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Primer design for HLA locus

Primer design for HLA locus


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i have designed primers for HLA locus DPA1(exon 2 region) based on Real-Time PCR (qPCR) Primer Design guidelines. primer will start from intron regions to cover full exonic region.

F-CAGCAACAGAGAATGTCAGC

R-CCCTGAAGCAGCAATTGATG

to check for amplification of only a single region i have used in silico PCR UCSC but it shows multiple region from chr6.

pls kindly help me with solving this.

thanks


When designing rtPCR primers always check the extensive and well validated taqman library for the ABI system, the primers for the region you want are already well characterized:

http://www.lifetechnologies.com/order/genome-database/details/gene-expression/Hs01072897_m1#more-information-section


Primer design for HLA locus - Biology

This is the first report on preimplantation HLA sequencing using the next generation sequencing technique.

High-resolution typing of HLA-A, -B, -C, -DRB1, and -DQB1 was done in preimplantation single cells.

Low-resolution typing was achieved in 92.2% of all alleles of embryos.

Conclusive high-resolution typing was attained in 88.9% of alleles.

Amplification efficiency of 93.3% with an allele drop-out rate of 22.2% was found.


Background

The major histocompatibility complex (MHC) region on the short arm of chromosome 6 is one of the most complex regions in the human genome with extreme levels of polymorphism and linkage disequilibrium [1–3]. With a span of about 4 Mb, the MHC comprises many hundreds of genes [4]. Of these, the human leukocyte antigen (HLA) genes are the most prominently studied. The HLA genes encode cell-surface proteins responsible for antigen peptide presentation in a cell-mediated immune response. Inherited DNA sequence variation within these genes is strongly associated with autoimmune and infectious diseases as well as severe adverse drug reactions [5–8]. Clinically, HLA sequence information is also widely used for matching donor and recipient in transplantation on the basis that more similar alleles will reduce the risk of rejection [9].

To date, 2048 unique 4-digit HLA alleles have been described in the IMGT/HLA database at class I, and 751 at class II [10]. Over many years, best practices in HLA typing have traditionally been disseminated by the participants of the International Histocompatibility Workshop. Established methods include sequence specific oligonucleotide (SSO) hybridization and, more recently, capillary sequencing (Sanger method). SSO hybridization uses oligonucleotide probes to detect the presence (or absence) of polymorphisms specific to each probe[11]. The Sanger method uses chain-termination fluorescence for DNA base-pair detection and sequencing.

Although these methods have proven effective for HLA typing, they remain labor-intensive, time-consuming and expensive. Further, one specific disadvantage of Sanger sequencing is that it does not generate two separate, haploid sequences, making it in some cases challenging to resolve the individual HLA haplotype sequences in a diploid pair of chromosome 6. The advent of next-generation sequencing technologies motivated us to develop an efficient protocol for genotyping of the classical HLA genes of class I.

Our strategy for HLA typing utilizes the 454 GS FLX Titanium sequencing platform (Roche) and allows the use of sequence tags, or barcodes, to label each DNA sample at either the amplicon-preparation stage (using PCR primers tailed with a molecular barcode) or during library construction (using barcoded adaptors) (Figure 1, Additional File 1).

HLA Class I amplification strategy. PCR amplification of the polymorphic exons 2 and 3 in the class I HLA loci was performed (using primers within introns surrounding each exon) prior to 454 sequencing.

The first method, termed "library construction-based barcoding," involves the addition of a molecular barcode to the standard 454 "A" adapter, which is ligated to double-stranded exon-specific amplicons during library construction. The exon-specific amplicons are pooled by sample (six products per pool) after PCR, and up to 96 samples are pooled after barcoded adapter ligation (Additional File 1).

The second method, termed "PCR-based barcoding" involves the addition of a barcode to the forward and reverse exon-specific PCR primers. The same unique barcode is added to all 6 exon-specific primer pairs (exons 2 and 3 of HLA-A, -B and -C) for a given sample. 95 different barcoded primer sets were designed in total, leaving an empty 96 th well as a positional key (Additional File 1). Post-PCR all amplicons from all 95 samples are pooled and the pool proceeds through standard library construction with the addition of a non-barcoded adapter.

Both methods facilitate sample multiplexing prior to emulsion PCR and sequencing, which dramatically reduces the overall cost per individual for an all-in cost of less than $40 per sample for typing of Class I genes HLA-A, -B and -C. Using these methods a single technician can process up to 96 samples at once, creating a sequence-ready library in under three days. Because we are using the FLX Titanium chemistry, sequence reads extend across entire exons of the HLA genes (

In parallel, we have developed an HLA calling algorithm to process sequence reads, from the now-standard SAM/BAM format, and to infer classical types for a given DNA sample (Figure 2)[12]. Recognizing that the improvements in next-generation sequencing technologies are rapid, we designed the HLA caller to be an integral part of the Genome Analysis Toolkit (GATK), a data processing tool for recalibration, quality control, and variant calling of next-generation sequence data [13].

Schematic of the HLA Caller Algorithm. The HLA calling algorithm determines the most likely pair of HLA types at each locus by systematically evaluating all possible pairs of 4-digit HLA types. A) The genotyping algorithm within the GATK calculates the probability of observing particular genotypes in the data given a pair of HLA alleles. Probabilities were combined multiplicatively across base positions to obtain the cumulative probability based on genotypes. B) A binomial distribution function was used to calculate of probability of observing particular haplotypes in the data given a pair of HLA alleles. Probabilities were combined multiplicatively across pairs of polymorphic positions to obtain the cumulative probability based phase information. C) Prior probabilities for specific allele pairs were calculated as the product of allele frequencies in a specific population. Probabilities based on genotypes, phase information, and allele frequencies were combined multiplicatively to obtain the posterior probability for each HLA allele pair.

