Relating the Disease Mutation Spectrum to the Evolution of the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR)

Abstract

Cystic fibrosis (CF) is the most common genetic disease among Caucasians, and accordingly the cystic fibrosis transmembrane conductance regulator (CFTR) protein has perhaps the best characterized disease mutation spectrum with more than 1,500 causative mutations having been identified. In this study, we took advantage of that wealth of mutational information in an effort to relate site-specific evolutionary parameters with the propensity and severity of CFTR disease-causing mutations. To do this, we devised a scoring scheme for known CFTR disease-causing mutations based on the Grantham amino acid chemical difference matrix. CFTR site-specific evolutionary constraint values were then computed for seven different evolutionary metrics across a range of increasing evolutionary depths. The CFTR mutational scores and the various site-specific evolutionary constraint values were compared in order to evaluate which evolutionary measures best reflect the disease-causing mutation spectrum. Site-specific evolutionary constraint values from the widely used comparative method PolyPhen2 show the best correlation with the CFTR mutation score spectrum, whereas more straightforward conservation based measures (ConSurf and ScoreCons) show the greatest ability to predict individual CFTR disease-causing mutations. While far greater than could be expected by chance alone, the fraction of the variability in mutation scores explained by the PolyPhen2 metric (3.6%), along with the best set of paired sensitivity (58%) and specificity (60%) values for the prediction of disease-causing residues, were marginal. These data indicate that evolutionary constraint levels are informative but far from determinant with respect to disease-causing mutations in CFTR. Nevertheless, this work shows that, when combined with additional lines of evidence, information on site-specific evolutionary conservation can and should be used to guide site-directed mutagenesis experiments by more narrowly defining the set of target residues, resulting in a potential savings of both time and money.

Multiple in silico tools predict phenotypic manifestations in congenital thrombotic thrombocytopenic purpura

“…Conservation has been shown to play a causative role in gene expression, proving to be the second-most predictive feature of the impact of a nucleotide variant (Lee et al, 2009). As previously demonstrated (Rishishwar et al, 2012), these tools have different but minor specificities and sensitivities in predicting disease-causing residues. Previously published studies on ADAMTS13 have used mainly Polyphen-2 and SIFT (Lotta et al, 2012;Moatti-Cohen et al, 2012), which utilize multiple sequence alignment.…”

Longevity and Plasticity of CFTR Provide an Argument for Noncanonical SNP Organization in Hominid DNA

“…Recent studies of CFTR sequence evolution [64] , protein residue co-evolution and structure [65] , selection for intronic regulatory sequences [33] , inferences regarding CFTR channel gating [66] , and characterization of the cystic fibrosis disease mutational spectrum [67] are predicated on a mechanism that treats CFTR exonic, intronic, synonymous, and non-synonymous SNP production as a random process. More classical aspects of genomics including DNA ‘clocks’ [28] , polymorphic SNP formation and non-neutral evolution [22] , [30] , [36] , rapidly evolving and ultra-conserved DNA [26] , genesis of phenotypic complexity [68] – [70] , and computational reconstruction of ancestral DNA [71] are likewise grounded to varying extent on SNP formation as essentially unbiased.…”

Curated multiple sequence alignment for the Adenomatous Polyposis Coli (APC) gene and accuracy of in silico pathogenicity predictions

“…These in silico methods have been validated for relatively few hereditary cancer genes in which pathogenic missense variants are not rare (BRCA1/2, the mismatch repair [MMR] genes, TP53, a few others) [5,[7][8][9][10][11]. They have not often been validated for other genes, and for some genes predictive value was not strong [12]. However, they are often cited as evidence in favor or against pathogenicity of variants for genes in which validation is lacking.…”