Phenotype prediction for mucopolysaccharidosis type I by in silico analysis

Protein modeling and in silico analysis to assess pathogenicity of ABCA4 variants in patients with inherited retinal disease

Purpose

The retina-specific ABCA transporter, ABCA4, plays an essential role in translocating retinoids required by the visual cycle. ABCA4 genetic variants are known to cause a wide range of inherited retinal disorders, including Stargardt disease and cone-rod dystrophy. More than 1,400 ABCA4 missense variants have been identified; however, more than half of these remain variants of uncertain significance (VUS). The purpose of this study was to employ a predictive strategy to assess the pathogenicity of ABCA4 variants in inherited retinal diseases using protein modeling and computational approaches.

Methods

We studied 13 clinically well-defined patients with ABCA4 retinopathies and identified the presence of 10 missense variants, including one novel variant in the ABCA4 gene, by next-generation sequencing (NGS). All variants were structurally analyzed using AlphaFold2 models and existing experimental structures of human ABCA4 protein. The results of these analyses were compared with patient clinical presentations to test the effectiveness of the methods employed in predicting variant pathogenicity.

Results

We conducted a phenotype-genotype comparison of 13 genetically and phenotypically well-defined retinal disease patients. The in silico protein structure analyses we employed successfully detected the deleterious effect of missense variants found in this affected patient cohort. Our study provides American College of Medical Genetics and Genomics (ACMG)-defined supporting evidence of the pathogenicity of nine missense ABCA4 variants, aligning with the observed clinical phenotypes in this cohort.

Conclusions

In this report, we describe a systematic approach to predicting the pathogenicity of ABCA4 variants by means of three-dimensional (3D) protein modeling and in silico structure analysis. Our results demonstrate concordance between disease severity and structural changes in protein models induced by genetic variations. Furthermore, the present study suggests that in silico protein structure analysis can be used as a predictor of pathogenicity and may facilitate the assessment of genetic VUS.

MPS I: Early diagnosis, bone disease and treatment, where are we now?

Mucopolysaccharidosis type I (MPS I) is a lysosomal storage disorder characterized by α-L-iduronidase deficiency. Patients present with a broad spectrum of disease severity ranging from the most severe phenotype (Hurler) with devastating neurocognitive decline, bone disease and early death to intermediate (Hurler-Scheie) and more attenuated (Scheie) phenotypes, with a normal life expectancy. The most severely affected patients are preferably treated with hematopoietic stem cell transplantation, which halts the neurocognitive decline. Patients with more attenuated phenotypes are treated with enzyme replacement therapy. There are several challenges to be met in the treatment of MPS I patients. First, to optimize outcome, early recognition of the disease and clinical phenotype is needed to guide decisions on therapeutic strategies. Second, there is thus far no effective treatment available for MPS I bone disease. The pathophysiological mechanisms behind bone disease are largely unknown, limiting the development of effective therapeutic strategies. This article is a state of the art that comprehensively discusses three of the most urgent open issues in MPS I: early diagnosis of MPS I patients, pathophysiology of MPS I bone disease, and emerging therapeutic strategies for MPS I bone disease.

Molecular Analysis of Vietnamese Patients with Mucopolysaccharidosis Type I

Mucopolysaccharidosis type I (MPS I) is a rare autosomal recessive disorder caused by deleterious mutations in the α-L-iduronidase (IDUA) gene. Until now, MPS I in Vietnamese has been poorly addressed. Five MPS I patients were studied with direct DNA sequencing using Illumina technology confirming pathogenic variants in the IDUA gene. Clinical characteristics, additional laboratory results, and family history were collected. All patients have presented with the classical characteristic of MPS I, and α-L-iduronidase activity was low with the accumulation of glycosaminoglycans. Three variants in the IDUA gene (c.1190-10C>A (Intronic), c.1046A>G (p.Asp349Gly), c.1862G>C (p.Arg621Pro) were identified. The c.1190-10C>A variant represents six of the ten disease alleles, indicating a founder effect for MPS I in the Vietnamese population. Using biochemical and genetic analyses, the precise incidence of MPS I in this population should accelerate early diagnosis, newborn screening, prognosis, and optimal treatment.

