Clinical, Molecular, and Bioinformatics Analysis of Axenfeld-Rieger Syndrome

• Author / Creator

  Seifi, Morteza

• Axenfeld-Rieger syndrome (ARS) is an autosomal-dominant inherited disorder that primarily affects the development of structures in the anterior segment of the eye. Approximately half of patients with ARS develop glaucoma, a progressively blinding condition associated with increased intraocular pressure, which is the most severe common consequence of ARS. In almost 40% of cases, variations in the forkhead box C1 (FOXC1) or pituitary homeobox 2 (PITX2) genes are associated with ARS. However, in the remaining cases, the genetic basis of the ARS is still unknown and identifying new pathogenetic variants is becoming increasingly important for ARS genetic testing. Thus, reliable computational approaches are necessary to accurately prioritize harmful variants for functional tests to substantiate the association of variant with disruption to function. The aim of this thesis is to identify rapid and efficient bioinformatics tools for clinical diagnostic lab researchers to prioritize predicted deleterious variants for further experimental characterization. Towards this goal, functional experiments and bioinformatics programs were performed on FOXC1 and PITX2 and then the results were analysed. To this end, FOXC1 and PITX2 variations were identified and characterized (Chapter 2). The results showed a novel deletion involving the coding region of PITX2 in a father-son pair associated with ARS. The proband (son) additionally, possessed a novel 2-bp deletion in a non-coding exon of the remaining PITX2 allele predicted to alter correct splicing. It is hypothesized that the removal of the entire PITX2 allele, plus a novel 2-bp deletion (observed in the proband) within the remaining PITX2 allele together underlie the atypical ARS phenotypes in this family. Then, the impact of variants on FOXC1 and PITX2 structure and function and the performance of bioinformatics tools for all missense variants reported in these genes were investigated (Chapter 3 and 4). Functional analysis indicated that c.378A> G (p.H128R), c.402G> A (p.C135Y), and c.481A> G(p.M161V) impair FOXC1 function via different mechanisms. C.1103C>A (p.T368N) variant was indistinguishable from wild-type FOXC1 in all tests, consistent with being a rare benign variant. Comparison of variants studied here and all previously characterized FOXC1 missense variants, with predictions from commonly used in silico bioinformatics programs indicated that SIFT, PolyPhen-2, and MutPred can reliably be used to predict missense variant pathogenicity for forkhead transcription factors. Regarding PITX2, the predictive value of bioinformatics programs was assessed by comparing their predictions to functional data for PITX2 variants. The results showed that MutPred, Provean, and PMUT are the most reliable tools for predicting the pathogenicity of PITX2 missense variants. The results of molecular modeling, performed on all the PITX2 missense variations located in the homeodomain (HD), were compared with the findings of different protein stability programs and the results showed that I-mutant3.0 (sequence based) is the most reliable tool in predicting the effect of missense variations on PITX2 stability. In the last chapter (Chapter 5), in silico analysis were used to identify and characterize the regulatory regions of FOXC1 gene. Using an integration of three different bioinformatics programs, seven conserved non-coding elements (CNEs) resided up- and downstream of FOXC1 were identified. Transactivation experiments indicated that none of the identified conserved regions have functional roles in the cell lines tested, suggesting that there is no association of expression of the FOXC1 gene with my detected conserved regions. As a conclusion, the results showed that in the absence of functional data, PMUT, Provean, MutPred, I-mutant3.0 and molecular modeling are all reliable means of predicting the pathogenicity of missense variations in the FOXC1 forkhead domain (FHD) and PITX2 HD. In addition, due to the sequence homology between the FHDs of FOX class and HD of PITX transcription factors, it is hypothesized that these bioinformatics programs can be applied to determine the potential pathogenicity of missense variants within other FOX and PITX proteins and to prioritize variants for functional characterization.

• Subjects / Keywords

  • Mutation Prediction
  • Functional analysis
  • Missense variants
  • Protein stability prediction methods
  • FOXC1
  • Axenfeld-Rieger syndrome
  • PITX2
  • Bioinformatics programs

• Graduation date

  Fall 2017

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/R3F766N0K

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.