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

Exploring deleterious non-synonymous SNPs in FUT2 gene, and implications for norovirus susceptibility and gut microbiota composition

Fucosyltransferase 2 (FUT2) gene has been extensively reported to play its role in potential gut microbiota changes and norovirus susceptibility. The normal activity of FUT2 has been found to be disrupted by non-synonymous single nucleotide polymorphisms (nsSNPs) in its gene. To explore the possible mutational changes and their deleterious effects, we employed state-of-the-art computational strategies. Firstly, nine widely-used bioinformatics tools were utilized for initial screening of possibly deleterious nsSNPs. Subsequently, the structural and functional effects of screened nsSNPs on FUT2 were evaluated by utilizing relevant computational tools. Following this, the two shortlisted nsSNPs, including G149S (rs200543547) and V196G (rs367923363), were further validated by their molecular docking with norovirus capsid protein, VP1. As compared to wild-type, the higher stability and lower binding energy scores of the both the mutants indicated their stable binding with VP1, which ultimately leads to norovirus implications. These docking results were further verified by a comprehensive computational approach, molecular dynamic simulation, which gave results in the form of lower RMSD, RMSF, RoG, and hydrogen bond values of both the mutants, depicted in relevant graphs. Overall, this research explores and validated the two FUT2 nsSNPs (G146S and V196G), which may possibly linked with the norovirus susceptibility and gut microbiota changes. Moreover, our findings highlights the value of computational strategies in mutational analysis and welcomes any further experimental validation.

Exploration of alcohol dehydrogenase EutG from Bacillus tropicus as an eco-friendly approach for the degradation of polycyclic aromatic compounds

Polycyclic aromatic compounds (PACs) are pervasive environmental contaminants derived from diverse sources including pyrogenic (e.g., combustion processes), petrogenic (e.g., crude oil), and biological origins. They are commonly found in gasoline, coal, and crude oil, reflecting their prevalence and varied origins in natural and anthropogenic activities. The aim of this study is to use Bacillus tropicus which is a spore-forming, gram-positive and facultative anaerobic bacteria, containing a gene for PACs degradtion. In this study bacterial sample was collected from women's vaginal discharge through streaking and spreading techniques. The DNA was extracted from bacterial culture and then the bacterium was identified through 16S rRNA which appeared to be B.tropicus. Then the computational analysis was conducted where the sequence similarity and functional analysis of alcohol dehydrogenase EutG protein from B.tropicus was analyzed through PSI-BLAT and SMART tool, respectively. The PSI-BLAST showed 100% query coverage score and 9 domains of alcohol dehydrogenase EutG protein were predicted through SMART tool. The quality of the protein was also assessed through ProQ server with a predicted LQ score of 8.091, a Maxsub score of - 0.350 and a z score of - 10.76. Then the phylogentic analysis was conducted to know the evolutionary relationship and closely related taxa. The 3D structure of the protein was predicted through SWISS MODEL and its quality was predicted through ERRAT with overall qauality factor of 98.708. The Ramachandran plot also predicted its quality and showed that 93.8% residues were in the most favored region. After this, 3D stucture of PACs were obtained from PubChem and molecular docking of the protein was performed with each of the compound. The lowest energy of - 10.3 was obtained with Indeno[1,2,3-cd] pyrene and the best docked complex was visulaized through discover studio to analyze its binding residues. Lastly, in-silico site-directed mutagenesis studies were performed which showed that the EutG gene (codes for alcoholic dehydrogenase) obtained from B. tropicus, will not get altered or have any decreasing effect on the enzyme's stability if it goes through any mutations. This suggests that B. tropicus can act as an efficient, non-virulent, and reliable candidate for the eco-friendly and cost-effective bioremediation of PACs.

Genomic instability in ovarian cancer: Through the lens of single nucleotide polymorphisms

Ovarian cancer (OC) is the deadliest gynecological malignancy among all female reproductive cancers. It is characterized by high mortality rate and poor prognosis. Genomic instability caused by mutations, single nucleotide polymorphisms (SNPs), copy number variations (CNVs), microsatellite instability (MSI), and chromosomal instability (CIN) are associated with OC predisposition. SNPs, which are highly prevalent in the general population, show a greater relative risk contribution, particularly in sporadic cancers. Understanding OC etiology in terms of genetic basis can increase the use of molecular diagnostics and provide promising approaches for designing novel treatment modalities. This will help deliver personalized medicine to OC patients, which may soon be within reach. Given the pivotal impact of SNPs in cancers, the primary emphasis of this review is to shed light on their prevalence in key caretaker genes that closely monitor genomic integrity, viz., DNA damage response, repair, cell cycle checkpoints, telomerase maintenance, and apoptosis and their clinical implications in OC. We highlight the current challenges faced in different SNP-based studies. Various computational methods and bioinformatic tools employed to predict the functional impact of SNPs have also been comprehensively reviewed concerning OC research. Overall, this review identifies that variants in the DDR and HRR pathways are the most studied, implying their critical role in the disease. Conversely, variants in other pathways, such as NHEJ, MMR, cell cycle, apoptosis, telomere maintenance, and PARP genes, have been explored the least.

Identification of novel candidate predisposing genes in familial nonmedullary thyroid carcinoma implicating DNA damage repair pathways

The genetic basis of nonsyndromic familial nonmedullary thyroid carcinoma (FNMTC) is still poorly understood, as the susceptibility genes identified so far only account for a small percentage of the genetic burden. Recently, germline mutations in DNA repair-related genes have been reported in cases with thyroid cancer. In order to clarify the genetic basis of FNMTC, 94 genes involved in hereditary cancer predisposition, including DNA repair genes, were analyzed in 48 probands from FNMTC families, through targeted next-generation sequencing (NGS). Genetic variants were selected upon bioinformatics analysis and in silico studies. Structural modeling and network analysis were also performed. In silico results of NGS data unveiled likely pathogenic germline variants in 15 families with FNMTC, in genes encoding proteins involved in DNA repair (ATM, CHEK2, ERCC2, BRCA2, ERCC4, FANCA, FANCD2, FANCF, and PALB2) and in the DICER1, FLCN, PTCH1, BUB1B, and RHBDF2 genes. Structural modeling predicted that most missense variants resulted in the disruption of networks of interactions between residues, with implications for local secondary and tertiary structure elements. Functional annotation and network analyses showed that the involved DNA repair proteins functionally interact with each other, within the same DNA repair pathway and across different pathways. MAPK activation was a common event in tumor progression. This study supports that rare germline variants in DNA repair genes may be accountable for FNMTC susceptibility, with potential future utility in patients' clinical management, and reinforces the relevance of DICER1 in disease etiology.

Expanding the phenotypic and genetic spectrum of GTPBP3 deficiency: findings from nine Chinese pedigrees

BACKGROUND

GTPBP3 catalyzes τm5(s2) U biosynthesis at the 34th wobble position of mitochondrial tRNAs, the hypomodification of τm5U leads to mitochondrial disease. While twenty-three variants of GTPBP3 have been reported worldwide, the genetic landscape in China remains uncertain.

METHODS

By using whole-exome sequencing, the candidate individuals carrying GTPBP3 variants were screened and identified. Pathogenicity analysis of variants was biochemically verified by patients-derived immortalized lymphocytes and cell models.

RESULTS

Through whole-exome sequencing, thirteen variants associated with GTPBP3 were identified in nine Chinese pedigrees, with eight of these variants being newly reported. Affected individuals displayed classic neurologic phenotypes and heart complications including developmental delay, seizures, hypotonia, exercise intolerance, and hypertrophic cardiomyopathy. Additionally, they displayed new symptoms such as eye problems like strabismus and heart issues related to valve function. Studies conducted on patient-derived cells provided evidence of reduced levels of GTPBP3 and impairment in mitochondrial energetic biogenesis. Re-expressing GTPBP3 variants in knockout cell lines further defined the pathogenicity of the novel variants. Analysis of the genetic spectrum in the Chinese population highlighted a concentration in exons 4 and 6, with c.689A > C being the prominent hotspot.

CONCLUSION

Our findings emphasize the extensive clinical and genetic implications of GTPBP3-related mitochondrial disorders, particularly within the Chinese population, but further investigations are needed to explore the phenotype-genotype correlation.