In silico analysis of mutations associated with occult hepatitis B virus (HBV) infection in South Africa

Occult hepatitis B virus (OBI) infection is defined by the presence of viral DNA in the liver and/or serum in absence of hepatitis B surface antigen (HBsAg). While multiple studies have identified mutations that are associated with OBI, only a small portion of these mutations have been functionally characterized in vitro. Using complementary in silico approaches, the effects of OBI-associated amino acid mutations on HBV protein function in HBV/HIV-positive ART-naïve South Africans were evaluated. Two OBI-associated mutations in the PreS1 region, one in the PreS2 region, and seven in the surface region of subgenotype A1 sequences were identified as deleterious. In subgenotype A2 sequences, 11 OBI-associated mutations in the PreS1 region, seven in the PreS2 region, and 31 in the surface region were identified as deleterious. In the polymerase region, 14 OBI-associated mutations in subgenotype A1 and 71 OBI-associated mutations in subgenotype A2 were identified as deleterious. This study utilized in silico approaches to characterize the likely impact of OBI-associated mutations on viral function, thereby identifying and prioritizing candidates and reducing the significant cost associated with functional studies that are essential for mechanistic studies of the OBI phenotype.

A computational approach to analyse the amino acid variants of GLB1 protein causing GM1 Gangliosidosis

Lysosomal storage diseases comprise different forms of autosomal recessive disorders from which GM1 gangliosidosis has categorized by the accumulation of complex glycolipids associated with a range of progressive neurologic phenotypes. GM1 gangliosidosis is an inherited disorder that progressively destroys nerve cells (neurons) in the brain and spinal cord. GM1 has three main types of onsets, namely infantile (type I), juvenile (type II), and adult (type III) forms. This study provides a series of computational methods that examine the mutations that occurred in GLB1 protein. Initially, the mutational analysis started with 689 amino acid variants for a sequence-based screening and it was done with quite a few In-silico tools to narrow down the most significant variants by utilizing the standard tools; namely, Evolutionary analysis (77 variants), Pathogenicity prediction (44 variants), Stability predictions (30 variants), Biophysical functions (19 variants) and according to the binding site of protein structure with PDB ID 3THC, seven variants (Y83D, Y83H, Y270S, Y270D, W273R, W273D, and Y333H) were narrowed down. Structure based analysis was performed to understand the interacting profile of the native protein and variants with Miglustat; which is the currently used FDA drug as an alternative to enzyme replacement therapy. Molecular Docking study was done to analyze the protein interaction with Miglustat (ligand), as a result native (3THC) structure had a binding affinity of -8.18 kcal/mol and two variant structures had an average binding affinities of -2.61 kcal/mol (Y83D) and - 7.63 kcal/mol (Y270D). Finally, Molecular Dynamics Simulation was performed to know the mutational activity of the protein structures on Miglustat for 50,000 ps. The Y83D variant showed higher deviation than native protein and Y270D in all trajectory analysis. The analysis was done to the protein structures to check the structural variations happened through simulations. This study aids to understand the most deleterious mutants, the activity of the drug to the protein structure and also gives an insight on the stability of the drug with the native and selected variants.

A novel gene editing system to treat both Tay-Sachs and Sandhoff diseases

The GM2-gangliosidoses are neurological diseases causing premature death, thus developing effective treatment protocols is urgent. GM2-gangliosidoses result from deficiency of a lysosomal enzyme β-hexosaminidase (Hex) and subsequent accumulation of GM2 gangliosides. Genetic changes in HEXA, encoding the Hex α subunit, or HEXB, encoding the Hex β subunit, causes Tay-Sachs disease and Sandhoff disease, respectively. Previous studies have showed that a modified human Hex μ subunit (HEXM) can treat both Tay-Sachs and Sandhoff diseases by forming a homodimer to degrade GM2 gangliosides. To this end, we applied this HEXM subunit in our PS813 gene editing system to treat neonatal Sandhoff mice. Through AAV delivery of the CRISPR system, a promoterless HEXM cDNA will be integrated into the albumin safe harbor locus, and lysosomal enzyme will be expressed and secreted from edited hepatocytes. Four months after the i.v. of AAV vectors, plasma MUGS and MUG activities reached up to 144- and 17-fold of wildtype levels (n=10, p<0.0001), respectively. More importantly, MUGS and MUG activities in the brain also increased significantly compared with untreated Sandhoff mice (p<0.001). Further, HPLC-MS/MS analysis showed that GM2 gangliosides in multiple tissues, except the brain, of treated mice were reduced to normal levels. Rotarod analysis showed that coordination and motor memory of treated mice were improved (p<0.05). Histological analysis of H&E stained tissues showed reduced cellular vacuolation in the brain and liver of treated Sandhoff mice. These results demonstrate the potential of developing a treatment of in vivo genome editing for Tay-Sachs and Sandhoff patients.