The HLA calling algorithm determines the most likely pair of HLA types at each locus by systematically evaluating all possible pairs of 4-digit HLA types. We use three key components to calculate the posterior probability for each HLA allele pair. First, we compare genotypes for each allele pair to the genotypes determined by the Genome Analysis Toolkit (GATK) based on sequence data. Second, we check the allelic phase of each HLA allele pair for consistency with the sequence data. Specifically, we calculate the binomial probability that the phase orientation for a specific HLA allele pair is consistent with the sequence data at a pair of adjacent polymorphic sites, and aggregate these probabilities across all pairs of polymorphic sites. Third, we use information about the expected allele frequency to determine the prior probability of observing each pair of HLA alleles in the population (if the ancestry is known). We then multiply the probabilities calculated from base genotypes, allelic phase information, and allele frequencies, rescale (to ensure all posteriors sum to 1), and output the posterior probability for each HLA allele pair. The pair with the highest posterior probability corresponds to the best-guess genotype for that DNA sample.

In this study, we benchmark our protocol on DNA samples used in the International HapMap Project with known HLA types: 270 samples for library construction-based barcoding and 95 samples for the barcoded PCR method. We limited the sample number in our validation test of the barcoded PCR method to 95 due to the similarities to the already tested library-construction based method, and the number of barcoded primers arrayed per plate (i.e. 95 barcodes plus 1 empty well). We demonstrate that we can generate reliable HLA calls, and in some cases improve upon the existing calls, but also highlight instances of problematic alleles where calls are less robust in our current protocol. Overall, our protocol offers comparable data quality but outperforms traditional Sanger sequencing in terms of cost-effectiveness and throughput.


INTRODUCTION

The human leukocyte antigen (HLA) complex is located on chromosome 6p21 and comprises dozens of genes important for immune function ( 1, 2). Of key importance for determining the antigenic specificities of the adaptive immune response, one gene family encoded in the HLA complex comprises the ‘classical’ HLA genes ( 3, 4). Accurate assignment of individual HLA alleles at these loci is essential within several disciplines, e.g. clinical transplantation medicine ( 5, 6), inflammatory disease susceptibility research ( 7–9), tumor immunology ( 10–12) and evolutionary biology ( 13).

Up to recently, HLA typing has mainly been carried out using sequence-specific oligonucleotide probes (SSOP), sequence-specific primers (SSP) and sequence-based typing (SBT) using Sanger sequencing of exons 2 and 3 in class I HLA genes (HLA-A/B/C) or exon 2 in class II HLA genes (HLA-DR/DQ/DP). High-resolution (i.e. covering all coding variation and by nomenclature consensus at least the first and second field, classically ‘four digits’) typing by means of SSOP and/or SSP usually is an iterative approach that starts with low-resolution typing (i.e. first field, classically two digits), followed by additional characterizations to the extent needed by the application. This process is time consuming and incompatible with any high-throughput research context. Sanger sequencing-based typing has the capability to perform high-resolution typing, but demands polymerase chain reaction (PCR) amplification of individual exons at each locus and often several separate sequencing reactions for each amplicon. Moreover, the results usually contain a large number of cis/trans ambiguities, i.e. heterozygous positions cannot be adequately phased and all alleles matching the sequencing outcome are thus listed as possible genotype combinations. A PCR and Sanger sequencing based method that generates unambiguous HLA typing for four HLA loci was published by Voorter et al. ( 14). Even though it is automatable and delivers reliable results, practical challenges of all PCR-based approaches remain.

In recent years alternative SBT methods using next-generation sequencing (NGS) technology have emerged ( 15–20). Most of these NGS methods rely on traditional PCR of the target regions followed by massive parallel sequencing of the amplicons. The advantage of NGS is the single strand sequencing nature combined with the increased amount of sequencing reads per sample and locus. This allows for a highly confident allele-determination, hereafter referred to as calling. Because of the single-strand derived NGS reads, the new typing approaches often allow for intragenic phasing between polymorphic nucleotides. However, the limitations of the initial PCR remain for these NGS approaches. Amplicon-based methods are laborious, require extensive PCR primer optimization steps and often demand a manual curation of results.

For targeted NGS, array-based ( 21) and bead-based ( 22) enrichment techniques are well established and widely used. The advantages of these oligonucleotide-based enrichments are their ease of use as no extra instrumentation and PCR optimizations are necessary, plus the flexibility to enrich genomic targets of different sizes and complexity. Today whole-exome enrichments are widely used by NGS platforms and different groups worldwide. Major et al. ( 23) published an HLA in silico typing approach where whole exome and whole genome NGS data from the 1000 genomes project was employed ( 24). As exemplified by this application, traditional exome enrichment for HLA genotyping may however result in allelic dropout, because the target baits are designed based on the standard human reference genome sequence not accounting for the allelic variation at the HLA loci. The complexity of the classical HLA loci challenges the development of specific diagnostic-grade enrichment kits. Nevertheless, the collection of known HLA allele sequences is large, captures probably all common alleles and is publicly available ( 25). Here we present an in-solution targeted enrichment approach for NGS-based HLA genotyping without PCR-based amplification. Our approach consists of a complete turnaround for HLA sequencing including a user-friendly software tool for assigning HLA alleles.


IPD-IMGT/HLA

The Probe and Primer Search Tool allows you to easily search the known coding sequence of any allele, for a particular nucleotide motif. The tool can be used to provide either an online output or a tab separated text file suitable for loading into Excel for use as a hit table or similar.

To use the Probe and Primer Search Tool:

  • Simply select the locus and database release required
  • Enter the nucleotide sequence or your probe or primer in the box provided. You can enter multiple sequences as long as each sequence is on a separate line.
  • To further aid searches approved IUB codes are also permitted within the search string. However due to the number of permutations generated by entering Ns in a sequence, you will be limited to 5 Ns per sequence.
  • The search tool only searches the known CDS sequences for each allele, if a probe or primer contains intronic, UTR or pseudoexon sequence, the tool will fail to match this.
  • The tool will also not span insertion or deletions with the search string.
  • All libraries prior to release 3.0.0 use the pre-2010 nomenclarture designations.

The following IUB Codes can be used in the query sequence. However it should be noted the more ambiguous codes used the slower the search will run.

IUB Code Nucleic Acid IUB Code Nucleic Acid
A A Y C or T
C C K G or T
G G V A or C or G
T T H A or C or T
M A or C D A or G or T
R A or G B C or G or T
W A or T N A or C or G or T
S C or G

Further Information

For more information about the database, queries (including website) or to subscribe to the IPD mailing lists please contact IPD Support.


