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Abstract

  1. Top of page
  2. Abstract
  3. Patients and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

Genetic predisposition to type 1 autoimmune hepatitis (AIH) is linked mainly to HLA class II genes. We previously searched the whole HLA region for AIH susceptibility genes using microsatellite markers and found only HLA-DR/DQ to be a candidate region for this suspected multifactorial disease. As such, the aim of this study was to broaden our search and screen the whole genome for additional genes that might contribute to type 1 AIH susceptibility. Eighty-one patients with type 1 AIH (15 men, 66 women, average age 55.9) and 80 healthy sex- and age-matched Japanese controls were enrolled in this study. We performed a case-control association study using 400 polymorphic microsatellite markers with an average spacing of 10.8 cM distributed throughout the whole genome. Two markers, one on chromosome 11 (D11S902, Pc = 0.013) and one on chromosome 18 (D18S464, Pc = 0.008), were revealed to have statistically significant associations with AIH. An additional 7 markers (D2S367, D6S309, D9S273, D11S1320, D16S423, D17S938, and D18S68) were also found to be candidate susceptibility regions. In addition, our results showed there were 17 regions that may contain genes of resistance to AIH. No specific markers were detected in HLA-DR4-negative patients, and no differences were seen in the clinical courses of patients (severe versus mild to moderate). Conclusion: This first genomewide scan of Japanese AIH patients revealed at least 26 candidate AIH susceptibility or resistance regions other than HLA class II loci. These results also suggested that the products of several genes interact to determine heritable susceptibility to AIH. (HEPATOLOGY 2007;45:384–390.)

Autoimmune hepatitis (AIH) is a chronic active hepatitis of unknown etiology characterized by hypergammaglobulinemia and autoantibodies; genetic and environmental factors are suspected to be important in its pathogenesis.1–3 Several studies from ethnically different countries have clarified strong genetic bases for both the susceptibility to and behavior of AIH.4–14 Among Caucasians susceptibility to developing type 1 AIH specifically is associated with the DRB1*0301 and DRB1*0401 alleles6–10 and among Japanese with the DRB1*0405 allele11 at the HLA class II DRB1 locus, which encodes a polymorphic β chain in the HLA-DR antigen. However, the association of these DRB1 antigens with susceptibility to developing type 1 AIH is not complete because not all AIH patients possess these antigens. This suggests that additional susceptibility genes may contribute to the development of type 1 AIH. Moreover, there may be resistance genes, which may protect against development of the disease. Whereas previously candidate susceptibility and resistance genes were searched for on an individual level in Caucasian patients with AIH,15, 16 the current study searched for them comprehensively throughout the whole genome.

Microsatellites (also called short tandem repeat polymorphisms) are tandem arrays of short stretches of noncoding nucleotide sequences that are usually repeated between 15 and 30 times.17 The obvious advantages of microsatellites are high heterozygosity, ubiquity throughout the genome, and PCR typability. Association analysis using microsatellite markers is a powerful yet cost-efficient method for mapping candidate susceptibility genes in multifactorial genetic diseases.18 If the frequencies of microsatellite markers differ significantly between patients and controls, there may be susceptibility or resistance genes may be near them, which can be further analyzed by sequencing. We previously screened the HLA region for AIH susceptibility genes using this method and found only HLA-DR/DQ to be a candidate region.19 To search for additional genes influencing the development of type 1 AIH in the whole genome, we performed an association analysis using 400 microsatellite markers spaced an average of 10.8 cM apart.

Patients and Methods

  1. Top of page
  2. Abstract
  3. Patients and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

Subjects.

Eighty-one patients with type 1 AIH (15 men, 66 women, average age 55.9 years) and 80 healthy sex- and age-matched Japanese controls were enrolled in this study. Seventy-seven of the 81 patients had been included in our previous study that scanned the HLA region.19 All subjects were residents of Nagano Prefecture, Japan, and their racial background was Japanese. Of the 81 patients, 10 were probable cases of AIH (score of 14-17 after treatment) and 71 were definite cases, according to the scoring system of the International Autoimmune Hepatitis Group.20 Ten patients had slightly elevated titers of ANA (40×), and 69 patients had high titers of ANA (more than 80×). ANA was not found in 2 of the patients, though they were both positive for anti–smooth muscle antibodies (80×). The patients were classified as having type 1 AIH based on antibody profiles. No viral markers, such as hepatitis B surface antigen, anti-hepatitis C virus antibody (second and third generations), or hepatitis C virus RNA, were detected in the serum. This study was approved by the Ethics Committee of the Shinshu University School of Medicine. Written informed consent was obtained from each subject.