In silico approach to the analysis of SNPs in the human APAF1 gene

The apoptotic protease activating factor 1 (APAF1) gene encodes a cytoplasmic protein that initiates apoptosis and is a crucial factor in the mitochondria-dependent death pathway. APAF1 is implicated in many pathways such as apoptosis, neurodegenerative diseases, and cancer. The purpose of this study was to predict deleterious/damaging SNPs in the APAF1 gene viain silicoanalysis. To this end, APAF1 missense SNPs were obtained from the NCBI dbSNP database. In silico analysis of the missense SNPs was carried out by using publicly available online software tools. The stabilization and three-dimensional modeling of mutant proteins were also determined by using the I-Mutant 2.0 and Project HOPE webservers, respectively. In total, 772 missense SNPs were found in the APAF1 gene from the NCBI dbSNP database, 18 SNPs of which were demonstrated to be deleterious or damaging. Of those, 13 SNPs had a decreasing effect on protein stability, while the other 5 SNPs had an increasing effect. Based on the modeling results, some dissimilarities of mutant type amino acids from wild-type amino acids such as size, charge, and hydrophobicity were revealed. The SNPs predicted to be deleterious in this study might be used in the selection of target SNPs for genotyping in disease association studies. Therefore, we could suggest that the present study could pave the way for future experimental studies.

Mutation analysis and clinical characterization of Iranian patients with mucopolysaccharidosis type I

Background

Mucopolysaccharidosis type I (MPSI) is a rare autosomal recessive disorder caused by a deficiency of α‐l‐iduronidase (IDUA) encoded by the IDUA gene. We examined the mutation spectrum of the IDUA gene to explain the clinical, biochemical, and molecular features in 21 Iranian patients with MPSI.

Methods

Sanger sequencing was used to measure the IDUA gene sequence in the coding region and exon‐intron boundaries. We recorded the clinical findings of studied patients at the first diagnosis of disease and then during the treatment and follow‐up.

Results

Five different missense disease‐causing mutations were determined in our patient groups, indicating 90.48% of detection rate. The most widespread mutation was the p.Y109H, occurring in 15.625% of all alleles, which was reported for the first time in our study. Other frequent mutations were as follows: p.Ser157Pro (12.5%), p.Gly84Arg (12.5%), p.Asp257His (9.375%), and p.Asp301Glu (9.375%). Three ones of them were new missense mutations: p.Ser157Pro, p.Asp257His, and p.Asp301Glu.

Discussion

The results of this study explain the different spectrum of IDUA gene mutations in our patients with MPSI. We introduced here 32 different variants including four new variants: p.Y109H (15.625%), p.S157P (12.5%), p.D257H (9.375%), and p.D301E (9.375%). In this series, there was no relationship between the happening of clinical features and genotype variations and biochemical findings.

Genotype-phenotype correlation of gangliosidosis mutations using in silico tools and homology modeling

Gangliosidoses, including GM1-gangliosidosis and GM2-gangliosidosis (Tay-Sachs disease and Sandhoff disease), are lysosomal disorders resulting from enzyme deficiencies and accumulation of gangliosides. Phenotypes of gangliosidoses range from infantile, late-infantile, juvenile, and to the adult form. The genotype-phenotype correlation is essential for prognosis and clinical care planning for patients with a gangliosidosis condition. Previously, we have developed a method to establish the genotype-phenotype correlation of another lysosomal disease, mucopolysaccharidosis type I, with in silico tools. This same method was applied to analyze the genotype and phenotype of 38 patients diagnosed with a gangliosidosis disease in the United States. Out of 40 mutations identified, 3 were novel, including p.Tyr192His and p.Phe556Ser of the GLB1 gene and p.Gly461Val of the HEXA gene. Furthermore, the mutant protein structure of all missense mutations was constructed by homology modeling. A systemic structural analysis of these models revealed the specific mechanisms of how each mutation may lead to the disease. In summary, the method developed in this study holds promise as a tool that can be broadly applicable to other lysosomal diseases and monogenic diseases.