Analysis of damaging non-synonymous SNPs in GPx1 gene associated with the progression of diverse cancers through a comprehensive in silico approach

Glutathione Peroxidase 1 (GPx1) gene has been reported for its role in cellular redox homeostasis, and the dysregulation of its expression is linked with the progression of diverse cancers. Non-synonymous single nucleotide polymorphism (nsSNPs) have been emerged as the crucial factors, playing their role in GPx1 overexpression. To understand the deleterious mutational effects on the structure and function of GPx1 enzyme, we delved deeper into the exploration of possibly damaging nsSNPs using in-silico based approaches. Eight widely utilized computational tools were employed to roughly shortlist the deleterious nsSNPs. Their damaging effects on structure and function of the genes were evaluated by using different bioinformatics tools. Subsequently, the three final proposed deleterious mutants including mutations rs373838463, rs2107818892, and rs763687242, were docked with their reported binder, TNF receptor-associated factor 2 (TRAF2). The lowest binding affinity and stability of the docked mutant complexes as compared to the wild type GPx1 were validated by molecular dynamic simulation. Finally, the comparison of RMSD, RMSF, RoG and hydrogen bond analyses between wild-type and mutant's complexes validated the deleterious effects of proposed nsSNPs. This study successfully identified and verified the possibly damaging nsSNPs in GPx1 enzyme, which may be linked the progression of various types of cancer. Our findings underscore the value of in-silico approaches in mutational analysis and encourage further preclinical and clinical trials.

Using computational approaches to enhance the interpretation of missense variants in the PAX6 gene

The PAX6 gene encodes a highly-conserved transcription factor involved in eye development. Heterozygous loss-of-function variants in PAX6 can cause a range of ophthalmic disorders including aniridia. A key molecular diagnostic challenge is that many PAX6 missense changes are presently classified as variants of uncertain significance. While computational tools can be used to assess the effect of genetic alterations, the accuracy of their predictions varies. Here, we evaluated and optimised the performance of computational prediction tools in relation to PAX6 missense variants. Through inspection of publicly available resources (including HGMD, ClinVar, LOVD and gnomAD), we identified 241 PAX6 missense variants that were used for model training and evaluation. The performance of ten commonly used computational tools was assessed and a threshold optimization approach was utilized to determine optimal cut-off values. Validation studies were subsequently undertaken using PAX6 variants from a local database. AlphaMissense, SIFT4G and REVEL emerged as the best-performing predictors; the optimized thresholds of these tools were 0.967, 0.025, and 0.772, respectively. Combining the prediction from these top-three tools resulted in lower performance compared to using AlphaMissense alone. Tailoring the use of computational tools by employing optimized thresholds specific to PAX6 can enhance algorithmic performance. Our findings have implications for PAX6 variant interpretation in clinical settings.

MTR3D-AF2: Expanding the coverage of spatially derived missense tolerance scores across the human proteome using AlphaFold2

The missense tolerance ratio (MTR) was developed as a novel approach to assess the deleteriousness of variants. Its three-dimensional successor, MTR3D, was demonstrated powerful at discriminating pathogenic from benign variants. However, its reliance on experimental structures and homologs limited its coverage of the proteome. We have now utilized AlphaFold2 models to develop MTR3D-AF2, which covers 89.31% of proteins and 85.39% of residues across the human proteome. This work has improved MTR3D's ability to distinguish clinically established pathogenic from benign variants. MTR3D-AF2 is freely available as an interactive web server at https://biosig.lab.uq.edu.au/mtr3daf2/.

Untapped Potential of Poly(ADP-Ribose) Polymerase Inhibitors: Lessons Learned From the Real-World Clinical Homologous Recombination Repair Mutation Testing

Background

Testing for homologous recombination deficiency (HRD) mutations is pivotal to assess individual risk, to proact preventive measures in healthy carriers and to tailor treatments for cancer patients. Increasing prominence of poly(ADP-ribose) polymerase (PARP) inhibitors with remarkable impact on molecular-selected patient survival across diverse nosologies, ingrains testing for BRCA genes and beyond in clinical practice. Nevertheless, testing strategies remain a question of debate. While several pathogenic BRCA1/2 gene variants have been described as founder pathogenic mutations frequently found in patients from Russia, other homologous recombination repair (HRR) genes have not been sufficiently explored. In this study, we present real-world data of routine HRR gene testing in Russia.

Methods

We evaluated clinical and sequencing data from cancer patients who had germline/somatic next-generation sequencing (NGS) HRR gene testing in Russia (BRCA1/2/ATM/CHEK2, or 15 HRR genes). The primary objectives of this study were to evaluate the frequency of BRCA1/2 and non-BRCA gene mutations in real-world unselected patients from Russia, and to determine whether testing beyond BRCA1/2 is feasible.

Results

Data of 2,032 patients were collected from February 2021 to February 2023. Most had breast (n = 715, 35.2%), ovarian (n = 259, 12.7%), pancreatic (n = 85, 4.2%), or prostate cancer (n = 58, 2.9%). We observed 586 variants of uncertain significance (VUS) and 372 deleterious variants (DVs) across 487 patients, with 17.6% HRR-mutation positivity. HRR testing identified 120 (11.8%) BRCA1/2-positive, and 172 (16.9%) HRR-positive patients. With 51 DVs identified in 242 formalin-fixed paraffin-embedded (FFPE), testing for variant origin clarification was required in one case (0.4%). Most BRCA1/2 germline variants were DV (121 DVs, 26 VUS); in non-BRCA1/2 genes, VUS were ubiquitous (53 DVs, 132 VUS). In silico prediction identified additional 4.9% HRR and 1.2% BRCA1/2/ATM/CHEK2 mutation patients.

Conclusions

Our study represents one of the first reports about the incidence of DV and VUS in HRR genes, including genes beyond BRCA1/2, identified in cancer patients from Russia, assessed by NGS. In silico predictions of the observed HRR gene variants suggest that non-BRCA gene testing is likely to result in higher frequency of patients who are candidates for PARP inhibitor therapy. Continuing sequencing efforts should clarify interpretation of frequently observed non-BRCA VUS.

In-Silico Method for Predicting Pathogenic Missense Variants Using Online Tools: AURKA Gene as a Model

Background

In-silico analysis provides a fast, simple, and cost-free method for identifying potentially pathogenic single nucleotide variants.

Objective

To propose a simple and relatively fast method for the prediction of variant pathogenicity using free online in-silico (IS) tools with AURKA gene as a model.

Materials and Methods

We aim to propose a methodology to predict variants with high pathogenic potential using computational analysis, using AURKA gene as model. We predicted a protein model and analyzed 209 out of 64,369 AURKA variants obtained from Ensembl database. We used bioinformatic tools to predict pathogenicity. The results were compared through the VarSome website, which includes its own pathogenicity score and the American College of Medical Genetics (ACMG) classification.

Results

Out of the 209 analyzed variants, 16 were considered pathogenic, and 13 were located in the catalytic domain. The most frequent protein changes were size and hydrophobicity modifications of amino acids. Proline and Glycine amino acid substitutions were the most frequent changes predicted as pathogenic. These bioinformatic tools predicted functional changes, such as protein up or down-regulation, gain or loss of molecule interactions, and structural protein modifications. When compared to the ACMG classification, 10 out of 16 variants were considered likely pathogenic, with 7 out of 10 changes at Proline/Glycine substitutions.

Conclusion

This method allows quick and cost-free bulk variant screening to identify variants with pathogenic potential for further association and/or functional studies.

Exploring the Role of Non-synonymous and Deleterious Variants Identified in Colorectal Cancer: A Multi-dimensional Computational Scrutiny of Exomes

Introduction

Colorectal cancers are the world's third most commonly diagnosed type of cancer. Currently, there are several diagnostic and treatment options to combat it. However, a delay in detection of the disease is life-threatening. Additionally, a thorough analysis of the exomes of cancers reveals potential variation data that can be used for early disease prognosis.

Methods

By utilizing a comprehensive computational investigation, the present study aimed to reveal mutations that could potentially predispose to colorectal cancer. Ten colorectal cancer exomes were retrieved. Quality control assessments were performed using FastQC and MultiQC, gapped alignment to the human reference genome (hg19) using Bowtie2 and calling the germline variants using Haplotype caller in the GATK pipeline. The variants were filtered and annotated using SIFT and PolyPhen2 successfully categorized the mutations into synonymous, non-synonymous, start loss and stop gain mutations as well as marked them as possibly damaging, probably damaging and benign. This mutational profile helped in shortlisting frequently occurring mutations and associated genes, for which the downstream multi-dimensional expression analyses were carried out.

