Literature DB >> 25151476

Genome resequencing and bioinformatic analysis of SNP containing candidate genes in the autoimmune vitiligo Smyth line chicken model.

Hyeon-Min Jang, Gisela F Erf, Kaylee C Rowland, Byung-Whi Kong1.   

Abstract

BACKGROUND: 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 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.
RESULTS: Illumina 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).
CONCLUSIONS: Various 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.

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Year:  2014        PMID: 25151476      PMCID: PMC4152579          DOI: 10.1186/1471-2164-15-707

Source DB:  PubMed          Journal:  BMC Genomics        ISSN: 1471-2164            Impact factor:   3.969


Background

Vitiligo is an acquired hypomelanotic disorder characterized by circumscribed depigmented macules in the skin resulting from the loss of melanocytes. Autoimmunity has been identified as the major etiological factor in vitiligo, although many other factors including infections, stress, neural abnormalities, aberrant melanocyte function, and genetic susceptibility have been implicated [1]. 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 [2, 3]. Like other autoimmune diseases, SL vitiligo (SLV) is multi-factorial in nature and involves the interplay of genetic, immune system, and environmental-factors. SLV susceptibility is manifested in part in an inherent melanocyte defect and loss of melanocytes is due to melanocyte-specific cell-mediated and humoral immune activities [2, 3]. 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 [5-10]. However, most susceptible loci identified by GWAS results were found in regulatory regions of gene expression, therefore 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). Recently, the encyclopedia of DNA elements (ENCODE) of mammalian species suggested that ~90% of disease associated genetic variations in human lie in noncoding regions, while only ~10% of variations in coding regions were causative mutations associated with human disease [11, 12]. Nevertheless, the identification of potential coding mutations that alter protein functionalities is a prerequisite process to understand disease etiology. Moreover, the functional study of candidate genetic risk factors is almost impossible without appropriate model systems. The SL chicken is an excellent model to conduct a functional verification study of candidate genes that underlie genetic susceptibility for vitiligo due to the tractable, definite phenotype, the high vitiligo incidence in the population (80-90%), the feasibility of in vivo characterization and the relatively short generation time. Recently, microarray analysis examined global gene expression during SLV development and provided comprehensive information at the transcriptome level that supported the multifactorial etiology of vitiligo [13]. In this study, whole genome resequencing analysis using an Illumina platform was performed to more deeply investigate the genetic aspects of SLV expression in comparison with the parental Brown line (BL) of chickens from which the SL was originally derived. BL chickens retain vitiligo susceptibility but with a very low (0 – 2%) incidence rate of vitiligo development [2, 3], although none of the BL chickens used in this study had vitiligo. Millions of single nucleotide polymorphisms (SNP) were identified by genome resequencing and only potentially causal genes containing non-synonymous mutations that can induce amino acid changes in proteins were focused on in this study.

Results and discussion

Genome resequencing for BL and SL chickens

Genome sequencing of pooled DNA from 10 non-vitiliginous BL and SL chickens each with confirmed SLV produced ~63 and 89 million sequence reads of 200 bp, respectively (Table 1). Of those, ~80% of the reads were used for sequence alignment, while 20% of sequence reads were not aligned. Therefore, genome coverage for BL and SL reached 5.1x and 7.0x, respectively, of the Red Jungle Fowl chicken genome. The total number of SNPs was 4.8 and 5.5 million (~0.5% of template genome) for the BL and SL genome, respectively. The large number of SNPs per examined chicken line was based on data of at least 2 read coverage depths (number of read counts per nucleotide location). Most SNPs were found on the larger chromosomes (Chr), including Chr 1 through 5 and Z (sex chromosome) (data not shown). To identify genetic biomarkers that are responsible for the incidence of SLV, unique SNPs that are found in SL only were selected by removing SNPs that overlapped with those found in the parental BL. Then, mutations with ≥75% SNP rates were chosen as reliable marker SNPs. 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]. Instead, SNP filtering was conducted by removing overlapping SNPs found in both BL and SL, and applying fixed %SNP rates (≥75%) as described in Methods. As a result, a total of ~1 million unique SNPs were identified throughout the SL chicken genome (Figure 1). Over 100,000 SNPs were found in Chr 1, 2, 3, and Z while Chr 32 did not contain any unique SNPs for SL. When SNPs were grouped by the feature types of chromosome regions, ~50% of SNPs were in the intergenic (heterochromatic) regions and 13,710 SNPs were found in CDS sequences (protein coding regions) (Figure 2). Most genes containing SNPs in regulatory regions, not in CDS regions, identified by human GWAS studies were also observed to contain unique SNPs in the current SL study (data not shown). Around 60% of SL SNPs in protein coding CDS regions were synonymous mutations that did not induce amino acid changes. To identify potentially causal mutations that induce protein coding alterations, SNP analysis focused on SNPs leading to changes in amino acid sequences. Using this approach, a total of 3518 SNPs were identified that could induce non-synonymous-, frameshift-, nonsense-, and no-start-changes in the CDS region (Figure 2 and Additional file 1: Table S1), suggesting that the 3518 SNPs are part of the genetic components in functional protein coding regions that may drive the high incidence rate of SLV. Of the 3518 candidate SNPs that are associated with amino acid changes, SNPs showing ≥10 read depths (considered to be more reliable candidate genetic markers) were chosen for the further analysis. Using this approach, 195 SNPs remained (data not shown). To reduce false positives due to possible errors in the assembly process, re-scanning of each SNP position for the 195 potentially more reliable protein coding SNPs was conducted using the Seqman-Pro viewer program. This process yielded 156 more reliable SNPs that were chosen as candidate marker SNPs for further analysis (Table 2).
Table 1

Results of Illumina sequencing and assembly

LineReads numbersTotal # of reads used for alignmentTotal # of reads not alignedCoverageTotal # of SNP
BL (P) 1 62,764,36850,536,8319,480,7945.1x4,846,132
SL2 88,525,26669,886,11014,225,5777.0x5,465,994

1(P) means the parental line, no vitiligo.

2 SL, with vitiligo.

Figure 1

Number of unique SNPs per chromosome found in vitiliginous SL chickens compared to non-vitiliginous BL chickens. Numbers are indicated for bars not clearly visible.

Figure 2

Summary of SNPs in SLV. A) Number of SNPs categorized by chromosomal region in SL chickens. B) Number of SNPs categorized by type of amino acid sequence changes.

