Comparison of predicted and actual consequences of missense mutations

Human genetics of infectious diseases: Unique insights into immunological redundancy

For almost any given human-tropic virus, bacterium, fungus, or parasite, the clinical outcome of primary infection is enormously variable, ranging from asymptomatic to lethal infection. This variability has long been thought to be largely determined by the germline genetics of the human host, and this is increasingly being demonstrated to be the case. The number and diversity of known inborn errors of immunity is continually increasing, and we focus here on autosomal and X-linked recessive traits underlying complete deficiencies of the encoded protein. Schematically, four types of infectious phenotype have been observed in individuals with such deficiencies, each providing information about the redundancy of the corresponding human gene, in terms of host defense in natural conditions. The lack of a protein can confer vulnerability to a broad range of microbes in most, if not all patients, through the disruption of a key immunological component. In such cases, the gene concerned is of low redundancy. However, the lack of a protein may also confer vulnerability to a narrow range of microbes, sometimes a single pathogen, and not necessarily in all patients. In such cases, the gene concerned is highly redundant. Conversely, the deficiency may be apparently neutral, conferring no detectable predisposition to infection in any individual. In such cases, the gene concerned is completely redundant. Finally, the lack of a protein may, paradoxically, be advantageous to the host, conferring resistance to one or more infections. In such cases, the gene is considered to display beneficial redundancy. These findings reflect the current state of evolution of humans and microbes, and should not be considered predictive of redundancy, or of a lack of redundancy, in the distant future. Nevertheless, these observations are of potential interest to present-day biologists testing immunological hypotheses experimentally and physicians managing patients with immunological or infectious conditions.

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.

Performance of in silico prediction tools for the classification of rare BRCA1/2 missense variants in clinical diagnostics

Background

The use of next-generation sequencing approaches in clinical diagnostics has led to a tremendous increase in data and a vast number of variants of uncertain significance that require interpretation. Therefore, prediction of the effects of missense mutations using in silico tools has become a frequently used approach. Aim of this study was to assess the reliability of in silico prediction as a basis for clinical decision making in the context of hereditary breast and/or ovarian cancer.

Methods

We tested the performance of four prediction tools (Align-GVGD, SIFT, PolyPhen-2, MutationTaster2) using a set of 236 BRCA1/2 missense variants that had previously been classified by expert committees. However, a major pitfall in the creation of a reliable evaluation set for our purpose is the generally accepted classification of BRCA1/2 missense variants using the multifactorial likelihood model, which is partially based on Align-GVGD results. To overcome this drawback we identified 161 variants whose classification is independent of any previous in silico prediction. In addition to the performance as stand-alone tools we examined the sensitivity, specificity, accuracy and Matthews correlation coefficient (MCC) of combined approaches.

Results

PolyPhen-2 achieved the lowest sensitivity (0.67), specificity (0.67), accuracy (0.67) and MCC (0.39). Align-GVGD achieved the highest values of specificity (0.92), accuracy (0.92) and MCC (0.73), but was outperformed regarding its sensitivity (0.90) by SIFT (1.00) and MutationTaster2 (1.00). All tools suffered from poor specificities, resulting in an unacceptable proportion of false positive results in a clinical setting. This shortcoming could not be bypassed by combination of these tools. In the best case scenario, 138 families would be affected by the misclassification of neutral variants within the cohort of patients of the German Consortium for Hereditary Breast and Ovarian Cancer.

Conclusion

We show that due to low specificities state-of-the-art in silico prediction tools are not suitable to predict pathogenicity of variants of uncertain significance in BRCA1/2. Thus, clinical consequences should never be based solely on in silico forecasts. However, our data suggests that SIFT and MutationTaster2 could be suitable to predict benignity, as both tools did not result in false negative predictions in our analysis.

Electronic supplementary material

The online version of this article (10.1186/s12920-018-0353-y) contains supplementary material, which is available to authorized users.

Prediction and interpretation of deleterious coding variants in terms of protein structural stability

The classification of human genetic variants into deleterious and neutral is a challenging issue, whose complexity is rooted in the large variety of biophysical mechanisms that can be responsible for disease conditions. For non-synonymous mutations in structured proteins, one of these is the protein stability change, which can lead to loss of protein structure or function. We developed a stability-driven knowledge-based classifier that uses protein structure, artificial neural networks and solvent accessibility-dependent combinations of statistical potentials to predict whether destabilizing or stabilizing mutations are disease-causing. Our predictor yields a balanced accuracy of 71% in cross validation. As expected, it has a very high positive predictive value of 89%: it predicts with high accuracy the subset of mutations that are deleterious because of stability issues, but is by construction unable of classifying variants that are deleterious for other reasons. Its combination with an evolutionary-based predictor increases the balanced accuracy up to 75%, and allowed predicting more than 1/4 of the variants with 95% positive predictive value. Our method, called SNPMuSiC, can be used with both experimental and modeled structures and compares favorably with other prediction tools on several independent test sets. It constitutes a step towards interpreting variant effects at the molecular scale. SNPMuSiC is freely available at https://soft.dezyme.com/.

Insights into protein structure, stability and function from saturation mutagenesis

Where convenient phenotypic readouts are available, saturation mutagenesis coupled to deep sequencing provides a rapid and facile method to infer sequence determinants of protein structure, stability and function. We provide brief descriptions and currently available options for the various steps involved, and mention limitations of current implementations. We also highlight recent applications such as estimating relative stabilities and affinities of protein variants, mapping epitopes, protein model discrimination and prediction of mutant phenotypes. Most mutational scans have so far been applied to single genes and proteins. Additional methodological improvements are required to expand the scope to study intergenic epistasis and intermolecular interactions in macromolecular complexes.

MERRF Classification: Implications for Diagnosis and Clinical Trials

BACKGROUND

Given the etiologic heterogeneity of disease classification using clinical phenomenology, we employed contemporary criteria to classify variants associated with myoclonic epilepsy with ragged-red fibers (MERRF) syndrome and to assess the strength of evidence of gene-disease associations. Standardized approaches are used to clarify the definition of MERRF, which is essential for patient diagnosis, patient classification, and clinical trial design.

METHODS

Systematic literature and database search with application of standardized assessment of gene-disease relationships using modified Smith criteria and of variants reported to be associated with MERRF using modified Yarham criteria.

RESULTS

Review of available evidence supports a gene-disease association for two MT-tRNAs and for POLG. Using modified Smith criteria, definitive evidence of a MERRF gene-disease association is identified for MT-TK. Strong gene-disease evidence is present for MT-TL1 and POLG. Functional assays that directly associate variants with oxidative phosphorylation impairment were critical to mtDNA variant classification. In silico analysis was of limited utility to the assessment of individual MT-tRNA variants. With the use of contemporary classification criteria, several mtDNA variants previously reported as pathogenic or possibly pathogenic are reclassified as neutral variants.

CONCLUSIONS

MERRF is primarily an MT-TK disease, with pathogenic variants in this gene accounting for ~90% of MERRF patients. Although MERRF is phenotypically and genotypically heterogeneous, myoclonic epilepsy is the clinical feature that distinguishes MERRF from other categories of mitochondrial disorders. Given its low frequency in mitochondrial disorders, myoclonic epilepsy is not explained simply by an impairment of cellular energetics. Although MERRF phenocopies can occur in other genes, additional data are needed to establish a MERRF disease-gene association. This approach to MERRF emphasizes standardized classification rather than clinical phenomenology, thus improving patient diagnosis and clinical trial design.