Results

Our work involved prioritizing the non-synonymous, deleterious SNPs since these polymorphisms bring about a functional alteration to the phenotype. The top variations associated with their genes with the highest frequency of occurrence included LGALS8, CTSB, RAD17, CPNE1, OPRM1, SEMA4D, MUC4, PDE4DIP, ELN and ADRA1A. An in-depth multi-dimensional downstream analysis of all these genes in terms of gene expression profiling and analysis and differential gene expression with regard to various cancer types revealed CTSB and CPNE1 as highly expressed and overregulated genes in colorectal cancer.

Conclusion

Our work provides insights into the various alterations that might possibly lead to colorectal cancer and suggests the possibility of utilizing the most important genes identified for wet-lab experimentation.

Uncovering the clinical relevance of unclassified variants in DNA repair genes: a focus on BRCA negative Tunisian cancer families

Introduction: Recent advances in sequencing technologies have significantly increased our capability to acquire large amounts of genetic data. However, the clinical relevance of the generated data continues to be challenging particularly with the identification of Variants of Uncertain Significance (VUSs) whose pathogenicity remains unclear. In the current report, we aim to evaluate the clinical relevance and the pathogenicity of VUSs in DNA repair genes among Tunisian breast cancer families. Methods: A total of 67 unsolved breast cancer cases have been investigated. The pathogenicity of VUSs identified within 26 DNA repair genes was assessed using different in silico prediction tools including SIFT, PolyPhen2, Align-GVGD and VarSEAK. Effects on the 3D structure were evaluated using the stability predictor DynaMut and molecular dynamics simulation with NAMD. Family segregation analysis was also performed. Results: Among a total of 37 VUSs identified, 11 variants are likely deleterious affecting ATM, BLM, CHEK2, ERCC3, FANCC, FANCG, MSH2, PMS2 and RAD50 genes. The BLM variant, c.3254dupT, is novel and seems to be associated with increased risk of breast, endometrial and colon cancer. Moreover, c.6115G>A in ATM and c.592+3A>T in CHEK2 were of keen interest identified in families with multiple breast cancer cases and their familial cosegregation with disease has been also confirmed. In addition, functional in silico analyses revealed that the ATM variant may lead to protein immobilization and rigidification thus decreasing its activity. We have also shown that FANCC and FANCG variants may lead to protein destabilization and alteration of the structure compactness which may affect FANCC and FANCG protein activity. Conclusion: Our findings revealed that VUSs in DNA repair genes might be associated with increased cancer risk and highlight the need for variant reclassification for better disease management. This will help to improve the genetic diagnosis and therapeutic strategies of cancer patients not only in Tunisia but also in neighboring countries.

Classification of MLH1 Missense VUS Using Protein Structure-Based Deep Learning-Ramachandran Plot-Molecular Dynamics Simulations Method

Pathogenic variation in DNA mismatch repair (MMR) gene MLH1 is associated with Lynch syndrome (LS), an autosomal dominant hereditary cancer. Of the 3798 MLH1 germline variants collected in the ClinVar database, 38.7% (1469) were missense variants, of which 81.6% (1199) were classified as Variants of Uncertain Significance (VUS) due to the lack of functional evidence. Further determination of the impact of VUS on MLH1 function is important for the VUS carriers to take preventive action. We recently developed a protein structure-based method named "Deep Learning-Ramachandran Plot-Molecular Dynamics Simulation (DL-RP-MDS)" to evaluate the deleteriousness of MLH1 missense VUS. The method extracts protein structural information by using the Ramachandran plot-molecular dynamics simulation (RP-MDS) method, then combines the variation data with an unsupervised learning model composed of auto-encoder and neural network classifier to identify the variants causing significant change in protein structure. In this report, we applied the method to classify 447 MLH1 missense VUS. We predicted 126/447 (28.2%) MLH1 missense VUS were deleterious. Our study demonstrates that DL-RP-MDS is able to classify the missense VUS based solely on their impact on protein structure.

Matching variants for functional characterization of genetic variants

Rapid and low-cost sequencing, as well as computer analysis, have facilitated the diagnosis of many genetic diseases, resulting in a substantial rise in the number of disease-associated genes. However, genetic diagnosis of many disorders remains problematic due to the lack of interpretation for many genetic variants, especially missenses, the infeasibility of high-throughput experiments on mammals, and the shortcomings of computational prediction technologies. Additionally, the available mutant databases are not well-utilized. Toward this end, we used Caenorhabditis elegans mutant resources to delineate the functions of eight missense variants (V444I, V517D, E610K, L732F, E817K, H873P, R1105K, and G1205E) and two stop codons (W937stop and Q1434stop), including several matching variants (MatchVar) with human in ciliopathy associated IFT-140 (also called CHE-11)//IFT140 (intraflagellar transport protein 140). Moreover, MatchVars carrying C. elegans mutants, including IFT-140(G680S) and IFT-140(P702A) for the human (G704S) (dbSNP: rs150745099) and P726A (dbSNP: rs1057518064 and a conflicting variation) were created using CRISPR/Cas9. IFT140 is a key component of IFT complex A (IFT-A), which is involved in the retrograde transport of IFT along cilia and the entrance of G protein-coupled receptors into cilia. Functional analysis of all 10 variants revealed that P702A and W937stop, but not others phenocopied the ciliary phenotypes (short cilia, IFT accumulations, mislocalization of membrane proteins, and cilia entry of nonciliary proteins) of the IFT-140 null mutant, indicating that both P702A and W937stop are phenotypic in C. elegans. Our functional data offered experimental support for interpreting human variants, by using ready-to-use mutants carrying MatchVars and generating MatchVars with CRISPR/Cas9.

A novel deleterious oxytocin variant is associated with the lower twinning ratio in Awassi ewes

This study aimed to assess the possible association of oxytocin (OXT) gene with reproductive traits in two groups of Awassi ewes that differ in their reproductive potentials. Sheep were genotyped using PCR-single-stranded conformation polymorphism approach. Three genotypes were detected in exon 2, CC, CA, and AA, and a novel SNP was identified with a missense effect on oxytocin (c.188C > A → p.Arg55Leu). A significant (p < 0.01) association of p.Arg55Leu with the twinning rate was found as ewes with AA and CA genotypes exhibited, respectively a lower twinning ratio than those with the wild-type CC genotype. The deleterious impact of p.Arg55Leu was demonstrated by all in silico tools that were utilized to assess the effect of this variant on the structure, function, and stability of oxytocin. Molecular docking showed that p.Arg55Leu caused a dramatic alteration in the binding of oxytocin with its receptor and reduced the number of interacted amino acids between them. Our study suggests that ewes with AA and CA genotypes showed a lower reproductive performance due to the presence of p.Arg55Leu, which caused damaging impacts on oxytocin and is binding with the OXT receptor. The utilization of the p.Arg55Leu could be useful for improving Awassi reproductive potential.

A review of genetic variant databases and machine learning tools for predicting the pathogenicity of breast cancer

Studies continue to uncover contributing risk factors for breast cancer (BC) development including genetic variants. Advances in machine learning and big data generated from genetic sequencing can now be used for predicting BC pathogenicity. However, it is unclear which tool developed for pathogenicity prediction is most suited for predicting the impact and pathogenicity of variant effects. A significant challenge is to determine the most suitable data source for each tool since different tools can yield different prediction results with different data inputs. To this end, this work reviews genetic variant databases and tools used specifically for the prediction of BC pathogenicity. We provide a description of existing genetic variants databases and, where appropriate, the diseases for which they have been established. Through example, we illustrate how they can be used for prediction of BC pathogenicity and discuss their associated advantages and disadvantages. We conclude that the tools that are specialized by training on multiple diverse datasets from different databases for the same disease have enhanced accuracy and specificity and are thereby more helpful to the clinicians in predicting and diagnosing BC as early as possible.

In silico and structure-based assessment to classify VUS identified in the α-helical domain of BRCA2

Breast cancer type 2 susceptibility (BRCA2) protein plays a crucial role in DNA double-strand breaks repair mechanism by homologous recombination. Pathogenic mutations in the BRCA2 gene confer an increased risk of hereditary breast and ovarian cancer (HBOC). Different missense mutations are identified from a larger cohort of patient populations in the BRCA2. However, most missense mutations are classified as 'Variants of Uncertain Significance' (VUS) due to a lack of data from structural, functional, and clinical assessments. Therefore, this study focused on assessing VUS identified in the α-helical domain of h-BRCA2 using different in silico tools and structure-based molecular dynamics simulation. A total of 286 identified VUS were evaluated using Align-GVGD, PROVEAN and PANTHER servers and 18 variants were predicted to be pathogenic. Further, out of 18 variants analyzed using the ConSurf server, 16 variants were found to be evolutionary conserved. These 16 conserved variants were submitted to PremPS and Dynamut server to assess the effect of the mutation at the protein structure level; 12 mutations were predicted to have a destabilizing effect on the native protein structure. Finally, molecular dynamics simulations revealed 5 variants BRCA2 Cys2646Tyr, Asp2665Val, Trp2619Arg, Trp2619Ser and Tyr2660Cys can alter the folding pattern and need further validation using in vitro, structural and in vivo studies to classify as pathogenic.Communicated by Ramaswamy H. Sarma.