Table 2

The 156 reliable marker SNPs that induced amino acid changes showing ≥10 read depths

Contig IDcRef PosRef baseCalled baseImpactSNP%Gene symbolDNA changeAA ChangeDepthA CntC CntG CntT CntDel
NC_006088112587537CTN-Syn0.80FGL2c.368C > TT123I100-080
NC_006088124296415GCN-Syn1.00CTTNBP2c.2600G > CR867P12012-00
NC_006088133755434GAN-Syn0.91TBC1D30c.304G > AV102I11100-00
NC_006088134386453GAN-Syn1.00HELBc.2342G > AG781E12120-00
NC_006088134386455CTN-Syn1.00HELBc.2344C > TR782W120-0120
NC_006088145803846AGN-Syn1.00LOC417921c.1457A > GQ486R11-01100
NC_006088145804772TAN-Syn0.92LOC417921c.1700 T > AI567K131210-0
NC_006088154936701GTN-Syn0.80STAB2c.6155G > TA2052D1000-80
NC_006088167320079CC|AN-Syn0.77CASC1c.[949C > C] + [949C > A]V317V, V317F1310-000
NC_006088182856208GTN-Syn1.00CD200R1c.197G > TR66L1100-110
NC_006088188049869CTN-Syn0.90LOC418423c.229C > TV77I100-090
NC_006088188157238CTN-Syn1.00CGGBP1c.428C > TR143Q100-0100
NC_0060881105941417TCN-Syn0.90SETD4c.1186 T > CI396V10090-0
NC_0060881107377588CGN-Syn0.92LCA5Lc.1331C > GG444A130-1200
NC_0060881108406755TT|CN-Syn0.77LOC770616c.[37 T > T] + [37 T > C]M13M, M13V130100-0
NC_0060881108764081CTN-Syn0.91UBASH3Ac.1144C > TL382F110-0100
NC_0060881109036880GTN-Syn1.00CBSc.1492G > TQ498K1000-100
NC_0060881112264749AGN-Syn1.00OTCc.5A > GL2P12-01200
NC_0060881112303781AGN-Syn1.00RPGRc.2359A > GI787V13-01300
NC_0060881112303950GAN-Syn1.00RPGRc.2528G > AG843E10100-00
NC_0060881121737171GAN-Syn1.00FANCBc.1988G > AR663K12120-00
NC_0060881121737188CAN-Syn1.00FANCBc.2005C > AL669M1212-000
NC_0060881133392696AGN-Syn1.00MFSD9c.790A > GF264L11-01100
NC_0060881137555484CT|CN-Syn0.79MYO16c.[1232C > T] + [1232C > C]T411T, T411I140-0110
NC_0060881138584430CTN-Syn0.90ANKRD10c.551C > TR184H100-090
NC_0060881152732014AGN-Syn1.00RNF219c.794A > GQ265R10-01000
NC_0060881153202063CTN-Syn1.00SLAIN1c.1486C > TG496S110-0110
NC_0060881166907615GCN-Syn0.90NUFIP1c.925G > CQ309E1009-00
NC_0060881169375045AGN-Syn0.91SERPINE3c.254A > GH85R11-01000
NC_0060881173852809TCN-Syn1.00BRCA2c.355 T > CT119A100100-0
NC_0060881176891377AGN-Syn1.00SACSc.6235A > GI2079V11-01100
NC_0060881177772554CAN-Syn0.86EFHA1c.659C > AA220D1412-000
NC_0060881177772568GAN-Syn0.86EFHA1c.673G > AV225I14120-00
NC_0060881178541726GTN-Syn0.92CENPJc.2041G > TV681L1200-110
NC_0060881187399843GAN-Syn0.90GRM5c.2842G > AA948T1090-00
NC_0060881194954389AGN-Syn1.00RNF169c.992A > GV331A10-01000
NC_00608922046630CTN-Syn1.00CCDC13c.860C > TS287N120-0120
NC_00608922059597TCN-Syn1.00CCDC13c.209 T > CD70G110110-0
NC_00608922141930AGN-Syn0.90OBSCNc.17225A > GI5742T10-0900
NC_00608922155796TCN-Syn1.00OBSCNc.14875 T > CN4959D110110-0
NC_00608924669968CTN-Syn0.90DLEC1c.1592C > TA531V100-090
NC_00608924948035CTN-Syn1.00XIRP1c.4696C > TA1566T110-0110
NC_00608927746258C-Frameshift1.00LOC100859401c.388C > -Y129fs100-0010
NC_006089220725367CAN-Syn1.00DBF4c.1114C > AP372T1111-000
NC_006089221576571CTN-Syn0.90C2H7orf63c.962C > TT321M100-090
NC_006089223984791GAN-Syn0.90PON2c.521G > AP174L1090-00
NC_006089226789159TCN-Syn1.00VWDEc.2678 T > CN893S110110-0
NC_006089242356129CTN-Syn1.00LOC100857506c.757C > TP253S120-0120
NC_006089243749632CTN-Syn1.00TGM4c.160C > TA54T100-0100
NC_006089247501151CAN-Syn1.00SEPT7c.764C > AG255V1010-000
NC_006089248103938CTN-Syn1.00BMPERc.1957C > TV653I110-0110
NC_006089267476715TAN-Syn1.00MYLK4c.2071 T > AI691L121200-0
NC_006089279168177CTN-Syn1.00MTRRc.2027C > TR676K110-0110
NC_006089296844408TGN-Syn1.00CEP192c.3655 T > GK1219Q110011-0
NC_0060892107668292CGN-Syn1.00PRKDCc.3938C > GC1313S100-1000
NC_0060892107767247AGN-Syn0.91LOC421108c.77A > GN26S11-01000
NC_0060892115050472CAN-Syn1.00CSPP1c.1303C > AH435N1010-000
NC_0060892120054459TGN-Syn1.00FAM164Ac.605 T > GV202G100010-0
NC_0060892126210593GAN-Syn0.80C2H8orf38c.730G > AV244I1080-00
NC_0060892148002272CTN-Syn1.00TOP1MTc.1149C > TM383I100-0100
NC_006090318177273CTN-Syn1.00EPRSc.778C > TH260Y100-0100
NC_006090318177438TCN-Syn1.00EPRSc.943 T > CC315R110110-0
NC_006090319964716ATN-Syn0.90USH2Ac.9152A > TE3051V10-0090
NC_006090320371026TCN-Syn0.92CENPFc.2726 T > CE909G130120-0
NC_006090320377057TT|AN-Syn0.75CENPFc.[1411 T > T] + [1411 T > A]N471N, N471Y12900-0
NC_006090328850511AGN-Syn1.00LOC100858112c.586A > GF196L10-01000
NC_006090347388200GG|AN-Syn0.77ZC3H12Dc.[998G > G] + [998G > A]T333T, T333M13100-00
NC_006090348575253CTN-Syn0.90SYNE1c.11674C > TA3892T100-090
NC_006090348577869CTN-Syn0.93SYNE1c.11335C > TD3779N140-0130
NC_006090354551285TCN-Syn1.00PDE7Bc.868 T > CT290A110110-0
NC_006090366462814CTN-Syn1.00LOC421765c.1076C > TR359Q120-0120
NC_006090366468226GTN-Syn1.00LOC421765c.334G > TQ112K1000-100
NC_006090375929093GAN-Syn0.93ZNF292c.7835G > AT2612I14130-00
NC_006090380234352GAN-Syn1.00COL12A1c.2659G > AA887T10100-00
NC_00609144124961CTNonsense1.00DDX26Bc.151C > TQ51.110-0110
NC_00609149883806GAN-Syn0.91SLITRK4c.1715G > AR572K11100-00
NC_006091449489771GTN-Syn0.91RUFY3c.1588G > TA530S1100-100
NC_00609253898544CTN-Syn0.80KIF18Ac.1567C > TD523N100-080
NC_00609258035275AGN-Syn1.00LOC100859209c.79A > GC27R11-01100
NC_006092520708767AGN-Syn1.00API5c.506A > GK169R10-01000