Two de novo novel mutations in one SHANK3 allele in a patient with autism and moderate intellectual disability

SHANK3 encodes for a scaffolding protein that links neurotransmitter receptors to the cytoskeleton and is enriched in postsynaptic densities of excitatory synapses. Deletions or mutations in one copy of the SHANK3 gene cause Phelan-McDermid syndrome, also called 22q13.3 deletion syndrome, a neurodevelopmental disorder with common features including global developmental delay, absent to severely impaired language, autistic behavior, and minor dysmorphic features. By whole exome sequencing, we identified two de novo novel variants including one frameshift pathogenic variant and one missense variant of unknown significance in a 14-year-old boy with delayed motor milestones, delayed language acquisition, autism, intellectual disability, ataxia, progressively worsening spasticity of the lower extremities, dysmorphic features, short stature, microcephaly, failure to thrive, chronic constipation, intrauterine growth restriction, and bilateral inguinal hernias. Both changes are within the CpG island in exon 21, separated by a 375 bp sequence. Next generation sequencing of PCR products revealed that the two variants are most frequently associated with each other. Sanger sequencing of the cloned PCR products further confirmed that both changes were on a single allele. The clinical presentation in this individual is consistent with other patients with a truncating mutation in exon 21, suggesting that the missense change contributes none or minimally to the phenotypes. This is the first report of two de novo mutations in one SHANK3 allele.

Estimating the mutational load for cardiovascular diseases in Pakistani population

The deleterious genetic variants contributing to certain diseases may differ in terms of number and allele frequency from population to population depending on their evolutionary background. Here, we prioritize the deleterious variants from Pakistani population in manually curated gene list already reported to be associated with common, Mendelian, and congenital cardiovascular diseases (CVDs) using the genome/exome sequencing data of Pakistani individuals publically available in 1000 Genomes Project (PJL), and Exome Aggregation Consortium (ExAC) South Asia. By applying a set of tools such as Combined Annotation Dependent Depletion (CADD), ANNOVAR, and Variant Effect Predictor (VEP), we highlighted 561 potentially detrimental variants from PJL data, and 7374 variants from ExAC South Asian data. Likewise, filtration from ClinVar for CVDs revealed 03 pathogenic and 02 likely pathogenic variants from PJL and 112 pathogenic and 42 likely pathogenic variants from ExAC South Asians. The comparison of derived allele frequencies (DAF) revealed many of these prioritized variants having two fold and higher DAF in Pakistani individuals than in other populations. The highest number of deleterious variants contributing to common CVDs in descending order includes hypertension, atherosclerosis, heart failure, aneurysm, and coronary heart disease, and for Mendelian and congenital CVDs cardiomyopathies, cardiac arrhythmias, and atrioventricular septal defects.

Probability of phenotypically detectable protein damage by ENU-induced mutations in the Mutagenetix database

Computational inference of mutation effects is necessary for genetic studies in which many mutations must be considered as etiologic candidates. Programs such as PolyPhen-2 predict the relative severity of damage caused by missense mutations, but not the actual probability that a mutation will reduce/eliminate protein function. Based on genotype and phenotype data for 116,330 ENU-induced mutations in the Mutagenetix database, we calculate that putative null mutations, and PolyPhen-2-classified “probably damaging”, “possibly damaging”, or “probably benign” mutations have, respectively, 61%, 17%, 9.8%, and 4.5% probabilities of causing phenotypically detectable damage in the homozygous state. We use these probabilities in the estimation of genome saturation and the probability that individual proteins have been adequately tested for function in specific genetic screens. We estimate the proportion of essential autosomal genes in Mus musculus (C57BL/6J) and show that viable mutations in essential genes are more likely to induce phenotype than mutations in non-essential genes.

Programs such as PolyPhen-2 predict the relative severity of damage by missense mutations. Here, Wang et al estimate probabilities that putative null or missense alleles would reduce protein function to cause detectable phenotype by analyzing data from ENU-induced mouse mutations.

Malignant Hyperthermia in the Post-Genomics Era: New Perspectives on an Old Concept

This article reviews advancements in the genetics of malignant hyperthermia, new technologies and approaches for its diagnosis, and the existing limitations of genetic testing for malignant hyperthermia. It also reviews the various RYR1-related disorders and phenotypes, such as myopathies, exertional rhabdomyolysis, and bleeding disorders, and examines the connection between these disorders and malignant hyperthermia.

Identification of Two Novel Fibrinogen Bβ Chain Mutations in Two Slovak Families with Quantitative Fibrinogen Disorders

Congenital fibrinogen disorders are caused by mutations in one of the three fibrinogen genes that affect the synthesis, assembly, intracellular processing, stability or secretion of fibrinogen. Functional studies of mutant Bβ-chains revealed the importance of individual residues as well as three-dimensional structures for fibrinogen assembly and secretion. This study describes two novel homozygous fibrinogen Bβ chain mutations in two Slovak families with afibrinogenemia and hypofibrinogenemia. Peripheral blood samples were collected from all subjects with the aim of identifying the causative mutation. Coagulation-related tests and rotational thromboelastometry were performed. All exons and exon–intron boundaries of the fibrinogen genes (FGA, FGB and FGG) were amplified by PCR followed by direct sequencing. Sequence analysis of the three fibrinogen genes allowed us to identify two novel homozygous mutations in the FGB gene. A novel Bβ chain truncation (BβGln180Stop) was detected in a 28-year-old afibrinogenemic man with bleeding episodes including repeated haemorrhaging into muscles, joints, and soft tissues, and mucocutaneous bleeding and a novel Bβ missense mutation (BβTyr368His) was found in a 62-year-old hypofibrinogenemic man with recurrent deep and superficial venous thromboses of the lower extremities. The novel missense mutation was confirmed by molecular modelling. Both studying the molecular anomalies and the modelling of fibrinogenic mutants help us to understand the extremely complex machinery of fibrinogen biosynthesis and finally better assess its correlation with the patient’s clinical course.

Pharmacogenomics of GPCR Drug Targets

Natural genetic variation in the human genome is a cause of individual differences in responses to medications and is an underappreciated burden on public health. Although 108 G-protein-coupled receptors (GPCRs) are the targets of 475 (∼34%) Food and Drug Administration (FDA)-approved drugs and account for a global sales volume of over 180 billion US dollars annually, the prevalence of genetic variation among GPCRs targeted by drugs is unknown. By analyzing data from 68,496 individuals, we find that GPCRs targeted by drugs show genetic variation within functional regions such as drug- and effector-binding sites in the human population. We experimentally show that certain variants of μ-opioid and Cholecystokinin-A receptors could lead to altered or adverse drug response. By analyzing UK National Health Service drug prescription and sales data, we suggest that characterizing GPCR variants could increase prescription precision, improving patients’ quality of life, and relieve the economic and societal burden due to variable drug responsiveness.

Video Abstract

•

GPCRs targeted by FDA-approved drugs show genetic variation in the human population

•

Genetic variation occurs in functional sites and may result in altered drug response

•

We present an online resource of GPCR genetic variants for pharmacogenomics research

•

Understanding variation in drug targets may help alleviate economic healthcare burden

Frequently used bioinformatics tools overestimate the damaging effect of allelic variants

We selected two sets of naturally occurring human missense allelic variants within innate immune genes. The first set represented eleven non-synonymous variants in six different genes involved in interferon (IFN) induction, present in a cohort of patients suffering from herpes simplex encephalitis (HSE) and the second set represented sixteen allelic variants of the IFNLR1 gene. We recreated the variants in vitro and tested their effect on protein function in a HEK293T cell based assay. We then used an array of 14 available bioinformatics tools to predict the effect of these variants upon protein function. To our surprise two of the most commonly used tools, CADD and SIFT, produced a high rate of false positives, whereas SNPs&GO exhibited the lowest rate of false positives in our test. As the problem in our test in general was false positive variants, inclusion of mutation significance cutoff (MSC) did not improve accuracy.