Genotype-phenotype relationships in mucopolysaccharidosis type I (MPS I): Insights from the International MPS I Registry

Mucopolysaccharidosis type I (MPS I) is a rare autosomal recessive disorder resulting from pathogenic variants in the α-L-iduronidase (IDUA) gene. Clinical phenotypes range from severe (Hurler syndrome) to attenuated (Hurler-Scheie and Scheie syndromes) and vary in age of onset, severity, and rate of progression. Defining the phenotype at diagnosis is essential for disease management. To date, no systematic analysis of genotype-phenotype correlation in large MPS I cohorts have been performed. Understanding genotype-phenotype is critical now that newborn screening for MPS I is being implemented. Data from 538 patients from the MPS I Registry (380 severe, 158 attenuated) who had 2 IDUA alleles identified were examined. In the 1076 alleles identified, 148 pathogenic variants were reported; of those, 75 were unique. Of the 538 genotypes, 147 (27%) were unique; 40% of patients with attenuated and 22% of patients with severe MPS I had unique genotypes. About 67.6% of severe patients had genotypes where both variants identified are predicted to severely disrupt protein/gene function and 96.1% of attenuated patients had at least one missense or intronic variant. This dataset illustrates a close genotype/phenotype correlation in MPS I but the presence of unique IDUA missense variants remains a challenge for disease prediction.

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

Background

Missense mutations in the first five exons of F9, which encodes factor FIX, represent 40% of all mutations that cause hemophilia B. To address the ongoing debate regarding in silico identification of disease-causing mutations at these exons, we analyzed 215 missense mutations from www.factorix.org using six in silico prediction tools, which are the most common used programs for analysis prediction of impact of mutations on the protein structure and function, with further advantage of using similar approaches. We developed different algorithms to integrate multiple predictions from such tools. In order to approach a structural analysis on FIX we performed a modeling of five selected pathogenic mutations.

Results

SIFT, PolyPhen-2 HumDiv, SNAP2, and MutationAssessor were the most successful in identifying true non-causative and causative mutations. A proposed function integrating these algorithms (wgP4) was the most sensitive (90.1%), specific (22.6%), and accurate (87%) than similar functions, and identified 187 variants as deleterious. Clinical phenotype was significantly associated with predicted causative mutations at all five exons. However, PolyPhen-2 HumDiv was more successful in linking clinical severity to specific exons, while functions that integrate 4–6 predictions were more successful in linking phenotype to genotypes at the light chain (exons 3–5). The most important value of integrating multiple predictions is the inclusion of scores derived from different approaches. Modeling of protein structure showed the effects of pathogenic nsSNPs on structure and function of FIX.

Conclusions

A simple function that integrates information from different in silico programs yields the best prediction of mutated phenotypes. However, the specificity, sensitivity, and accuracy of genotype-phenotype predictions depend on specific characteristics of the protein domain and the disease of interest as we validated by the structural analysis of selected pathogenic F9 mutations. The proposed function integrating algorithm (wgP4) might be useful for the analysis of nsSNPs impact on other genes.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2919-x) contains supplementary material, which is available to authorized users.

A longitudinal study of neurocognition and behavior in patients with Hurler-Scheie syndrome heterozygous for the L238Q mutation

Previous research has demonstrated the mutation, c.712T>A (p.L238Q) of the gene for α-L- iduronidase (IDUA) in patients with Hurler-Scheie syndrome is relatively severe when paired with a nonsense or deletion or splice-site mutation. This mutation was also found to be associated with psychiatric symptoms. This research presents longitudinal data and protein analysis to further investigate the severity and natural history of these unique patients.

Methods

Six patients heterozygous for L238Q were compared to six patients with Hurler-Scheie without the L238Q mutations. Somatic burden of disease, IQ, memory, attention, adaptive functioning and behavioral measures were given yearly over 2 to 4 years from 2009 to 2014. The impact of L238Q on the IDUA enzyme was examined using 7 bioinformatics tools and a 3D structural analysis.

