservation of CFTR codon frequency through primates suggests synonymous mutations could have a functional effect

Cystic fibrosis is an inherited chronic disease that affects the lungs and digestive system, with a prevalence of about 1:3000 people. Cystic fibrosis is caused by mutations in CFTR gene, which lead to a defective function of the chloride channel, the cystic fibrosis transmembrane conductance regulator (CFTR). Up-to-date, more than 1900 mutations have been reported in CFTR. However for an important proportion of them, their functional effects and the relation to disease are still not understood. Many of these mutations are silent (or synonymous), namely they do not alter the encoded amino acid. These synonymous mutations have been considered as neutral to protein function. However, more recent evidence in bacterial and human proteins has put this concept under revision. With the aim of understanding possible functional effects of synonymous mutations in CFTR, we analyzed human and primates CFTR codon usage and divergence patterns. We report the presence of regions enriched in rare and frequent codons. This spatial pattern of codon preferences is conserved in primates, but this cannot be explained by sequence conservation alone. In sum, the results presented herein suggest a functional implication of these regions of the gene that may be maintained by purifying selection acting to preserve a particular codon usage pattern along the sequence. Overall these results support the idea that several synonymous mutations in CFTR may have functional importance, and could be involved in the disease.

In silico analyses of missense mutations in coagulation factor VIII: identification of severity determinants of haemophilia A

Factor VIII (FVIII) mutations cause haemophilia A (HA), an X-linked recessive coagulation disorder. Over 1000 missense mutations in FVIII are known and they lead to variable clinical phenotypes (severe, moderate and mild). The exact molecular basis of this phenotypic heterogeneity by FVIII missense mutations is elusive to date. In this study, we aimed to identify the severity determinants that cause phenotypic heterogeneity of HA. We compiled and curated a data set of 766 missense mutations from the repertoire of missense mutations in FVIII. We analysed these mutations by computational programs (e.g. Swiss-PdbViewer) and different mutation analysis servers (e.g. SIFT, PROVEAN, CUPSAT, PolyPhen2, MutPred); and various sequence- and structure-based parameters were assessed for any significant distribution bias among different HA phenotypes. Our analyses suggest that 'mutations in evolutionary conserved residues', 'mutations in buried residues', mutation-induced 'steric clash' and 'surface electrostatic potential alteration' act as risk factors towards severe HA. We have developed a grading system for FVIII mutations combining the severity determinants, and the grading pattern correlates with HA phenotype. This study will help to correctly associate the HA phenotype with a mutation and aid early characterization of novel variants.

Exposing synonymous mutations

Synonymous codon changes, which do not alter protein sequence, were previously thought to have no functional consequence. Although this concept has been overturned in recent years, there is no unique mechanism by which these changes exert biological effects. A large repertoire of both experimental and bioinformatic methods has been developed to understand the effects of synonymous variants. Results from this body of work have provided global insights into how biological systems exploit the degeneracy of the genetic code to control gene expression, protein folding efficiency, and the coordinated expression of functionally related gene families. Although it is now clear that synonymous variants are important in a variety of contexts, from human disease to the safety and efficacy of therapeutic proteins, there is no clear consensus on the approaches to identify and validate these changes. Here, we review the diverse methods to understand the effects of synonymous mutations.