Predicting the pathogenicity of novel variants in mitochondrial tRNA with MitoTIP.
Ontology highlight
ABSTRACT:
SUBMITTER: Sonney S
PROVIDER: S-EPMC5739504 | biostudies-literature | 2017 Dec
REPOSITORIES: biostudies-literature
Publications
Predicting the pathogenicity of novel variants in mitochondrial tRNA with MitoTIP.
Sonney Sanjay S Leipzig Jeremy J Lott Marie T MT Zhang Shiping S Procaccio Vincent V Wallace Douglas C DC Sondheimer Neal N
PLoS computational biology 20171211 12
Novel or rare variants in mitochondrial tRNA sequences may be observed after mitochondrial DNA analysis. Determining whether these variants are pathogenic is critical, but confirmation of the effect of a variant on mitochondrial function can be challenging. We have used available databases of benign and pathogenic variants, alignment between diverse tRNAs, structural information and comparative genomics to predict the impact of all possible single-base variants and deletions. The Mitochondrial t ...[more]
Similar Datasets
Project description:Confirming the pathogenicity of mitochondrial tRNA point mutations is one of the classical challenges in the field of mitochondrial medicine. In addition to genetic and functional studies, the evaluation of a genetic change using a pathogenicity scoring system is extremely useful to discriminate between disease-causing mutations from neutral polymorphisms. The pathogenicity scoring system is very robust for confirming pathogenicity, especially of mutations that show impaired activity in functional studies. However, mutations giving normal results using the same functional approaches are disregarded, and this compromises the power of the system to rule out pathogenicity. We propose to include a new criterion in the pathogenicity scoring systems regarding mutations which fail to show any mitochondrial defect in functional studies. To evaluate this proposal we characterized two mutations, m.8296A>G and m.8347A>G, in the mitochondrial tRNA(L) (ys) gene (MT-TK) using trans-mitochondrial cybrid analysis. m.8347A>G mutation severely impairs oxidative phosphorylation, suggesting that it is highly pathogenic. By contrast, the behavior of cybrids homoplasmic for the m.8296A>G mutation is similar to cybrids containing wild-type mitochondrial DNA (mtDNA). The results indicate that including not only positive but also negative outcomes of functional studies in the scoring system is critical for facilitating the diagnosis of this complex group of diseases.
| S-EPMC3960052 | biostudies-literature
Project description:MOTIVATION:The biological effects of human missense variants have been studied experimentally for decades but predicting their effects in clinical molecular diagnostics remains challenging. Available computational tools are usually based on the analysis of sequence conservation and structural properties of the mutant protein. We recently introduced a new machine learning method that demonstrated for the first time the significance of protein dynamics in determining the pathogenicity of missense variants. RESULTS:Here, we present a new interface (Rhapsody) that enables fully automated assessment of pathogenicity, incorporating both sequence coevolution data and structure- and dynamics-based features. Benchmarked against a dataset of about 20 000 annotated variants, the methodology is shown to outperform well-established and/or advanced prediction tools. We illustrate the utility of Rhapsody by in silico saturation mutagenesis studies of human H-Ras, phosphatase and tensin homolog and thiopurine S-methyltransferase. AVAILABILITY AND IMPLEMENTATION:The new tool is available both as an online webserver at http://rhapsody.csb.pitt.edu and as an open-source Python package (GitHub repository: https://github.com/prody/rhapsody; PyPI package installation: pip install prody-rhapsody). Links to additional resources, tutorials and package documentation are provided in the 'Python package' section of the website. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
| S-EPMC7214033 | biostudies-literature
Project description:Quickly growing genetic variation data of unknown clinical significance demand computational methods that can reliably predict clinical phenotypes and deeply unravel molecular mechanisms. On the platform enabled by the Critical Assessment of Genome Interpretation (CAGI), we develop a novel "weakly supervised" regression (WSR) model that not only predicts precise clinical significance (probability of pathogenicity) from inexact training annotations (class of pathogenicity) but also infers underlying molecular mechanisms in a variant-specific manner. Compared to multiclass logistic regression, a representative multiclass classifier, our kernelized WSR improves the performance for the ENIGMA Challenge set from 0.72 to 0.97 in binary area under the receiver operating characteristic curve (AUC) and from 0.64 to 0.80 in ordinal multiclass AUC. WSR model interpretation and protein structural interpretation reach consensus in corroborating the most probable molecular mechanisms by which some pathogenic BRCA1 variants confer clinical significance, namely metal-binding disruption for p.C44F and p.C47Y, protein-binding disruption for p.M18T, and structure destabilization for p.S1715N.
