https://tigerprints.clemson.edu/cgi/viewcontent.cgi?article=1418&context=physastro_pubs

Molecular Mechanisms of Disease-Causing Missense Mutations

The number of reported germline mutations in human nuclear genes, either underlying or associated with inherited disease, has now exceeded 100,000 in more than 3,700 different genes. The availability of these data has both revolutionized the study of the morbid anatomy of the human genome and facilitated “personalized genomics.” With ∼300 new “inherited disease genes” (and ∼10,000 new mutations) being identified annually, it is pertinent to ask how many “inherited disease genes” there are in the human genome, how many mutations reside within them, and where such lesions are likely to be located? To address these questions, it is necessary not only to reconsider how we define human genes but also to explore notions of gene “essentiality” and “dispensability.” Answers to these questions are now emerging from recent novel insights into genome structure and function and through complete genome sequence information derived from multiple individual human genomes. However, a change in focus toward screening functional genomic elements as opposed to genes sensu stricto will be required if we are to capitalize fully on recent technical and conceptual advances and identify new types of disease-associated mutation within noncoding regions remote from the genes whose function they disrupt.

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Genes, Mutations, and Human Inherited Disease at the Dawn of the Age of Personalized Genomics

Large datasets describing the quantitative effects of mutations on protein function are becoming increasingly available. Here, we leverage these datasets to develop Envision, which predicts the magnitude of a missense variant's molecular effect. Envision combines 21,026 variant effect measurements from nine large-scale experimental mutagenesis datasets, a hitherto untapped training resource, with a supervised, stochastic gradient boosting learning algorithm. Envision outperforms other missense variant effect predictors both on large-scale mutagenesis data and on an independent test dataset comprising 2,312 TP53 variants whose effects were measured using a low-throughput approach. This dataset was never used for hyperparameter tuning or model training and thus serves as an independent validation set. Envision prediction accuracy is also more consistent across amino acids than other predictors. Finally, we demonstrate that Envision's performance improves as more large-scale mutagenesis data are incorporated. We precompute Envision predictions for every possible single amino acid variant in human, mouse, frog, zebrafish, fruit fly, worm, and yeast proteomes (https://envision.gs.washington.edu/).

Quantitative Missense Variant Effect Prediction Using Large-Scale Mutagenesis Data

It is widely accepted that at least 10% of all mutations causing human inherited disease disrupt splice-site consensus sequences. In contrast to splice-site mutations, the role of auxiliary cis-acting elements such as exonic splicing enhancers (ESE) and exonic splicing silencers (ESS) in human inherited disease is still poorly understood. Here we use a top-down approach to determine rates of loss or gain of known human exonic splicing regulatory (ESR) sequences associated with either disease-causing mutations or putatively neutral single nucleotide polymorphisms (SNPs). We observe significant enrichment toward loss of ESEs and gain of ESSs among inherited disease-causing variants relative to neutral polymorphisms, indicating that exon skipping may play a prominent role in aberrant gene regulation. Both computational and biochemical approaches underscore the relevance of exonic splicing enhancer loss and silencer gain in inherited disease. Additionally, we provide direct evidence that both SRp20 (SRSF3) and possibly PTB (PTBP1) are involved in the function of a splicing silencer that is created de novo by a total of 83 different inherited disease mutations in 67 different disease genes. Taken together, we find that ∼25% (7154/27,681) of known mis-sense and nonsense disease-causing mutations alter functional splicing signals within exons, suggesting a much more widespread role for aberrant mRNA processing in causing human inherited disease than has hitherto been appreciated.

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Loss of exon identity is a common mechanism of human inherited disease

Many nonsynonymous single nucleotide polymorphisms (nsSNPs) are disease causing due to effects at protein-protein interfaces. We have integrated a database of the three-dimensional (3D) structures of human protein/protein complexes and the humsavar database of nsSNPs. We analyzed the location of nsSNPS in terms of their location in the protein core, at protein-protein interfaces, and on the surface when not at an interface. Disease-causing nsSNPs that do not occur in the protein core are preferentially located at protein-protein interfaces rather than surface noninterface regions when compared to random segregation. The disruption of the protein-protein interaction can be explained by a range of structural effects including the loss of an electrostatic salt bridge, the destabilization due to reduction of the hydrophobic effect, the formation of a steric clash, and the introduction of a proline altering the main-chain conformation.

Protein-protein interaction sites are hot spots for disease-associated nonsynonymous SNPs.

As the cost of DNA sequencing drops, we are moving beyond one genome per species to one genome per individual to improve prevention, diagnosis, and treatment of disease by using personal genotypes. Computational methods are frequently applied to predict impairment of gene function by nonsynonymous mutations in individual genomes and single nucleotide polymorphisms (nSNPs) in populations. These computational tools are, however, known to fail 15%-40% of the time. We find that accurate discrimination between benign and deleterious mutations is strongly influenced by the long-term (among species) history of positions that harbor those mutations. Successful prediction of known disease-associated mutations (DAMs) is much higher for evolutionarily conserved positions and for original-mutant amino acid pairs that are rarely seen among species. Prediction accuracies for nSNPs show opposite patterns, forecasting impediments to building diagnostic tools aiming to simultaneously reduce both false-positive and false-negative errors. The relative allele frequencies of mutations diagnosed as benign and damaging are predicted by positional evolutionary rates. These allele frequencies are modulated by the relative preponderance of the mutant allele in the set of amino acids found at homologous sites in other species (evolutionarily permissible alleles [EPAs]). The nSNPs found in EPAs are biochemically less severe than those missing from EPAs across all allele frequency categories. Therefore, it is important to consider position evolutionary rates and EPAs when interpreting the consequences and population frequencies of human mutations. The impending sequencing of thousands of human and many more vertebrate genomes will lead to more accurate classifiers needed in real-world applications.

/pdf/positional-conservation-and-amino-acids-shape-the-correct-3jm5i7uoaq.pdf

Simon Lawrence

Papers

Positional conservation and amino acids shape the correct diagnosis and population frequencies of benign and damaging personal amino acid mutations