Results

Similar to the cross sectional study, the L238Q patients had more severe abnormalities in IQ, attention, adaptive functioning, and behavioral functioning than the comparison group. There were no major differences between the two groups in change over time; IQ for both groups was stable with some behavioral areas showing improvement. Over time, both groups declined in visual spatial memory and, attention/visual processing. They also showed increased anxiety. Structural and bioinformatics analysis of the L238Q suggest that this mutation causes a significant reduction in the IDUA enzyme's potential catalytic activity, and this mutation may be more severe than other mutations contributing to the Hurler-Scheie syndrome phenotype, presumably causing the psychiatric disease.

Conclusion

L238Q patients demonstrate severe neurocognitive and neurobehavioral deficits but are relatively stable. Like the comparison group, decreasing visual spatial memory and attention and increasing anxiety suggest more intervention in life skills and emotional social supports are needed.

Identification and functional analysis of missense mutations in the lecithin cholesterol acyltransferase gene in a Chilean patient with hypoalphalipoproteinemia

Background

Lecithin-cholesterol acyltransferase (LCAT) is a plasma enzyme that esterifies cholesterol in high- and low-density lipoproteins (HDL and LDL). Mutations in LCAT gene causes familial LCAT deficiency, which is characterized by very low plasma HDL-cholesterol levels (Hypoalphalipoproteinemia), corneal opacity and anemia, among other lipid-related traits. Our aim is to evaluate clinical/biochemical features of a Chilean family with a proband showing clinical signs of familial LCAT deficiency, as well as to identify and assess the functional effects of LCAT mutations.

Methods

An adult female proband with hypoalphalipoproteinemia, corneal opacity and mild anemia, as well as her first-degree relatives, were recruited for clinical, biochemical, genetic, in-silico and in-vitro LCAT analysis. Sequencing of exons and intron-exon boundaries was performed to identify mutations. Site-directed mutagenesis was carried out to generate plasmids containing cDNA with wild type or mutant sequences. Such expression vectors were transfected to HEK-239 T cells to asses the effect of LCAT variants in expression, synthesis, secretion and enzyme activity. In-silico prediction analysis and molecular modeling was also used to evaluate the effect of LCAT variants.

Results

LCAT sequencing identified rare p.V333 M and p.M404 V missense mutations in compound heterozygous state in the proband, as well the common synonymous p.L363 L variant. LCAT protein was detected in proband’s plasma, but with undetectable enzyme activity compared to control relatives. HEK-293 T transfected cells with vector expression plasmids containing either p.M404 V or p.V333 M cDNA showed detectable LCAT protein expression both in supernatants and lysates from cultured cells, but with much lower enzyme activity compared to cells transfected with the wild-type sequence. Bioinformatic analyses also supported a causal role of such rare variations in LCAT lack of function. Additionally, the proband carried the minor allele of the synonymous p.L363 L variant. However, this variant is unlikely to affect the clinical phenotype of the proband given its relatively high frequency in the Chilean population (4%) and its small putative effect on plasma HDL-cholesterol levels.

Conclusion

Genetic, biochemical, in vitro and in silico analyses indicate that the rare mutations p.M404 V and p.V333 M in LCAT gene lead to suppression of LCAT enzyme activity and cause clinical features of familial LCAT deficiency.

Electronic supplementary material

The online version of this article (10.1186/s12944-019-1045-0) contains supplementary material, which is available to authorized users.

Computational modelling approaches as a potential platform to understand the molecular genetics association between Parkinson's and Gaucher diseases

Gaucher's disease (GD) is a genetic disorder in which glucocerebroside accumulates in cells and specific organs. It is broadly classified into type I, type II and type III. Patients with GD are at high risk of Parkinson's disease (PD), and the clinical and pathological presentation of GD patients with PD is almost identical to idiopathic PD. Several experimental models like cell culture, animal models, and transgenic mice models were used to understand the molecular mechanism behind GD and PD association; however, such mechanism remains unclear. In this context, based on literature reports, we identified the most common mutations K198T, E326K, T369M, N370S, V394L, D409H, L444P, and R496H, in the Glucosylceramidase (GBA) protein that are known to cause GD1, and represent a risk of developing PD. However, to date, no computational analyses have designed to elucidate the potential functional role of GD mutations with increased risk of PD. The present computational pipeline allows us to understand the structural and functional significance of these GBA mutations with PD. Based on the published data, the most common and severe mutations were E326K, N370S, and L444P, which further selected for our computational analysis. PredictSNP and iStable servers predicted L444P mutant to be the most deleterious and responsible for the protein destabilization, followed by the N370S mutation. Further, we used the structural analysis and molecular dynamics approach to compare the most frequent deleterious mutations (N370S and L444P) with the mild mutation E326K. The structural analysis demonstrated that the location of E326K and N370S in the alpha helix region of the protein whereas the mutant L444P was in the starting region of the beta sheet, which might explain the predicted pathogenicity level and destabilization effect of the L444P mutant. Finally, Molecular Dynamics (MD) at 50 ns showed the highest deviation and fluctuation pattern in the L444P mutant compared to the two mutants E326K and N370S and the native protein. This was consistent with more loss of intramolecular hydrogen bonds and less compaction of the radius of gyration in the L444P mutant. The proposed study is anticipated to serve as a potential platform to understand the mechanism of the association between GD and PD, and might facilitate the process of drug discovery against both GD and PD.