| S-EPMC6744350 | biostudies-literature
Project description:In most eukaryotes, mitochondrial protein synthesis is essential for oxidative phosphorylation (OXPHOS) as some subunits of the respiratory chain complexes are encoded by the mitochondrial DNA (mtDNA). Mutations affecting the mitochondrial translation apparatus have been identified as a major cause of mitochondrial diseases. These mutations include either heteroplasmic mtDNA mutations in genes encoding for the mitochondrial rRNA (mtrRNA) and tRNAs (mttRNAs) or mutations in nuclear genes encoding ribosomal proteins, initiation, elongation and termination factors, tRNA-modifying enzymes, and aminoacyl-tRNA synthetases (mtARSs). Aminoacyl-tRNA synthetases (ARSs) catalyze the attachment of specific amino acids to their cognate tRNAs. Differently from most mttRNAs, which are encoded by mitochondrial genome, mtARSs are encoded by nuclear genes and then imported into the mitochondria after translation in the cytosol. Due to the extensive use of next-generation sequencing (NGS), an increasing number of mtARSs variants associated with large clinical heterogeneity have been identified in recent years. Being most of these variants private or sporadic, it is crucial to assess their causative role in the disease by functional analysis in model systems. This review will focus on the contributions of the yeast Saccharomyces cerevisiae in the functional validation of mutations found in mtARSs genes associated with human disorders.
| S-EPMC8123711 | biostudies-literature
Project description:Coronary heart disease is the leading cause of death worldwide. Mitochondrial genetic determinants for the development of this disorder remain less explored.We performed a clinical and genetic evaluation and mutational screening of 22 mitochondrial tRNA genes in a cohort of 80 genetically unrelated Han Chinese subjects and 125 members of 4 families with coronary heart disease and 512 Chinese control subjects. This analysis identified 16 nucleotide changes among 9 tRNA genes. Of these, the T5592C mutation creates a highly conservative base pairing (5G-68C) on the acceptor stem of tRNA(Gln), whereas the G15927A mutation destabilizes a highly conserved base pairing (28C-42G) in the anticodon stem of tRNA(Thr). However, the other tRNA variants were polymorphisms. The pedigrees of BJH24 carrying the T5592C mutation, BJH15, and BJH45 harboring the G15927A mutation exhibited maternal transmission of coronary heart disease. Sequence analysis of their mitochondrial genomes revealed the presence of T5592C or G15927A mutation but the absence of other functionally significant mutations in all matrilineal relatives of these families.Our previous observations showed that altered structures of tRNAs by these mtDNA mutations caused mitochondrial dysfunction. These may be the first evidence that mtDNA mutations increase the risk of coronary heart disease. Our findings may provide new insights into the pathophysiology of this disorder.
| S-EPMC3959674 | biostudies-literature
Project description:The rapid advance of gene sequencing technologies has produced an unprecedented rate of discovery of genome variation in humans. A growing number of authoritative clinical repositories archive gene variants and disease phenotypes, yet there are currently many more gene variants that lack clear annotation or disease association. To date, there has been very limited coverage of gene-specific predictors in the literature. Here the evaluation is presented of "gene-specific" predictor models based on a naïve Bayesian classifier for 20 gene-disease datasets, containing 3986 variants with clinically characterized patient conditions. The utility of gene-specific prediction is then compared with "all-gene" generalized prediction and also with existing popular predictors. Gene-specific computational prediction models derived from clinically curated gene variant disease datasets often outperform established generalized algorithms for novel and uncertain gene variants.
| S-EPMC3277614 | biostudies-literature
Project description:The vast majority of coding variants are rare, and assessment of the contribution of rare variants to complex traits is hampered by low statistical power and limited functional data. Improved methods for predicting the pathogenicity of rare coding variants are needed to facilitate the discovery of disease variants from exome sequencing studies. We developed REVEL (rare exome variant ensemble learner), an ensemble method for predicting the pathogenicity of missense variants on the basis of individual tools: MutPred, FATHMM, VEST, PolyPhen, SIFT, PROVEAN, MutationAssessor, MutationTaster, LRT, GERP, SiPhy, phyloP, and phastCons. REVEL was trained with recently discovered pathogenic and rare neutral missense variants, excluding those previously used to train its constituent tools. When applied to two independent test sets, REVEL had the best overall performance (p < 10-12) as compared to any individual tool and seven ensemble methods: MetaSVM, MetaLR, KGGSeq, Condel, CADD, DANN, and Eigen. Importantly, REVEL also had the best performance for distinguishing pathogenic from rare neutral variants with allele frequencies <0.5%. The area under the receiver operating characteristic curve (AUC) for REVEL was 0.046-0.182 higher in an independent test set of 935 recent SwissVar disease variants and 123,935 putatively neutral exome sequencing variants and 0.027-0.143 higher in an independent test set of 1,953 pathogenic and 2,406 benign variants recently reported in ClinVar than the AUCs for other ensemble methods. We provide pre-computed REVEL scores for all possible human missense variants to facilitate the identification of pathogenic variants in the sea of rare variants discovered as sequencing studies expand in scale.