Identification of twenty-nine novel germline unclassified variants of BRCA1 and BRCA2 genes in 1400 Italian individuals

OBJECTIVES

Breast and/or ovarian cancers are complex multifactorial diseases caused by interaction of both genetic and non-genetic factors and characterized by predisposition to inheritance. BRCA1 and BRCA2 genes are the most clinically involved with these kinds of cancer and the spectrum of variants affecting these genes is very wide. In fact, point variants, large or small insertions/deletions, genomic rearrangements can be found in these patients, although a large number of variants with uncertain biological and clinical significance continues to be identified. Next-generation sequencing (NGS) technology is actually the most powerful tool for the discovering of causative mutations and novel disease genes, moreover it allows to make a rapid diagnosis of genetic variants giving fast, inexpensive and detailed genetic information.

MATERIAL AND METHODS

In this study, we report the screening of BRCA1 and BRCA2 genes on 1400 consecutive Caucasian patients with breast and/or ovarian cancer history or family risk, attending the oncogenetic ambulatory at the Foundation Policlinico Agostino Gemelli in Rome.

RESULTS

We describe twenty-nine novel BRCA1 and BRCA2 variants detected in Italian individuals suffering from hereditary breast and ovarian cancer syndrome (HBOC).

CONCLUSION

Data regarding novel variants can provide useful information not only at epidemiological but also at clinical level, allowing for the better managing of breast and ovarian cancer patients and their family members.

Variant Interpretation: Functional Assays to the Rescue

Classical genetic approaches for interpreting variants, such as case-control or co-segregation studies, require finding many individuals with each variant. Because the overwhelming majority of variants are present in only a few living humans, this strategy has clear limits. Fully realizing the clinical potential of genetics requires that we accurately infer pathogenicity even for rare or private variation. Many computational approaches to predicting variant effects have been developed, but they can identify only a small fraction of pathogenic variants with the high confidence that is required in the clinic. Experimentally measuring a variant's functional consequences can provide clearer guidance, but individual assays performed only after the discovery of the variant are both time and resource intensive. Here, we discuss how multiplex assays of variant effect (MAVEs) can be used to measure the functional consequences of all possible variants in disease-relevant loci for a variety of molecular and cellular phenotypes. The resulting large-scale functional data can be combined with machine learning and clinical knowledge for the development of "lookup tables" of accurate pathogenicity predictions. A coordinated effort to produce, analyze, and disseminate large-scale functional data generated by multiplex assays could be essential to addressing the variant-interpretation crisis.

Blocking protein quality control to counter hereditary cancers

Inhibitors of molecular chaperones and the ubiquitin-proteasome system have already been clinically implemented to counter certain cancers, including multiple myeloma and mantle cell lymphoma. The efficacy of this treatment relies on genomic alterations in cancer cells causing a proteostatic imbalance, which makes them more dependent on protein quality control (PQC) mechanisms than normal cells. Accordingly, blocking PQC, e.g. by proteasome inhibitors, may cause a lethal proteotoxic crisis in cancer cells, while leaving normal cells unaffected. Evidence, however, suggests that the PQC system operates by following a better-safe-than-sorry principle and is thus prone to target proteins that are only slightly structurally perturbed, but still functional. Accordingly, implementing PQC inhibitors may also, through an entirely different mechanism, hold potential for other cancers. Several inherited cancer susceptibility syndromes, such as Lynch syndrome and von Hippel-Lindau disease, are caused by missense mutations in tumor suppressor genes, and in some cases, the resulting amino acid substitutions in the encoded proteins cause the cellular PQC system to target them for degradation, although they may still retain function. As a consequence of this over-meticulous PQC mechanism, the cell may end up with an insufficient amount of the abnormal, but functional, protein, which in turn leads to a loss-of-function phenotype and manifestation of the disease. Increasing the amounts of such proteins by stabilizing with chemical chaperones, or by targeting molecular chaperones or the ubiquitin-proteasome system, may thus avert or delay the disease onset. Here, we review the potential of targeting the PQC system in hereditary cancer susceptibility syndromes.

Disease reservoirs: from conceptual frameworks to applicable criteria

Central to the One Health approach and any disease eradication program is the question of whether a pathogen has a non-human reservoir. Despite well-established conceptual frameworks that define a reservoir of infection, empirical characterization of reservoirs often remains controversial, challenging and sometimes misleading. What is essentially missing are applicable requirements that standardize the use of the term ‘reservoir of infection’ across multiple disciplines. We propose an empirical framework, considering maintenance and feasible transmission of a pathogen, to standardize the acceptance of a disease reservoir across multiple disciplines. We demonstrate the intended use of these requirements by applying them to different diseases that are known to infect both humans and animals.

Assessing predictions of fitness effects of missense mutations in SUMO-conjugating enzyme UBE2I

The exponential growth of genomic variants uncovered by next-generation sequencing necessitates efficient and accurate computational analyses to predict their functional effects. A number of computational methods have been developed for the task, but few unbiased comparisons of their performance are available. To fill the gap, The Critical Assessment of Genome Interpretation (CAGI) comprehensively assesses phenotypic predictions on newly collected experimental datasets. Here, we present the results of the SUMO conjugase challenge where participants were predicting functional effects of missense mutations in human SUMO-conjugating enzyme UBE2I. The performance of the predictors is similar to each other and is far from perfection. Evolutionary information from sequence alignments dominates the success: deleterious mutations at conserved positions and benign mutations at variable positions are accurately predicted. Prediction accuracy of other mutations remains unsatisfactory, and this fast-growing field of research is yet to learn the use of spatial structure information to improve the predictions significantly.

Aggregation of population-based genetic variation over protein domain homologues and its potential use in genetic diagnostics

Whole exomes of patients with a genetic disorder are nowadays routinely sequenced but interpretation of the identified genetic variants remains a major challenge. The increased availability of population‐based human genetic variation has given rise to measures of genetic tolerance that have been used, for example, to predict disease‐causing genes in neurodevelopmental disorders. Here, we investigated whether combining variant information from homologous protein domains can improve variant interpretation. For this purpose, we developed a framework that maps population variation and known pathogenic mutations onto 2,750 “meta‐domains.” These meta‐domains consist of 30,853 homologous Pfam protein domain instances that cover 36% of all human protein coding sequences. We find that genetic tolerance is consistent across protein domain homologues, and that patterns of genetic tolerance faithfully mimic patterns of evolutionary conservation. Furthermore, for a significant fraction (68%) of the meta‐domains high‐frequency population variation re‐occurs at the same positions across domain homologues more often than expected. In addition, we observe that the presence of pathogenic missense variants at an aligned homologous domain position is often paired with the absence of population variation and vice versa. The use of these meta‐domains can improve the interpretation of genetic variation.

BRCA1/2 missense mutations and the value of in-silico analyses

INTRODUCTION

The clinical implications of genetic variants in BRCA1/2 in healthy and affected individuals are considerable. Variant interpretation, however, is especially challenging for missense variants. The majority of them are classified as variants of unknown clinical significance (VUS). Computational (in-silico) predictive programs are easy to access, but represent only one tool out of a wide range of complemental approaches to classify VUS. With this single-center study, we aimed to evaluate the impact of in-silico analyses in a spectrum of different BRCA1/2 missense variants.