DNA Preparation.

Genomic DNA from patients and controls was isolated by phenolic extraction of sodium dodecyl sulfate–lysed and proteinase K–treated cells as described previously.19, 21

HLA Typing.

HLA classes I and II alleles were determined using a Micro SSP™ DNA Typing Kit (One Lambda, Canoga Park, CA). DNA typing of the DRB1 and DQB1 alleles was performed by polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLP) analysis as previously described.19, 21

Microsatellite.

Fluorescence-tagged primers for 400 microsatellite markers that defined a 10.8-cM resolution human index map (ABI PRISM Linkage Mapping Set Version 2.5 MD10) were purchased from Applied Biosystems (Foster City, CA). PCR was performed following the manufacturer's instructions. PCR-amplified products were denatured for 5 minutes at 100°C, mixed with formamide-containing stop buffer, and then electrophoresed on a 4% polyacrylamide denaturing gel containing 8 M urea in a Model 377 automated DNA sequencer (Applied Biosystems). Fragment sizes were determined automatically by GeneScan software (Applied Biosystems) as described previously.19

Statistical Analysis.

Phenotypic frequency at polymorphic sites in the 400 microsatellites was estimated by direct counting. The significance of differences between patients and controls in allele distribution was tested by the χ2 method with continuity correction. The P value was corrected by multiplication by the number of alleles observed in each locus tested (corrected P value: Pc value). A Pc value of less than 0.1 was considered statistically significant.

Results

  1. Top of page
  2. Abstract
  3. Patients and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

Four hundred microsatellite markers were used in a genomewide linkage search in AIH patients with age- and sex-matched controls to localize genetic intervals that might contain AIH susceptibility or resistance loci. This search revealed several candidate susceptibility and resistance regions throughout the genome (Tables 1 and 2). Strong evidence for linkage was detected by marker D11S902 (28.4% vs. 7.5%, Pc = 0.013) on chromosome 11p15.1 (Fig. 1A) and D18S464 (25.9% vs. 6.0%, Pc = 0.008) on chromosome 18p11.22 (Fig. 1B). An additional 7 markers (D2S367, D6S309, D9S273, D11S1320, D16S423, D17S938, and D18S68) were also found to be candidate susceptibility regions (Table 1). We found 17 additional regions in which there might be genes that confer resistance to AIH (Table 2). We used the National Center for Biotechnology Information Map Viewer, National Library of Medicine, National Institute of Health (http://www.ncbi.nlm.nih.gov/mapview/; Table 3) to identify several candidate genes within 500-kb perimeters of D11S902 and D18S464, as well as to investigate the areas other markers (data not shown).

Table 1. Statistically Significant Alleles Associated with AIH
ChromosomeMarkerSignificant AlleleAIH (n = 81), %Control (n = 80), %ORχ2PPc
  1. Abbreviations: OR, odds ratio; Pc, corrected P.

2p22.3D2S36731211.1017.697.930.00490.068
6p24.3D6S30931228.410.43.407.310.00690.082
9q21.11D9S27321519.84.55.257.650.00570.085
11p15.1D11S90215628.47.54.9210.470.00120.013
11q25D11S132026796.385.14.565.760.01640.082
16p13.3D16S42313553.131.32.487.060.00790.094
17p13.2D17S93825228.410.43.407.310.00690.062
18p11.22D18S46430425.96.05.5110.400.00130.008
18q21.33D18S682879.9015.617.000.00820.082
Table 2. Statistically Significant Alleles Associated with Resistance to AIH
ChromosomeMarkerSignificant AlleleAIH (n = 81), %Control (n = 80), %ORχ2PPc
  1. Abbreviations: OR, odds ratio; Pc, corrected P.

1p13.1D1S2521042.414.90.147.640.00570.051
1q42.2D1S28002172.414.90.147.640.00570.057
1q41D1S278517312.329.80.336.950.00840.084
3q28D3S15802258.625.40.287.560.00600.090
4p14D4S40529527.149.20.387.660.00560.062
4q21D4S29641279.825.40.326.270.01230.086
5p14D5S64131324.746.20.387.560.00600.072
5q35.1D5S40022917.340.30.319.700.00180.018
7q22.1D7S51517039.567.20.3211.240.00080.011
7q32.1D7S5301193.717.90.188.130.00440.052
8p21.2D8S17713592.514.90.147.640.00570.046
10p15.3D10S24911919.841.80.348.520.00350.039
10p13D10S16531266.220.90.257.100.00770.092
14q22.3D14S276235010.40.008.880.00290.032
15q12D15S10021207.425.40.249.020.00270.032
15q13.3D15S16518690.11000.007.000.00820.065
17p13.3D17S84926125.946.30.416.660.00990.049
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Figure 1. AIH susceptibility gene mapping by association analysis on (A) chromosome 11 and (B) chromosome 18. P values of association between control and patient groups are displayed with the location of microsatellite markers used for mapping.