| S-EPMC5065685 | biostudies-literature
Project description:SummarySingle amino acid variations (SAVs) are a primary contributor to variations in the human genome. Identifying pathogenic SAVs can provide insights to the genetic architecture of complex diseases. Most approaches for predicting the functional effects or pathogenicity of SAVs rely on either sequence or structural information. This study presents 〈Lai Yang Rubenstein Uzun Sarkar〉 (LYRUS), a machine learning method that uses an XGBoost classifier to predict the pathogenicity of SAVs. LYRUS incorporates five sequence-based, six structure-based and four dynamics-based features. Uniquely, LYRUS includes a newly proposed sequence co-evolution feature called the variation number. LYRUS was trained using a dataset that contains 4363 protein structures corresponding to 22 639 SAVs from the ClinVar database, and tested using the VariBench testing dataset. Performance analysis showed that LYRUS achieved comparable performance to current variant effect predictors. LYRUS's performance was also benchmarked against six Deep Mutational Scanning datasets for PTEN and TP53.Availability and implementationLYRUS is freely available and the source code can be found at https://github.com/jiaying2508/LYRUS.Supplementary informationSupplementary data are available at Bioinformatics Advances online.
| S-EPMC8754197 | biostudies-literature
Project description:Nonsynonymous single nucleotide variants (nsSNVs) constitute about 50% of known disease-causing mutations and understanding their functional impact is an area of active research. Existing algorithms predict pathogenicity of nsSNVs; however, they are unable to differentiate heterozygous, dominant disease-causing variants from heterozygous carrier variants that lead to disease only in the homozygous state. Here, we present MAPPIN (Method for Annotating, Predicting Pathogenicity, and mode of Inheritance for Nonsynonymous variants), a prediction method which utilizes a random forest algorithm to distinguish between nsSNVs with dominant, recessive, and benign effects. We apply MAPPIN to a set of Mendelian disease-causing mutations and accurately predict pathogenicity for all mutations. Furthermore, MAPPIN predicts mode of inheritance correctly for 70.3% of nsSNVs. MAPPIN also correctly predicts pathogenicity for 87.3% of mutations from the Deciphering Developmental Disorders Study with a 78.5% accuracy for mode of inheritance. When tested on a larger collection of mutations from the Human Gene Mutation Database, MAPPIN is able to significantly discriminate between mutations in known dominant and recessive genes. Finally, we demonstrate that MAPPIN outperforms CADD and Eigen in predicting disease inheritance modes for all validation datasets. To our knowledge, MAPPIN is the first nsSNV pathogenicity prediction algorithm that provides mode of inheritance predictions, adding another layer of information for variant prioritization.
| S-EPMC5737764 | biostudies-literature
Project description:Thyroid cancer is a very common form of endocrine system malignancy. To date, the molecular mechanism underlying thyroid cancer remains poorly understood. Studies of oncocytic tumors have led to a hypothesis which proposes that defects in oxidative phosphorylation (OX- PHOS) may result in a compensatory increase in mitochondrial replication and gene expression. As a result, mitochondrial DNA (mtDNA) mutation analysis has become a useful tool to explore the molecular basis of this disease. Among these mutations, mitochondrial transfer RNAs (mttRNAs) are the hot spots for pathogenic mutations associated with thyroid cancer. However, due to its high mutation rate, the role of mt-tRNA variants in thyroid cancer is still controversial. To address this problem, in this study, we reassessed seven reported mt-tRNA variants: tRNAAsp G7521A, tRNAArg T10411C and T10463C, tRNALeu(CUN) A12308G, tRNAIle G4292C and C4312T, and tRNAAla T5655C, in clinical manifestations of thyroid cancer. We first performed the phylogenetic conservation analysis for these variants; moreover, we used a bioinformatic tool to compare the minimum free energy (G) of mt-tRNA with and without mutations. Most strikingly, none of these variants caused the significant change of the G between the wild-type and the mutant form, suggesting that they may not play an important roles in thyroid cancer. In addition, we screened the frequency of the "pathogenic" A12308G alternation in 300 patients with thyroid cancer and 200 healthy controls. We found that there were five patients and three control subjects carrying this variant. It seemed that the A12308G variant may be a common polymorphism in the human population. Taken together, our study indicated that variants in mt-tRNA genes may not play active roles in patients with thyroid cancer.
| S-EPMC5026273 | biostudies-literature