METHODS

We conducted mutation analysis of BRCA1/2 in 523 index patients with suspected hereditary breast and ovarian cancer (HBOC). Classification of the genetic variants was performed according to the German Consortium (GC)-HBOC database. Additionally, all missense variants were classified by the following three in-silico prediction tools: SIFT, Mutation Taster (MT2) and PolyPhen2 (PPH2).

RESULTS

Overall 201 different variants, 68 of which constituted missense variants were ranked as pathogenic, neutral, or unknown. The classification of missense variants by in-silico tools resulted in a higher amount of pathogenic mutations (25% vs. 13.2%) compared to the GC-HBOC-classification. Altogether, more than fifty percent (38/68, 55.9%) of missense variants were ranked differently. Sensitivity of in-silico-tools for mutation prediction was 88.9% (PPH2), 100% (SIFT) and 100% (MT2).

CONCLUSION

We found a relevant discrepancy in variant classification by using in-silico prediction tools, resulting in potential overestimation and/or underestimation of cancer risk. More reliable, notably gene-specific, prediction tools and functional tests are needed to improve clinical counseling.

Uses of Next-Generation Sequencing Technologies for the Diagnosis of Primary Immunodeficiencies

Primary immunodeficiencies (PIDs) are genetic disorders impairing host immunity, leading to life-threatening infections, autoimmunity, and/or malignancies. Genomic technologies have been critical for expediting the discovery of novel genetic defects underlying PIDs, expanding our knowledge of the complex clinical phenotypes associated with PIDs, and in shifting paradigms of PID pathogenesis. Once considered Mendelian, monogenic, and completely penetrant disorders, genomic studies have redefined PIDs as a heterogeneous group of diseases found in the global population that may arise through multigenic defects, non-germline transmission, and with variable penetrance. This review examines the uses of next-generation DNA sequencing (NGS) in the diagnosis of PIDs. While whole genome sequencing identifies variants throughout the genome, whole exome sequencing sequences only the protein-coding regions within a genome, and targeted gene panels sequence only a specific cohort of genes. The advantages and limitations of each sequencing approach are compared. The complexities of variant interpretation and variant validation remain the major challenge in wide-spread implementation of these technologies. Lastly, the roles of NGS in newborn screening and precision therapeutics for individuals with PID are also addressed.

Advances in the Genetics of Congenital Heart Disease: A Clinician's Guide

Our understanding of the genetics of congenital heart disease (CHD) is rapidly expanding; however, many questions, particularly those relating to sporadic forms of disease, remain unanswered. Massively parallel sequencing technology has made significant contributions to the field, both from a diagnostic perspective for patients and, importantly, also from the perspective of disease mechanism. The importance of de novo variation in sporadic disease is a recent highlight, and the genetic link between heart and brain development has been established. Furthermore, evidence of an underlying burden of genetic variation contributing to sporadic and familial forms of CHD has been identified. Although we are still unable to identify the cause of CHD for most patients, recent findings have provided us with a much clearer understanding of the types of variants and their individual contributions and collectively mark an important milestone in our understanding of both familial and sporadic forms of disease.

Functional analyses of a novel missense and other mutations of the vitamin D receptor in association with alopecia

Hereditary 1,25-dihydroxyvitamin D-resistant rickets (HVDRR) is a rare disorder, caused by bialellic mutations of the vitamin D receptor (VDR) gene, sometimes associated with alopecia. The aim of this study is to elucidate the mechanism of functional disruption of a novel mutation, detected in a patient with HVDRR, comparing to other mutations with or without alopecia. The patient was a 2-year-old girl with alopecia, who was clinically diagnosed as HVDRR. Genetic analysis revealed a novel homozygous mutation, S360P, located in ligand binding domain (LBD). The mutation was predicted as not disease causing by Polyphen2 and SIFT. But the transcriptional activity of S360P was disrupted as well as other reported mutations, Q152X (located in the hinge lesion), and R274L, H305Q (located in LBD). Following assays revealed no ligand binding affinity, no interaction with cofactors or RXR and no functioning of nuclear localization signals. Our results provide an additional evidence for the previous findings suggesting that DNA binding by the VDR/RXR heterodimer is essential for the function of the VDR in hair development. In conclusion, we identified a novel missense mutation of VDR causing HVDRR with alopecia. Functional analyses revealed that the single amino acid substitution could disrupt the function of the protein.

Objective assessment of the evolutionary action equation for the fitness effect of missense mutations across CAGI-blinded contests

A major challenge in genome interpretation is to estimate the fitness effect of coding variants of unknown significance (VUS). Labor, limited understanding of protein functions, and lack of assays generally limit direct experimental assessment of VUS, and make robust and accurate computational approaches a necessity. Often, however, algorithms that predict mutational effect disagree among themselves and with experimental data, slowing their adoption for clinical diagnostics. To objectively assess such methods, the Critical Assessment of Genome Interpretation (CAGI) community organizes contests to predict unpublished experimental data, available only to CAGI assessors. We review here the CAGI performance of evolutionary action (EA) predictions of mutational impact. EA models the fitness effect of coding mutations analytically, as a product of the gradient of the fitness landscape times the perturbation size. In practice, these terms are computed from phylogenetic considerations as the functional sensitivity of the mutated site and as the magnitude of amino acid substitution, respectively, and yield the percentage loss of wild-type activity. In five CAGI challenges, EA consistently performed on par or better than sophisticated machine learning approaches. This objective assessment suggests that a simple differential model of evolution can interpret the fitness effect of coding variations, opening diverse clinical applications.

Systems-guided forward genetic screen reveals a critical role of the replication stress response protein ETAA1 in T cell clonal expansion

T-cell immunity requires extremely rapid clonal proliferation of rare, antigen-specific T lymphocytes to form effector cells. Here we identify a critical role for ETAA1 in this process by surveying random germ line mutations in mice using exome sequencing and bioinformatic annotation to prioritize mutations in genes of unknown function with potential effects on the immune system, followed by breeding to homozygosity and testing for immune system phenotypes. Effector CD8⁺ and CD4⁺ T-cell formation following immunization, lymphocytic choriomeningitis virus (LCMV) infection, or herpes simplex virus 1 (HSV1) infection was profoundly decreased despite normal immune cell development in adult mice homozygous for two different Etaa1 mutations: an exon 2 skipping allele that deletes Gly78-Leu119, and a Cys166Stop truncating allele that eliminates most of the 877-aa protein. ETAA1 deficiency decreased clonal expansion cell autonomously within the responding T cells, causing no decrease in their division rate but increasing TP53-induced mRNAs and phosphorylation of H2AX, a marker of DNA replication stress induced by the ATM and ATR kinases. Homozygous ETAA1-deficient adult mice were otherwise normal, healthy, and fertile, although slightly smaller, and homozygotes were born at lower frequency than expected, consistent with partial lethality after embryonic day 12. Taken together with recently reported evidence in human cancer cell lines that ETAA1 activates ATR kinase through an exon 2-encoded domain, these findings reveal a surprisingly specific requirement for this ATR activator in adult mice restricted to rapidly dividing effector T cells. This specific requirement may provide new ways to suppress pathological T-cell responses in transplantation or autoimmunity.

