A novel mutation (c.121‑13T>A) in the polypyrimidine tract of the splice acceptor site of intron 2 causes exon 3 skipping in mitochondrial acetoacetyl-CoA thiolase gene

CRISPR/Cas9-generated mouse model with humanizing single-base substitution in the Gnao1 for safety studies of RNA therapeutics

The development of personalized medicine for genetic diseases requires preclinical testing in the appropriate animal models. GNAO1 encephalopathy is a severe neurodevelopmental disorder caused by heterozygous de novo mutations in the GNAO1 gene. GNAO1 c.607 G>A is one of the most common pathogenic variants, and the mutant protein Gαo-G203R likely adversely affects neuronal signaling. As an innovative approach, sequence-specific RNA-based therapeutics such as antisense oligonucleotides or effectors of RNA interference are potentially applicable for selective suppression of the mutant GNAO1 transcript. While in vitro validation can be performed in patient-derived cells, a humanized mouse model to rule out the safety of RNA therapeutics is currently lacking. In the present work, we employed CRISPR/Cas9 technology to introduce a single-base substitution into exon 6 of the Gnao1 to replace the murine Gly203-coding triplet (GGG) with the codon used in the human gene (GGA). We verified that genome-editing did not interfere with the Gnao1 mRNA or Gαo protein synthesis and did not alter localization of the protein in the brain structures. The analysis of blastocysts revealed the off-target activity of the CRISPR/Cas9 complexes; however, no modifications of the predicted off-target sites were detected in the founder mouse. Histological staining confirmed the absence of abnormal changes in the brain of genome-edited mice. The created mouse model with the “humanized” fragment of the endogenous Gnao1 is suitable to rule out unintended targeting of the wild-type allele by RNA therapeutics directed at lowering GNAO1 c.607 G>A transcripts.

Machine Learning Approaches for the Prioritization of Genomic Variants Impacting Pre-mRNA Splicing

Defects in pre-mRNA splicing are frequently a cause of Mendelian disease. Despite the advent of next-generation sequencing, allowing a deeper insight into a patient's variant landscape, the ability to characterize variants causing splicing defects has not progressed with the same speed. To address this, recent years have seen a sharp spike in the number of splice prediction tools leveraging machine learning approaches, leaving clinical geneticists with a plethora of choices for in silico analysis. In this review, some basic principles of machine learning are introduced in the context of genomics and splicing analysis. A critical comparative approach is then used to describe seven recent machine learning-based splice prediction tools, revealing highly diverse approaches and common caveats. We find that, although great progress has been made in producing specific and sensitive tools, there is still much scope for personalized approaches to prediction of variant impact on splicing. Such approaches may increase diagnostic yields and underpin improvements to patient care.

Mutation update on ACAT1 variants associated with mitochondrial acetoacetyl-CoA thiolase (T2) deficiency

Mitochondrial acetoacetyl‐CoA thiolase (T2, encoded by the ACAT1 gene) deficiency is an inherited disorder of ketone body and isoleucine metabolism. It typically manifests with episodic ketoacidosis. The presence of isoleucine‐derived metabolites is the key marker for biochemical diagnosis. To date, 105 ACAT1 variants have been reported in 149 T2‐deficient patients. The 56 disease‐associated missense ACAT1 variants have been mapped onto the crystal structure of T2. Almost all these missense variants concern residues that are completely or partially buried in the T2 structure. Such variants are expected to cause T2 deficiency by having lower in vivo T2 activity because of lower folding efficiency and/or stability. Expression and activity data of 30 disease‐associated missense ACAT1 variants have been measured by expressing them in human SV40‐transformed fibroblasts. Only two variants (p.Cys126Ser and p.Tyr219His) appear to have equal stability as wild‐type. For these variants, which are inactive, the side chains point into the active site. In patients with T2 deficiency, the genotype does not correlate with the clinical phenotype but exerts a considerable effect on the biochemical phenotype. This could be related to variable remaining residual T2 activity in vivo and has important clinical implications concerning disease management and newborn screening.

RNA splicing analysis in genomic medicine

High-throughput next-generation sequencing technologies have led to a rapid increase in the number of sequence variants identified in clinical practice via diagnostic genetic tests. Current bioinformatic analysis pipelines fail to take adequate account of the possible splicing effects of such variants, particularly where variants fall outwith canonical splice site sequences, and consequently the pathogenicity of such variants may often be missed. The regulation of splicing is highly complex and as a result, in silico prediction tools lack sufficient sensitivity and specificity for reliable use. Variants of all kinds can be linked to aberrant splicing in disease and the need for correct identification and diagnosis grows ever more crucial as novel splice-switching antisense oligonucleotide therapies start to enter clinical usage. RT-PCR provides a useful targeted assay of the splicing effects of identified variants, while minigene assays, massive parallel reporter assays and animal models can also be used for more detailed study of a particular splicing system, given enough time and resources. However, RNA-sequencing (RNA-seq) has the potential to be used as a rapid diagnostic tool in genomic medicine. By utilising data science approaches and machine learning, it may prove possible to finally understand and interpret the 'splicing code' and apply this knowledge in human disease diagnostics.

Recent advances in understanding beta-ketothiolase (mitochondrial acetoacetyl-CoA thiolase, T2) deficiency

Beta-ketothiolase (mitochondrial acetoacetyl-CoA thiolase, T2) deficiency (OMIM #203750, *607809) is an inborn error of metabolism that affects isoleucine catabolism and ketone body metabolism. This disorder is clinically characterized by intermittent ketoacidotic crises under ketogenic stresses. In addition to a previous 26-case series, four series of T2-deficient patients were recently reported from different regions. In these series, most T2-deficient patients developed their first ketoacidotic crises between the ages of 6 months and 3 years. Most patients experienced less than three metabolic crises. Newborn screening (NBS) for T2 deficiency is performed in some countries but some T2-deficient patients have been missed by NBS. Therefore, T2 deficiency should be considered in patients with severe metabolic acidosis, even in regions where NBS for T2 deficiency is performed. Neurological manifestations, especially extrapyramidal manifestations, can occur as sequelae to severe metabolic acidosis; however, this can also occur in patients without any apparent metabolic crisis or before the onset of metabolic crisis.

Splicing mutations in human genetic disorders: examples, detection, and confirmation

Precise pre-mRNA splicing, essential for appropriate protein translation, depends on the presence of consensus "cis" sequences that define exon-intron boundaries and regulatory sequences recognized by splicing machinery. Point mutations at these consensus sequences can cause improper exon and intron recognition and may result in the formation of an aberrant transcript of the mutated gene. The splicing mutation may occur in both introns and exons and disrupt existing splice sites or splicing regulatory sequences (intronic and exonic splicing silencers and enhancers), create new ones, or activate the cryptic ones. Usually such mutations result in errors during the splicing process and may lead to improper intron removal and thus cause alterations of the open reading frame. Recent research has underlined the abundance and importance of splicing mutations in the etiology of inherited diseases. The application of modern techniques allowed to identify synonymous and nonsynonymous variants as well as deep intronic mutations that affected pre-mRNA splicing. The bioinformatic algorithms can be applied as a tool to assess the possible effect of the identified changes. However, it should be underlined that the results of such tests are only predictive, and the exact effect of the specific mutation should be verified in functional studies. This article summarizes the current knowledge about the "splicing mutations" and methods that help to identify such changes in clinical diagnosis.