Premature termination codons in modern human genomes

rs67047829 genotypes of ERV3-1/ZNF117 are associated with lower body mass index in the Polish population

There is now substantial evidence that zinc-finger proteins are implicated in adiposity. Aims were to datamine for high-frequency (near-neutral selection) pretermination-codon (PTC) single-nucleotide polymorphisms (SNPs; n = 141) from a database with > 550,000 variants and analyze possible association with body mass index in a large Polish sample (n = 5757). BMI was regressed (males/females together or separately) against genetic models. Regression for rs67047829 uncovered an interaction-independent association with BMI with both sexes together: mean ± standard deviation, kg/m2: [G];[G], 25.4 ± 4.59 (n = 3650); [G](;)[A], 25.0 ± 4.28 (n = 731); [A];[A], 23.4 ± 3.60 (n = 44); additive model adjusted for age and sex: p = 4.08 × 10-5; beta: - 0.0458, 95% confidence interval (CI) - 0.0732 : - 0.0183; surviving Bonferroni correction; for males: [G];[G], 24.8 ± 4.94 (n = 1878); [G](;)[A], 24.2 ± 4.31 (n = 386); [A];[A], 22.4 ± 3.69 (n = 23); p = 4.20 × 10-4; beta: - 0.0573, CI - 0.0947 : - 0.0199. For average-height males the difference between [G];[G] and [A];[A] genotypes would correspond to ~ 6 kg, suggesting considerable protection against increased BMI. rs67047829 gives a pretermination codon in ERV3-1 which shares an exonic region and possibly promoter with ZNF117, previously associated with adiposity and type-2 diabetes. As this result occurs in a near-neutral Mendelian setting, a drug targetting ERV3-1/ZNF117 might potentially provide considerable benefits with minimal side-effects. This result needs to be replicated, followed by analyses of splice-variant mRNAs and protein expression.

Adeno-associated viral delivery of engineered tRNA-enzyme pairs into nonsense mutation mouse models

In this protocol, we describe how to utilize the unnatural amino acid (UAA) incorporation system to read through endogenous premature termination codons in a Duchenne muscular dystrophy mouse model. We detail how to screen and optimize tRNA-enzyme pairs for efficient UAA incorporation, deliver the system intraperitoneally or intramuscularly in pathogenic mice by an adeno-associated viral (AAV) vector, and evaluate the restoration of endogenous dystrophin and increase in muscle strength after AAV injection. For complete details on the use and execution of this protocol, please refer to Shi et al. (2021).1.

Mini-dCas13X-mediated RNA editing restores dystrophin expression in a humanized mouse model of Duchenne muscular dystrophy

Approximately 10% of monogenic diseases are caused by nonsense point mutations that generate premature termination codons (PTCs), resulting in a truncated protein and nonsense-mediated decay of the mutant mRNAs. Here, we demonstrate a mini-dCas13X-mediated RNA adenine base editing (mxABE) strategy to treat nonsense mutation-related monogenic diseases via A-to-G editing in a genetically humanized mouse model of Duchenne muscular dystrophy (DMD). Initially, we identified a nonsense point mutation (c.4174C>T, p.Gln1392*) in the DMD gene of a patient and validated its pathogenicity in humanized mice. In this model, mxABE packaged in a single adeno-associated virus (AAV) reached A-to-G editing rates up to 84% in vivo, at least 20-fold greater than rates reported in previous studies using other RNA editing modalities. Furthermore, mxABE restored robust expression of dystrophin protein to over 50% of WT levels by enabling PTC read-through in multiple muscle tissues. Importantly, systemic delivery of mxABE by AAV also rescued dystrophin expression to averages of 37%, 6%, and 54% of WT levels in the diaphragm, tibialis anterior, and heart muscle, respectively, as well as rescued muscle function. Our data strongly suggest that mxABE-based strategies may be a viable new treatment modality for DMD and other monogenic diseases.

