Challenges in the Definitive Diagnosis of Niemann-Pick Type C-Leaky Variants and Alternative Transcripts

Abstract

Niemann-Pick type C (NPC, ORPHA: 646) is a neuro-visceral, psychiatric disease caused predominantly by pathogenic variants in the NPC1 gene or seldom in NPC2. The rarity of the disease, and its wide range of clinical phenotypes and ages of onset, turn the diagnosis into a significant challenge. Other than the detailed clinical history, the typical diagnostic work-up for NPC includes the quantification of pathognomonic metabolites. However, the molecular basis diagnosis is still of utmost importance to fully characterize the disorder. Here, the authors provide an overview of splicing variants in the NPC1 and NPC2 genes and propose a new workflow for NPC diagnosis. Splicing variants cover a significant part of the disease-causing variants in NPC. The authors used cDNA analysis to study the impact of such variants, including the collection of data to classify them as leaky or non-leaky pathogenic variants. However, the presence of naturally occurring spliced transcripts can misdiagnose or mask a pathogenic variant and make the analysis even more difficult. Analysis of the NPC1 cDNA in NPC patients in parallel with controls is vital to assess and detect alternatively spliced forms. Moreover, nonsense-mediated mRNA decay (NMD) analysis plays an essential role in evaluating the naturally occurring transcripts during cDNA analysis and distinguishing them from other pathogenic variants' associated transcripts.

Keywords: NPC1 gene; Niemann–Pick type C; leaky variants; molecular diagnosis; splicing variants.

Figure 1

Recommendations for the detection and diagnosis of NPC, based on Patterson et al. [18] with slight updates to accommodate the most recent technologies, which are now commonly used for diagnostic purposes (e.g., clinical exome), as well as the current nomenclature. # Negative biomarkers may be suggestive that the diagnosis is not NPC; * Biomarker(s) profiling (if not initially conducted) or extended biomarker(s) profiling (in addition to those already conducted).

Figure 2

Schematic representation of the silent variants in the NPC1 exonic region affecting splicing and the effect on splicing based on in silico predictions (Human Splicing Finder—HSF and EX-SKIP tools and Maxent). P.Val562= localization on Exon 11 (red) and the effect on splicing based on in silico predictions. EX-SKIP compares the Exonic Splicing Enhancer (ESE)/Exonic Splicing Silencer (ESS) profile of a wild type (WT) and a mutated allele to determine if a specific exonic variant increases the chance of exon skipping. It calculates the total number of ESSs, ESEs, and their ratio. The p.Val562= mutant is associated with a change in the ESE/ESS ratio, which is compatible with a higher chance of exon skipping than in the WT allele. In addition, the HSF (a tool to predict the effects of pathogenic variants on splicing signals or to identify splicing motifs in any human sequence) predicts that the p.Val562= mutant leads to the creation of an ESS site. It involves the cDNA sequences CTTGTAAT (orange) [39] and CTTGTA (yellow) [40], which might be associated with a potential alteration of splicing. In the case of the silent variant p.Val562=, functional cDNA analysis was performed [19], confirming the bioinformatic prediction of Exon 11 skippings.

Figure 3

Schematic representation of NPC1 protein with the different domains. In red are the missing amino acids, if the differently spliced form would give rise to a protein. That segment is located in Middle Luminal Domain 3 (MLD3), between transmembrane domains (TMDs) II and III, and it contains the amino acids 519 to 586 (68 amino acids in total).