Molecular and in-silico analysis of single nucleotide polymorphism targeting human TP53 gene exon 5-8 in Sudanese esophageal cancer patients

Background: The protein product of the normal TP53 gene performs an essential function in cell cycle control and tumor suppression, and the mutation of a TP53 gene is an essential step in the development of many cancers. Despite the reported association of TP53 gene mutations with many human cancers, the comprehensive computational analysis of single nucleotide polymorphisms (SNPs), and their functional impacts, still remains rare. Methods: In this study DNA were extracted from formalin fixed paraffin embedded samples followed by the conventional polymerase chain reaction and DNA sequencing. Computational analysis was performed using different algorithms to screen for deleterious SNPs. Results: The results demonstrate that there are synonymous SNPs (sSNPs) and non-synonymous SNPs (nsSNPs) in the TP53 gene that may be deleterious to p53 structure and function. Additionally, TP53 gene mutations were found in 40% of samples. Six out of ten of TP53 gene mutations occurred in exon 5, two mutation in exon 6 and other two were present in exon 8. Only one SNP in position E298Q was predicted to have a neutral effect and other SNPs were predicted to be disease related according to Mutation Taster software. A total of 37.2% of squamous cell carcinoma (SCC) samples were found to be mutated, 87.5% of them exist in exon 5, 12.5% in exon 6 and 6.3% in exon 8, whereas adenocarcinoma (AC) achieved a higher rate of mutation (57.1%) with 100% exon 5 involvement. Conclusions: Mutation of TP53 exon 5 in esophageal cancer patients were the most frequent. Genomic results have identified a higher TP53 mutation rate in esophageal AC in contrast to SCC.

In Silico Analysis of Hepatitis B Virus Occult Associated Mutations in Botswana Using a Novel Algorithm

Occult hepatitis B infections (OBI) represent a reservoir of undiagnosed and untreated hepatitis B virus (HBV), hence the need to identify mutations that lead to this phenotype. Functionally characterizing these mutations by in vitro studies is time-consuming and expensive. To bridge this gap, in silico approaches, which predict the effect of amino acid (aa) variants on HBV protein function, are necessary. We developed an algorithm for determining the relevance of OBI-associated mutations using in silico approaches. A 3 kb fragment of subgenotypes A1 and D3 from 24 chronic HBV-infected (CHB) and 24 OBI participants was analyzed. To develop and validate the algorithm, the effects of 68 previously characterized occult-associated mutations were determined using three computational tools: PolyPhen2, SNAP2, and PROVEAN. The percentage of deleterious mutations (with impact on protein function) predicted were 52 (76.5%) by PolyPhen2, 55 (80.9%) by SNAP2, and 65 (95.6%) by PROVEAN. At least two tools correctly predicted 59 (86.8%) mutations as deleterious. To identify OBI-associated mutations exclusive to Botswana, study sequences were compared to CHB sequences from GenBank. Of the 43 OBI-associated mutations identified, 26 (60.5%) were predicted by at least two tools to have an impact on protein function. To our knowledge, this is the first study to use in silico approaches to determine the impact of OBI-associated mutations, thereby identifying potential candidates for functional analysis to facilitate mechanistic studies of the OBI phenotype.

In silico VHL Gene Mutation Analysis and Prognosis of Pancreatic Neuroendocrine Tumors in von Hippel-Lindau Disease

Context

Patients with von Hippel-Lindau (vHL) disease caused by a missense VHL mutation have a more severe phenotype compared with other VHL mutation types.