MARRVEL: Integration of Human and Model Organism Genetic Resources to Facilitate Functional Annotation of the Human Genome

One major challenge encountered with interpreting human genetic variants is the limited understanding of the functional impact of genetic alterations on biological processes. Furthermore, there remains an unmet demand for an efficient survey of the wealth of information on human homologs in model organisms across numerous databases. To efficiently assess the large volume of publically available information, it is important to provide a concise summary of the most relevant information in a rapid user-friendly format. To this end, we created MARRVEL (model organism aggregated resources for rare variant exploration). MARRVEL is a publicly available website that integrates information from six human genetic databases and seven model organism databases. For any given variant or gene, MARRVEL displays information from OMIM, ExAC, ClinVar, Geno2MP, DGV, and DECIPHER. Importantly, it curates model organism-specific databases to concurrently display a concise summary regarding the human gene homologs in budding and fission yeast, worm, fly, fish, mouse, and rat on a single webpage. Experiment-based information on tissue expression, protein subcellular localization, biological process, and molecular function for the human gene and homologs in the seven model organisms are arranged into a concise output. Hence, rather than visiting multiple separate databases for variant and gene analysis, users can obtain important information by searching once through MARRVEL. Altogether, MARRVEL dramatically improves efficiency and accessibility to data collection and facilitates analysis of human genes and variants by cross-disciplinary integration of 18 million records available in public databases to facilitate clinical diagnosis and basic research.

Variant effect prediction tools assessed using independent, functional assay-based datasets: implications for discovery and diagnostics

Background

Genetic variant effect prediction algorithms are used extensively in clinical genomics and research to determine the likely consequences of amino acid substitutions on protein function. It is vital that we better understand their accuracies and limitations because published performance metrics are confounded by serious problems of circularity and error propagation. Here, we derive three independent, functionally determined human mutation datasets, UniFun, BRCA1-DMS and TP53-TA, and employ them, alongside previously described datasets, to assess the pre-eminent variant effect prediction tools.

Results

Apparent accuracies of variant effect prediction tools were influenced significantly by the benchmarking dataset. Benchmarking with the assay-determined datasets UniFun and BRCA1-DMS yielded areas under the receiver operating characteristic curves in the modest ranges of 0.52 to 0.63 and 0.54 to 0.75, respectively, considerably lower than observed for other, potentially more conflicted datasets.

Conclusions

These results raise concerns about how such algorithms should be employed, particularly in a clinical setting. Contemporary variant effect prediction tools are unlikely to be as accurate at the general prediction of functional impacts on proteins as reported prior. Use of functional assay-based datasets that avoid prior dependencies promises to be valuable for the ongoing development and accurate benchmarking of such tools.

Electronic supplementary material

The online version of this article (doi:10.1186/s40246-017-0104-8) contains supplementary material, which is available to authorized users.

Prediction of harmful variants on mitochondrial genes: Test of habitat-dependent and demographic effects in a euryhaline fish

Both effective population size and life history may influence the efficacy of purifying selection, but it remains unclear if the environment affects the accumulation of weakly deleterious nonsynonymous polymorphisms. We hypothesize that the reduced energetic cost of osmoregulation in brackish water habitat may cause relaxation of selective constraints at mitochondrial oxidative phosphorylation (OXPHOS) genes. To test this hypothesis, we analyzed 57 complete mitochondrial genomes of Pungitius pungitius collected from brackish and freshwater habitats. Based on inter‐ and intraspecific comparisons, we estimated that 84% and 68% of the nonsynonymous polymorphisms in the freshwater and brackish water populations, respectively, are weakly or moderately deleterious. Using in silico prediction tools (MutPred, SNAP2), we subsequently identified nonsynonymous polymorphisms with potentially harmful effect. Both prediction methods indicated that the functional effects of the fixed nonsynonymous substitutions between nine‐ and three‐spined stickleback were weaker than for polymorphisms within species, indicating that harmful nonsynonymous polymorphisms within populations rarely become fixed between species. No significant differences in mean estimated functional effects were identified between freshwater and brackish water nine‐spined stickleback to support the hypothesis that reduced osmoregulatory energy demand in the brackish water environment reduces the strength of purifying selection at OXPHOS genes. Instead, elevated frequency of nonsynonymous polymorphisms in the freshwater environment (P_n/P_s = 0.549 vs. 0.283; Fisher's exact test p = .032) suggested that purifying selection is less efficient in small freshwater populations. This study shows the utility of in silico functional prediction tools in population genetic and evolutionary research in a nonmammalian vertebrate and demonstrates that mitochondrial energy production genes represent a promising system to characterize the demographic, life history and potential habitat‐dependent effects of segregating amino acid variants.

Targeted Next Generation Sequencing in patients with Myotonia Congenita

INTRODUCTION

Myotonia Congenita (MC) is a nondystrophic skeletal muscle disease characterized by muscle stiffness, weakness, delayed skeletal relaxation and hypertrophic muscle. The disease can be inherited as dominant or recessive. More than 130 mutations in CLCN1 gene have been identified.

MATERIALS AND METHODS

We analyzed the entire coding region and exon-intron boundaries of the CLCN1 gene in 40 MC patients. Samples already Sanger-sequenced were successively evaluated by Next Generation Sequencing (NGS), on Ion Torrent PGM. Moreover, additional 15 patients were sequenced directly by NGS.

RESULTS

NGS allowed us to identify all CLCN1 mutations except those located within exon 3, demonstrating a 96% of sensitivity. Due to primer design, one SNP (exactly rs7794560) also failed to be detected. Our results enlarge the spectrum of CLCN1 mutations and showed a novel approach for molecular analysis of MC.

Predicting phenotype from genotype: Improving accuracy through more robust experimental and computational modeling

Computational prediction yields efficient and scalable initial assessments of how variants of unknown significance may affect human health. However, when discrepancies between these predictions and direct experimental measurements of functional impact arise, inaccurate computational predictions are frequently assumed as the source. Here, we present a methodological analysis indicating that shortcomings in both computational and biological data can contribute to these disagreements. We demonstrate that incomplete assaying of multifunctional proteins can affect the strength of correlations between prediction and experiments; a variant's full impact on function is better quantified by considering multiple assays that probe an ensemble of protein functions. Additionally, many variants predictions are sensitive to protein alignment construction and can be customized to maximize relevance of predictions to a specific experimental question. We conclude that inconsistencies between computation and experiment can often be attributed to the fact that they do not test identical hypotheses. Aligning the design of the computational input with the design of the experimental output will require cooperation between computational and biological scientists, but will also lead to improved estimations of computational prediction accuracy and a better understanding of the genotype–phenotype relationship.

Characterization of BRCA1 and BRCA2 variants in multi-ethnic Asian cohort from a Malaysian case-control study

Background

Genetic testing for BRCA1 and BRCA2 has led to the accurate identification of individuals at higher risk of cancer and the development of new therapies. Approximately 10-20% of the genetic testing for BRCA1 and BRCA2 leads to the identification of variants of uncertain significance (VUS), with higher proportions in Asians. We investigated the functional significance of 7 BRCA1 and 25 BRCA2 variants in a multi-ethnic Asian cohort using a case-control approach.

Methods

The MassARRAY genotyping was conducted in 1,394 Chinese, 406 Malay and 310 Indian breast cancer cases and 1,071 Chinese, 167 Malay and 255 Indian healthy controls. The association of individual variant with breast cancer risk was analyzed using logistic regression model adjusted for ethnicity, age and family history.