NC_006092521158570-AFrameshift0.90LOC770458c.105- > AA35fs1090001
NC_006092522076516TCN-Syn1.00C1QTNF4c.230 T > CI77T110110-0
NC_006092538183079CTN-Syn0.83LOC100859479c.82C > TP28S120-0100
NC_006092544443744GG|CN-Syn0.75KIAA1409c.[2076G > G] + [2076G > C]Q692H, Q692Q1209-00
NC_006092544443754GAN-Syn0.80KIAA1409c.2086G > AE696K1080-00
NC_006092544443755ATN-Syn0.80KIAA1409c.2087A > TE696V10-0080
NC_006092545296293GG|AN-Syn0.75C5H14orf49c.[596G > G] + [596G > A]A199A, A199V1290-00
NC_006092550316949AGN-Syn1.00APOPT1c.440A > GH147R12-01200
NC_00609364563260GCN-Syn0.90BMS1c.1666G > CL556V1009-00
NC_006093615062678AGN-Syn0.80PLAUc.925A > GW309R10-0800
NC_006093625651167GAN-Syn1.00RBM20c.1687G > AD563N10100-00
NC_006093630617911CTN-Syn0.80ATE1c.518C > TG173D100-080
NC_006093632898174TCN-Syn1.00MKI67c.2414 T > CM805T100100-0
NC_00609479605730TCN-Syn1.00ANKRD44c.584 T > CK195R150150-0
NC_006094718282262GG|AN-Syn0.75LRP2c.[3775G > G] + [3775G > A]A1259T, A1259A1290-00
NC_006094721828274AG|AN-Syn0.75CCDC108c.[2902A > G] + [2902A > A]N968N, N968D12-0900
NC_006094725803714TGN-Syn0.80IQCB1c.952 T > GT318P10008-0
NC_00609581373656GAN-Syn1.00CAMSAP1L1c.1955G > AA652V12120-00
NC_00609582290270AGN-Syn1.00LOC100859900c.65A > GL22P13-01300
NC_00609582506276CTN-Syn1.00CRB1c.1814C > TS605N120-0120
NC_00609582612732TCN-Syn1.00ASPMc.6907 T > CC2303R110110-0
NC_00609587596287GTN-Syn0.91NCF2c.1234G > TP412T1100-100
NC_00609588053190GCN-Syn0.81LOC768407c.875G > CG292A16013-00
NC_00609588188888TAN-Syn0.82LOC768392c.512 T > AE171V11900-0
NC_006095812666139GG|AN-Syn0.75ABCA4c.[4678G > G] + [4678G > A]V1560I, V1560V1290-00
NC_006095823585119CGN-Syn0.93LRP8c.583C > GG195R140-1300
NC_0060969800651GG|AN-Syn0.75LOC424748c.[5G > G] + [5G > A]R2K, R2R1290-00
NC_00609692123518AGN-Syn1.00YEATS2c.602A > GN201S11-01100
NC_00609694127341GAN-Syn1.00GAL3ST4c.505G > AA169T10100-00
NC_00609694217231AGN-Syn0.92RNF168c.806A > GD269G13-01200
NC_006096915175477TGN-Syn1.00CAPN10c.1660 T > GT554P110011-0
NC_006096921419266CAN-Syn0.82SPTSSBc.44C > AP15Q119-000
NC_006097103986458TCN-Syn0.82VPS13Cc.3727 T > CY1243H11090-0
NC_006099129103119GAN-Syn1.00COPGc.1771G > AA591T12120-00
NC_0060991215133837TCN-Syn1.00LOC100858715c.89 T > CV30A110110-0
NC_0060991219738746CTN-Syn1.00NR2C2c.1061C > TS354N100-0100
NC_006100132703509AG|AN-Syn0.77LOC769940c.[2875A > G] + [2875A > A]S959P, S959S13-01000
NC_006100132703511AG|AN-Syn0.77LOC769940c.[2873A > G] + [2873A > A]F958S, F958F13-01000
NC_0061001311337736CGN-Syn0.90GEMIN5c.849C > GH283Q100-900
NC_0061001311349274AGN-Syn0.83GEMIN5c.3739A > GS1247G12-01000
NC_006101145552664GAN-Syn0.82TEKT4c.1192G > AV398I1190-00
NC_006101145975913TCN-Syn0.93CHTF18c.1934 T > CE645G140130-0
NC_006101146002290GG|CN-Syn0.75MSLNc.[774G > G] + [774G > C]S258S, S258R1209-00
NC_006101147697077CTN-Syn0.80ABCC6c.1213C > TV405M100-080
NC_0061011413378210TCN-Syn0.93C14H16orf89c.68 T > CD23G141130-0
NC_006102153706306TCN-Syn1.00GLT1D1c.968 T > CQ323R100100-0
NC_006102154915706TCN-Syn0.90ATP6V0A2c.1246 T > CI416V10090-0
NC_006102154926428GAN-Syn1.00LOC100857705c.1552G > AR518C10100-00
NC_006102155060494GAN-Syn1.00MPHOSPH9c.755G > AR252K14140-00
NC_006102155695994GAN-Syn0.90WDR66c.1690G > AD564N1090-00
NC_006102156317399CTN-Syn1.00C15H12orf51c.7777C > TD2593N100-0100
NC_006102156633596TCN-Syn1.00USP30c.488 T > CI163T130130-0
NC_006102158096384TCN-Syn1.00DDX51c.1493 T > CM498T110110-0
NC_006102158693805GCN-Syn1.00RTDR1c.118G > CV40L10010-00
NC_006104176506143TCN-Syn0.87SETXc.4564 T > CI1522V150130-0
NC_006104176581087GAN-Syn1.00C17H9orf171c.682G > AD228N12120-00
NC_006104176922987AGN-Syn1.00ADAMTS13c.3103A > GI1035V12-01200
NC_006104177858127ATN-Syn1.00SEC16Ac.4079A > TH1360L15-00150
NC_006104177905701GCN-Syn1.00SNAPC4c.2609G > CG870A11011-00
NC_006105181082307CTN-Syn0.90DNAH9c.6700C > TP2234S100-090
NC_006105181111244GAN-Syn0.80DNAH9c.8491G > AV2831M1080-00
NC_006105186605385CGN-Syn1.00ATAD5c.3652C > GP1218A110-1100
NC_006105186605422GAN-Syn1.00ATAD5c.3689G > AR1230K10100-00
NC_006105186967029CTN-Syn1.00C18H17orf58c.1018C > TL340F120-0120
NC_006106195858876TCN-Syn1.00ERAL1c.1186 T > CM396V140140-0
NC_006106196213750GCN-Syn1.00SLC6A4c.242G > CA81G10010-00
NC_006106197495965CGN-Syn0.82BRIP1c.1718C > GP573R110-900
NC_0061072012338968TGN-Syn0.83CASS4c.1169 T > GN390T120010-0
NC_0061072012696859TAN-Syn1.00CYP24A1c.878 T > AL293Q121200-0
NC_006108212336588GANonsense0.80TAS1R3c.1108G > AR370.1080-00
NC_006108213215527CTN-Syn0.92ENO1c.1265C > TR422H120-0110
NC_006108214657639GAN-Syn1.00KIAA0090c.2716G > AR906C11110-00
NC_006110235163924TCN-Syn1.00ZBTB8Ac.232 T > CF78L100100-0
NC_006112251498400GAN-Syn1.00HORMAD1c.844G > AA282T11110-00
NC_006113264679877GAN-Syn1.00LOC768535c.1097G > AP366L10100-00
NC_006127Z71529316GAN-Syn1.00DMXL1c.4394G > AA1465V11110-00

Contig ID, chromosome (Chr) numbers, reference position (Ref Pos), reference base (Ref Base), called (SNP) base, impact (kinds of protein mutation), SNP%, feature name (gene name), DNA change, amino acid (AA) change, Depths, and five columns for SNP counts (cnts) are indicated.

