Tag: variant

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.

1. Eilbeck K et al. Settling the score: variant prioritization and Mendelian disease. Nat Rev Genet. 2017 Oct;18(10):599-612. doi: 10.1038/nrg.2017.52. https://www.ncbi.nlm.nih.gov/pubmed/28804138

2. Hol et al. Comparison and optimization of in silico algorithms for predicting the pathogenicity of sodium channel variants in epilepsy. Epilepsia. 2017 Jul;58(7):1190-1198. doi: 10.1111/epi.13798. Epub 2017 May 18. https://www.ncbi.nlm.nih.gov/pubmed/28518218

3. Rodríguez-Escudero I et al. Yeast-based methods to assess PTEN phosphoinositide phosphatase activity in vivo. Methods. 2015 May;77-78:172-9. doi: 10.1016/j.ymeth.2014.10.020. Epub 2014 Oct 25. https://www.ncbi.nlm.nih.gov/pubmed/25448481

4. Woods NT et al. Functional assays provide a robust tool for the clinical annotation of genetic variants of uncertain significance. NPJ Genom Med. 2016;1. pii: 16001. doi: 10.1038/npjgenmed.2016.1. Epub 2016 Mar 2. https://www.ncbi.nlm.nih.gov/pubmed/28781887

5. Liu YL et al. The impacts of nineteen mutations on the enzymatic activity of USP26. Gene. 2018 Jan 30;641:292-296. doi: 10.1016/j.gene.2017.10.074. Epub 2017 Oct 27. https://www.ncbi.nlm.nih.gov/pubmed/29111204

6. McGonigle P. Animal Models of CNS Disorders. Biochemical Pharmacology 87, no. 1 (January 1, 2014): 140–49. doi:10.1016/j.bcp.2013.06.016. https://www.ncbi.nlm.nih.gov/pubmed/23811310

7. Csöbönyeiová M et al. Recent Advances in iPSC Technologies Involving Cardiovascular and Neurodegenerative Disease Modeling. General Physiology and Biophysics 35, no. 1 (January 2016): 1–12. doi:10.4149/gpb_2015023. https://www.ncbi.nlm.nih.gov/pubmed/26492069

8. Breslin S and O’Driscoll L. Three-Dimensional Cell Culture: The Missing Link in Drug Discovery. Drug Discovery Today 18, no. 5–6 (March 2013): 240–49. doi:10.1016/j.drudis.2012.10.003. https://www.ncbi.nlm.nih.gov/pubmed/23073387

9. Wang J et al. MARRVEL: Integration of Human and Model Organism Genetic Resources to Facilitate Functional Annotation of the Human Genome. Am J Hum Genet. 2017 Jun 1;100(6):843-853. doi: 10.1016/j.ajhg.2017.04.010. Epub 2017 May 11. https://www.ncbi.nlm.nih.gov/pubmed/28502612

10. Luo X et al. Clinically severe CACNA1A alleles affect synaptic function and neurodegeneration differentially. PLoS Genet. 2017 Jul 24;13(7):e1006905. doi: 10.1371/journal.pgen.1006905. eCollection 2017 Jul. https://www.ncbi.nlm.nih.gov/pubmed/28742085

11. Bend EG et al. NALCN channelopathies: Distinguishing gain-of-function and loss-of-function mutations. Neurology. 2016 Sep 13;87(11):1131-9. doi: 10.1212/WNL.0000000000003095. Epub 2016 Aug 24. https://www.ncbi.nlm.nih.gov/pubmed/27558372

12. Sorkaç A et al. In Vivo Modelling of ATP1A3 G316S-Induced Ataxia in C. elegans Using CRISPR/Cas9-Mediated Homologous Recombination Reveals Dominant Loss of Function Defects. PLoS One. 2016 Dec 9;11(12):e0167963. doi: 10.1371/journal.pone.0167963. https://www.ncbi.nlm.nih.gov/pubmed/27936181

13. Bulger DA et al. Caenorhabditis elegans DAF-2 as a Model for Human Insulin Receptoropathies.G3 (Bethesda). 2017 Jan 5;7(1):257-268. doi: 10.1534/g3.116.037184.. Available at: https://www.ncbi.nlm.nih.gov/pubmed/27856697

14. Prior H et al. Highly Efficient, Rapid and Co-CRISPR-Independent Genome Editing in Caenorhabditis elegans. G3 (Bethesda). 2017 Nov 6;7(11):3693-3698. doi: 10.1534/g3.117.300216. https://www.ncbi.nlm.nih.gov/pubmed/28893845