Beyond genetics: Deciphering the impact of missense variants in CAD deficiency

CAD is a large, 2225 amino acid multienzymatic protein required for de novo pyrimidine biosynthesis. Pathological CAD variants cause a developmental and epileptic encephalopathy which is highly responsive to uridine supplements. CAD deficiency is difficult to diagnose because symptoms are nonspecific, there is no biomarker, and the protein has over 1000 known variants. To improve diagnosis, we assessed the pathogenicity of 20 unreported missense CAD variants using a growth complementation assay that identified 11 pathogenic variants in seven affected individuals; they would benefit from uridine treatment. We also tested nine variants previously reported as pathogenic and confirmed the damaging effect of seven. However, we reclassified two variants as likely benign based on our assay, which is consistent with their long-term follow-up with uridine. We found that several computational methods are unreliable predictors of pathogenic CAD variants, so we extended the functional assay results by studying the impact of pathogenic variants at the protein level. We focused on CAD's dihydroorotase (DHO) domain because it accumulates the largest density of damaging missense changes. The atomic-resolution structures of eight DHO pathogenic variants, combined with functional and molecular dynamics analyses, provided a comprehensive structural and functional understanding of the activity, stability, and oligomerization of CAD's DHO domain. Combining our functional and protein structural analysis can help refine clinical diagnostic workflow for CAD variants in the genomics era.

Rare disease variant curation from literature: assessing gaps with creatine transport deficiency in focus

BACKGROUND

Approximately 4-8% of the world suffers from a rare disease. Rare diseases are often difficult to diagnose, and many do not have approved therapies. Genetic sequencing has the potential to shorten the current diagnostic process, increase mechanistic understanding, and facilitate research on therapeutic approaches but is limited by the difficulty of novel variant pathogenicity interpretation and the communication of known causative variants. It is unknown how many published rare disease variants are currently accessible in the public domain.

RESULTS

This study investigated the translation of knowledge of variants reported in published manuscripts to publicly accessible variant databases. Variants, symptoms, biochemical assay results, and protein function from literature on the SLC6A8 gene associated with X-linked Creatine Transporter Deficiency (CTD) were curated and reported as a highly annotated dataset of variants with clinical context and functional details. Variants were harmonized, their availability in existing variant databases was analyzed and pathogenicity assignments were compared with impact algorithm predictions. 24% of the pathogenic variants found in PubMed articles were not captured in any database used in this analysis while only 65% of the published variants received an accurate pathogenicity prediction from at least one impact prediction algorithm.

CONCLUSIONS

Despite being published in the literature, pathogenicity data on patient variants may remain inaccessible for genetic diagnosis, therapeutic target identification, mechanistic understanding, or hypothesis generation. Clinical and functional details presented in the literature are important to make pathogenicity assessments. Impact predictions remain imperfect but are improving, especially for single nucleotide exonic variants, however such predictions are less accurate or unavailable for intronic and multi-nucleotide variants. Developing text mining workflows that use natural language processing for identifying diseases, genes and variants, along with impact prediction algorithms and integrating with details on clinical phenotypes and functional assessments might be a promising approach to scale literature mining of variants and assigning correct pathogenicity. The curated variants list created by this effort includes context details to improve any such efforts on variant curation for rare diseases.

Choosing Variant Interpretation Tools for Clinical Applications: Context Matters

Pathogenicity predictors are computational tools that classify genetic variants as benign or pathogenic; this is currently a major challenge in genomic medicine. With more than fifty such predictors available, selecting the most suitable tool for clinical applications like genetic screening, molecular diagnostics, and companion diagnostics has become increasingly challenging. To address this issue, we have developed a cost-based framework that naturally considers the various components of the problem. This framework encodes clinical scenarios using a minimal set of parameters and treats pathogenicity predictors as rejection classifiers, a common practice in clinical applications where low-confidence predictions are routinely rejected. We illustrate our approach in four examples where we compare different numbers of pathogenicity predictors for missense variants. Our results show that no single predictor is optimal for all clinical scenarios and that considering rejection yields a different perspective on classifiers.

Predicting disease severity in metachromatic leukodystrophy using protein activity and a patient phenotype matrix

BACKGROUND

Metachromatic leukodystrophy (MLD) is a lysosomal storage disorder caused by mutations in the arylsulfatase A gene (ARSA) and categorized into three subtypes according to age of onset. The functional effect of most ARSA mutants remains unknown; better understanding of the genotype-phenotype relationship is required to support newborn screening (NBS) and guide treatment.

RESULTS

We collected a patient data set from the literature that relates disease severity to ARSA genotype in 489 individuals with MLD. Patient-based data were used to develop a phenotype matrix that predicts MLD phenotype given ARSA alleles in a patient's genotype with 76% accuracy. We then employed a high-throughput enzyme activity assay using mass spectrometry to explore the function of ARSA variants from the curated patient data set and the Genome Aggregation Database (gnomAD). We observed evidence that 36% of variants of unknown significance (VUS) in ARSA may be pathogenic. By classifying functional effects for 251 VUS from gnomAD, we reduced the incidence of genotypes of unknown significance (GUS) by over 98.5% in the overall population.

CONCLUSIONS

These results provide an additional tool for clinicians to anticipate the disease course in MLD patients, identifying individuals at high risk of severe disease to support treatment access. Our results suggest that more than 1 in 3 VUS in ARSA may be pathogenic. We show that combining genetic and biochemical information increases diagnostic yield. Our strategy may apply to other recessive diseases, providing a tool to address the challenge of interpreting VUS within genotype-phenotype relationships and NBS.

Gene-specific machine learning for pathogenicity prediction of rare BRCA1 and BRCA2 missense variants

Machine learning-based pathogenicity prediction helps interpret rare missense variants of BRCA1 and BRCA2, which are associated with hereditary cancers. Recent studies have shown that classifiers trained using variants of a specific gene or a set of genes related to a particular disease perform better than those trained using all variants, due to their higher specificity, despite the smaller training dataset size. In this study, we further investigated the advantages of "gene-specific" machine learning compared to "disease-specific" machine learning. We used 1068 rare (gnomAD minor allele frequency (MAF) < 0.005) missense variants of 28 genes associated with hereditary cancers for our investigation. Popular machine learning classifiers were employed: regularized logistic regression, extreme gradient boosting, random forests, support vector machines, and deep neural networks. As features, we used MAFs from multiple populations, functional prediction and conservation scores, and positions of variants. The disease-specific training dataset included the gene-specific training dataset and was > 7 × larger. However, we observed that gene-specific training variants were sufficient to produce the optimal pathogenicity predictor if a suitable machine learning classifier was employed. Therefore, we recommend gene-specific over disease-specific machine learning as an efficient and effective method for predicting the pathogenicity of rare BRCA1 and BRCA2 missense variants.

The Spectra of Disease-Causing Mutations in the Ferroportin 1 (SLC40A1) Encoding Gene and Related Iron Overload Phenotypes (Hemochromatosis Type 4 and Ferroportin Disease)

SLC40A1 is the sole iron export protein reported in mammals and is a key player in both cellular and systemic iron homeostasis. This unique iron exporter, which belongs to the major facilitator superfamily, is predominantly regulated by the hyposideremic hormone hepcidin. SLC40A1 dysfunction causes ferroportin disease, and autosomal dominant iron overload disorder characterized by cellular iron retention, principally in reticuloendothelial cells, correlating with high serum ferritin and low to normal transferrin saturation. Resistant to hepcidin, SLC40A1 mutations are rather associated with elevated plasma iron and parenchymal iron deposition, a condition that resembles HFE-related hemochromatosis and is associated with more clinical complications. With very few exceptions, only missense variations are reported at the SLC40A1 locus; this situation increasingly limits the establishment of pathogenicity. In this mutation update, we provide a comprehensive review of all the pathogenic or likely pathogenic variants, variants of unknown significance, and benign or likely benign SLC40A1 variants. The classification is essentially determined using functional, structural, segregation, and recurrence data. We furnish new information on genotype-phenotype correlations for loss-of-function, gain-of-function, and other SLC40A1 variants, confirming the existence of wide clinical heterogeneity and the potential for misdiagnosis. All information is recorded in a locus-specific online database.