Objective

To define pancreatic neuroendocrine tumor (PNET) aggressiveness according to VHL genotype.

Design

A prospective natural history study.

Setting

The National Institutes of Health clinical center.

Patients

Patients with vHL disease, pancreatic manifestations, and germline missense VHL gene mutations.

Intervention

In-silico prediction of VHL mutation via five computational prediction models. Patients with >80% prediction for disease-causing mutations in all models [high predicted risk (HPR)] were compared with others [low predicted risk (LPR)].

Main Outcome Measure

Rates of metastases, surgical intervention, and disease progression.

Results

Sixty-nine patients were included: 2 developed metastases, 12 needed surgery, and 31 had disease progression during a median follow-up of 60 months (range 13 to 84 months). Thirteen patients were excluded for low prediction reliability. In the remaining 56 patients (45 with PNETs, 11 with pancreatic cysts), the HPR group (n = 13) had a higher rate of disease progression than the LPR group (n = 43) in multivariable analysis (hazard ratio 3.6; 95% confidence interval, 1.1 to 11.9; P = 0.037). The HPR group also had a higher risk of developing metastases (P = 0.015). Among patients with codon 167 hotspot mutations (n = 26), those in the HPR group had a higher risk for disease progression (P = 0.03) than other patients.

Conclusions

Computational models for predicting the impact of missense VHL gene mutations may be used as a prognostic factor in patients with PNETs in the context of vHL disease.

SAAMP 2.0: An algorithm to predict genotype-phenotype correlation of lysosomal storage diseases

Lysosomal storage diseases (LSDs) are a group of genetic disorders, resulting from deficiencies of lysosomal enzyme. Genotype-phenotype correlation is essential for timely and proper treatment allocation. Recently, by integrating prediction outcomes of 7 bioinformatics tools, we developed a SAAMP algorithm to predict the impact of individual amino-acid substitution. To optimize this approach, we evaluated the performance of these bioinformatics tools in a broad array of genes. PolyPhen and PROVEAN had the best performances, while SNP&GOs, PANTHER and I-Mutant had the worst performances. Therefore, SAAMP 2.0 was developed by excluding 3 tools with worst performance, yielding a sensitivity of 94% and a specificity of 90%. To generalize the guideline to proteins without known structures, we built the three-dimensional model of iduronate-2-sulfatase by homology modeling. Further, we investigated the phenotype severity of known disease-causing mutations of the GLB1 gene, which lead to 2 LSDs (GM1 gangliosidosis and Morquio disease type B). Based on the previous literature and structural analysis, we associated these mutations with disease subtypes and proposed a theory to explain the complicated genotype-phenotype correlation. Collectively, an updated guideline for phenotype prediction with SAAMP 2.0 was proposed, which will provide essential information for early diagnosis and proper treatment allocation, and they may be generalized to many monogenic diseases.

Coping Strategies, Stress, and Support Needs in Caregivers of Children with Mucopolysaccharidosis

The mucopolysaccharidoses are a set of rare, inherited conditions that can have a catastrophic impact on those affected and their families. Because of the rarity of these disorders, little is known regarding the challenges faced by families of those affected and what coping mechanisms are commonly used. Coping is a way to manage demands that occur in one's environment or within oneself. Medical social workers historically have facilitated this process while providing support to patients who are responding to pressures of their diagnosis and the system.A questionnaire of demographics and qualitative questions, along with the Pediatric Inventory for Parents (PIP) and Brief COPE, was sent by electronic survey to caregivers of children with MPS. The results of Brief COPE showed that problem-focused coping was more frequently used than emotion-focused (p < 0.001) or dysfunctional coping (p < 0.0001). Acceptance was the most frequently used coping strategy (p < 0.05). The results of PIP showed that emotionally distressing events were the most difficult (p < 0.001), while events related to medical care occurred at the highest frequency (p < 0.001). Psychosocial support provided by medical social workers significantly increased acceptance of caregivers (p = 0.04). Guidance on what to expect provided by any member of the care team increased denial (p = 0.02) and the difficulty of emotional distress (p = 0.04). This study identified commonly used coping strategies and measured stress among caregivers of children with MPS, as well as access to and use of psychosocial support services. Results highlight the urgency to improve the coverage and quality of psychosocial support and other support services.