15. Canning P et al. CDKL Family Kinases Have Evolved Distinct Structural Features and Ciliary Function. Cell Rep. 2018 Jan 23;22(4):885-894. doi: 10.1016/j.celrep.2017.12.083. https://www.ncbi.nlm.nih.gov/pubmed/29420175

16. Pierce SB et al. De novo mutation in RING1 with epigenetic effects on neurodevelopment. Proc Natl Acad Sci U S A. 2018 Feb 13;115(7):1558-1563. doi: 10.1073/pnas.1721290115. https://www.ncbi.nlm.nih.gov/pubmed/29386386

17. Troulinaki K et al. WAH-1/AIF regulates mitochondrial oxidative phosphorylation in the nematode Caenorhabditis elegans. Cell Death Discov. 2018 Jan 29;4:2. doi: 10.1038/s41420-017-0005-6. https://www.ncbi.nlm.nih.gov/pubmed/29531799

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.

1. Wetterstrand KA. DNA Sequencing Costs: Data from the NHGRI Genome Sequencing Program (GSP) Accessed July 12, 2018. https://www.genome.gov/sequencingcostsdata

2. Richards S et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015 May;17(5):405-24. doi: 10.1038/gim.2015.30. Epub 2015 Mar 5. https://www.ncbi.nlm.nih.gov/pubmed/25741868

3. Manolio TA et al. Bedside Back to Bench: Building Bridges between Basic and Clinical Genomic Research. Cell. 2017 Mar 23;169(1):6-12. doi: 10.1016/j.cell.2017.03.005. https://www.ncbi.nlm.nih.gov/pubmed/28340351

4. Henrie A. et al. ClinVar Miner: Demonstrating utility of a Web-based tool for viewing and filtering ClinVar data. Hum Mutat. 2018 May 23. doi: 10.1002/humu.23555. https://www.ncbi.nlm.nih.gov/pubmed/29790234

5. Stavropoulos DJ et al. Whole Genome Sequencing Expands Diagnostic Utility and Improves Clinical Management in Pediatric Medicine. NPJ Genom Med. 2016 Jan 13;1. pii: 15012. doi: 10.1038/npjgenmed.2015.12. https://www.ncbi.nlm.nih.gov/pubmed/28567303

6. Ellingford JM et al. Whole Genome Sequencing Increases Molecular Diagnostic Yield Compared with Current Diagnostic Testing for Inherited Retinal Disease. Ophthalmology. 2016 May;123(5):1143-50. doi: 10.1016/j.ophtha.2016.01.009. https://www.ncbi.nlm.nih.gov/pubmed/26872967

7. Volk AE and Kubisch C. The rapid evolution of molecular genetic diagnostics in neuromuscular diseases. Curr Opin Neurol. 2017 Oct;30(5):523-528. doi: 10.1097/WCO.0000000000000478. https://www.ncbi.nlm.nih.gov/pubmed/28665809

8. Oliver GR et al. Bioinformatics for clinical next generation sequencing. Clin Chem. 2015 Jan;61(1):124-35. doi: 10.1373/clinchem.2014.224360. https://www.ncbi.nlm.nih.gov/pubmed/25451870

9. Cox TC. Utility and limitations of animal models for the functional validation of human sequence variants. Mol Genet Genomic Med. 2015 Sep;3(5):375-82. doi: 10.1002/mgg3.167. https://www.ncbi.nlm.nih.gov/pubmed/26436102

10. Eilbeck K et al. Settling the score: variant prioritization and Mendelian disease. Nat Rev Genet. 2017 Oct;18(10):599-612. doi: 10.1038/nrg.2017.52. https://www.ncbi.nlm.nih.gov/pubmed/28804138

Beginning of the Journey to the End

The purpose of this blog has many uses.  It allows me to create content exploring genome biology in more depth than what can be achieved in a mere 140 characters.  But more ambitiously, I will be embarking on a journey of self-discovery, of the molecular and structural kind.  I plan to acquire my own genome’s data at high-density read depth.  I expect to catalog over 1,000,000 variations from my genome when compared to the norm.   Of these variations, I expect about 1000 will be in the coding sequence of my genes.  About 7% of these will be obviously deleterious to gene function.  Yet, because we are diploids organisms with two copies of every gene, many of these “bad” genes will be backed up by a good copy.  As a result, I will know what is my carrier status for bad alleles. Intriguingly, I will be uncovering which important genes (or hopefully not so important genes) have had two hits that render their phenotypic consequence as defective.