IPD-IMGT/HLA

The IPD-IMGT/HLA Database provides a specialist database for sequences of the human major histocompatibility complex (MHC) and includes the official sequences named by the WHO Nomenclature Committee For Factors of the HLA System. The IPD-IMGT/HLA Database is part of the international ImMunoGeneTics project (IMGT).

The database uses the 2010 naming convention for HLA alleles in all tools herein. To aid in the adoption of the new nomenclature, all search tools can be used with both the current and pre-2010 allele designations. The pre-2010 nomenclature designations are only used where older reports or outputs have been made available for download.

Latest Developments

Recent developments of the IPD database include

Latest Publications

  • Robinson J, Barker DJ, Georgiou X, Cooper MA, Flicek P, Marsh SGE. IPD-IMGT/HLA Database. Nucleic Acids Research (2020) 48:D948-55
    Full Text PDF available from Nucleic Acids Research
  • For further IPD publications, please see our citations page.

Funding and Support

Disclaimer

Where discrepancies have arisen between reported sequences and those stored in the databases, the original authors have been contacted where possible, and necessary amendments to published sequences have been incorporated. Future sequencing may identify errors and the Nomenclature Committees would welcome any evidence that helps to maintain the accuracy of the database. We therefore make no warranties regarding the correctness of the data, and disclaim liability for damages resulting from its use. We cannot provide unrestricted permission regarding the use of the data, as some data may be covered by patents or other rights. Any medical or genetic information is provided for research, educational and informational purposes only. It is not in any way intended to be used as a substitute for professional medical advice, diagnosis, treatment or care.

Further Information

For more information about the database, queries (including website) or to subscribe to the IPD mailing lists please contact IPD Support.


Contents

The proteins encoded by HLAs are those on the outer part of body cells that are (in effect) unique to that person. The immune system uses the HLAs to differentiate self cells and non-self cells. Any cell displaying that person's HLA type belongs to that person and, therefore, is not an invader.

In infectious disease Edit

When a foreign pathogen enters the body, specific cells called antigen-presenting cells (APCs) engulf the pathogen through a process called phagocytosis. Proteins from the pathogen are digested into small pieces (peptides) and loaded onto HLA antigens (to be specific, MHC class II). They are then displayed by the antigen-presenting cells to CD4+ helper T cells, [7] which then produce a variety of effects and cell to cell interactions to eliminate the pathogen.

Through a similar process, proteins (both native and foreign, such as the proteins of virus) produced inside most cells are displayed on HLAs (to be specific, MHC class I) on the cell surface. Infected cells can be recognized and destroyed by CD8+ T cells. [7]

The image off to the side shows a piece of a poisonous bacterial protein (SEI peptide) bound within the binding cleft portion of the HLA-DR1 molecule. In the illustration far below, a different view, one can see an entire DQ with a bound peptide in a similar cleft, as viewed from the side. Disease-related peptides fit into these "slots" much like a hand fits into a glove. When bound, peptides are presented to T cells. T cells require presentation via MHC molecules to recognize foreign antigens — a requirement known as MHC restriction. T cells have receptors that are similar to B cell receptors, and each T cell recognizes only a few MHC class II-peptide combinations. Once a T cell recognizes a peptide within an MHC class II molecule, it can stimulate B-cells that also recognize the same molecule in their B cell receptors. Thus, T cells help B cells make antibodies to the same foreign antigens. Each HLA can bind many peptides, and each person has 3 HLA types and can have 4 isoforms of DP, 4 isoforms of DQ and 4 Isoforms of DR (2 of DRB1, and 2 of DRB3, DRB4, or DRB5) for a total of 12 isoforms. In such heterozygotes, it is difficult for disease-related proteins to escape detection.

In graft rejection Edit

Any cell displaying some other HLA type is "non-self" and is seen as an invader by the body's immune system, resulting in the rejection of the tissue bearing those cells. This is particularly important in the case of transplanted tissue, because it could lead to transplant rejection. Because of the importance of HLA in transplantation, the HLA loci are some of the most frequently typed by serology and PCR. It has been shown that high resolution HLA typing (HLA-A, HLA-B, HLA-C, HLA-DRB1, HLA-DQB1 and HLA-DPB1) may be relevant in transplantation to identify a full match, even when the donor is related. [8]

HLA and autoimmune diseases
HLA allele Diseases with increased risk Relative risk
HLA-B27 Ankylosing spondylitis 12 [9]
Reactive arthritis 14 [9]
Acute anterior uveitis 15 [9]
HLA-B47 21-hydroxylase deficiency 15 [9]
HLA-DR2 Systemic lupus erythematosus 2 to 3 [10]
HLA-DR3 Autoimmune hepatitis 14 [9]
Primary Sjögren syndrome 10 [9]
Diabetes mellitus type 1 5 [9]
Systemic lupus erythematosus 2 to 3 [10]
HLA-DR4 Rheumatoid arthritis 4 [9]
Diabetes mellitus type 1 6 [9]
HLA-DR3 and
-DR4 combined
Diabetes mellitus type 1 15 [9]
HLA-DQ2 and HLA-DQ8 Coeliac disease 7 [11]

In autoimmunity Edit

HLA types are inherited, and some of them are connected with autoimmune disorders and other diseases. People with certain HLA antigens are more likely to develop certain autoimmune diseases, such as type I diabetes, ankylosing spondylitis, rheumatoid arthritis, [12] celiac disease, SLE (systemic lupus erythematosus), myasthenia gravis, inclusion body myositis, Sjögren syndrome, and narcolepsy. [13] HLA typing has led to some improvement and acceleration in the diagnosis of celiac disease and type 1 diabetes however, for DQ2 typing to be useful, it requires either high-resolution B1*typing (resolving *02:01 from *02:02), DQA1*typing, or DR serotyping. Current serotyping can resolve, in one step, DQ8. HLA typing in autoimmunity is being increasingly used as a tool in diagnosis. In celiac disease, it is the only effective means of discriminating between first-degree relatives that are at risk from those that are not at risk, prior to the appearance of sometimes-irreversible symptoms such as allergies and secondary autoimmune disease.

In cancer Edit

Some HLA-mediated diseases are directly involved in the promotion of cancer. Gluten-sensitive enteropathy is associated with increased prevalence of enteropathy-associated T-cell lymphoma, and DR3-DQ2 homozygotes are within the highest risk group, with close to 80% of gluten-sensitive enteropathy-associated T-cell lymphoma cases. More often, however, HLA molecules play a protective role, recognizing increases in antigens that are not tolerated because of low levels in the normal state. Abnormal cells might be targeted for apoptosis, which is thought to mediate many cancers before diagnosis.