In silico versus functional characterization of genetic variants: lessons from muscle channelopathies

Accurate determination of the pathogenicity of missense genetic variants of uncertain significance is a huge challenge for implementing genetic data in clinical practice. In silico predictive tools are used to score variants' pathogenicity. However, their value in clinical settings is often unclear, as they have not usually been validated against robust functional assays. We compared nine widely used in silico predictive tools, including more recently developed tools (EVE and REVEL) with detailed cell-based electrophysiology, for 126 CLCN1 variants discovered in patients with the skeletal muscle channelopathy myotonia congenita. We found poor accuracy for most tools. The highest accuracy was obtained with MutationTaster (84.58%) and REVEL (82.54%). Both of these scores showed poor specificity, although specificity was better using EVE. Combining methods based on concordance improved performance overall but still lacked specificity. Our calculated statistics for the predictive tools were different to reported values for other genes in the literature, suggesting that the utility of the tools varies between genes. Overall, current predictive tools for this chloride channel are not reliable for clinical use, and tools with better specificity are urgently required. Improving the accuracy of predictive tools is a wider issue and a huge challenge for effective clinical implementation of genetic data.

In Silico-Based Structural Evaluation to Categorize the Pathogenicity of Mutations Identified in the RAD Class of Proteins

RAD genes, known as double-strand break repair proteins, play a major role in maintaining the genomic integrity of a cell by carrying out essential DNA repair functions via double-strand break repair pathways. Mutations in the RAD class of proteins show high susceptibility to breast and ovarian cancers; however, adequate research on the mutations identified in these genes has not been extensively reported for their deleterious effects. Changes in the folding pattern of RAD proteins play an important role in protein-protein interactions and also functions. Missense mutations identified from four cancer databases, cBioPortal, COSMIC, ClinVar, and gnomAD, cause aberrant conformations, which may lead to faulty DNA repair mechanisms. It is therefore necessary to evaluate the effects of pathogenic mutations of RAD proteins and their subsequent role in breast and ovarian cancers. In this study, we have used eight computational prediction servers to analyze pathogenic mutations and understand their effects on the protein structure and function. A total of 5122 missense mutations were identified from four different cancer databases, of which 1165 were predicted to be pathogenic using at least five pathogenicity prediction servers. These mutations were characterized as high-risk mutations based on their location in the conserved domains and subsequently subjected to structural stability characterization. The mutations included in the present study were selected from clinically relevant mutants in breast cancer pedigrees. Comparative folding patterns and intra-atomic interaction results showed alterations in the structural behavior of RAD proteins, specifically RAD51C triggered by mutations G125V and L138F and RAD51D triggered by mutations S207L and E233G.

Lynch syndrome, molecular mechanisms and variant classification

Patients with the heritable cancer disease, Lynch syndrome, carry germline variants in the MLH1, MSH2, MSH6 and PMS2 genes, encoding the central components of the DNA mismatch repair system. Loss-of-function variants disrupt the DNA mismatch repair system and give rise to a detrimental increase in the cellular mutational burden and cancer development. The treatment prospects for Lynch syndrome rely heavily on early diagnosis; however, accurate diagnosis is inextricably linked to correct clinical interpretation of individual variants. Protein variant classification traditionally relies on cumulative information from occurrence in patients, as well as experimental testing of the individual variants. The complexity of variant classification is due to (1) that variants of unknown significance are rare in the population and phenotypic information on the specific variants is missing, and (2) that individual variant testing is challenging, costly and slow. Here, we summarise recent developments in high-throughput technologies and computational prediction tools for the assessment of variants of unknown significance in Lynch syndrome. These approaches may vastly increase the number of interpretable variants and could also provide important mechanistic insights into the disease. These insights may in turn pave the road towards developing personalised treatment approaches for Lynch syndrome.

HBOC syndrome with an uncharacterized variant in the BRCA1 gene in a patient diagnosed with endometrial cancer after surgery for bilateral breast cancer: A case report

The association between endometrial cancer and the BRCA1 and BRCA2 genes is not fully understood, and the risk elevation of endometrial cancer in patients with hereditary breast and ovarian cancer (HBOC) is not understood. The present report examines a rare case of HBOC syndrome and an uncharacterized variant of the BRCA1 gene in a patient diagnosed with endometrial cancer. A 46-year-old woman, gravida 1 para 1, was referred to Wakayama Medical University Hospital (Wakayama, Japan) because positron emission tomography/computed tomography (PET/CT) showed a high FDG uptake in the corpus uteri and the left ovary. PET/CT was performed just after mastectomy for left-sided breast cancer (triple negative). The patient had previously undergone partial mastectomy for right-sided breast cancer (triple negative) and was treated with radiation therapy to the right residual breast when she was 39 years old. Laparoscopic hysterectomy and bilateral adnexectomy were performed, and the histological diagnosis was endometrioid carcinoma, grade 1. Her germline BRCA status was tested by blood examination and the result was ‘NM_007294.4(BRCA1):c.49G>C (p.Ala17Pro)’. The variant was evaluated as ‘likely pathogenic’. The patient was diagnosed with HBOC syndrome and endometrial cancer, pT1ANxM0. The patient had no recurrence of breast or endometrial cancer 16 months after gynecologic surgery.

A Comprehensive Evaluation of the Performance of Prediction Algorithms on Clinically Relevant Missense Variants

The rapid integration of genomic technologies in clinical diagnostics has resulted in the detection of a multitude of missense variants whose clinical significance is often unknown. As a result, a plethora of computational tools have been developed to facilitate variant interpretation. However, choosing an appropriate software from such a broad range of tools can be challenging; therefore, systematic benchmarking with high-quality, independent datasets is critical. Using three independent benchmarking datasets compiled from the ClinVar database, we evaluated the performance of ten widely used prediction algorithms with missense variants from 21 clinically relevant genes, including BRCA1 and BRCA2. A fourth dataset consisting of 1053 missense variants was also used to investigate the impact of type 1 circularity on their performance. The performance of the prediction algorithms varied widely across datasets. Based on Matthews Correlation Coefficient and Area Under the Curve, SNPs&GO and PMut consistently displayed an overall above-average performance across the datasets. Most of the tools demonstrated greater sensitivity and negative predictive values at the expense of lower specificity and positive predictive values. We also demonstrated that type 1 circularity significantly impacts the performance of these tools and, if not accounted for, may confound the selection of the best performing algorithms.

Understanding and predicting the functional consequences of missense mutations in BRCA1 and BRCA2

BRCA1 and BRCA2 are tumour suppressor genes that play a critical role in maintaining genomic stability via the DNA repair mechanism. DNA repair defects caused by BRCA1 and BRCA2 missense variants increase the risk of developing breast and ovarian cancers. Accurate identification of these variants becomes clinically relevant, as means to guide personalized patient management and early detection. Next-generation sequencing efforts have significantly increased data availability but also the discovery of variants of uncertain significance that need interpretation. Experimental approaches used to measure the molecular consequences of these variants, however, are usually costly and time-consuming. Therefore, computational tools have emerged as faster alternatives for assisting in the interpretation of the clinical significance of newly discovered variants. To better understand and predict variant pathogenicity in BRCA1 and BRCA2, various machine learning algorithms have been proposed, however presented limited performance. Here we present BRCA1 and BRCA2 gene-specific models and a generic model for quantifying the functional impacts of single-point missense variants in these genes. Across tenfold cross-validation, our final models achieved a Matthew's Correlation Coefficient (MCC) of up to 0.98 and comparable performance of up to 0.89 across independent, non-redundant blind tests, outperforming alternative approaches. We believe our predictive tool will be a valuable resource for providing insights into understanding and interpreting the functional consequences of missense variants in these genes and as a tool for guiding the interpretation of newly discovered variants and prioritizing mutations for experimental validation.