Results of Illumina sequencing and assembly 1(P) means the parental line, no vitiligo. 2 SL, with vitiligo. Number of unique SNPs per chromosome found in vitiliginous SL chickens compared to non-vitiliginous BL chickens. Numbers are indicated for bars not clearly visible. Summary of SNPs in SLV. A) Number of SNPs categorized by chromosomal region in SL chickens. B) Number of SNPs categorized by type of amino acid sequence changes. The 156 reliable marker SNPs that induced amino acid changes showing ≥10 read depths Contig ID, chromosome (Chr) numbers, reference position (Ref Pos), reference base (Ref Base), called (SNP) base, impact (kinds of protein mutation), SNP%, feature name (gene name), DNA change, amino acid (AA) change, Depths, and five columns for SNP counts (cnts) are indicated.

SNP validation using PCR and Sanger sequencing

Since pooled DNA samples of 10 chickens for each line were used for genome sequencing, individual SNPs were subjected to the verification process with larger bird populations. For this, 14 SNPs were randomly chosen from the 156 candidate marker SNPs showing ≥10 read depths and were subjected to SNP verification analysis using PCR and Sanger sequencing to detect SNP positions with larger numbers of birds; specifically 20 non-vitiliginous BL chickens and 70 SL chickens exhibiting vitiligo. The results clearly showed differential frequencies of nucleotide bases in the 14 SNP positions between BL and SL chickens (Table 3). Thus, the 156 SNPs known to induce amino acid changes can become potential genetic biomarkers for vitiligo in SL chickens.
Table 3

Verification of 14 SNPs using PCR and Sanger sequencing in larger numbers of non-vitiliginous parental BL (20) vs. vitiliginous SL (70) chickens

ChRef PosRef baseCalled baseImpactSNP%Feature nameProtein changeResults of larger population
BL (20 birds)SL (70 birds)
27746258CT-FS1LOC100859401Y129fs All_CT All_Del
521158570-AFS0.9LOC770458A35fs Ins_A_30% Ins_A_100%
921419266CAN-Syn0.82SPTSSB [1]P15Q C:A = 3:1 C:A = 1:4
154936701GTN-Syn0.8STAB2A2052D All_G G:T = 1:1
348575253CTN-Syn0.9SYNE1A3892T C:T = 10:1 C:T = 1:10
318177438TCN-Syn1EPRSC315R T:C = 4:1 T:C = 1:7
1105941417TCN-Syn0.9SETD4I396V T:C = 10:1 All_C
195858876TCN-Syn1ERAL1M396V All_T T:C = 1:10
1177772554CAN-Syn0.86EFHA1A220D C:A = 4:1 C:A = 1:10
1177772568GAN-Syn0.86EFHA1V225I All_G G:A = 1:10
1311337736CGN-Syn0.9GEMIN5H283Q All_C All_G
544443744GG|CN-Syn0.75KIAA1409Q692H All_G G:C = 1:10
544443754GAN-Syn0.8KIAA1409E696K All_G G:A = 1:4
544443755ATN-Syn0.8KIAA1409E696V All_A A:T = 1:4

Ratios between reference base and SNP (Called base) in large chicken populations were bolded. FS; frameshift mutation.

Verification of 14 SNPs using PCR and Sanger sequencing in larger numbers of non-vitiliginous parental BL (20) vs. vitiliginous SL (70) chickens Ratios between reference base and SNP (Called base) in large chicken populations were bolded. FS; frameshift mutation.

Bioinformatic analyses of genes containing amino acid change SNPs

Amino acid changes may have impacts on the functional interpretations for vitiligo induction in SL chickens. The Ingenuity Pathway Analysis (IPA) program generated bioinformatics data sets including functional groups (gene ontology; GO) and gene networks for genes containing amino acid changes in SL chicken. The 156 SNPs were found in 139 genes encompassing known- and unknown functions, chromosomal open reading frames, and hypothetical proteins (Additional file 2: Table S2).

Functional roles

Genes were categorized in 76 functional groups (Additional file 3: Table S3). Of these, six functional groups are of particular interest to autoimmune vitiligo development, including dermatological diseases/conditions, inflammatory response, inflammatory disease, immunological disease, immune cell trafficking, and infectious disease (Table 4). The functional group of genes for dermatological diseases/conditions contained the following genes: ADAMTS13 (ADAM metallopeptidase with thrombospondin type 1 motif 13); ASPM [asp (abnormal spindle) homolog, microcephaly associated; Drosophila)]; ATP6V0A2 (ATPase, H + transporting, lysosomal V0 subunit A2); BRCA2 (breast cancer 2, early onset); COL12A1 (collagen, type XII, alpha 1); GRM5 (glutamate receptor, metabotropic 5); LRP2 (low density lipoprotein receptor-related protein 2); MKI67 (marker of proliferation Ki-67); OBSCN (obscurin, cytoskeletal calmodulin and titin-interacting RhoGEF); PLAU (plasminogen activator, urokinase); RNF168 (ring finger protein 168, E3 ubiquitin protein ligase); STAB2 [stabilin 2 or FEEL2 (fasciclin EGF-like, laminin-type EGF-like, and link domain-containing scavenger receptor 2)]; and XIRP1 (xin actin-binding repeat containing 1). General and dermatological disease related functions for these genes are summarized in Table 5.
Table 4

Vitiligo related functions of candidate genes

Functions# moleculesGenes involved
Dermatological diseases/conditions14ADAMTS13, ASPM, ATP6V0A2, BRCA2, COL12A1, GRM5, LRP2, MKI67, OBSCN, PLAU, RNF168, STAB2, XIRP1
Inflammatory response10ADAMTS13, CBS, OL3A1, LRP2, LRP8, NR2C2, PDE7B, PLAU, PON2, SLC6A4
Inflammatory disease9CBS, LRP2, MKI67, NCF2, NR2C2, PDE7B, PLAU, SLC6A4
Immunological disease5BRCA2, LRP2, NR2C2,PRKDC, RNF168
Immune cell trafficking2CBS, PLAU
Infectious disease1PLAU
Table 5

Function of candidate genes containing SNPs in CDS region related to dermatological diseases/conditions