In mate selection Edit

There is evidence for non-random mate choice with respect to certain genetic characteristics. [14] [15] This has led to a field known as Genetic matchmaking.

MHC class I proteins form a functional receptor on most nucleated cells of the body. [16]

There are 3 major and 3 minor MHC class I genes in HLA.

Minor genes are HLA-E, HLA-F and HLA-G. β2-microglobulin binds with major and minor gene subunits to produce a heterodimer

There are 3 major and 2 minor MHC class II proteins encoded by the HLA. The genes of the class II combine to form heterodimeric (αβ) protein receptors that are typically expressed on the surface of antigen-presenting cells.

Major MHC class II proteins only occur on antigen-presenting cells, B cells, and T cells. [16]

    • α-chain encoded by HLA-DPA1 locus
    • β-chain encoded by HLA-DPB1 locus
    • α-chain encoded by HLA-DQA1 locus
    • β-chain encoded by HLA-DQB1 locus
    • α-chain encoded by HLA-DRA locus
    • 4 β-chains (only 3 possible per person), encoded by HLA-DRB1, DRB3, DRB4, DRB5 loci

    The other MHC class II proteins, DM and DO, are used in the internal processing of antigens, loading the antigenic peptides generated from pathogens onto the HLA molecules of antigen-presenting cell.

    Nomenclature Edit

    Modern HLA alleles are typically noted with a variety of levels of detail. Most designations begin with HLA- and the locus name, then * and some (even) number of digits specifying the allele. The first two digits specify a group of alleles, also known as supertypes. Older typing methodologies often could not completely distinguish alleles and so stopped at this level. The third through fourth digits specify a nonsynonymous allele. Digits five through six denote any synonymous mutations within the coding frame of the gene. The seventh and eighth digits distinguish mutations outside the coding region. Letters such as L, N, Q, or S may follow an allele's designation to specify an expression level or other non-genomic data known about it. Thus, a completely described allele may be up to 9 digits long, not including the HLA-prefix and locus notation. [17]

    Variability Edit

    MHC loci are some of the most genetically variable coding loci in mammals, and the human HLA loci are no exceptions. Despite the fact that the human population went through a constriction several times during its history that was capable of fixing many loci, the HLA loci appear to have survived such a constriction with a great deal of variation. [18] Of the 9 loci mentioned above, most retained a dozen or more allele-groups for each locus, far more preserved variation than the vast majority of human loci. This is consistent with a heterozygous or balancing selection coefficient for these loci. In addition, some HLA loci are among the fastest-evolving coding regions in the human genome. One mechanism of diversification has been noted in the study of Amazonian tribes of South America that appear to have undergone intense gene conversion between variable alleles and loci within each HLA gene class. [19] Less frequently, longer-range productive recombinations through HLA genes have been noted producing chimeric genes.

    Six loci have over 100 alleles that have been detected in the human population. Of these, the most variable are HLA B and HLA DRB1. As of 2012, the number of alleles that have been determined are listed in the table below. To interpret this table, it is necessary to consider that an allele is a variant of the nucleotide (DNA) sequence at a locus, such that each allele differs from all other alleles in at least one (single nucleotide polymorphism, SNP) position. Most of these changes result in a change in the amino acid sequences that result in slight to major functional differences in the protein.

    There are issues that limit this variation. Certain alleles like DQA1*05:01 and DQA1*05:05 encode proteins with identically processed products. Other alleles like DQB1*0201 and DQB1*0202 produce proteins that are functionally similar. For class II (DR, DP and DQ), amino acid variants within the receptor's peptide-binding cleft tend to produce molecules with different binding capability.

    However, the gene frequencies of the most common alleles (>5%) of HLA-A, -B, -C and HLA-DPA1, -DPB1, -DQA1, -DQB1, and -DRB1 from South America have been reported from the typing and sequencing carried out in genetic diversity studies and cases and controls. [20] In addition, information on the allele frequencies of HLA-I and HLA-II genes for the European population has been compiled. [21] [22] In both cases the distribution of allele frequencies reveals a regional variation related with the history of the populations.

    Tables of variant alleles Edit

    Number of variant alleles at class I loci according to the IMGT-HLA database, last updated October 2018:

    MHC class I
    locus # [23] [24]
    Major Antigens
    HLA A 4,340
    HLA B 5,212
    HLA C 3,930
    Minor Antigens
    HLA E 27
    HLA F 31
    HLA G 61

    Number of variant alleles at class II loci (DM, DO, DP, DQ, and DR):

    MHC class II
    HLA -A1 -B1 -B3 to -B5 1 Theor. possible
    locus # [24] # [24] # [24] combinations
    DM- 7 13 91
    DO- 12 13 156
    DP- 67 1,014 16,036
    DQ- 95 1,257 34,528
    DR- 7 2,593 312 11,431
    1 DRB3, DRB4, DRB5 have variable presence in humans

    Sequence feature variant type (SFVT) Edit

    The large extent of variability in HLA genes poses significant challenges in investigating the role of HLA genetic variations in diseases. Disease association studies typically treat each HLA allele as a single complete unit, which does not illuminate the parts of the molecule associated with disease. Karp D. R. et al. describes a novel sequence feature variant type (SFVT) approach for HLA genetic analysis that categorizes HLA proteins into biologically relevant smaller sequence features (SFs), and their variant types (VTs). [25] Sequence features are combinations of amino acid sites defined based on structural information (e.g., beta-sheet 1), functional information (e.g., peptide antigen-binding), and polymorphism. These sequence features can be overlapping and continuous or discontinuous in the linear sequence. Variant types for each sequence feature are defined based upon all known polymorphisms in the HLA locus being described. SFVT categorization of HLA is applied in genetic association analysis so that the effects and roles of the epitopes shared by several HLA alleles can be identified. Sequence features and their variant types have been described for all classical HLA proteins the international repository of HLA SFVTs will be maintained at IMGT/HLA database. [26] A tool to convert HLA alleles into their component SFVTs can be found on the Immunology Database and Analysis Portal (ImmPort) website. [27]