Genetic Analysis of ABCB4 Mutations and Variants Related to the Pathogenesis and Pathophysiology of Low Phospholipid-Associated Cholelithiasis

Clinical studies have revealed that the ABCB4 gene encodes the phospholipid transporter on the canalicular membrane of hepatocytes, and its mutations and variants are the genetic basis of low phospholipid-associated cholelithiasis (LPAC), a rare type of gallstone disease caused by a single-gene mutation or variation. The main features of LPAC include a reduction or deficiency of phospholipids in bile, symptomatic cholelithiasis at <40 years of age, intrahepatic sludge and microlithiasis, mild chronic cholestasis, a high cholesterol/phospholipid ratio in bile, and recurrence of biliary symptoms after cholecystectomy. Needle-like cholesterol crystals, putatively “anhydrous” cholesterol crystallization at low phospholipid concentrations in model and native bile, are characterized in ABCB4 knockout mice, a unique animal model for LPAC. Gallbladder bile with only trace amounts of phospholipids in these mice is supersaturated with cholesterol, with lipid composition plotting in the left two-phase zone of the ternary phase diagram, consistent with “anhydrous” cholesterol crystallization. In this review, we summarize the molecular biology and physiological functions of ABCB4 and comprehensively discuss the latest advances in the genetic analysis of ABCB4 mutations and variations and their roles in the pathogenesis and pathophysiology of LPAC in humans, based on the results from clinical studies and mouse experiments. To date, approximately 158 distinct LPAC-causing ABCB4 mutations and variants in humans have been reported in the literature, indicating that it is a monogenic risk factor for LPAC. The elucidation of the ABCB4 function in the liver, the identification of ABCB4 mutations and variants in LPAC patients, and the exploration of gene therapy for ABCB4 deficiency in animal models can help us to better understand the cellular, molecular, and genetic mechanisms underlying the onset of the disease, and will pave the way for early diagnosis and prevention of susceptible subjects and effective intervention for LPAC in patients.

A machine learning approach based on ACMG/AMP guidelines for genomic variant classification and prioritization

Genomic variant interpretation is a critical step of the diagnostic procedure, often supported by the application of tools that may predict the damaging impact of each variant or provide a guidelines-based classification. We propose the application of Machine Learning methodologies, in particular Penalized Logistic Regression, to support variant classification and prioritization. Our approach combines ACMG/AMP guidelines for germline variant interpretation as well as variant annotation features and provides a probabilistic score of pathogenicity, thus supporting the prioritization and classification of variants that would be interpreted as uncertain by the ACMG/AMP guidelines. We compared different approaches in terms of variant prioritization and classification on different datasets, showing that our data-driven approach is able to solve more variant of uncertain significance (VUS) cases in comparison with guidelines-based approaches and in silico prediction tools.

SWAAT Bioinformatics Workflow for Protein Structure-Based Annotation of ADME Gene Variants

Recent genomic studies have revealed the critical impact of genetic diversity within small population groups in determining the way individuals respond to drugs. One of the biggest challenges is to accurately predict the effect of single nucleotide variants and to get the relevant information that allows for a better functional interpretation of genetic data. Different conformational scenarios upon the changing in amino acid sequences of pharmacologically important proteins might impact their stability and plasticity, which in turn might alter the interaction with the drug. Current sequence-based annotation methods have limited power to access this type of information. Motivated by these calls, we have developed the Structural Workflow for Annotating ADME Targets (SWAAT) that allows for the prediction of the variant effect based on structural properties. SWAAT annotates a panel of 36 ADME genes including 22 out of the 23 clinically important members identified by the PharmVar consortium. The workflow consists of a set of Python codes of which the execution is managed within Nextflow to annotate coding variants based on 37 criteria. SWAAT also includes an auxiliary workflow allowing a versatile use for genes other than ADME members. Our tool also includes a machine learning random forest binary classifier that showed an accuracy of 73%. Moreover, SWAAT outperformed six commonly used sequence-based variant prediction tools (PROVEAN, SIFT, PolyPhen-2, CADD, MetaSVM, and FATHMM) in terms of sensitivity and has comparable specificity. SWAAT is available as an open-source tool.

BRCA1/2 variants of unknown significance in hereditary breast and ovarian cancer (HBOC) syndrome: looking for the hidden meaning

Hereditary breast and ovarian cancer syndrome is caused by germline mutations in BRCA1/2 genes. These genes are very large and their mutations are heterogeneous and scattered throughout the coding sequence. In addition to the above-mentioned mutations, variants of uncertain/unknown significance (VUSs) have been identified in BRCA genes, which make more difficult the clinical management of the patient and risk assessment. In the last decades, several laboratories have developed different databases that contain more than 2000 variants for the two genes and integrated strategies which include multifactorial prediction models based on direct and indirect genetic evidence, to classify the VUS and attribute them a clinical significance associated with a deleterious, high-low or neutral risk. This review provides a comprehensive overview of literature studies concerning the VUSs, in order to assess their impact on the population and provide new insight for the appropriate patient management in clinical practice.

Theranostic Interpolation of Genomic Instability in Breast Cancer

Breast cancer is a diverse disease caused by mutations in multiple genes accompanying epigenetic aberrations of hazardous genes and protein pathways, which distress tumor-suppressor genes and the expression of oncogenes. Alteration in any of the several physiological mechanisms such as cell cycle checkpoints, DNA repair machinery, mitotic checkpoints, and telomere maintenance results in genomic instability. Theranostic has the potential to foretell and estimate therapy response, contributing a valuable opportunity to modify the ongoing treatments and has developed new treatment strategies in a personalized manner. “Omics” technologies play a key role while studying genomic instability in breast cancer, and broadly include various aspects of proteomics, genomics, metabolomics, and tumor grading. Certain computational techniques have been designed to facilitate the early diagnosis of cancer and predict disease-specific therapies, which can produce many effective results. Several diverse tools are used to investigate genomic instability and underlying mechanisms. The current review aimed to explore the genomic landscape, tumor heterogeneity, and possible mechanisms of genomic instability involved in initiating breast cancer. We also discuss the implications of computational biology regarding mutational and pathway analyses, identification of prognostic markers, and the development of strategies for precision medicine. We also review different technologies required for the investigation of genomic instability in breast cancer cells, including recent therapeutic and preventive advances in breast cancer.

Artificial Selection Drives SNPs of Olfactory Receptor Genes into Different Working Traits in Labrador Retrievers

Labs as guide dogs or sniffer dogs in usage have been introduced into China for more than 20 years. These two types of working dogs own blunt or acute olfactory senses, which have been obtained by artificial selection in relatively closed populations. In order to attain stable olfactory attributes and meet use-oriented demands, Chinese breeders keep doing the same artificial selection. Though olfactory behavior is canine genetic behavior, genotypes of OR genes formed by breeding schemes are largely unknown. Here, we characterized 26 SNPs, 2 deletions, and 2 insertions of 7 OR genes between sniffer dogs and guide dogs in order to find out the candidate alleles associated with working specific traits. The results showed that there were candidate functional SNP alleles in one locus that had statistically severely significant differences between the two subpopulations. Furthermore, the levels of polymorphism were not high in all loci and linkage disequilibrium only happened within one OR gene. Hardy–Weinberg equilibrium (HWE) tests showed that there was a higher ratio not in HWE and lower FST within the two working dog populations. We conclude that artificial selection in working capacities has acted on SNP alleles of OR genes in a dog breed and driven the evolution in compliance with people's intentions though the changes are limited in decades of strategic breeding.

Evaluation of in silico predictors on short nucleotide variants in HBA1, HBA2, and HBB associated with haemoglobinopathies

Haemoglobinopathies are the commonest monogenic diseases worldwide and are caused by variants in the globin gene clusters. With over 2400 variants detected to date, their interpretation using the American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) guidelines is challenging and computational evidence can provide valuable input about their functional annotation. While many in silico predictors have already been developed, their performance varies for different genes and diseases. In this study, we evaluate 31 in silico predictors using a dataset of 1627 variants in HBA1, HBA2, and HBB. By varying the decision threshold for each tool, we analyse their performance (a) as binary classifiers of pathogenicity and (b) by using different non-overlapping pathogenic and benign thresholds for their optimal use in the ACMG/AMP framework. Our results show that CADD, Eigen-PC, and REVEL are the overall top performers, with the former reaching moderate strength level for pathogenic prediction. Eigen-PC and REVEL achieve the highest accuracies for missense variants, while CADD is also a reliable predictor of non-missense variants. Moreover, SpliceAI is the top performing splicing predictor, reaching strong level of evidence, while GERP++ and phyloP are the most accurate conservation tools. This study provides evidence about the optimal use of computational tools in globin gene clusters under the ACMG/AMP framework.

