Abstract
BackgroundThe Smyth line (SL) chicken is the only animal model for autoimmune vitiligo that spontaneously displays all clinical and biological manifestations of the human disorder. To understand the genetic components underlying the susceptibility to develop SL vitiligo (SLV), whole genome resequencing analysis was performed in SLV chickens compared with non-vitiliginous parental Brown line (BL) chickens, which maintain a very low incidence rate of vitiligo.ResultsIllumina sequencing technology and reference based assembly on Red Jungle Fowl genome sequences were used. Results of genome resequencing of pooled DNA of each 10 BL and SL chickens reached 5.1x and 7.0x coverage, respectively. The total number of SNPs was 4.8 and 5.5 million in BL and SL genome, respectively. Through a series of filtering processes, a total of ~1 million unique SNPs were found in the SL alone. Eventually of the 156 reliable marker SNPs, which can induce non-synonymous-, frameshift-, nonsense-, and no-start mutations in amino acid sequences in proteins, 139 genes were chosen for further analysis. Of these, 14 randomly chosen SNPs were examined for SNP verification by PCR and Sanger sequencing to detect SNP positions in 20 BL and 70 SL chickens. The results of the analysis of the 14 SNPs clearly showed differential frequencies of nucleotide bases in the SNP positions between BL and SL chickens. Bioinformatic analysis showed that the 156 most reliable marker SNPs included genes involved in dermatological diseases/conditions such as ADAMTS13, ASPM, ATP6V0A2, BRCA2, COL12A1, GRM5, LRP2, OBSCN, PLAU, RNF168, STAB2, and XIRP1. Intermolecular gene network analysis revealed that candidate genes identified in SLV play a role in networks centered on protein kinases (MAPK, ERK1/2, PKC, PRKDC), phosphatase (PPP1CA), ubiquitinylation (UBC) and amyloid production (APP).ConclusionsVarious potential genetic markers showing amino acid changes and potential roles in vitiligo development were identified in the SLV chicken through genome resequencing. The genetic markers and bioinformatic interpretations of amino acid mutations found in SLV chickens may provide insight into the genetic component responsible for the onset and the progression of autoimmune vitiligo and serve as valuable markers to develop diagnostic tools to detect vitiligo susceptibility.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-707) contains supplementary material, which is available to authorized users.
Highlights
The Smyth line (SL) chicken is the only animal model for autoimmune vitiligo that spontaneously displays all clinical and biological manifestations of the human disorder
To identify genetic biomarkers that are responsible for the incidence of SL vitiligo (SLV), unique single nucleotide polymorphisms (SNP) that are found in SL only were selected by removing SNPs that overlapped with those found in the parental Brown line (BL)
Since the objective of this study was to identify mutational SNPs uniquely found in SL compared to the parental BL, the filtering process used did not involve a typical SNP calling and filtering method based on quality score (Q call column in Additional file 1: Table S1) [14]
Summary
The Smyth line (SL) chicken is the only animal model for autoimmune vitiligo that spontaneously displays all clinical and biological manifestations of the human disorder. Recent genome wide association studies (GWAS) in humans to understand the role of genetic components in a variety of autoimmune diseases including vitiligo have identified hundreds of loci harboring risk alleles [4]. Several GWAS results identified vitiligo susceptible loci in human populations [5,6,7,8,9,10]. Most susceptible loci identified by GWAS results were found in regulatory regions of gene expression, the identified associations were not sufficient to identify the causal gene or deduce alterations caused by risk variants, which generally do not induce profound changes to genes (e.g. coding sequence changes, deletions, or duplications). The identification of potential coding mutations that alter protein functionalities is a prerequisite process to understand disease etiology. The functional study of candidate genetic risk factors is almost impossible without appropriate model systems
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