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Functional Analysis of the PCCA and PCCB Gene Variants Predicted to Affect Splicing

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Functional Analysis of the PCCA and PCCB Gene Variants Predicted to Affect Splicing International Journal of Molecular Sciences Article Functional Analysis of the PCCA and PCCB Gene Variants Predicted to Affect Splicing Igor Bychkov * , Artur Galushkin, Alexandra Filatova , Andrey Nekrasov, Marina Kurkina, Galina Baydakova, Alexandra Ilyushkina , Mikhail Skoblov and Ekaterina Zakharova Research Centre for Medical Genetics, 115522 Moscow, Russia; [email protected] (A.G.); [email protected] (A.F.); [email protected] (A.N.); [email protected] (M.K.); [email protected] (G.B.); [email protected] (A.I.); [email protected] (M.S.); [email protected] (E.Z.) * Correspondence: [email protected] Abstract: It is estimated that up to one-third of all variants causing inherited diseases affect splicing; however, their deleterious effects and roles in disease pathogenesis are often not fully character- ized. Given their prevalence and the development of various antisense-based splice-modulating approaches, pathogenic splicing variants have become an important object of genomic medicine. To improve the accuracy of variant interpretation in public mutation repositories, we applied the minigene splicing assay to study the effects of 24 variants that were predicted to affect normal splicing in the genes associated with propionic acidemia (PA)—PCCA and PCCB. As a result, 13 variants (including one missense and two synonymous variants) demonstrated a significant alter- ation of splicing with the predicted deleterious effect at the protein level and were characterized as spliceogenic loss-of-function variants. The analysis of the available data for the studied variants Citation: Bychkov, I.; Galushkin, A.; and application of the American College of Medical Genetics and the Association for Molecular Filatova, A.; Nekrasov, A.; Kurkina, Pathology (ACMG/AMP) guidelines allowed us to precisely classify five of the variants and change M.; Baydakova, G.; Ilyushkina, A.; the pathogenic status of nine. Using the example of the PA genes, we demonstrated the utility of the Skoblov, M.; Zakharova, E. Functional minigene splicing assay in the fast and effective assessment of the spliceogenic effect for identified Analysis of the PCCA and PCCB Gene variants and highlight the necessity of their standardized classification. Variants Predicted to Affect Splicing. Int. J. Mol. Sci. 2021, 22, 4154. Keywords: splicing variants; variant classification; functional analysis; PCCA and PCCB https://doi.org/10.3390/ijms22084154 Academic Editor: Peter Bross 1. Introduction Received: 25 February 2021 Pathogenic variants in the PCCA and PCCB genes are responsible for the rare auto- Accepted: 12 April 2021 Published: 16 April 2021 somal recessive metabolic disease called propionic acidemia (PA) (OMIM#606054). The products of these genes form the heterododecameric enzyme propionyl CoA carboxy- Publisher’s Note: MDPI stays neutral lase (PCC), which converts propionyl CoA to methymalonyl CoA in the mitochondrial with regard to jurisdictional claims in matrix [1]. The deficient activity of PCC leads to accumulation of toxic propionic acid published maps and institutional affil- metabolites and dysfunction of the respiratory chain and the urea cycle pathway. PA is iations. clinically heterogeneous and has a variable age of onset ranging from severe neonatal forms to mild adult-onset forms [2]. In most cases, the symptoms of PA manifest in the early neonatal period and without treatment quickly become life-threatening. The symp- toms include seizures, poor feeding, vomiting, hypotonia, metabolic acidosis, ketonuria, hypoglycemia, hyperammonemia and cytopenia [3]. Timely medical and dietary inter- Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. ventions could significantly improve the patient’s condition and stabilize the metabolic This article is an open access article state. Therefore, fast and proper genetic diagnosis can play an important role in patient distributed under the terms and management [3–5]. conditions of the Creative Commons The ClinVar (https://www.ncbi.nlm.nih.gov/clinvar) (accessed on 15 April 2021) Attribution (CC BY) license (https:// and Human Gene Mutation (http://www.hgmd.cf.ac.uk/) (accessed on 15 April 2021) creativecommons.org/licenses/by/ databases are the main repositories of PA variants and currently comprise 720 and 322 4.0/). unique variants, respectively. Missense variants are the predominant cause of PA, followed Int. J. Mol. Sci. 2021, 22, 4154. https://doi.org/10.3390/ijms22084154 https://www.mdpi.com/journal/ijms Int. J. Mol. Sci. 2021, 22, 4154 2 of 11 by small insertions and deletions, splicing variants and large genomic deletions [6]. Due to the increasing availability of molecular