    Common, well-documented and rare alleles Edit

    Although the number of individual HLA alleles that have been identified is large, approximately 40% of these alleles appear to be unique, having only been identified in single individuals. [28] [29] Roughly a third of alleles have been reported more than three times in unrelated individuals. [29] [30] Because of this variation in the rate at which of individual HLA alleles are detected, attempts have been made to categorize alleles at each expressed HLA locus in terms of their prevalence. The result is a catalog of common and well-documented (CWD) HLA alleles, [30] [31] and a catalogue of rare and very rare HLA alleles. [28] [29]

    Common HLA alleles are defined as having been observed with a frequency of at least 0.001 in reference populations of at least 1500 individuals. [30] [31] Well-documented HLA alleles were originally defined as having been reported at least three times in unrelated individuals, [30] and are now defined as having been detected at least five times in unrelated individuals via the application of a sequence-based typing (SBT) method, or at least three times via a SBT method and in a specific haplotype in unrelated individuals. [31] Rare alleles are defined as those that have been reported one to four times, and very rare alleles as those reported only once. [28] [29]

    Table of HLA alleles in each prevalence category Edit

    While the current CWD and rare or very rare designations were developed using different datasets and different versions of the IMGT/HLA Database, [29] [31] the approximate fraction of alleles at each HLA locus in each category is shown below.

    Examining HLA types Edit

    Serotype and allele names Edit

    There are two parallel systems of nomenclature that are applied to HLA. The first, and oldest, system is based on serological (antibody based) recognition. In this system, antigens were eventually assigned letters and numbers (e.g., HLA-B27 or, shortened, B27). A parallel system that allowed more refined definition of alleles was developed. In this system, an "HLA" is used in conjunction with a letter, *, and a four-or-more-digit number (e.g., HLA-B*08:01, A*68:01, A*24:02:01N N=Null) to designate a specific allele at a given HLA locus. HLA loci can be further classified into MHC class I and MHC class II (or rarely, D locus). Every two years, a nomenclature is put forth to aid researchers in interpreting serotypes to alleles. [23]

    Serotyping Edit

    In order to create a typing reagent, blood from animals or humans would be taken, the blood cells allowed to separate from the serum, and the serum diluted to its optimal sensitivity and used to type cells from other individuals or animals. Thus, serotyping became a way of crudely identifying HLA receptors and receptor isoforms. Over the years, serotyping antibodies became more refined as techniques for increasing sensitivity improved and new serotyping antibodies continue to appear. One of the goals of serotype analysis is to fill gaps in the analysis. It is possible to predict based on 'square root','maximum-likelihood' method, or analysis of familial haplotypes to account for adequately typed alleles. These studies using serotyping techniques frequently revealed, in particular for non-European or north East Asian populations many null or blank serotypes. This was particularly problematic for the Cw locus until recently, and almost half of the Cw serotypes went untyped in the 1991 survey of the human population.

    There are several types of serotypes. A broad antigen serotype is a crude measure of identity of cells. For example, HLA A9 serotype recognizes cells of A23- and A24-bearing individuals. It may also recognize cells that A23 and A24 miss because of small variations. A23 and A24 are split antigens, but antibodies specific to either are typically used more often than antibodies to broad antigens.

    Cellular typing Edit

    A representative cellular assay is the mixed lymphocyte culture (MLC) and used to determine the HLA class II types. [32] The cellular assay is more sensitive in detecting HLA differences than serotyping. This is because minor differences unrecognized by alloantisera can stimulate T cells. This typing is designated as Dw types. Serotyped DR1 has cellularly defined as either of Dw1 or of Dw20 and so on for other serotyped DRs. Table [33] shows associated cellular specificities for DR alleles. However, cellular typing has inconsistency in the reaction between cellular-type individuals, sometimes resulting differently from predicted. Together with difficulty of cellular assay in generating and maintaining cellular typing reagents, cellular assay is being replaced by DNA-based typing method. [32]

    Gene sequencing Edit

    Minor reactions to subregions that show similarity to other types can be observed to the gene products of alleles of a serotype group. The sequence of the antigens determines the antibody reactivities, and so having a good sequencing capability (or sequence-based typing) obviates the need for serological reactions. Therefore, different serotype reactions may indicate the need to sequence a person's HLA to determine a new gene sequence.

    Broad antigen types are still useful, such as typing very diverse populations with many unidentified HLA alleles (Africa, Arabia, [34] Southeastern Iran [35] and Pakistan, India [36] ). Africa, Southern Iran, and Arabia show the difficulty in typing areas that were settled earlier. Allelic diversity makes it necessary to use broad antigen typing followed by gene sequencing because there is an increased risk of misidentifying by serotyping techniques.

    In the end, a workshop, based on sequence, decides which new allele goes into which serogroup either by sequence or by reactivity. Once the sequence is verified, it is assigned a number. For example, a new allele of B44 may get a serotype (i.e. B44) and allele ID i.e. B*44:65, as it is the 65th B44 allele discovered. Marsh et al. (2005) [23] can be considered a code book for HLA serotypes and genotypes, and a new book biannually with monthly updates in Tissue Antigens.

    Phenotyping Edit

    Gene typing is different from gene sequencing and serotyping. With this strategy, PCR primers specific to a variant region of DNA are used (called SSP-PCR). If a product of the right size is found, the assumption is that the HLA allele has been identified. New gene sequences often result in an increasing appearance of ambiguity. Because gene typing is based on SSP-PCR, it is possible that new variants, in particular in the class I and DRB1 loci, may be missed.

    For example, SSP-PCR within the clinical situation is often used for identifying HLA phenotypes. An example of an extended phenotype for a person might be:

    A *01:01 / *03:01 , C *07:01 / *07:02 , B *07:02 / *08:01 , DRB1 *03:01 / *15:01 , DQA1 *05:01 / *01:02 , DQB1 *02:01 / *06:02

    In general, this is identical to the extended serotype: A1,A3,B7,B8,DR3,DR15(2), DQ2,DQ6(1)

    For many populations, such as the Japanese or European populations, so many patients have been typed that new alleles are relatively rare, and thus SSP-PCR is more than adequate for allele resolution. Haplotypes can be obtained by typing family members in areas of the world where SSP-PCR is unable to recognize alleles and typing requires the sequencing of new alleles. Areas of the world where SSP-PCR or serotyping may be inadequate include Central Africa, Eastern Africa, parts of southern Africa, Arabia, S. Iran, Pakistan, and India.