Sub-groupsMolecules
Malignant cutaneous melanoma cancerOBSCN [15]
- Is a RhoGEF protein that interacts with cytoskeletal calmodulin and titin and is part of the giant sarcomeric signaling protein family of myosin light chain kinases
- Mutant human OBSCN protein (E4574K) is associated with melanoma in human.
STAB2 (or FEEL2) [16, 17]
- is a large, transmembrane receptor protein which may function in angiogenesis, lymphocyte homing, cell adhesion, or receptor scavenging.
- Somatic missense homozygous mutant human STAB2 gene (c.3862 T > G translating to p.S1288A) is associated with melanoma in skin from human leg (observed in 2 of 2 samples)
LRP2 (or megalin) [17, 18]
- Is a member of the low density lipoprotein (LDL) receptor gene family essential for brain development.
- Somatic missense heterozygous mutant human LRP2 gene (c.6284G > A translating to p.R2095Q) is associated with melanoma in skin from human leg (observed in 2 of 2 samples).
ASPM [17, 19]
- This gene is the human ortholog of the Drosophila melanogaster ’abnormal spindle’ gene (asp), which is essential for normal mitotic spindle function in embryonic neuroblasts.
- Somatic nonsense heterozygous mutant human ASPM gene (c.7174C > T translating to p.Q2392*) is associated with melanoma in skin from human leg (observed in 2 of 2 samples).
GRM5 [20]
- Is a member of G protein-coupled receptor that are widely expressed in the brain and modulate many diverse signaling pathways
- In mouse melanocytes, transgenic rat GRM5 protein (S901A mutant) affects development of melanoma in mouse.
BRCA2 [21, 22]
- At the cellular level, loss of BRCA2 function results in sensitivity to cross-linking agents, a decrease in homology-directed repair of double-stranded DNA breaks (DSBs), and defects in replication and checkpoint control
- Inherited mutations in BRCA1 and this gene, BRCA2, confer increased lifetime risk of developing breast or ovarian cancer.
- Mutant human BRCA2 gene is associated with malignant melanoma in Homo sapiens.
XIRP1 [17, 23]
- Its function is unknown, but it is upregulated in wounded skeletal muscle cells in zebrafish
- Somatic nonsense heterozygous mutant human XIRP1 gene (c.2838G > A translating to p.W946*) is associated with melanoma in skin from human leg (observed in 2 of 2 samples).
RIDDLE syndromeRNF168 [24]
- Is an E3 ubiquitin ligase
- Mutant human RNF168 gene (deletion c.1323_1326del of ACAA and DNA duplication mutation) is associated with RIDDLE (radiosensitivity, immunodeficiency, dysmorphic features, and learning difficulties) syndrome, which a novel human immunodeficiency disorder associated with defective double strand break repair.
Schulman-Upshaw syndromeADAMTS13 [25]
- Is von Willebrand Factor (VWF) cleaving metalloproteinase.
- Is associated with the development of thrombotic thrombocytopenic purpura (TTP), known as the Schulman-Upshaw syndrome.
- Mutant human ADAMTS13 gene (deletion c.1783_1784delTT) is associated with congenital TTP.
Autosomal recessive cutis laxa type IIAATP6V0A2 [26]
- Is a subunit of the vacuolar ATPase (v-ATPase), a heteromultimeric enzyme that is essential for the acidification of diverse cellular components.
- Mutations in human ATP6V0A2 protein (p.Q765* (rs80356758), p.R63* (rs80356750), deletion, and insertion) is associated with autosomal recessive cutis laxa type IIA.
Wrinkly skin syndromeATP6V0A2 [26]
- Mutant human ATP6V0A2 gene (g.10132G > A) is associated with wrinkly skin syndrome
HyperpigmentationGRM5 [20]
- In mouse melanocytes, transgenic rat mGlur5 [GRM5] protein increases hyperpigmentation of pinna in mouse ear.
Development of blisterPLAU [27, 28]
- Is a serine protease involved in degradation of the extracellular matrix and possibly tumor cell migration and proliferation.
- Heterozygous- and heterozygous mutant mouse Plg gene in mouse affects development of blister in mouse subepidermal skin that is altered by transgenic uPAR (PLAUR) protein and transgenic uPA (product of PLAU) protein and development of blister.
Vitiligo related functions of candidate genes Function of candidate genes containing SNPs in CDS region related to dermatological diseases/conditions Interestingly, a recent report by Nikolaev et al. (2012) [17] indicated that amino acid changes found in ASPM, LRP2, STAB2, and XIRP1 proteins were associated with human melanoma by exome sequencing. Melanocytes in vitiligo also exhibit morphological and biological melanocyte defects/alterations compared to melanocytes from individuals with normal pigmentation [29]. While these alterations may be different from those observed in melanoma, e.g. slower growth, and higher sensitivity to oxidative stress of cultured melanocytes [30, 31], alterations in amino acid sequences found in homolog proteins but different residues may result in opposite phenotypes of dermatological diseases/conditions [32]. In addition to these molecules, BRCA2, GRM5, MKI67, and OBSCN associated with SLV are also known to be associated with human melanoma. The relationship between candidate genes and other dermatological diseases including melanoma is summarized in Table 5.

Gene networks

Gene network analysis, which represents the intermolecular connections among interacting genes based on functional knowledge inputs, was performed on genes with amino acid changes in SLV chickens using the IPA program. The gene network analysis was carried out using the simplest setting of 35 focus molecules to facilitate and summarize the intermolecular connections (Table 6 and Figures 3, 4, 5, 6, 7, 8, and 9). A discussion of the 7 gene networks is provided below and gene information for focus molecules in each network is listed in Additional file 4: Table S4.
Table 6