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Table 3. Candidate Genes Within 500-kb Perimeter of D11S902 and D18S464
MarkerPosition (kbp)SymbolDescription
 16765.8-16992.5PLEKHA7Pleckstrin homology domain containing, family A member 7
 16950.0-16983.2LOC644889Similar to large subunit ribosomal protein L36a
 17030.4-17031.0OR7E14POlfactory receptor, family 7, subfamily E, member 14 pseudogene
 17052.5-17022.8PRS13Ribosomal protein S13
 17052.8-17052.9RNU14RNA, U14 small nucleolar
 17053.9-17054.0RNU14BRNA, U14B small nucleolar
 17067.9-17147.9PIK3C2APhosphoinositide-3-kinase, class 2, alpha polypeptide
 17205.8-17214.8LOC91561Similar to ribosomal protein S2; 40S ribosomal protein S2
 17254.9-17309.6NUCB2Nucleobindin 2
 17330.0-17355.4DKFZp686O24166Hypothetical protein DKFZp686O24166
 17363.4-17366.8KCNJ11Potassium inwardly rectifying channel, subfamily J, member 11
 17371.0-17455.0ABCC8ATP-binding cassette, subfamily C (CFTR/MRP), member 8
D11S90217403  
 17472.0-17522.5USH1CUsher syndrome 1C (autosomal recessive, severe)
 17525.5-17624.1OTOGOtogelin
 17697.7-17700.3MYOD1Myogenic differentiation 1
 17714.1-17750.8KCNC1Potassium voltage-gated channel, Shaw-related subfamily, member 1
 17766.1-17991.2DELGEFDeafness locus-associated putative guanine nucleotide exchange factor
 9324.0-9325.4LOC645604Hypothetical protein LOC645604
 9324.9-9392.4TWSG1Twisted gastrulation homolog 1 (Drosophila)
 9465.5-9528.1RALBP1ralA-binding protein 1
 9523.1-9604.6PPP4R1Protein phosphatase 4, regulatory subunit 1
 9668.2-9668.6LOC124242Similar to keratin, type I cytoskeletal 18 (cytokeratin 18; K18, CK 18)
 9698.3-9852.5RAB21RAB31, member RAS oncogene family
D18S4649819  
 9875.8-9878.2TXNDC2Thioredoxin domain containing 2 (spermatozoa)
 9904.0-9949.6VAPAVAMP (vesicle-associated membrane protein)–associated protein A, 33 kDa

Stratifying the patients according to whether they had HLA-DR4 allowed us to examine potential relationships between susceptibility or resistance loci with HLA. No specific markers were detected in HLA-DR4-negative patients, though D11S902 was found to be weakly associated with DR4-positive patients (Table 4).

Table 4. Comparison of Allele Frequencies in DR4-Positive and DR4-Negative Patients
Susceptible MarkerDR4(+) (n = 61), %DR4(−) (n = 20), %PResistance MarkerDR4(+) (n = 61), %DR4(−) (n = 20), %P
D2S36711.510.00.855D1S2523.30.00.412
D6S30929.525.00.698D1S278514.85.00.250
D9S27324.65.00.056D1S28003.30.00.412
D11S132092.795.00.724D3S15809.85.00.481
D11S90234.410.00.036D4S40526.230.00.742
D16S42349.265.00.214D4S296411.55.00.400
D17S93823.025.00.851D5S64119.740.00.067
D18S46426.225.00.913D5S40019.710.00.321
D18S689.810.00.983D7S51541.036.30.635
    D7S5301.610.00.086
    D8S17711.65.00.401
    D10S24922.025.00.497
    D10S16536.65.00.802
    D14S2760.00.0 
    D15S10026.610.00.610
    D15S16590.290.00.983
    D17S84926.225.00.913