    Haplotypes Edit

    An HLA haplotype is a series of HLA "genes" (loci-alleles) by chromosome, one passed from the mother and one from the father.

    The phenotype exampled above is one of the more common in Ireland and is the result of two common genetic haplotypes:

    A *01:01 C *07:01 B *08:01 DRB1 *03:01 DQA1 *05:01 DQB1 *02:01 (By serotyping A1-Cw7-B8-DR3-DQ2)

    which is called ' 'super B8' ' or ' 'ancestral haplotype' ' and

    A *03:01 C *07:02 B *07:02 DRB1 *15:01 DQA1 *01:02 DQB1 *06:02 (By serotyping A3-Cw7-B7-DR15-DQ6 or the older version "A3-B7-DR2-DQ1")

    These haplotypes can be used to trace migrations in the human population because they are often much like a fingerprint of an event that has occurred in evolution. The Super-B8 haplotype is enriched in the Western Irish, declines along gradients away from that region, and is found only in areas of the world where Western Europeans have migrated. The "A3-B7-DR2-DQ1" is more widely spread, from Eastern Asia to Iberia. The Super-B8 haplotype is associated with a number of diet-associated autoimmune diseases. There are 100,000s of extended haplotypes, but only a few show a visible and nodal character in the human population.

    Studies of humans and animals imply a heterozygous selection mechanism operating on these loci as an explanation for this variability. [37] One proposed mechanism is sexual selection in which females are able to detect males with different HLA relative to their own type. [38] While the DQ and DP encoding loci have fewer alleles, combinations of A1:B1 can produce a theoretical potential of 7,755 DQ and 5,270 DP αβ heterodimers, respectively. While nowhere near this number of isoforms exist in the human population, each individual can carry 4 variable DQ and DP isoforms, increasing the potential number of antigens that these receptors can present to the immune system.

    Studies of the variable positions of DP, DR, and DQ reveal that peptide antigen contact residues on class II molecules are most frequently the site of variation in the protein primary structure. Therefore, through a combination of intense allelic variation and/or subunit pairing, the class II peptide receptors are capable of binding an almost endless variation of peptides of 9 amino acids or longer in length, protecting interbreeding subpopulations from nascent or epidemic diseases. Individuals in a population frequently have different haplotypes, and this results in many combinations, even in small groups. This diversity enhances the survival of such groups, and thwarts evolution of epitopes in pathogens, which would otherwise be able to be shielded from the immune system.

    HLA antibodies are typically not naturally occurring, and with few exceptions are formed as a result of an immunologic challenge to a foreign material containing non-self HLAs via blood transfusion, pregnancy (paternally inherited antigens), or organ or tissue transplant.

    Antibodies against disease-associated HLA haplotypes have been proposed as a treatment for severe autoimmune diseases. [39]

    Donor-specific HLA antibodies have been found to be associated with graft failure in renal, heart, lung, and liver transplantation.

    In some diseases requiring hematopoietic stem cell transplantation, preimplantation genetic diagnosis may be used to give rise to a sibling with matching HLA, although there are ethical considerations. [40]


    HLA-B locus products resist degradation by the human cytomegalovirus immunoevasin US11

    To escape CD8+ T-cell immunity, human cytomegalovirus (HCMV) US11 redirects MHC-I for rapid ER-associated proteolytic degradation (ERAD). In humans, classical MHC-I molecules are encoded by the highly polymorphic HLA-A, -B and -C gene loci. While HLA-C resists US11 degradation, the specificity for HLA-A and HLA-B products has not been systematically studied. In this study we analyzed the MHC-I peptide ligands in HCMV-infected cells. A US11-dependent loss of HLA-A ligands was observed, but not of HLA-B. We revealed a general ability of HLA-B to assemble with β2m and exit from the ER in the presence of US11. Surprisingly, a low-complexity region between the signal peptide sequence and the Ig-like domain of US11, was necessary to form a stable interaction with assembled MHC-I and, moreover, this region was also responsible for changing the pool of HLA-B ligands. Our data suggest a two-pronged strategy by US11 to escape CD8+ T-cell immunity, firstly, by degrading HLA-A molecules, and secondly, by manipulating the HLA-B ligandome.

    Conflict of interest statement

    The authors have declared that no competing interests exist.

    Figures

    Fig 1. Changes in the relative abundance…

    Fig 1. Changes in the relative abundance of HLA class I ligands in HCMV-infected fibroblasts.

    ) HLA-A*02:01, A*03:01, B*07:02 or CD99 molecules were transduced with lentiviruses encoding US11 in front of an IRES-EGFP sequence. At 0, 24 and 48 h post-transduction the cells were analyzed by flow cytometry using an anti-HA mAb. The MFI in EGFP + cells relative to MFI at 0 h post transduction is depicted. Two independent biological replicates of the experiment with similar outcomes were performed.

    Fig 2. HLA locus specific downregulation by…

    Fig 2. HLA locus specific downregulation by US11.

    HeLa cells were transiently co-transfected with US11…

    ) HLA molecules expressed from the pUC-IP vector (SFFV U3 promoter). (A) At 20 h post-transfection HLA-I cell surface expression of EGFP positive cells was measured by flow cytometry using anti-HA mAbs. (B) The HLA-I expression from (A) was defined as ratio of the MFI in US11 expressing cells compared to control cells, the value of which was normalized to the downregulation of a control molecule (HA-CD99). Bars represent normalized mean values ± SEM from three independent experiments. Statistical analyses were performed to compare HLA-A or -B alleles among themselves, applying one-way ANOVA followed by a Tuckey’s multiple comparisons method for all pairwise differences of means. Endogenous HLA-I expressed in MRC-5 or HeLa cells are indicated. (C) At 20 h post-transfection cells were labeled with [ 35 S]-Met/Cys for 30 min and chased for 0 or 45 min and an immunoprecipitation was performed using anti-HA mAb. An uncropped autoradiography is depicted in S3 Fig. (D) Whole cell lysates were prepared and digested with EndoH prior to analysis by Western blot with antibodies as indicated. Equal loading of lysates was controlled by Ponceau S staining. (E) Cells were analyzed as described in A. In addition, the cells were incubated with LIR1-Fc. In the upper panel binding of LIR1-Fc to the CD99/ctrl transfected cells is shown in green. Representatives of two independent biological replicates with similar outcomes are shown.