Associated network functions of candidate genes

IDMolecules in networkScoreFocus moleculesTop functions
126 s Proteasome, ADAMTS13, ATE1, BMPER, CAPN10, CD200R1, CENPF, Cg, COL12A1, collagen, CTTNBP2, DBF4, ENO1, ERK1/2, FGL2, GRM5, LDL, LRP2, LRP8, LRP, MKI67, NCF2, P38 MAPK, PDGF BB, Pkc(s), PLAU, PON2, Ppp2c, SACS, SLC6A4, SYNE1, TAS1R3, UBASH3A, Vegf4824Cardiovascular Disease, Hematological Disease, Cardiac Infarction
2ANKRD44, ANKRD52, API5, APOPT1, ATAD5, BMS1, C9orf114, CAMSAP2, CCDC115, CENPQ, CEP192, CGGBP1, CPVL, DDX26B, DNHD1, EMC1, ERAL1, ERCC6L, GTSE1, MTR, MTRR, PLK1, PPP6R1, PRRC1, RGPD5 (includes others), RNF169, RNF219, SIMC1, SPATA2L, STK10, STXBP4, TTC4, UBC, VRK3, ZNF2922615Cell Cycle, DNA Replication, Recombination, and Repair, Developmental Disorder
3ASPM, ATP6V0A2, ATP6V1E2, C15orf39, CCDC8, CHTF18, COPG1, DUS2L, EARS2, EPRS, FANCB, GEMIN5, HELB, INPP5B, KAT5, OGFOD2, PARS2, RNF168, SEC16A, SEPT3, SEPT7, SETX, SNX10, TMEM106B, TRIM68, UBC, USP30, USP34, USP35, USP40, USP48, USP27X, USP9Y, YEATS2, ZBTB8A2615Developmental Disorder, Endocrine System Disorders, Hereditary Disorder
41810009J06Rik/Gm2663, ABCA4, ABCC6, ADCYAP1, BRCA1, BRIP1, Ca2+, CASC1, DNAH9, DPP7, DQX1, Gbp8, IFNG, IL4, KIF16B, KIF18A, LOC290071, MAPK8, MPHOSPH9, MPPE1, MYO16, NUFIP1, Pde, PDE1C, PDE7B, PPP1CA, PSEN1, Rb, RTDR1, SLAIN1, ST18, STAB2, TOP1MT, UTP, ZC3H12D2615Cell Morphology, Cellular Function and Maintenance, Hair and Skin Development and Function
5ABCD4, ABHD2, ADCK4, ANKRD10, AS3MT, C1QTNF4, CEP120, CLN6, CSPP1, DDX51, DLEC1, DMXL1, DPP7, EFHA1, FASTKD1, GAL3ST4, ILVBL, KRT12, KRT15, LRRC8A, MORC1, NR3C1, OGDHL, PEX1, SETD4, SPTLC2, SPTLC3, SPTSSB, STAT3, TEKT4, UBC, VPS13C, XRCC6, ZFYVE28, ZNF4832012Lipid Metabolism, Small Molecule Biochemistry, Digestive System Development and Function
6AIFM3, APP, BCL2L15, CASP3, DNAJB14, DNAJC4, DNAJC12, DNAJC19, HORMAD1, Hsp84-2, HSP90AB1, HSPB7, HSPD1, IARS2, IQCB1, KLHL32, MYLK4, NEK11, norepinephrine, OBSCN, OTC, RPGR, RUFY3, SMC3, TARP, TBX22, testosterone, TGM4, THAP4, TTPA, ULK4, UNC79, USH2A, YWHAQ, ZC2HC1A1711Hereditary Disorder, Ophthalmic Disease, Neurological Disease
7Act1, Akt, BRCA2, CBS, CD3, CENPJ, Ck2, Collagen type I, CRB1, CYP24A1, estrogen receptor, Immunoglobulin, INSRR, Jnk, LAG3, miR-101-3p (and other miRNAs w/seed ACAGUAC), MSLN, MZB1, NFkB (complex), NKTR, NR2C2, PI3K (complex), PIK3IP1, PIK3R6, PRKDC, Prl4a1, Psg16, SEC14L2, SNAPC4, Taok2, Tnfrsf22/Tnfrsf23, TRAF1-TRAF2-TRAF3, VTCN1, Zfp110/Zfp369, ZNF675149Cellular Response to Therapeutics, Cellular Assembly and Organization, DNA Replication, Recombination, and Repair

Functions associated with 7 networks are listed. Score means the number of network eligible molecules out of differentially expressed genes.

Figure 3

Gene network #1. Molecular interactions among important focus molecules are displayed. Gray symbols show the genes found in the list of SNP while white symbols indicate neighboring genes that are functionally associated, but not included, in the gene list of SNP. Symbols for each molecule are presented according to molecular functions and type of interactions.

Figure 4

Gene network #2. Molecular interaction and symbols are the same as the description in Figure 3.

Figure 5

Gene network #3. Molecular interaction and symbols are the same as the description in Figure 3.

Figure 6

Gene network #4. Molecular interaction and symbols are the same as the description in Figure 3.

Figure 7

Gene network #5. Molecular interaction and symbols are the same as the description in Figure 3.

Figure 8

Gene network #6. Molecular interaction and symbols are the same as the description in Figure 3.

Figure 9

Gene network #7. Molecular interaction and symbols are the same as the description in Figure 3.