Noncanonical Amino Acid Incorporation in Mice

Genetic code expansion enables in cellulo biosynthesis of curative proteins with enhanced specificity, improved stability, and even novel functions, due to the incorporation of artificial, designed, noncanonical amino acids (ncAAs). In addition, this orthogonal system also holds great potential for in vivo suppressing nonsense mutations during protein translation, providing an alternative strategy for alleviating inherited diseases caused by premature termination codons (PTCs). Here we describe the approach to explore the therapeutic efficacy and long-term safety of this strategy in transgenic mdx mice with stably expanded genetic codes. Theoretically, this method is applicable to about 11% of monogenic diseases involving nonsense mutations.

Restoration of dystrophin expression in mice by suppressing a nonsense mutation through the incorporation of unnatural amino acids

Approximately 11% of monogenic diseases involve nonsense mutations that are caused by premature termination codons. These codons can in principle be read-through via the site-specific incorporation of unnatural amino acids to generate full-length proteins with minimal loss of function. Here we report that aminoacyl-tRNA-synthase-tRNA pairs specific for the desired unnatural amino acids can be used to read through a nonsense mutation in the dystrophin gene. We show partial restoration of dystrophin expression in differentiated primary myoblasts (from a mdx mouse model and a patient with Duchenne muscular dystrophy), and restoration of muscle function in two mouse models: mdx mice, via viral delivery of the engineered tRNA-synthase-tRNA pair intraperitoneally or intramuscularly and of the associated unnatural amino acid intraperitoneally; and mice produced by crossing mdx mice and transgenic mice with a chromosomally integrated pair, via intraperitoneal delivery of the unnatural amino acid. The incorporation of unnatural amino acids to restore endogenous protein expression could be explored for therapeutic use.

Emerging strategies to bridge the gap between pharmacogenomic research and its clinical implementation

The genomic inter-individual heterogeneity remains a significant challenge for both clinical decision-making and the design of clinical trials. Although next-generation sequencing (NGS) is increasingly implemented in drug development and clinical trials, translation of the obtained genomic information into actionable clinical advice lags behind. Major reasons are the paucity of sufficiently powered trials that can quantify the added value of pharmacogenetic testing, and the considerable pharmacogenetic complexity with millions of rare variants with unclear functional consequences. The resulting uncertainty is reflected in inconsistencies of pharmacogenomic drug labels in Europe and the United States. In this review, we discuss how the knowledge gap for bridging pharmacogenomics into the clinics can be reduced. First, emerging methods that allow the high-throughput experimental characterization of pharmacogenomic variants combined with novel computational tools hold promise to improve the accuracy of drug response predictions. Second, tapping of large biobanks of therapeutic drug monitoring data allows to conduct high-powered retrospective studies that can validate the clinical importance of genetic variants, which are currently incompletely characterized. Combined, we are confident that these methods will improve the accuracy of drug response predictions and will narrow the gap between variant identification and its utilization for clinical decision-support.

Computational Methods for the Pharmacogenetic Interpretation of Next Generation Sequencing Data

Up to half of all patients do not respond to pharmacological treatment as intended. A substantial fraction of these inter-individual differences is due to heritable factors and a growing number of associations between genetic variations and drug response phenotypes have been identified. Importantly, the rapid progress in Next Generation Sequencing technologies in recent years unveiled the true complexity of the genetic landscape in pharmacogenes with tens of thousands of rare genetic variants. As each individual was found to harbor numerous such rare variants they are anticipated to be important contributors to the genetically encoded inter-individual variability in drug effects. The fundamental challenge however is their functional interpretation due to the sheer scale of the problem that renders systematic experimental characterization of these variants currently unfeasible. Here, we review concepts and important progress in the development of computational prediction methods that allow to evaluate the effect of amino acid sequence alterations in drug metabolizing enzymes and transporters. In addition, we discuss recent advances in the interpretation of functional effects of non-coding variants, such as variations in splice sites, regulatory regions and miRNA binding sites. We anticipate that these methodologies will provide a useful toolkit to facilitate the integration of the vast extent of rare genetic variability into drug response predictions in a precision medicine framework.