    Fig 3. Analysis of factors that could…

    Fig 3. Analysis of factors that could affect HLA-B resistance against US11.

    ) HLA in pIRES-EGFP (CMV major IE promoter). At 20 h post-transfection cells were labeled with [ 35 S]-Met/Cys for 30 min and chased for 0 or 45 min and a co-immunoprecipitation experiment was performed using anti-HA mAb or anti-US11 antiserum. A complete autoradiography is depicted in S5 Fig. A representative of two independent biological replicates with similar outcomes is shown. (B-C) HeLa cells were transiently co-transfected with US11 or a ctrl pIRES-EGFP plasmid together with indicated HA-tagged (

    ) MHC-I molecules and mutants encoded by the pUC-IP vector. At 20 h post-transfection flow cytometry and statistical analysis were performed as described in Fig 2A and 2B. (D) β2m-deficient FO-1 cells were co-transfected with US11 or a control pIRES-EGFP plasmid together with indicated HA-tagged (

    ) HLA alleles encoded by the pUC-IP vector. At 20 h post-transfection cell surface expression of EGFP positive cells was measured by flow cytometry using anti-HA mAbs. (E) FO-1 cells were transfected as described in (D). At 20 h post-transfection cells were labeled with [ 35 S]-Met/Cys for 1 or 3 h and an immunoprecipitation was performed using anti-HA antibodies. A representative of two independent biological replicates with similar outcomes is shown. A complete autoradiography is depicted in S6 Fig.

    Fig 4. US11 co-immunoprecipitation with the PLC…

    Fig 4. US11 co-immunoprecipitation with the PLC in HCMV-infected cells.

    Fig 5. The N-terminal LCR of US11…

    Fig 5. The N-terminal LCR of US11 is required for MHC-I ER retention but not…

    Fig 6. Resistant HLA-B in cells ectopically…

    Fig 6. Resistant HLA-B in cells ectopically expressing US11.

    US11) or control cells (-) were treated for 36 h with IFN-γ (500 U/ml) or left untreated. Cells were labeled with [ 35 S]-Met/Cys for 2 h and immunoprecipitation was performed using indicated antibodies. Distinct MHC-I HCs are indicated with blue and red asterisks. A representative of two independent biological replicates with similar outcomes is shown. (B) Hela cells were transiently transfected with HA-tagged MHC-I encoded by the pUC-IP vector. At 20 h post-transfection an immunoprecipitation experiment as described in (A) was performed.

    Fig 7. Anchor residue usage of HLA-B…

    Fig 7. Anchor residue usage of HLA-B ligands is modified by US11.

    ) US11Q/A, ΔLCRUS11Q/A or US3. Cells were collected and MHC-I molecules were isolated using the mAb W6/32. Peptide ligands were eluted and analyzed by mass spectrometry. (A) The relative distribution of MHC-I specific 9-mer ligands between HLA-A*68:02 and B*15:03 is shown. (B) The frequency of P2 peptide anchor residues of HLA-B*15:03 9-mer ligands was determined and depicted as percentage of total pool at that specific position. Two independent biological replicates of the experiment are shown (#1 and #2). (C-D) Pooled #1 and #2 ligands from Fig 1A predicted by NetMHC3.4 to bind to HLA-B*07:02 and B*44:02 with an affinity of <500 and <1000 nM, respectively, were divided into common and unique ΔUS2-6 and ΔUS2-6/US11 ligands respectively (S13A Fig). From these pools the frequency of specific amino acids (x-axis) at positions P1 and P3 of HLA-B*07:02 (C) and positions P3 and P4 of HLA-B*44:02 (D) was determined and depicted as percentage of total pool at that specific position.


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    Discussion

    The technology of HLA typing has significantly evolved since the PCR method was introduced by Mullis and Faloona [17]. Nowadays, there are several kinds of PCR-based HLA typing being used: DNA amplification with sequence specific primers (SSP) [18], sequence specific oligonucleotide probes (SSO) [19], single-stranded conformation polymorphism (SSCP) [20], sequence-based typing (SBT) [21], and DNA chip technology [22]. These HLA typing techniques are of much higher accuracy as well as reliability than the conventional serologic typing method, and also may facilitate the standardization of HLA typing processes [23]. The SSP and SSO methods are low-intermediate resolution techniques at two-digit level. The advantage of the SSO method based on Luminex system lies in its high throughput, because 96 samples can be completed in one day by a skilled technician. SBT is a gold-standard [24], high-resolution method at the four-digit level that was used to resolve theambiguous types and to identify novel alleles. In this paper, the data of HLA genotyping from 2003 to 2006 were based on the SSP and SSO methods. The SBT method was used only to resolve the ambiguous types and to identify novel alleles. In 2007, we began to use the SBT method in the routine HLA genotyping. The high-resolution data are being analyzed and will be reported in another paper.

    HLA functions as a useful tool to clarify the differences between genetic makeup of Liaoning residents and other parts of China. The Han Chinese is divided into two major groups, namely northern Han and southern Han [25]. The HLA allele frequencies of the Liaoning Han show close similarity to the frequencies of the Shaanxi Northern Han, because both have less than 20% variation in allele frequencies. Shaanxi played a central role in ancient northern Chinese history for thousands of years. This is predictable given that Liaoning is one of the three northeastern Chinese provinces that was populated by the geographic expansion of the Northern Han Chinese.

    There are striking similarities between Liaoning Han and Liaoning Manchu ethnic group in HLA allele and haplotype frequencies. This genetic closeness is also supported by the genetic distance calculation. This can be explained by the fact that Manchus are descended from the Jurchen people with North East Asia origin (http://en.wikipedia.org/wiki/Manchu_people#Origins_and_early_history). There is difference in Manchu HLA allele frequencies between our study and previous one [26]. For example, A*33, B*38, B*46, and DRB*14 have frequencies of 6.57%, 2.10%, 7.11%, 6.31% in the current study, but 2.8%, 0.9%, 4.1% and 12.3% in the previous study conducted with Manchu residents living in Harbin. Harbin is located about 600 km to the north of Liaoning. It is not clear about what contributes to this difference beside different geographic locations. Liaoning was the historic home for Manchu. It is possible that more intensive gene exchange took place between Liaoning Han and Liaoning Manchu in the Liaoning region. The HLA allele frequencies of Liaoning Mongol in our dataset are similar to those published previously [27], [28]. Both B*37 and B*57 distinguish Mongol from the northern Han based on much higher frequencies in Mongol.