Associated network functions of candidate genes Functions associated with 7 networks are listed. Score means the number of network eligible molecules out of differentially expressed genes. Gene network #1. Molecular interactions among important focus molecules are displayed. Gray symbols show the genes found in the list of SNP while white symbols indicate neighboring genes that are functionally associated, but not included, in the gene list of SNP. Symbols for each molecule are presented according to molecular functions and type of interactions. Gene network #2. Molecular interaction and symbols are the same as the description in Figure 3. Gene network #3. Molecular interaction and symbols are the same as the description in Figure 3. Gene network #4. Molecular interaction and symbols are the same as the description in Figure 3. Gene network #5. Molecular interaction and symbols are the same as the description in Figure 3. Gene network #6. Molecular interaction and symbols are the same as the description in Figure 3. Gene network #7. Molecular interaction and symbols are the same as the description in Figure 3. Candidate genes in Network #1 are associated with signaling pathways of the mitogen activated protein kinase (MAPK; also ERK1/2) and protein kinase C (Pkc) connected to VEGF (vascular endothelial growth factor) and PDGF (platelet derived growth factor) with PLAU in the center (Figure 3). The top functions related to network #1 are cardiovascular disease, hematological disease, and cardiac infarction. Interestingly, molecules including LRP2, PLAU, ADAMTS13, and GRM5 that are part of Network #1 were also identified as functional factors for melanoma as described above [17]. Additionally, mutations in the amino acid sequence of LRP2, altered function of MAP2K1 and MAP2K2 induced by genetic mutations in melanoma patients [17], and mutations in GRM5 in mouse melanoma models [20] were also reported. The connections in Network #1 therefore suggest genetic mutations that generated amino acid changes in LRP2, PLAU, ADAMTS13, and GRM5 may influence dermatological diseases, including vitiligo, through MAPK and ERK1/2 signaling pathways. The top functions of Network #2 include Cell Cycle, DNA Replication, Recombination and Repair, and Developmental Disorder (Figure 4) and Network #3 is involved in Developmental Disorder, Endocrine System Disorders, Hereditary Disorder (Figure 5). Most molecules in Networks #2 and #3 directly bind to UBC (ubiquitin C). Ubiquitinylation, the covalent attachment of ubiquitin to proteins, regulates numerous cellular processes such as protein degradation and signal transduction. Recently, many ubiquitinylated proteins and their lysine ubiquitinylation site were identified using proteomic technologies in mammalian species [33-37]. Indeed, in SLV, the amino acid changes in lysine residues for CEP192 (centrosomal protein 192 kDa; pK169R) and API5 (apoptosis inhibitor 5; pK1219Q) were identified (Table 2), suggesting that the various cellular functions including protein degradation by altered ubiquitinylation properties of proteins may play a significant role in the induction of vitiligo. Molecules in Network #4 are involved in Cell Morphology, Cellular Function and Maintenance, Hair and Skin Development and Function (Figure 6). Molecules in network #4 mainly interact with IFNG (interferon gamma), IL4 (interleukin 4), MAPK8, and calcium signaling pathways in addition to protein phosphatase (PPP1CA; protein phosphatase 1 catalytic subunit alpha isozyme) functions. MYO16 (myocin 16), KIF18A (kinesin family member 18A), and CASC1 (cancer susceptibility candidate 1) are known to directly bind to PPP1CA [38-40], suggesting that amino acid changes in those proteins may induce alterations (hyper vs hypo) in protein phophorylation states in SL chickens, resulting in vitiligo development. Molecules interacting with IFNG, IL4, MAPK8 and calcium signaling pathways showed an indirect relationship, not a direct relationship with each other making it difficult to explain how amino acid changes in these molecules affect vitiligo induction in SL chicken. Molecules in Network #5 also mainly bind to UBC as discussed in Networks #2 and #3 and the functions include Lipid Metabolism, Small Molecule Biochemistry, Digestive System Development and Function (Figure 7). Network #6 contains molecules involved in Hereditary Disorder, Ophthalmic Disease, Neurological Disease (Figure 8). In this network, ZC2HC1A (zinc finger, C2HC-type containing 1A) and RUFY3 (RUN and FYVE domain containing 3) directly bind to APP [amyloide beta (A4) precursor protein]. APP is a precursor protein for beta-amyloide, which is the main constituent of amyloid plaques in the brains of Alzheimer disease patients [41]. RUFY3 (also known as single axon-related; singar1), which is a brain specific protein, regulates neuronal polarity by suppressing formation of surplus axons [42]. Though the binding of ZC2HC1A and RUFY3 to APP was found during the progression of Alzheimer disease [41], the functional roles for this binding in the progression of this disease has not been characterized. Similarly, amino acid changes found in ZC2HC1A and RUFY3 are implicated in SLV development possibly as a result of altered APP binding properties. USH2A [Usher syndrome 2A (autosomal recessive, mild)] was included in network #6. Various mutations in USH2A have been identified in patients of Usher syndrome type II, which is characterized by moderate to severe sensorineural hearing loss and progressive retinitis pigmentosa [43]. Vitiliginous SL chickens may also develop severe visual impairment and blindness due to autoimmune activity directed against choroidal melanocytes and subsequent damage to the retinal pigment epithelium. [44, 45]. Taken together, the amino acid change in USH2A also may affect vitiligo progression and retinal depigmentation. Molecules in Network #7 mainly function in Cellular Response to Therapeutics, Cellular Assembly and Organization, DNA Replication, Recombination, and Repair. PRKDC (protein kinase, DNA-activated, catalytic polypeptide) and its interacting protein kinases are mainly involved in this network (Figure 9). In addition to knock-out and inactive mutations, alteration of autophosphorylation capability by single amino acid change of PRKDC has been known to influence rejoining of DNA double stranded breaks in mammalian cells [46], suggesting that vitiligo development may be affected by aberrant PRKDC kinase activity due to an observed amino acid change. Vitiligo susceptible loci in human populations identified by several GWAS [5-10] also showed several loci that induced amino acid changes in various proteins such as STRN3 (Striatin, calmodulin binding protein 3), DNAH5 (dynein, axonemal, heavy chain 5), KIAA1005 (immunoglobulin heavy variable 3), TYR (tyrosinase), OCA2 (oculocutaneous albinism II), PTPN22 [protein tyrosine phosphatase, non-receptor type 22 (lymphoid)], IFIH1 (interferon induced with helicase C domain 1), SLA (SRC-like-adaptor), CD44, MC1R [melanocortin 1 receptor (alpha melanocyte stimulating hormone receptor)], UBASH3A (ubiquitin associated and SH3 domain containing A), C1QTNF6 (C1Q and tumor necrosis factor related protein 6), CASP7 (caspase 7), and GZMB (granzyme B). One similar mutation in UBASH3A protein coding region from human [6] was also found in the SLV chicken model (Table 2). Additionally, when the long list of 3518 candidate amino acid altering SNPs (read depth <10) was considered, several genes, including IFIH1 (interferon-induced helicase C domain-containing protein 1), CD44 antigen and DNAH5 (dynein, axonemal, heavy chain 5), matched those identified by human GWAS studies [Additional file 1: Table S1 and [5, 6]], although the amino acid position and alterations did not match. UBASH3A is one of the two family members belonging to the T cell ubiquitin ligand (TULA) family and can negatively regulate T cell signaling [47]. Together with the UBC molecule discussed elsewhere in this paper, functions for UBASH3A related to ubiquitinylation and T cell signaling pathway may be important for SLV development. IFIH1 encodes an interferon-induced RNA helicase involved in antiviral innate immune responses, associated with type 1 diabetes, Graves’ disease, multiple sclerosis, psoriasis, and perhaps lupus [48-53]. CD44 encodes a cell surface glycoprotein with various functions, including a role in T cell development [54], and is associated with lupus [55]. DNAH5 gene mutation is found in patients with primary ciliary dyskinesia (PCD) [56], a rare disease transmitted as an autosomal recessive trait and characterized by recurrent airway infections due to abnormal ciliary structure and function. Primary defects in the structure and function of sensory and motile cilia result in multiple ciliopathies [57].

Conclusions

In this study, various potential genetic markers showing amino acid changes were identified in the SLV model through genome re-sequencing. When considering functionality based on the interpretation of factors involved, development of vitiligo appeared to be associated with the interactions among cytoskeletal factors (OBSCN, ASPM, XIRP1, ADAMTS13), protein kinases (MAPK, ERK1/2, PKC, PRKDC), phosphatase (PPP1CA), ubiquitinylation (UBC) and amyloid (APP) production. Further functional validation study, such as allele specific expression of the candidate genes with candidate SNPs at the target tissues involved in SLV development will be carried out using the SL chicken model for spontaneous autoimmune vitiligo.

Methods

Animals and Illumina sequencing

Adult SL chickens with vitiligo and parental non-vitiliginous BL chickens, maintained by G. Erf at the University of Arkansas (Fayetteville, AR), were selected from the breeder populations. Blood (3 ml) was collected from 12 birds each following an animal use protocol approved by the University of Arkansas Institutional Animal Care and Use Committee (IACUC; approval number: 11019). Genomic DNA was isolated from each whole blood sample using QiaAmp DNA mini kit (Qiagen, Hilden, Germany) following manufacturer’s instructions. DNA quality was determined by agarose gel electrophoresis and 10 samples having the highest quality in each line were pooled to represent each line. Library preparation and Illumina genome sequencing for the pooled DNA samples were performed by the National Center for Genome Resources (NCGR; Santa Fe, NM). Illumina HiSeq system 2x100 bp paired end read technology was used for genome sequencing.

Genome sequence assembly and data analysis

Illumina sequencing data received from NCGR was aligned to the chicken reference genome sequence for Red Jungle Fowl (GBK 4.0) that was retrieved from NCBI. For the reference based genome alignment, the NGen genome sequence assembly program of the Lasergene software package (DNAStar, Madison, WI) was used. Assembly parameters were as follows: file format, BAM; mer Size, 21; mer skip query, 2; minimum match percentage, 93; maximum gap size, 6; minimum aligned length, 35; match score, 10; mismatch penalty, 20; gap penalty, 30; SNP calculation method, diploid bayesian; minimum SNP percentage, 5; SNP confidence threshold, 10; minimum SNP count, 2; minimum base quality score, 5. After assembly, the SeqMan Pro program of the Lasergene package was used for further analyses including SNP data.