Next, the patients were classified into 2 groups, the severe group or the mild to moderate group, according to disease severity (Table 5).We defined severe disease as a total bilirubin of more than 5 mg/dL and/or a prothrombin time (PT) of less than 40% and mild to moderate disease as a total bilirubin of 5 mg/dL or less and a PT of 40% or more. All patients with severe disease had had acute exaggerating-phase episodes, in which total bilirubin had transiently risen to more than 5.0 mg/DL and/or PT had fallen to less than 40%, that improved after treatment. All patients with mild to moderate disease had not had any episodes of jaundice or symptoms of hepatitis. We excluded 14 patients because information on PT was lacking. From this, we observed that patients with severe disease were more likely to be male (P = 0.001) and that ALT (P < 0.0001) and total bilirubin (P < 0.0001) were significantly higher in patients with severe disease than in patients with mild to moderate disease (Table 5). No differences in the frequency of having susceptibility or resistance markers were seen over the clinical courses of the patients (Table 6).

Table 5. Clinical Characteristics of Patients with Mild to Moderate and Severe AIH
FeaturesMild to Moderate (n = 34)Severe (n = 33)
  • Abbreviations: ALT, alanine aminotransferase; ALP, alkaline phosphatase; nl, normal range.

  • *

    P = 0.001;

  • P < 0.0001.

Mean age (years)55.7 (23-85)56.1 (34-79)
Women:men33:1*22:11*
DR4(+):DR4(−)25:922:11
ALT (nl: 7-45 U/L)429.4 (47-1800)925.6 (128-2159)
Bilirubin (nl: 0.3-1.2 mg/dL)1.49 (0.4-4.5)11.86 (0.3-30.4)
ALP (nl: 124-367 U/L)456.3 (169-1180)339.4 (131-1405)
IgG (nl: 800-2000 mg/dL)3275.5 (1639-7248)3114.2 (1134-5665)
Table 6. Comparison of Allele Frequencies Between Patients with Mild to Moderate and Patients with Severe AIH
Susceptible MarkerMild-Moderate (n = 34), %Severe (n = 33), %PResistance MarkerMild-Moderate (n = 34), %Severe (n = 33), %P
D2S3678.812.10.659D1S2522.90.00.321
D6S30923.533.30.373D1S27858.818.10.261
D9S27314.718.20.701D1S28005.90.00.157
D11S132097.197.00.983D3S15808.812.10.659
D11S90229.430.30.937D4S40532.421.20.304
D16S42350.054.50.710D4S29645.99.00.617
D17S93817.630.30.225D5S64138.218.10.069
D18S46426.530.30.728D5S40011.818.10.461
D18S682.915.20.080D7S51538.245.50.549
    D7S5305.93.00.573
    D8S17710.06.10.145
    D10S24920.621.20.950
    D10S16535.99.00.617
    D14S2760.00.0 
    D15S10028.83.00.317
    D15S16585.390.90.479
    D17S84932.418.10.183

Discussion

  1. Top of page
  2. Abstract
  3. Patients and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

Although this is the first case-control association study to search for candidate genes of AIH pathogenesis throughout the whole genome, there have been several previous genomewide studies—of rheumatoid arthritis (RA),22, 23 autoimmune thyroid disease,24 multiple sclerosis,25 and systemic lupus erythematosus (SLE)26, 27 and of common diseases such as hypertension28 and type 1 diabetes29—that attest to the effectiveness of microsatellite analysis. Most of these studies took multilocus, nonparametric approaches using affected-sibling pairs to scan novel disease susceptibility loci. In AIH, however, it is very rare for 2 or more family members to be affected—of the 130 AIH patients we have encountered in our hospital and in several regional hospitals in Nagano prefecture over the last 3 decades, only 2 patients had more than 2 affected members affected with AIH in their families. As such, there have been no reports of wide-scale searches for family clustering of AIH. In contrast, familial occurrence of primary biliary cirrhosis (PBC) is relatively high, up to 6.4%,30 though the reason for the scarcity of multiplex families in AIH compared with other autoimmune diseases such as PBC, RA, and SLE is still unclear. We therefore performed a case-control association study, which did not require a large number of sib pairs for analysis. Such studies are performed by genotyping a set of anonymous markers in independent cohorts of affected and healthy individuals.