Results

Our study confirmed BRCA2 p.Ile3412Val is presented in >2% of unaffected women and is likely benign, and BRCA2 p.Ala1996Thr which is predicted to be likely pathogenic by in-silico models is presented in 2% of healthy Indian women suggesting that it may not be associated with breast cancer risk. Single-variant analysis suggests that BRCA1 p.Arg762Ser may be associated with breast cancer risk (OR = 7.4; 95% CI, 0.9–62.3; p = 0.06).

Conclusions

Our study shows that BRCA2 p.Ile3412Val and p.Ala1996Thr are likely benign and highlights the need for population-specific studies to determine the likely functional significance of population-specific variants. Our study also suggests that BRCA1 p.Arg762Ser may be associated with increased risk of breast cancer but other methods or larger studies are required to determine a more precise estimate of breast cancer risk.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-017-3099-6) contains supplementary material, which is available to authorized users.

Integrative Variation Analysis Reveals that a Complex Genotype May Specify Phenotype in Siblings with Syndromic Autism Spectrum Disorder

It has been proposed that copy number variations (CNVs) are associated with increased risk of autism spectrum disorder (ASD) and, in conjunction with other genetic changes, contribute to the heterogeneity of ASD phenotypes. Array comparative genomic hybridization (aCGH) and exome sequencing, together with systems genetics and network analyses, are being used as tools for the study of complex disorders of unknown etiology, especially those characterized by significant genetic and phenotypic heterogeneity. Therefore, to characterize the complex genotype-phenotype relationship, we performed aCGH and sequenced the exomes of two affected siblings with ASD symptoms, dysmorphic features, and intellectual disability, searching for de novo CNVs, as well as for de novo and rare inherited point variations—single nucleotide variants (SNVs) or small insertions and deletions (indels)—with probable functional impacts. With aCGH, we identified, in both siblings, a duplication in the 4p16.3 region and a deletion at 8p23.3, inherited by a paternal balanced translocation, t(4, 8) (p16; p23). Exome variant analysis found a total of 316 variants, of which 102 were shared by both siblings, 128 were in the male sibling exome data, and 86 were in the female exome data. Our integrative network analysis showed that the siblings’ shared translocation could explain their similar syndromic phenotype, including overgrowth, macrocephaly, and intellectual disability. However, exome data aggregate genes to those already connected from their translocation, which are important to the robustness of the network and contribute to the understanding of the broader spectrum of psychiatric symptoms. This study shows the importance of using an integrative approach to explore genotype-phenotype variability.

Assessment of in silico protein sequence analysis in the clinical classification of variants in cancer risk genes

Missense variants represent a significant proportion of variants identified in clinical genetic testing. In the absence of strong clinical or functional evidence, the American College of Medical Genetics recommends that these findings be classified as variants of uncertain significance (VUS). VUSs may be reclassified to better inform patient care when new evidence is available. It is critical that the methods used for reclassification are robust in order to prevent inappropriate medical management strategies and unnecessary, life-altering surgeries. In an effort to provide evidence for classification, several in silico algorithms have been developed that attempt to predict the functional impact of missense variants through amino acid sequence conservation analysis. We report an analysis comparing internally derived, evidence-based classifications with the results obtained from six commonly used algorithms. We compiled a dataset of 1118 variants in BRCA1, BRCA2, MLH1, and MSH2 previously classified by our laboratory’s evidence-based variant classification program. We compared internally derived classifications with those obtained from the following in silico tools: Align-GVGD, CONDEL, Grantham Analysis, MAPP-MMR, PolyPhen-2, and SIFT. Despite being based on similar underlying principles, all algorithms displayed marked divergence in accuracy, specificity, and sensitivity. Overall, accuracy ranged from 58.7 to 90.8% while the Matthews Correlation Coefficient ranged from 0.26–0.65. CONDEL, a weighted average of multiple algorithms, did not perform significantly better than its individual components evaluated here. These results suggest that the in silico algorithms evaluated here do not provide reliable evidence regarding the clinical significance of missense variants in genes associated with hereditary cancer.

A practical guide to filtering and prioritizing genetic variants

Next-generation sequencing (NGS) of whole genomes and exomes is a powerful tool in biomedical research and clinical diagnostics. However, the vast amount of data produced by NGS introduces new challenges and opportunities, many of which require novel computational and theoretical approaches when it comes to identifying the causal variant(s) for a disease of interest. While workflows and associated software to process raw data and produce high-confidence variant calls have significantly improved, filtering tens of thousands of candidates to identify a subset relevant to a specific study is still a complex exercise best left to bioinformaticists. However, as this prioritization procedure requires biological/biomedical reasoning, biologists and clinicians are increasingly motivated to handle the task themselves. Here, we describe a set of guidelines, tools, and online resources that can be used to identify functional variants from whole-genome and whole-exome variant calls and then prioritize these variants with potential associations to phenotypes of interest. Insights gained from a recently published analysis of protein-coding gene variation in >60,000 humans by the Exome Aggregation Consortium (ExAC) are also taken into account.

Somatic Mutation Patterns in Hemizygous Genomic Regions Unveil Purifying Selection during Tumor Evolution

Identification of cancer driver genes using somatic mutation patterns indicative of positive selection has become a major goal in cancer genomics. However, cancer cells additionally depend on a large number of genes involved in basic cellular processes. While such genes should in theory be subject to strong purifying (negative) selection against damaging somatic mutations, these patterns have been elusive and purifying selection remains inadequately explored in cancer. Here, we hypothesized that purifying selection should be evident in hemizygous genomic regions, where damaging mutations cannot be compensated for by healthy alleles. Using a 7,781-sample pan-cancer dataset, we first confirmed this in POLR2A, an essential gene where hemizygous deletions are known to confer elevated sensitivity to pharmacological suppression. We next used this principle to identify several genes and pathways that show patterns indicative of purifying selection to avoid deleterious mutations. These include the POLR2A interacting protein INTS10 as well as genes involved in mRNA splicing, nonsense-mediated mRNA decay and other RNA processing pathways. Many of these genes belong to large protein complexes, and strong overlaps were observed with recent functional screens for gene essentiality in human cells. Our analysis supports that purifying selection acts to preserve the remaining function of many hemizygously deleted essential genes in tumors, indicating vulnerabilities that might be exploited by future therapeutic strategies.

Global inference of disease-causing single nucleotide variants from exome sequencing data

Background

Whole exome sequencing (WES) has recently emerged as an effective approach for identifying genetic variants underlying human diseases. However, considerable time and labour is needed for careful investigation of candidate variants. Although filtration based on population frequencies and functional prediction scores could effectively remove common and neutral variants, hundreds or even thousands of rare deleterious variants still remain. In addition, current WES platforms also provide variant information in flanking noncoding regions, such as promoters, introns and splice sites. Despite of being recognized to harbour causal variants, these regions are usually ignored by current analysis pipelines.

Results

We present a novel computational method, called Glints, to overcome the above limitations. Glints is capable of identifying disease-causing SNVs in both coding and flanking noncoding regions from exome sequencing data. The principle behind Glints is that disease-causing variants should manifest their effect at both variant and gene levels. Specifically, Glints integrates 14 types of functional scores, including predictions for both coding and noncoding variants, and 9 types of association scores, which help identifying disease relevant genes. We conducted a large-scale simulation studies based on 1000 Genomes Project data and demonstrated the effectiveness of our method in both coding and flanking noncoding regions. We also applied Glints in two real exome sequencing and demonstrated its effectiveness for uncovering disease-causing SNVs. Both standalone software and web server are available at our website http://bioinfo.au.tsinghua.edu.cn/jianglab/glints.