End-stage renal disease in a child with focal segmental glomerulosclerosis associated with a homozygous NUP93 variant

This report highlights that the genetic causes of FSGS, including NUP93 gene variant, such as the one described in this report, progress to end-stage renal disease rapidly and that the risk of recurrence post-renal transplantation is less likely.

Epidemiological aspects of hereditary fructose intolerance: A database study

Hereditary fructose intolerance (HFI) is an inborn error of fructose metabolism of autosomal recessive inheritance caused by pathogenic variants in the ALDOB gene that lead to aldolase B deficiency in the liver, kidneys, and intestine. Patients manifest symptoms, such as ketotic hypoglycemia, vomiting, nausea, in addition to hepatomegaly and other liver and kidney dysfunctions. The treatment consists of a fructose-restricted diet, which results in a good prognosis. To analyze the distribution of ALDOB variants described in patients and to estimate the prevalence of HFI based on carrier frequency in the gnomAD database, a systematic review was conducted to assess ALDOB gene variants among patients with HFI. The prevalence of HFI was estimated from the carrier frequency of variants described in patients, as well as rare variants predicted as pathogenic by in silico tools. The p.(Ala150Pro) and p.(Ala175Asp) variants are the most frequent and are distributed worldwide. However, these variants have particular distribution patterns in Europe. The analysis of the prevalence of HFI showed that the inclusion of rare alleles predicted as pathogenic is a more informative approach for populations with few patients. The data show that HFI has a wide distribution and an estimated prevalence of ~1:10,000.

The clinical and functional effects of TERT variants in myelodysplastic syndrome

Germline pathogenic TERT variants are associated with short telomeres and an increased risk of developing myelodysplastic syndrome (MDS) among patients with a telomere biology disorder. We identified TERT rare variants in 41 of 1514 MDS patients (2.7%) without a clinical diagnosis of a telomere biology disorder who underwent allogeneic transplantation. Patients with a TERT rare variant had shorter telomere length (P < .001) and younger age at MDS diagnosis (52 vs 59 years, P = .03) than patients without a TERT rare variant. In multivariable models, TERT rare variants were associated with inferior overall survival (P = .034) driven by an increased incidence of nonrelapse mortality (NRM; P = .015). Death from a noninfectious pulmonary cause was more frequent among patients with a TERT rare variant. Most variants were missense substitutions and classified as variants of unknown significance. Therefore, we cloned all rare missense variants and quantified their impact on telomere elongation in a cell-based assay. We found that 90% of TERT rare variants had severe or intermediate impairment in their capacity to elongate telomeres. Using a homology model of human TERT bound to the shelterin protein TPP1, we inferred that TERT rare variants disrupt domain-specific functions, including catalysis, protein-RNA interactions, and recruitment to telomeres. Our results indicate that the contribution of TERT rare variants to MDS pathogenesis and NRM risk is underrecognized. Routine screening for TERT rare variants in MDS patients regardless of age or clinical suspicion may identify clinically inapparent telomere biology disorders and improve transplant outcomes through risk-adapted approaches.

Two progressed malignant phyllodes tumors of the breast harbor alterations in genes frequently involved in other advanced cancers

Background

The genomic landscape of phyllodes tumors (PTs) of the breast is not well defined, especially in patients with advanced disease. To shed light on this topic, paired primary and progressed tumor samples from two patients with malignant PTs were subjected to next-generation sequencing (NGS) followed by functional analysis of genetic alterations using two prediction tools.

Methods

The DNA of both the primary tumor and distant metastases of Patient 1 and the primary and recurrent tumor of Patient 2 were subjected to molecular profiling. NGS with the FoundationOne® assay was performed in a commercial molecular pathology laboratory. Two in silico prediction tools were used to estimate the pathogenicity of indicated genetic alterations.

Results

In total, 38 genomic alterations were detected, of which 11 were predicted to be probably benign. In Patient 1, 14 aberrations were identified in the primary tumor and 17 in pulmonary metastases, 12 of which were identical. In the primary and recurrent tumor of Patient 2, 17 and 15 sequence variants, respectively, were found, with 13 overlapping findings. Affected genes included seven (TP53, TERT, APC, ARID1A, EGFR, KMT2D, and RB1) of the top 10 most frequently altered genes in other advanced cancer entities, as well as four actionable therapeutic targets (EGFR, KIT, PDGFRA, and BRIP1). Of note, seven genes coding for receptor tyrosine kinases were affected: three in Patient 1 and four in Patient 2. Several genes (e.g. EPHA3, EPHA7, and EPHB1) were shown to be altered for the first time in PTs.

Conclusions

The two progressed malignant PTs investigated here share some of the major genetic events occurring in other advanced cancers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13023-021-01986-z.

Decoding disease: from genomes to networks to phenotypes

Interpreting the effects of genetic variants is key to understanding individual susceptibility to disease and designing personalized therapeutic approaches. Modern experimental technologies are enabling the generation of massive compendia of human genome sequence data and associated molecular and phenotypic traits, together with genome-scale expression, epigenomics and other functional genomic data. Integrative computational models can leverage these data to understand variant impact, elucidate the effect of dysregulated genes on biological pathways in specific disease and tissue contexts, and interpret disease risk beyond what is feasible with experiments alone. In this Review, we discuss recent developments in machine learning algorithms for genome interpretation and for integrative molecular-level modelling of cells, tissues and organs relevant to disease. More specifically, we highlight existing methods and key challenges and opportunities in identifying specific disease-causing genetic variants and linking them to molecular pathways and, ultimately, to disease phenotypes.

Prevalence and Spectrum of Germline BRCA1 and BRCA2 Variants of Uncertain Significance in Breast/Ovarian Cancer: Mysterious Signals From the Genome

About 10–20% of breast/ovarian (BC/OC) cancer patients undergoing germline BRCA1/2 genetic testing have been shown to harbor Variants of Uncertain Significance (VUSs). Since little is known about the prevalence of germline BRCA1/2 VUS in Southern Italy, our study aimed at describing the spectrum of these variants detected in BC/OC patients in order to improve the identification of potentially high-risk BRCA variants helpful in patient clinical management. Eight hundred and seventy-four BC or OC patients, enrolled from October 2016 to December 2020 at the “Sicilian Regional Center for the Prevention, Diagnosis and Treatment of Rare and Heredo-Familial Tumors” of University Hospital Policlinico “P. Giaccone” of Palermo, were genetically tested for germline BRCA1/2 variants through Next-Generation Sequencing analysis. The mutational screening showed that 639 (73.1%) out of 874 patients were BRCA-w.t., whereas 67 (7.7%) were carriers of germline BRCA1/2 VUSs, and 168 (19.2%) harbored germline BRCA1/2 pathogenic/likely pathogenic variants. Our analysis revealed the presence of 59 different VUSs detected in 67 patients, 46 of which were affected by BC and 21 by OC. Twenty-one (35.6%) out of 59 variants were located on BRCA1 gene, whereas 38 (64.4%) on BRCA2. We detected six alterations in BRCA1 and two in BRCA2 with unclear interpretation of clinical significance. Familial anamnesis of a patient harboring the BRCA1-c.3367G>T suggests for this variant a potential of pathogenicity, therefore it should be carefully investigated. Understanding clinical significance of germline BRCA1/2 VUS could improve, in future, the identification of potentially high-risk variants useful for clinical management of BC or OC patients and family members.

Towards a New, Endophenotype-Based Strategy for Pathogenicity Prediction in BRCA1 and BRCA2: In Silico Modeling of the Outcome of HDR/SGE Assays for Missense Variants

The present limitations in the pathogenicity prediction of BRCA1 and BRCA2 (BRCA1/2) missense variants constitute an important problem with negative consequences for the diagnosis of hereditary breast and ovarian cancer. However, it has been proposed that the use of endophenotype predictions, i.e., computational estimates of the outcomes of functional assays, can be a good option to address this bottleneck. The application of this idea to the BRCA1/2 variants in the CAGI 5-ENIGMA international challenge has shown promising results. Here, we developed this approach, exploring the predictive performances of the regression models applied to the BRCA1/2 variants for which the values of the homology-directed DNA repair and saturation genome editing assays are available. Our results first showed that we can generate endophenotype estimates using a few molecular-level properties. Second, we show that the accuracy of these estimates is enough to obtain pathogenicity predictions comparable to those of many standard tools. Third, endophenotype-based predictions are complementary to, but do not outperform, those of a Random Forest model trained using variant pathogenicity annotations instead of endophenotype values. In summary, our results confirmed the usefulness of the endophenotype approach for the pathogenicity prediction of the BRCA1/2 missense variants, suggesting different options for future improvements.