There are 2 common ways to investigate genetic associations of autoimmune diseases: the candidate gene approach, which is hypothesis driven by knowledge of an immunological process, and the genomewide association approach, in which the entire genome is searched at once in an unbiased fashion. The former approach is performed on gene polymorphisms of tumor necrosis factor α and cytotoxic T-lymphocyte antigen 4 in AIH patients.15, 16 However, the main bottleneck of the microsatellite approach is sufficient marker density. For instance, Tamiya et al. used 27,039 microsatellite markers to perform their whole-genome association study of RA, using pooled DNA samples to reduce the costs and labor.23 As we used only 400 microsatellite markers, each an average of 10.8 cM apart, we must concede there may be genes undetected by our study. This current genomewide scan in Japanese AIH patients found at least 9 candidate regions other than the HLA class II region to be associated with type 1 AIH susceptibility and up to 17 regions associated with resistance to AIH. We could not find any common loci among the susceptibility and resistance marker regions between our AIH patients and previously reported Japanese patients with RA or thyroid diseases (data not shown).22–24 On chromosome 1, loci that specify ANA reactivity in SLE in humans and mice have been found.27, 31 ANA is increased in type 1 AIH, making this, and other likely susceptibility loci worth investigating. Although our current data did not find any significant linkages on chromosome 1, this region may be worth further analysis using narrow-interval microsatellite markers, as we did in the HLA region,19 in order to test for any relationships.

Because the average length of linkage disequilibrium between disease-susceptible genes and nearby microsatellite alleles was more than 100 kb,23 we therefore searched several candidate genes within a 500-kb perimeter of D11S902 and D18S464 (Table 3) and other markers (data not shown). However, none of the genes described here has been reported to be associated with autoimmune diseases. Nonetheless, several candidate genes found in this study are involved in interesting cell functions. For example, the protein encoded by phosphoinositide-3-kinase, class 2, alpha polypeptide (PIK3C2A) belongs to the phosphoinositide 3-kinase (PI3K) family. PI3 kinases play roles in signaling pathways involved in cell proliferation, oncogenic transformation, cell survival, cell migration, and intracellular protein trafficking. This protein contains a lipid kinase catalytic domain as well as a C-terminal C2 domain, a characteristic of class II PI3 kinases. C2 domains act as calcium-dependent phospholipid-binding motifs that mediate translocation of proteins to membranes and may also mediate protein–protein interactions.32 The protein encoded by KCNJ11 is an integral membrane protein and inward-rectifier-type potassium channel. In addition, ABCC8 is a member of the superfamily of ATP-binding cassette transporters. Mutations in these genes are causes of familial persistent hyperinsulinemic hypoglycemia of infancy.33 The KCNJ11 E23K variant was reported to be associated with type 2 diabetes.34 The protein encoded by receptor-interacting protein kinase; RIP1 (RALBP1) and NF-kappaB activation is associated with tumor necrosis factor receptor 1 signaling, which initiates several cellular responses including apoptosis.35 Last, the protein encoded by VAPA is a type IV membrane protein. It is present in the plasma membrane and intracellular vesicles. This protein may function in vesicle trafficking, membrane fusion, protein complex assembly, and cell motility.36 Recently, gene expression profiles of the liver tissues of AIH patients were shown using cDNA microarrays containing 1,080 cDNA clones.37 Several genes were up-regulated in the liver tissues of AIH patients compared with those with PBC, chronic hepatitis C, and nonalcoholic steatohepatitis. However, no such genes were found in our current study, except for MHC class II DR.

Interestingly, D11S902 was found to be weakly associated with DR4-positive patients, whereas D18S464 was associated with both DR4+ and DR4−. This potentially important finding needs confirmation in studies with larger numbers of cases.

To find genes associated with mild to moderate or severe disease, we divided the patients into 2 groups and compared our findings. However, there were no specific regions associated with severity of AIH, so we cannot yet clarify the regions that regulate the different clinical course of AIH.

In conclusion, we were able to use genomewide microsatellite analysis as an effective strategy for identifying positive associations between microsatellite markers and AIH. Our current study was preliminary in nature because of the small number of test cases and controls used and the limited number of markers. Further genomewide studies are needed in a second cohort or in a larger test group with more markers, in addition to analysis of the single-nucleotide polymorphisms of the positive marker sites found in our study. Future studies are needed to analyze the relationship between gene polymorphisms and the expression and functions of these gene products, as well as research on any therapy-effectiveness- or disease-severity-related genes. These results may provide the specific tools necessary for therapeutic intervention of AIH.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Patients and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

The authors thank Toyo Amaki, Yuki Akahane, and Asami Yamazaki for their technical assistance.

References

  1. Top of page
  2. Abstract
  3. Patients and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References