    The HLA haplotype analysis is widely used in human population genetics, anthropological studies, as well as the optimal marrow donor bank size planning. It carries more specific information than allele frequencies. A number of studies have addressed the Chinese HLA allele and haplotype distribution from different regions including Sichuan, Jiangsu, Fujian, Guangdong, Xi'an, Yunan [29]–[34]. However, there is no report of any large sample study about HLA haplotypes in Liaoning.

    Linkage disequilibrium is the common characteristic of HLA genetics. This is true for Liaoning Han population according to Table 5. The top four most common haplotypes found among Liaoning Han in this study include A*30-B*13-DRB1*07, A*02-B*46-DRB1*09, A*02-B*13-DRB1*12 and A*33-B*58-DRB1*03(17) which actually consist of allele groups ranking the 5 th (A*30, B*46) and 10 th (B*58, DRB1*03(17)) positions in the allele frequency ranking (Table 2). B*57 and B*37 rank at the 20 th and 21 st places in the frequency list but their corresponding haplotypes, A*01-B*37-DRB1*10 and A*01-B*57-DRB1*07 ranked within the top 10 most frequent haplotypes. The LDs are similar to the findings in the studies conducted in Shaanxi and Inner Mongolia, China [10], [28].

    The fact that HLA-A-B-DRB1 haplotype frequencies of Liaoning Han show high similarities to those in Shaanxi can be expected since both of them belong to the northern China regions (the North of Yangtze River). There is below 30% difference in haplotype frequencies among 18 out of the top 20 haplotypes when Liaoning is compared with Shaanxi (The data were not shown).

    Korea is adjacent to Liaoning on its southeast side. Our haplotype data do show that the Korean population is much closer to the northern Han than to the southern Han. Nine out of top 10 haplotype rankings in Liaoning Han are also listed in the top 10 most frequent haplotypes in Liaoning Korean. Korean do have own unique haplotypes that show a very different frequency pattern compared to Han. The most common haplotype A*02-B*15(62)-DRB1*15 in Liaoning Korean is five-time less frequent than that in Liaoning Han (Table 3). A*33-B*58-DRB1*13 (3.91%) and A*33-B*44-DRB1*13 (3.13%), the very common haplotypes in Liaoning Korean, reflect also a 3-fold lower frequency in Liaoning Han.

    It is well known that A*30-B*13-DRB1*07 is the most common haplotype in the northern Han Chinese, while A*02-B*46-DRB1*09 is the most common haplotype in the southern Han Chinese [29]–[34]. Indeed, the A*30-B*13-DRB1*07 turned out to be the most common haplotype in all ethnic groups in our study with the exception of Liaoning Korean.

    A*02-B*35-DRB1*04 and A*24-B*52-DRB1*15, two high-frequency haplotypes appear to be unique to the Mongolian population, i.e. both of them clearly present a lower frequency in Liaoning and Shaanxi Han, but much less in southern Han (The data were not shown).

    Our phylogenetic tree study has placed Liaoning Han, Liaoning Manchu, Liaoning Mongol, Liaoning Hui and Liaoning Xibe in the same close cluster as expected. In addition to the Liaoning Han cluster, Liaoning Hui differs substantially from others. A*01 is significantly less common in Liaoning Hui than in Liaoning Han (1.980% vs. 4.686%), and B*14(65), B*18 and B*38 are 3–7 times more common in Liaoning Hui than in Liaoning Han volunteers. We have noted that Liaoning Manchu, Liaoning Hui, Liaoning Korean are quite different in their HLA profiles from the same ethnic groups reported previously. The differences between Liaoning Manchu and Harbin Manchu were already discussed. Liaoning Hui also differs in many alleles from Qinghai Hui [26]. A*30, A*31, B*14(65), and B*18 show 2.5–12-fold higher allele frequencies in Liaoning Hui than in Qinghai Hui, while A*01, B*27, and B*40(61) behave in the opposite way. Liaoning Korean display a large genetic distance to Liaoning Han, but it clearly differs from the Korean residents in Seoul, Korea [11]. It is possible that Liaoning Koreans have undergone substantial gene exchange with the Liaoning Han population in the Liaoning region and have acquired certain HLA characteristics from the Liaoning Han while still maintaining Korean unique HLA gene composition. The genetic distance between Shaanxi Han and Liaoning Han is three times shorter than that between Guangdong Han and Liaoning Han. This explains the fact that both Shaanxi and Liaoning belong to northern China while Guangdong represents a southern China province. Thailand, Taiwan/Minnan and Japan lie relative close to each other in the same branch since they are all from the Southeast Asia region. Japan population shows the most distinct features with the longest distance from the rest of all the study groups. This can be explained with the fact that Japan is most isolated nation from their neighbors during the thousand years of history.

    Finally it is important to emphasize that our data may not reflect the true ethnic makeup of the Liaoning population due to the fact that the minority ethnic groups are less represented in the marrow donor registry population. The frequency data are also based on local bone marrow donor volunteers who could be a biased group in terms of representing the entire Liaoning population. We have no way to verify the ethnicity of samples from minority groups.

    In summary, we have analyzed HLA-A-B-DRB1 allele and haplotype frequencies of Liaoning residents based on the local marrow donor registry volunteers classified by various ethnic backgrounds and compared to the populations from other parts of China including its geographic neighbors, Koreans and Mongolians. The HLA haplotypes of Liaoning Han carry a clear Northern Han signature while other ethnic groups possess their unique characteristics except Liaoning Manchu who show almost identical HLA-A, -B and -DRB1 allele frequencies to those of Liaoning Han. Liaoning Mongolians and Koreans show clearly more similarities to Liaoning Han than to southern Han in respect to their HLA haplotypes. No specific haplotype has been found to be uniquely shared between Liaoning and Koreans or between Liaoning and Mongolians.


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