Focused Strategies for Defining the Genetic Architecture of Congenital Heart Defects

Congenital heart defects (CHD) are malformations present at birth that occur during heart development. Increasing evidence supports a genetic origin of CHD, but in the process important challenges have been identified. This review begins with information about CHD and the importance of detailed phenotyping of study subjects. To facilitate appropriate genetic study design, we review DNA structure, genetic variation in the human genome and tools to identify the genetic variation of interest. Analytic approaches powered for both common and rare variants are assessed. While the ideal outcome of genetic studies is to identify variants that have a causal role, a more realistic goal for genetic analytics is to identify variants in specific genes that influence the occurrence of a phenotype and which provide keys to open biologic doors that inform how the genetic variants modulate heart development. It has never been truer that good genetic studies start with good planning. Continued progress in unraveling the genetic underpinnings of CHD will require multidisciplinary collaboration between geneticists, quantitative scientists, clinicians, and developmental biologists.

In silico analysis of BRCA1 and BRCA2 missense variants and the relevance in molecular genetic testing

Over the years since the genetic testing of BRCA1 and BRCA2 has been conducted for research and later introduced into clinical practice, a high number of missense variants have been reported in the literature and deposited in public databases. Polymorphism Phenotyping v2 (PolyPhen-2) and Sorting Intolerant from Tolerant (SIFT) are two widely applied bioinformatics tools used to assess the functional impacts of missense variants. A total of 2605 BRCA1 and 4763 BRCA2 variants from the ClinVar database were analysed with PolyPhen2 and SIFT. When SIFT was evaluated alongside PolyPhen-2 HumDiv and HumVar, it had shown top performance in terms of negative predictive value (NPV) (100%) and sensitivity (100%) for ClinVar classified benign and pathogenic BRCA1 variants. Both SIFT and PolyPhen-2 HumDiv achieved 100% NPV and 100% sensitivity in prediction of pathogenicity of the BRCA2 variants. Agreement was achieved in prediction outcomes from the three tested approaches in 55.04% and 68.97% of the variants of unknown significance (VUS) for BRCA1 and BRCA2, respectively. The performances of PolyPhen-2 and SIFT in predicting functional impacts varied across the two genes. Due to lack of high concordance in prediction outcomes among the two tested algorithms, their usefulness in classifying the pathogenicity of VUS identified through molecular testing of BRCA1 and BRCA2 is hence limited in the clinical setting.

In-Silico Analyses of Nonsynonymous Variants in the BRCA1 Gene

BReast CAncer gene 1 (BRCA1)-a tumor suppressor gene plays an important role in the DNA repair mechanism. Several BRCA1 variants perturb its structure and function, including synonymous and nonsynonymous single nucleotide polymorphisms (SNPs). In the present study, we performed in-silico analyses of nonsynonymous SNPs (nsSNPs) of the BRCA1 gene. In total, 122 nsSNPs were retrieved from the NCBI SNP database and in-silico analyses were performed using computational prediction tools: SIFT, PROVEAN, Mutation Taster, PolyPhen-2, MutPred, and ConSurf. Of these tools, SIFT, PROVEAN, and Mutation Taster predicted 61 out of 122 nsSNPs as "damaging", based on structural homology analysis. PolyPhen-2 classified 22 nsSNPs as "probably damaging". These nsSNPs were further analyzed by MutPred to predict basic molecular mechanisms of amino acid alteration. ConSurf analysis predicted eleven conserved amino acid residues with structural and functional consequences. We identified five amino acid residues in the RING finger domain (L22, C39, H41, C44, and C47) and two in the BRCT domain (P1771 and I1707) with the potential to deter the BRCA1 protein function. This study provides insights into the effect of nsSNPs and amino acid substitutions in BRCA1.

Pathogenic and likely pathogenic variants in at least five genes account for approximately 3% of mild isolated nonsyndromic thrombocytopenia

BACKGROUND

Thrombocytopenia has a variety of different etiologies, both acquired and hereditary. Inherited thrombocytopenia may be associated with other symptoms (syndromic forms) or may be strictly isolated. To date, only about half of all the familial forms of thrombocytopenia have been accounted for in terms of well-defined genetic abnormalities. However, data are limited on the nature and frequency of the underlying causative genetic variants in individuals with mild isolated nonsyndromic thrombocytopenia.

STUDY DESIGN AND METHODS

Thirteen known or candidate genes for isolated thrombocytopenia were included in a gene panel analysis in which targeted next-generation sequencing was performed on 448 French blood donors with mild isolated nonsyndromic thrombocytopenia.

RESULTS

A total of 68 rare variants, including missense, splice site, frameshift, nonsense, and in-frame variants (all heterozygous) were identified in 11 of the 13 genes screened. Twenty-nine percent (N = 20) of the variants detected were absent from both the French Exome Project and gnomAD exome databases. Using stringent criteria and an unbiased approach, we classified seven predicted loss-of-function variants (three in ITGA2B and four in TUBB1) and four missense variants (one in GP1BA, two in ITGB3 and one in ACTN1) as being pathogenic or likely pathogenic. Altogether, they were found in 13 members (approx. 3%) of our studied cohort.

CONCLUSION

We present the results of gene panel sequencing of known and candidate thrombocytopenia genes in mild isolated nonsyndromic thrombocytopenia. Pathogenic and likely pathogenic variants in five known thrombocytopenia genes were identified, accounting for approximately 3% of individuals with the condition.

A yeast-based complementation assay elucidates the functional impact of 200 missense variants in human PSAT1

Defects in serine biosynthesis resulting from loss of function mutations in PHGDH, PSAT1, and PSPH cause a set of rare, autosomal recessive diseases known as Neu-Laxova syndrome (NLS) or serine-deficiency disorders. The diseases present with a broad range of phenotypes including lethality, severe neurological manifestations, seizures, and intellectual disability. However, because L-serine supplementation, especially if started prenatally, can ameliorate and in some cases even prevent symptoms, knowledge of pathogenic variants is medically actionable. Here, we describe a functional assay that leverages the evolutionary conservation of an enzyme in the serine biosynthesis pathway, phosphoserine aminotransferase, and the ability of the human protein-coding sequence (PSAT1) to functionally replace its yeast ortholog (SER1). Results from our quantitative, yeast-based assay agree well with clinical annotations and expectations based on the disease literature. Using this assay, we have measured the functional impact of the 199 PSAT1 variants currently listed in ClinVar, gnomAD, and the literature. We anticipate that the assay could be used to comprehensively assess the functional impact of all SNP-accessible amino acid substitution mutations in PSAT1, a resource that could aid variant interpretation and identify potential NLS carriers.

Functional Categorization of BRCA1 Variants of Uncertain Clinical Significance in Homologous Recombination Repair Complementation Assays

PURPOSE

Because BRCA1 is a high-risk breast/ovarian cancer susceptibility gene, BRCA1 sequence variants of uncertain clinical significance (VUS) complicate genetic counseling. As most VUS are rare, reliable classification based on clinical and genetic data is often impossible. However, all pathogenic BRCA1 variants analyzed result in defective homologous recombination DNA repair (HRR). Thus, BRCA1 VUS may be categorized based on their functional impact on this pathway.

EXPERIMENTAL DESIGN

Two hundred thirty-eight BRCA1 VUS-comprising most BRCA1 VUS known in the Netherlands and Belgium-were tested for their ability to complement Brca1-deficient mouse embryonic stem cells in HRR, using cisplatin and olaparib sensitivity assays and a direct repeat GFP (DR-GFP) HRR assay. Assays were validated using 25 known benign and 25 known pathogenic BRCA1 variants. For assessment of pathogenicity by a multifactorial likelihood analysis method, we collected clinical and genetic data for functionally deleterious VUS and VUS occurring in three or more families.

RESULTS

All three assays showed 100% sensitivity and specificity (95% confidence interval, 83%-100%). Out of 238 VUS, 45 showed functional defects, 26 of which were deleterious in all three assays. For 13 of these 26 variants, we could calculate the probability of pathogenicity using clinical and genetic data, resulting in the identification of 7 (likely) pathogenic variants.

CONCLUSIONS

We have functionally categorized 238 BRCA1 VUS using three different HRR-related assays. Classification based on clinical and genetic data alone for a subset of these variants confirmed the high sensitivity and specificity of our functional assays.