C. elegans as Fast and Affordable System for Variant Phenotyping

Systems for Functional Studies

A variety of modeling systems can be used to explore variant function. Initially, many researchers turn to a computational approach to aid variant assessments (Eilbeck 2017). A recent bioinformatics study was used to refine the variant classification of voltage-gated sodium channels (KCNQs) for their contributions to epilepsy (Hol 2017). Yet many of the KCNQ variants remained VUS alleles after the bioinformatic refinement, so alternative functional assays are needed to capture full functional assessment. Various techniques applied for obtaining functional data from clinical variants range from bacterial and yeast expression systems to mammalian cell models (Rodríguez-Escudero 2015, Woods 2016). In one example, bacterial expression of recombinant USP6 was used to detect pathological enzymatic activity in one of 18 clinical variants (liu 2016). Recombinant protein assays can be effective for enzymatic genes, but for disease genes with complex interaction phenotypes, expression in bacteria or yeast removes the gene from its native context and prevents the exploration of pathologies that involve these complex interactions. A mouse or rat animal model is the “gold standard” for finding clear phenotypes from a clinical variant, yet the cost and time spent are more than 10x relative to C. elegans animal models. Further, humanized rodent models are expensive to deploy early in drug development (McGonigle 2014). Disease modeling with Induced Pluripotent Stem Cells (iPSC) offers an exciting platform to study clinical variants (Csöbönyeiová 2016), but the removal of the cells from their native context of the intact animal regrettably removes the important effect of a tissue-based environment. 3D cell culturing techniques and organs-on-a-chip can be useful in restoring proper microenvironment context (Breslin 2013), but the ease of use for routine analysis clinical variants has yet to evolve.

Alternative Animal Models

An emerging approach used by the Undiagnosed Disease Network (Wang 2017), a variant observed in humans is homology modeled by installing the same amino acid change at a conserved position in the disease gene homologs. For instance, clinical variants in CACNA1A were installed in zebrafish and Drosophila and pathogenic activities were observed (Luo 2017). In a recent publication using C. elegans, CRISPR was used to install a patient sequence variant suspect of having CLIFAHDD syndrome (Congenital Contractures of the Limbs and Face with Hypotonia and Developmental Delay) due to defects in the NALCN gene (Bend 2016). These authors demonstrated a gain-of-function pathogenicity assignment could be made for a patient V637F sequence variation. Similar results have been observed for variant installs in other C. elegans disease gene orthologs (Sorkaç 2016, Bulger 2017, Prior 2017, Canning 2018, Pierce 2018, Troulinaki 2018). These results reinforce the C. elegans animal model as a good platform for modeling gene function of pathological variants. The fast life-cycle and ease of genome engineering allow direct modeling of pathogenic alleles to occur within a one-month timeline needed to create and analyze the transgenic animals.

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Explosive Growth of Gene Variant Numbers

Genetic Testing

Clinical geneticists have an acute need to understand pathogenicity in genomes of their patients (Figure 1). Cost per human genome has now approached $1000 each (Wetterstrand 2018). This affordable cost is allowing clinicians to start incorporating next-generation sequencing (NGS) technology into the patient diagnosis.

Variant Diversity

The American College of Medical Genetics and Genomics and the Association for Molecular Pathology recommend that variants be classified in five groups (Pathogenic, Likely Pathogenic, Uncertain Significance, Likely Benign, Benign) (Richards 2015). Further, they suggest greater efforts are needed to resolve variants of Uncertain Significance (VUS) into either Pathogenic or Benign status. The re-classification of a VUS is no small task. In a recent publication on the analysis of the clinical variant database (ClinVar), the number of known genetic variants in human disease was reported on 9/9/2016 to be 72,472 (Manolio 2017). One year later (9/21/2017), a survey of ClinVar using an online database viewer reported 359,938 known variants (Henrie 2018). This 4.9-fold increase in the number of known clinical variants over one year reflects the explosive application of whole genome sequencing in clinical diagnostics (Stavropoulos 2015, Ellingford 2016, Volk 2017).

Need for Innovation

The daunting task now is to determine the significance of each new variant. Bioinformatics can provide some insight into variant pathogenicity (Oliver 2015). Unfortunately, the number of VUS alleles has remained close to 40% year-on-year since 2015. As of the July 2018, there are 192,843 identified VUS alleles. With such high numbers, there is a pressing need to quickly correlate genotype to phenotype and determine if VUS alleles are benign or pathogenic (Cox 2015). Model systems that reconstitute mutations in a physiological context are a robust method to demonstrate variant pathogenicity (Eilbeck 2017). Traditionally, mouse models have been used to characterize defective function in VUS alleles, but the expanding universe of clinical variants is overwhelming the current capacity. Higher throughput animal modeling is needed to address the growing demand.

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