Conclusions

Glints is effective for uncovering disease-causing SNVs in coding and flanking noncoding regions, which is supported by both simulation and real case studies. Glints is expected to be a useful tool for human genetics research based on exome sequencing data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1325-x) contains supplementary material, which is available to authorized users.

Alanine-scanning mutagenesis of human signal transducer and activator of transcription 1 to estimate loss- or gain-of-function variants

BACKGROUND

Germline heterozygous mutations in human signal transducer and activator of transcription 1 (STAT1) can cause loss of function (LOF), as in patients with Mendelian susceptibility to mycobacterial diseases, or gain of function (GOF), as in patients with chronic mucocutaneous candidiasis. LOF and GOF mutations are equally rare and can affect the same domains of STAT1, especially the coiled-coil domain (CCD) and DNA-binding domain (DBD). Moreover, 6% of patients with chronic mucocutaneous candidiasis with a GOF STAT1 mutation have mycobacterial disease, obscuring the functional significance of the identified STAT1 mutations. Current computational approaches, such as combined annotation-dependent depletion, do not distinguish LOF and GOF variants.

OBJECTIVE

We estimated variations in the CCD/DBD of STAT1.

METHODS

We mutagenized 342 individual wild-type amino acids in the CCD/DBD (45.6% of full-length STAT1) to alanine and tested the mutants for STAT1 transcriptional activity.

RESULTS

Of these 342 mutants, 201 were neutral, 30 were LOF, and 111 were GOF mutations in a luciferase assay. This assay system correctly estimated all previously reported LOF mutations (100%) and slightly fewer GOF mutations (78.1%) in the CCD/DBD of STAT1. We found that GOF alanine mutants occurred at the interface of the antiparallel STAT1 dimer, suggesting that they destabilize this dimer. This assay also precisely predicted the effect of 2 hypomorphic and dominant negative mutations, E157K and G250E, in the CCD of STAT1 that we found in 2 unrelated patients with Mendelian susceptibility to mycobacterial diseases.

CONCLUSION

The systematic alanine-scanning assay is a useful tool to estimate the GOF or LOF status and the effect of heterozygous missense mutations in STAT1 identified in patients with severe infectious diseases, including mycobacterial and fungal diseases.

Alpha Helices Are More Robust to Mutations than Beta Strands

The rapidly increasing amount of data on human genetic variation has resulted in a growing demand to identify pathogenic mutations computationally, as their experimental validation is currently beyond reach. Here we show that alpha helices and beta strands differ significantly in their ability to tolerate mutations: helices can accumulate more mutations than strands without change, due to the higher numbers of inter-residue contacts in helices. This results in two patterns: a) the same number of mutations causes less structural change in helices than in strands; b) helices diverge more rapidly in sequence than strands within the same domains. Additionally, both helices and strands are significantly more robust than coils. Based on this observation we show that human missense mutations that change secondary structure are more likely to be pathogenic than those that do not. Moreover, inclusion of predicted secondary structure changes shows significant utility for improving upon state-of-the-art pathogenicity predictions.

The factors that determine the robustness and evolvability of proteins are still largely unknown. In this work the authors show that different secondary structure elements of proteins (helices and strands) differ in their ability to tolerate mutations, and demonstrate that it is caused by differences in the number of non-covalent residue interactions within these secondary structure units. The results suggest that engineering de novo all-alpha proteins should be easier than all-beta ones, as more sequences can to fold to the same topology. Additionally, secondary structure can be used to improve current methods of pathogenicity predictions; mutations that change secondary structure are more likely to be pathogenic than mutations that do not, due to their strong destabilizing effect on protein structure.

Dataset of eye disease-related proteins analyzed using the unfolding mutation screen

A number of genetic diseases are a result of missense mutations in protein structure. These mutations can lead to severe protein destabilization and misfolding. The unfolding mutation screen (UMS) is a computational method that calculates unfolding propensities for every possible missense mutation in a protein structure. The UMS validation demonstrated a good agreement with experimental and phenotypical data. 15 protein structures (a combination of homology models and crystal structures) were analyzed using UMS. The standard and clustered heat maps, and patterned protein structure from the analysis were stored in a UMS library. The library is currently composed of 15 protein structures from 14 inherited eye diseases including retina degenerations, glaucoma, and cataracts, and contains data for 181,110 mutations. The UMS protein library introduces 13 new human models of eye disease related proteins and is the first collection of the consistently calculated unfolding propensities, which could be used as a tool for the express analysis of novel mutations in clinical practice, next generation sequencing, and genotype-to-phenotype relationships in inherited eye disease.

In silico Mapping of Protein Unfolding Mutations for Inherited Disease

The effect of disease-causing missense mutations on protein folding is difficult to evaluate. To understand this relationship, we developed the unfolding mutation screen (UMS) for in silico evaluation of the severity of genetic perturbations at the atomic level of protein structure. The program takes into account the protein-unfolding curve and generates propensities using calculated free energy changes for every possible missense mutation at once. These results are presented in a series of unfolding heat maps and a colored protein 3D structure to show the residues critical to the protein folding and are available for quick reference. UMS was tested with 16 crystal structures to evaluate the unfolding for 1391 mutations from the ProTherm database. Our results showed that the computational accuracy of the unfolding calculations was similar to the accuracy of previously published free energy changes but provided a better scale. Our residue identity control helps to improve protein homology models. The unfolding predictions for proteins involved in age-related macular degeneration, retinitis pigmentosa, and Leber's congenital amaurosis matched well with data from previous studies. These results suggest that UMS could be a useful tool in the analysis of genotype-to-phenotype associations and next-generation sequencing data for inherited diseases.

Exome and genome sequencing for inborn errors of immunity

The advent of next-generation sequencing (NGS) in 2010 has transformed medicine, particularly the growing field of inborn errors of immunity. NGS has facilitated the discovery of novel disease-causing genes and the genetic diagnosis of patients with monogenic inborn errors of immunity. Whole-exome sequencing (WES) is presently the most cost-effective approach for research and diagnostics, although whole-genome sequencing offers several advantages. The scientific or diagnostic challenge consists in selecting 1 or 2 candidate variants among thousands of NGS calls. Variant- and gene-level computational methods, as well as immunologic hypotheses, can help narrow down this genome-wide search. The key to success is a well-informed genetic hypothesis on 3 key aspects: mode of inheritance, clinical penetrance, and genetic heterogeneity of the condition. This determines the search strategy and selection criteria for candidate alleles. Subsequent functional validation of the disease-causing effect of the candidate variant is critical. Even the most up-to-date dry lab cannot clinch this validation without a seasoned wet lab. The multifariousness of variations entails an experimental rigor even greater than traditional Sanger sequencing-based approaches in order not to assign a condition to an irrelevant variant. Finding the needle in the haystack takes patience, prudence, and discernment.