SNP detection and analysis

JMP genomics (SAS Institute, Inc., Cary, NC) program was used for filtering unique SNPs for vitiligo SL chickens. SNPs occurring in both SL and BL lines were filtered out, leaving behind unique SNPs for each line. To identify highly fixed and homozygous SNPs, the SNPs were filtered based on SNP percentages (SNP%). SNPs with a SNP% of ≥75 (%) (for example, number of SNP = 3 of read depth = 4) were chosen. The 75% cutoff for SNP selection was set by considering potential sequencing errors that can be generated by the massively parallel sequencing method. Potential causal SL SNPs that induce non-synonymous changes in CDS regions were chosen for further analysis. Since the read depth of many SL SNPs was low, unique SNPs showing ≥10 read depths were considered as reliable SNPs. Reliable and causal SNPs, which were chosen by criteria described above were confirmed by double-checking the raw assembly data with alignment view to reduce false positives. Fourteen randomly chosen SNPs, which induce amino acid changes in the CDS region, were subjected to validation using PCR and Sanger sequencing with larger numbers of SL and BL chickens. Twenty BL and 70 SL chickens that were verified phenotypically to be non-vitiliginous and vitiliginous, respectively, were used for blood sampling. Approximately 100 μL of blood was collected from each bird by wing vein puncture into tubes containing citrate (anticoagulant). Genomic DNA was isolated from whole blood using the Wizard SV 96 Genomic DNA Purification System (Promega; Madison, WI) following manufacturer’s instructions with modifications. Isolated DNA was quantified using a Nanodrop 1000 spectrophotometer (Thermo Fisher Scientific Inc., Waltham, MA) and a dilution of 1 ng/μL was prepared in 96 well PCR format for all samples. For PCR reaction, forward and reverse primers were designed based on the RJF genome sequence (GenBank assembly ID: GCA_000002315.2) using Primer 3 online software (Table 7). The sequencing primers were designed to anneal at least 50 bp upstream of the SNP position and forward/reverse primers were chosen at the flanking regions of the seq primer and the SNP position. All primers were commercially synthesized by Integrated DNA Technology (Ames, IA). PCR was carried out as 25 μL reaction volumes in 96 well plates with cyle conditions as follows: denaturation 95°C for 1 min, 40 cycles of amplification (95°C for 30 s, 55-63°C for 1 min, 72°C for 1 min), final extension 72°C for 10 min. Verification of PCR was performed by 1% agarose gel electrophoresis. PCR products were purified using the Wizard SV 96 PCR Clean-Up System (Promega; Madison, WI) following manufacturer’s instructions. Briefly, four plates (four different PCR products) were pooled into one plate and were subjected to PCR clean-up. Cross-specificity of seq-primers to the four pooled PCR products was examined by BLAST function (NCBI) and only products that were not cross-specific with other seq primers were pooled. Purified PCR products were subjected to Sanger sequencing performed by the University of Arkansas DNA Resources Center (Fayetteville, AR). Results were analyzed using ABI Sequence scanner software (Life Technologies, Carlsbad, CA). Ratios of bases occurring at SNP locations were recorded.
Table 7

Primers used for PCR and Sanger sequencing

GenePrimer namePrimer sequence (5′→3′)
EFHA1ForwardCAAAAACCTAAATGGGTTTCCA
ReverseAAACTTCATCAGGACATGCAGA
SequencingGTGCAAGTTTCTGAAAGACT
EPRSForwardCAATTCCACACTTTGCAGGTTA
ReverseGCTGTGATGCCAAATTTAAACA
SequencingGAAGGGAAGGCATATGTGGA
ERAL1ForwardAGGACACACACATGCTGGATAC
ReverseGCCCTTTTTGTGTTTTAAGTGG
SequencingGAGACTTCCTTGGGACCACA
LOC100859401ForwardGAATTTACCAGTCCAGGCACTC
ReverseACTACCTTGGGCCTTGTCAGTA
SequencingATGCTCCTTTTTTCCAGACC
LOC770458ForwardTGCAGAGAATACAGCACGATCT
ReverseACTCACTCCATAAGGGGAGACA
SequencingGCCTTTACCAGACAAA
SPTSSBForwardCTTGTTGGGAATCAGCTCTCTT
ReverseTGCCTTTGTCAATACTGTGACC
SequencingAACGCAGAGTCCTAACGTGG
STAB2ForwardCACTGTTACTGCAGTGATGCAA
ReverseGATAGGAACAGCATCCCTAACG
SequencingGCACTGGCACGTGTTGTTCT
SYNE1ForwardACTCACCTTGTGGTTGGCTAGT
ReverseCCTCACTGTCTTCCTCTGCT
SequencingCCAGCCTGCTAGACATATGT
SETD4ForwardCAAATCGTTGTCACGTTCAAGT
ReverseTTCCTTTTGGTGTGCTTCCTAT
SequencingAGATCTTCCA AGCGTAGTGC
GEMIN5-1ForwardTAGGCTTCATTTGCTGACTCAA
ReverseTACAGCAGGAAGGAAGGATGAT
SequencingCCCTTTCTTTTCCAAAGGTG
GEMIN5-2ForwardCCTGGGATGAGGTAGTGAAAAG
ReverseAAGCAGAAAAGCAAAAGCAGAC
SequencingATTCTTGGTGCTGTGGCCCG
KIAA1409_1ForwardCTTTGGGCTTAGAGAACAGCAT
ReverseAAATTCAGTGGCATTTTTGCTT
SequencingGGGGTGGTTCTCACACATGTCA
Primers used for PCR and Sanger sequencing

Bioinformatics

Functional interpretation of 139 genes showing ≥75 SNP%, ≥10 read depths and non-synonymous changes was analyzed in the context of gene ontology and molecular networks using Ingenuity Pathways Analysis (IPA; Ingenuity Systems®; http://www.ingenuity.com). Since IPA is based on human and mouse bioinformatics, functionalities for differentially expressed genes in the chicken were interpreted based primarily on mammalian biological mechanisms. The limit of number of molecules in the network was set to 35, leaving only the most important molecules based on the number of connections for each focus gene (a subset of uploaded significant genes having direct interactions with other genes in the database) to other significant genes [58].

Availability of supporting data

All sequence reads described in the manuscript are available under BioProject accession PRJNA256208. Illumina sequence reads have been deposited at NCBI’s SRA archive under following numbers (SL: Sample: SRS670088, Experiment: SRX665272, Read: SRR1531502; BL: Sample: SRS670098, Experiment: SRX665286, Reads: SRR1531503). Additional file 1: List of 3518 SNPs to induce amino acid changes in SL chicken. (XLSX 432 KB) Additional file 2: Gene name and functions of 139 gene containing amino acid changes showing ≥10 depth counts in SL chicken. Gene symbol, Entrez gene name, cellular location and type were provided. (XLSX 18 KB) Additional file 3: Functional groups of genes containing amino acid changes. (XLSX 12 KB) Additional file 4: List of focus molecules in gene networks. Gene symbols and Entrez gene names were displayed for the illustrations of network analysis. (XLSX 17 KB)
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