genetic diagnostics, the rapidly growing number of novel variants presents a challenge to researchers to establish their molecular consequence. In the case of missense variants, enzymatic assay and Western blot are widely used to reveal their deleterious effect on the PCC structure and activity [7,8]. Another class of variants that disrupts normal splicing is of particular interest due to development of the specific splice-modulating approaches for PA [9,10]. Splicing variants account for up to 50% of pathogenic variants in some genes and are suggested to be underrepresented in others due to the lack of their functional analysis at the mRNA level [11–13]. Splicing variants could be located anywhere within genes and be occasionally classified as synonymous, missense or nonsense variants. The standard for functional analysis of splicing variants is the analysis of mRNA from patient cells. This approach does, however, have some limitations, e.g., the aberrant mRNA isoform could be degraded by the nonsense-mediated mRNA decay mechanism or the gene of interest may be expressed only in tissue that is difficult to assess. To overcome these issues, the in-vitro minigene splicing assay presents a fast and robust tool for analysis of potentially spliceogenic variants [14–16]. The need for precise classification of studied variants led to the development of ACMG/AMP guidelines and their gene- and disease-specific refinements curated by Clin- Gen Workgroups (https://clinicalgenome.org/) (accessed on 15 April 2021) [17,18]. During the analysis of public repositories of PA variants, we faced the problem of incorrect classi- fication of splicing variants, which is inconsistent with ACMG/AMP recommendations. In addition, we revealed a number of variants classified as missense or synonymous, but which were highly spliceogenic according to bioinformatic analysis, and variants classified as splicing but lacking any functional characterization at the mRNA level. Therefore, the aim of this work was to improve the PA variant classification, expand the knowledge of splicing variants in the studied genes and evaluate the utility of bioinformatic analysis and the minigene assay for the characterization of PA variants. 2. Materials and Methods 2.1. Variant Selection and Bioinformatic Analysis The subjects of this study were the PCCA and PCCB gene variants available in the ClinVar and HGMD databases and classified as pathogenic, likely pathogenic and of uncertain significance. In addition, we included four splicing variants previously identified in our lab [19]. Bioinformatic analysis of probable splicing alterations was performed with freely available tools: HumanSplicingFinder 3.1 (HSF3.1) (https://www.genomnis.com/access- hsf) (accessed on 12 June 2020) [20], SpliceAI [21], MMSplice [22], SpiP [23], Ex-Skip (https: //ex-skip.img.cas.cz/) (accessed on 15 April 2021) [24], Hexplorer (https://www2.hhu. de/rna/html/hexplorer_score.php) (accessed on 15 April 2021) [25] and HExoSplice (http: //bioinfo.univ-rouen.fr/HExoSplice_submit/index.php) (accessed on 15 April 2021) [26]. For the variants which create new splicing sites (SS) or disrupt wild type (WT) SSs, the results of HSF3.1, SpliceAI, MMSplice and SpiP were considered. Exonic variants which do not create or disrupt SSs were analyzed using Ex-Skip, Hexplorer and HExoSplice, which are the recommended tools for analysis of probable alteration of exonic regulatory splicing motifs [27]. The standard cutoffs and values for the variant to be significant are as follows: HSF3.1 matrices, >15% difference in SS strength; MaxEntScan module of HSF3.1, >30% difference in SS strength; and SpliceAI, delta score > 0.5. MMSplice and SpiP provide the discrete values and corresponding probabilities. For the analysis of exonic regulatory splicing motif alteration, the variants with the highest Ex-Skip, Hexplorer and HExoSplice scores were selected, because this type of prediction has low specificity and depends largely on whether the splicing of the studied exon relies on the recognition of regulatory motifs and their specific positioning [27]. Int. J. Mol. Sci. 2021, 22, 4154 3 of 11 To visualize the probable effect of in-frame deletions caused by exon skipping, the human PCC heterodimer was modeled using SWISS-MODEL (https://swissmodel.expasy. org/) (accessed on 15 April 2021) [28] with the protein databank (https://www.rcsb.org/): 3n6r template (accessed on 15 April 2021). Amino acid conservation was visualized by We- bLogo (https://weblogo.berkeley.edu/logo.cgi (accessed on 15 April 2021)) using the PCCA and PCCB gene trees from Ensemble (ENSGT00940000156083 and ENSGT00940000157741). 2.2. Minigene Assay The PCCA and PCCB exons with a minimum 100 bp adjacent intronic sequence were amplified by PCR with high fidelity polymerase and cloned into multiple cloning sites between two constitutionally spliced
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