Six Germline Genetic Variations Impair the Translesion Synthesis Activity of Human DNA Polymerase κ

DNA polymerase (pol) κ efficiently catalyzes error-free translesion DNA synthesis (TLS) opposite bulky N²-guanyl lesions induced by carcinogens such as polycyclic aromatic hydrocarbons. We investigated the biochemical effects of nine human nonsynonymous germline POLK variations on the TLS properties of pol κ, utilizing recombinant pol κ (residues 1-526) enzymes and DNA templates containing an N²-CH₂(9-anthracenyl)G (N²-AnthG), 8-oxo-7,8-dihydroguanine (8-oxoG), O⁶-methyl(Me)G, or an abasic site. In steady-state kinetic analyses, the R246X, R298H, T473A, and R512W variants displayed 7- to 18-fold decreases in k_cat/K_m for dCTP insertion opposite G and N²-AnthG, with 2- to 3-fold decreases in DNA binding affinity, compared to that of the wild-type, and further showed 5- to 190-fold decreases in k_cat/K_m for next-base extension from C paired with N²-AnthG. The A471V variant showed 2- to 4-fold decreases in k_cat/K_m for correct nucleotide insertion opposite and beyond G (or N²-AnthG) compared to that of the wild-type. These five hypoactive variants also showed similar patterns of attenuation of TLS activity opposite 8-oxoG, O⁶-MeG, and abasic lesions. By contrast, the T44M variant exhibited 7- to 11-fold decreases in k_cat/K_m for dCTP insertion opposite N²-AnthG and O⁶-MeG (as well as for dATP insertion opposite an abasic site) but not opposite both G and 8-oxoG, nor beyond N²-AnthG, compared to that of the wild-type. These results suggest that the R246X, R298H, T473A, R512W, and A471V variants cause a general catalytic impairment of pol κ opposite G and all four lesions, whereas the T44M variant induces opposite lesion-dependent catalytic impairment, i.e., only opposite O⁶-MeG, abasic, and bulky N²-G lesions but not opposite G and 8-oxoG, in pol κ, which might indicate that these hypoactive pol κ variants are genetic factors in modifying individual susceptibility to genotoxic carcinogens in certain subsets of populations.

Targeted Next-Generation Sequencing Successfully Detects Causative Genes in Chinese Patients with Hereditary Hearing Loss

AIMS

We attempted to identify the genetic epidemiology of hereditary hearing loss among the Chinese Han population using next-generation sequencing (NGS).

MATERIALS AND METHODS

The entire length of the genes GJB2, SLC26A4, and GJB3, as well as exons of 57 additional candidate genes were sequenced from 116 individuals suffering from hearing loss.

RESULTS

Thirty potentially causative mutations from these 60 genes were identified as the likely etiologies of hearing loss in 67 of the cases. In our study, SLC26A4 and GJB2 were the most frequently affected genes among the Chinese Han population with hearing loss. Collectively, they account for 52.8% of the cases, followed by MTRNR1, PCDH15, and TECTA. These data also illustrate that NGS can be used to identify rare alleles responsible for hereditary hearing loss: 22 of the 30 (73.3%) genes identified with mutations are rarely mutated in hereditary hearing loss and only account for 21.5% (42/195) of the total mutation frequency, explaining no more than 2% for each gene. These rarely mutated genes would be missed by conventional diagnostic sequencing approaches.

CONCLUSIONS

NGS can be used effectively to identify both the common and rare genes causing hereditary hearing loss.

Molecular Determinants of Mutant Phenotypes, Inferred from Saturation Mutagenesis Data

Understanding how mutations affect protein activity and organismal fitness is a major challenge. We used saturation mutagenesis combined with deep sequencing to determine mutational sensitivity scores for 1,664 single-site mutants of the 101 residue Escherichia coli cytotoxin, CcdB at seven different expression levels. Active-site residues could be distinguished from buried ones, based on their differential tolerance to aliphatic and charged amino acid substitutions. At nonactive-site positions, the average mutational tolerance correlated better with depth from the protein surface than with accessibility. Remarkably, similar results were observed for two other small proteins, PDZ domain (PSD95^pdz3) and IgG-binding domain of protein G (GB1). Mutational sensitivity data obtained with CcdB were used to derive a procedure for predicting functional effects of mutations. Results compared favorably with those of two widely used computational predictors. In vitro characterization of 80 single, nonactive-site mutants of CcdB showed that activity in vivo correlates moderately with thermal stability and solubility. The inability to refold reversibly, as well as a decreased folding rate in vitro, is associated with decreased activity in vivo. Upon probing the effect of modulating expression of various proteases and chaperones on mutant phenotypes, most deleterious mutants showed an increased in vivo activity and solubility only upon over-expression of either Trigger factor or SecB ATP-independent chaperones. Collectively, these data suggest that folding kinetics rather than protein stability is the primary determinant of activity in vivo. This study enhances our understanding of how mutations affect phenotype, as well as the ability to predict fitness effects of point mutations.

The severity of hereditary porphyria is modulated by the porphyrin exporter and Lan antigen ABCB6

Hereditary porphyrias are caused by mutations in genes that encode haem biosynthetic enzymes with resultant buildup of cytotoxic metabolic porphyrin intermediates. A long-standing open question is why the same causal porphyria mutations exhibit widely variable penetrance and expressivity in different individuals. Here we show that severely affected porphyria patients harbour variant alleles in the ABCB6 gene, also known as Lan, which encodes an ATP-binding cassette (ABC) transporter. Plasma membrane ABCB6 exports a variety of disease-related porphyrins. Functional studies show that most of these ABCB6 variants are expressed poorly and/or have impaired function. Accordingly, homozygous disruption of the Abcb6 gene in mice exacerbates porphyria phenotypes in the Fech^m1Pas mouse model, as evidenced by increased porphyrin accumulation, and marked liver injury. Collectively, these studies support ABCB6 role as a genetic modifier of porphyria and suggest that porphyrin-inducing drugs may produce excessive toxicities in individuals with the rare Lan(−) blood type.

Accumulation of intermediates of haem biosynthesis, porphyrins, is harmful and usually inherited, but it is unclear how the same mutation may make some individuals more ill than others. Here, the authors show that a porphyrin transporter ABCB6 is a modulator of porphyria, and that patients with functionally defective ABCB6 show more severe symptoms.

Phenotypic spectrum of POLR3B mutations: isolated hypogonadotropic hypogonadism without neurological or dental anomalies

BACKGROUND

A constellation of neurodegenerative disorders exists (Gordon Holmes syndrome, 4H leucodystrophy, Boucher-Neuhauser syndrome) in which patients suffer from both neurological disease (typically manifested by ataxia) and reproductive failure (idiopathic hypogonadotropic hypogonadism (IHH)). POLR3B, which encodes the second largest subunit of RNA polymerase III (pol III), and POLR3A, which forms the pol III catalytic centre, are associated with 4H leucodystrophy.

METHODS

Whole exome sequencing was performed on a large cohort of subjects with IHH (n=565). Detailed neuroendocrine studies were performed in some individuals within this cohort.

RESULTS

Four individuals (two of them siblings) were identified with two rare nucleotide variants in POLR3B. On initial evaluation, all subjects were free of neurological disease. One patient underwent treatment with exogenous pulsatile gonadotropin-releasing hormone for 8 weeks which failed to result in normalisation of his sex steroid milieu due to pituitary resistance.

CONCLUSIONS

These findings suggest that the spectrum of phenotypes resulting from POLR3B mutations is wider than previously believed and that POLR3B can be associated exclusively with disorders characterised by abnormal gonadotropin secretion.

Novel bioinformatic developments for exome sequencing

With the widespread adoption of next generation sequencing technologies by the genetics community and the rapid decrease in costs per base, exome sequencing has become a standard within the repertoire of genetic experiments for both research and diagnostics. Although bioinformatics now offers standard solutions for the analysis of exome sequencing data, many challenges still remain; especially the increasing scale at which exome data are now being generated has given rise to novel challenges in how to efficiently store, analyze and interpret exome data of this magnitude. In this review we discuss some of the recent developments in bioinformatics for exome sequencing and the directions that this is taking us to. With these developments, exome sequencing is paving the way for the next big challenge, the application of whole genome sequencing.