We recently acquired a MiSeq sequencer which has allowed us to begin developing an NGS method for KIR genotyping. Presently, we genotype for presence/absence of KIR genes and perform Sanger sequencing for allelic typing of select loci. KIR genotyping is challenging because the locus is characterized by unusually high diversity in the numbers of genes and their alleles. The KIR locus varies in size from 100 to 350 kb due to duplicated segments, large deletions and gene fusions. KIR genes can be combined in numerous ways resulting in haplotypes having between 4 and 20 KIR genes, each of which is polymorphic. Thus, it has been very difficult to develop a reliable high-resolution typing method. Dr. Paul Norman at the University of Colorado, Denver has successfully developed a sequencing and bioinformatics method that determines complete KIR genomic diversity as well as HLA class I and II genotypes using the MiSeq platform. The method involves an integrated exome capture/NGS system that can be easily automated. Implementation of this method allows us to collect comprehensive information on KIR diversity, and to genotype various cohorts to test for disease associations in a much more refined manner. IGHG. Constant regions of immunoglobulin G (IgG) subclasses, which include CH1, hinge, CH2 and CH3 domains, are encoded by IGHG genes within the IGHC complex on human chromosome 14q31.32-33. Four genes, IGHG1, IGHG2, IGHG3, and IGHG4, correspond to four subclasses of IgG. The Fc portion of the constant region (CH2+CH3 domains) mediates antibody stability and its effector functions, such as cytotoxicity, phagocytosis, and complement activation. Therefore, polymorphism in this region may directly affect immune responses. For example, the naturally occurring change from arginine to histidine at position 435 in IgG3 causes a dramatic increase in the antibody half-life. Variation in the constant regions has been characterized primarily by serological methods (Gm-Am allotypes) with limited information on nucleotide diversity. This region of the genome is not well covered in genome-wide studies due to high homology between the IGHG genes. We have developed a genotyping method based on Sanger sequencing, which covers all exons of IGHG1, IGHG2, and IGHG3 genes. In addition, we can distinguish hinge exon copy number, which is variable for IGHG3. The method has been applied to several population groups, including healthy whites, HIV infected whites and blacks from the US, as well as healthy blacks from South Africa. We have observed striking differences in frequency distributions among these groups. We will continue to improve our genotyping method to make it more high-throughput. In addition, we plan to develop methods for detecting copy number variation, which is rare for IGHG1-3, but appears common for IGHG4 (this is why we were not able to develop Sanger genotyping for this gene). Given the involvement of the locus in immune responses and clinical use of Fc-fusion proteins and monoclonal antibodies, the variation in IGHG genes and its functional consequences requires more in-depth investigation. Determine tapasin dependence of HLA alleles. HLA is loaded with peptide by a multiprotein complex within the ER. One key protein of this complex is tapasin, which mediates the peptide editing process. A limited set of HLA allotypes have been shown to exhibit differential dependence on tapasin to form a stable peptide complex. Tapasin dependence is likely to influence the peptide repertoire and HLA-peptide complex stability, and thus, the quality of immune responses. The exact molecular features of HLA class I alleles that define levels of tapasin dependence are unknown and there is no prediction tool that can characterize tapasin dependence based on the HLA class I structure. Just one amino acid difference can dramatically change tapasin dependency, as in the case of HLA-B*4402 and HLA-B*4405, which differ only at position 116. We experimentally determined tapasin dependence for all HLA class I alleles with frequencies greater than 0.5% in either white or black US populations, including 27 HLA-A, 42 HLA-B and 24 HLA-C alleles. We expressed each allotype with an N-terminal FLAG-tag in the tapasin negative lymphoblastoid cell line 721.220 using lentiviral constructs. In addition, the same set was expressed in 721.220 cells reconstituted with tapasin. Expression levels were measured by flow cytometry using anti-FLAG antibody. The ratio of expression levels in tapasin positive vs tapasin negative cells was defined as the tapasin dependency value. We are now able to assign a tapasin dependency score to any individual based on his/her HLA class I genotype. This score can be applied to various tests for association with human disease outcomes. Variation within Tapasin and other genes involved in peptide processing/loading. Polymorphism within TAPBP and other genes involved in peptide processing/loading may influence antigen presentation by HLA class I molecules. The TAPBP gene contains a single polymorphic site encoding either arginine or threonine at position 240 of the mature protein (rs2071888G/C). The variants are almost equally represented among whites, whereas arginine is more common in blacks (72% allelic frequency). Dr. Buyong Ma in the CIP performed molecular dynamic simulations and demonstrated that the Arg240 form facilitates interactions with ERp57 (another component of the peptide loading complex) and HLA class I better than does the Thr240 form. We are planning to genotype rs2071888 in various cohorts to test its influence on human diseases and its potential interaction with HLA class I tapasin dependence scores. We have found that variation in the UTRs of TAPBP marks differential expression levels of the gene as measured by qPCR in people of African ancestry, whereas these positions are fixed in Europeans. The 5'UTR SNP rs111686073C/G and the 3'UTR SNP rs73410010A/G were identified to significantly and independently associate with mRNA levels of TAPBP in two South African cohorts (p0.001 for each SNP). The 5'UTR SNP has been verified as a direct modulator of tapasin expression via luciferase assays in which the promoter region of tapasin was cloned into a luciferase expression vector. The G variant induced greater expression of luciferase relative to the C variant. Elecrophoretic mobility shift assays (EMSAs) using HeLa extract indicate that AP-2a, a transcription factor predicted to have a binding site overlapping the SNP, binds both the G and C variants of the 5'UTR SNP, but the G variant appears to bind AP-2a more strongly than does the C variant. Luciferase assays in which the tapasin promoter constructs are cotransfected with an AP-2a expression plasmid are planned to verify the EMSA results.
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