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JoVE Journal Genetics

Genetics

qKAT: Quantitative Semi-automated Typing of Killer-cell Immunoglobulin-like Receptor Genes

Published: March 6, 2019 doi: 10.3791/58646
Automatic Translation
1,2,3,4, 1, 1,5, 1,6, 1,7, 1
1Department of Pathology, University of Cambridge, 2Department of Physiology, Development and Neuroscience, University of Cambridge, 3Department of Obstetrics and Gynaecology, University of Cambridge School of Medicine, NIHR Cambridge Biomedical Research Centre, 4Centre for Trophoblast Research, University of Cambridge, 5Department of Genetics & Evolution, University of Geneva, 6Royal Papworth Hospital, 7Department of Plant Sciences, University of Cambridge

Summary

Quantitative killer cell immunoglobulin-like receptor (KIR) semi-automated typing (qKAT) is a simple, high-throughput, and cost-effective method to copy number type KIR genes for their application in population and disease association studies.

Abstract

Killer cell immunoglobulin-like receptors (KIRs) are a set of inhibitory and activating immune receptors, on natural killer (NK) and T cells, encoded by a polymorphic cluster of genes on chromosome 19. Their best-characterized ligands are the human leukocyte antigen (HLA) molecules that are encoded within the major histocompatibility complex (MHC) locus on chromosome 6. There is substantial evidence that they play a significant role in immunity, reproduction, and transplantation, making it crucial to have techniques that can accurately genotype them. However, high-sequence homology, as well as allelic and copy number variation, make it difficult to design methods that can accurately and efficiently genotype all KIR genes. Traditional methods are usually limited in the resolution of data obtained, throughput, cost-effectiveness, and the time taken for setting up and running the experiments. We describe a method called quantitative KIR semi-automated typing (qKAT), which is a high-throughput multiplex real-time polymerase chain reaction method that can determine the gene copy numbers for all genes in the KIR locus. qKAT is a simple high-throughput method that can provide high-resolution KIR copy number data, which can be further used to infer the variations in the structurally polymorphic haplotypes that encompass them. This copy number and haplotype data can be beneficial for studies on large-scale disease associations, population genetics, as well as investigations on expression and functional interactions between KIR and HLA.

Introduction

In humans, the killer immunoglobulin-like receptor(KIR) locus is mapped on the long arm of chromosome 19 within the leukocyte receptor complex (LRC). This locus is around 150 kb in length and includes 15 KIR genes arranged head-to-tail. The KIR loci that are currently known are KIR2DL1, KIR2DL2/KIR2DL3, KIR2DL4, KIR2DL5A, KIR2DL5B, KIR2DS1-5, KIR3DL1/KIR3DS1, KIR3DL2-3, and two pseudogenes, KIR2DP1 and KIR3DP1. The KIR genes encode for two-dimensional (2D) and three-dimensional (3D) immunoglobulin-like domain receptors with short (S; activating) or long (L; inhibitory) cytoplasmic tails, which are expressed by natural killer (NK) cells and subsets of T cells. Copy number variation exhibited within the KIR locus forms diverse haplotypes with variable gene content1. Non-allelic homologous recombination (NAHR), facilitated by a close head-to-tail gene arrangement and high-sequence homology, is the mechanism proposed to be responsible for the haplotypic variability. Over 100 different haplotypes have been reported in populations worldwide1,2,3,4. All these haplotypes could be divided into two major groups: A and B haplotypes. The A haplotype contains 7 KIR genes: KIR3DL3, KIR2DL1, KIR2DL3, KIR2DL4, KIR3DL1, and KIR3DL2, which are inhibitory KIR genes, and the activating KIR gene KIR2DS4. However, up to 70% of European-origin individuals who are homozygous for KIR haplotype A exclusively carry a non-functional "deletion" form of KIR2DS45,6. All other KIR gene combinations form group B haplotypes, including at least one of the specific KIR genes KIR2DS1, KIR2DS2, KIR2DS3, KIR2DS5, KIR3DS1, KIR2DL2, and KIR2DL5, and typically include two or more activating KIR genes.

HLA Class I molecules have been identified as the ligands for certain inhibitory receptors (KIR2DL1, KIR2DL2, KIR2DL3, and KIR3DL1), activating receptors (KIR2DS1, KIR2DS2, KIR2DS4, KIR2DS5, and KIR3DS1), and for KIR2DL4, which is a unique KIR that contains a long cytoplasmic tails like other inhibitory KIR receptors but also has a positively charged residue near the extracellular domain which is a common feature of other activating KIR receptors. The combination of variants within the KIR genes and the HLA genes influences receptor ligand interaction that shapes potential NK cell responsiveness at the individual level7,8. Evidence from genetic association studies has indicated that KIR plays a role in viral resistance (e.g., human immunodeficiency virus [HIV]9 and hepatitis C virus [HCV]10), the success of transplantation11, the risk of pregnancy disorders and reproductive success12,13, the protection against relapse after allogeneic hematopoietic stem cell transplantation (HSCT)14,15,16, and the risk of cancers17.

The combination of high-sequence homology and allelic and haplotypic diversity presents challenges in the task of accurately genotyping KIR genes. Conventional methods to type KIR genes include sequence-specific primer (SSP) polymerase chain reaction (PCR)18,19,20, sequence-specific oligonucleotide probe (SSOP) PCR21, and matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS)22. The drawbacks of these techniques are that they only provide partial insight into the genotype of an individual whilst also being laborious to perform. Recently next-generation sequencing (NGS) has been applied to type the KIR locus specifically. While this method is very powerful, it can be expensive to run, and it is time-consuming to conduct in-depth analysis and data checks.

qKAT is a high-throughput quantitative PCR method. While conventional methods are laborious and time-consuming, this method makes it possible to run nearly 1,000 genomic DNA (gDNA) samples in five days and gives the KIR genotype, as well as the gene copy number. qKAT consists of ten multiplex reactions, each of which targets two KIR loci and one reference gene of a fixed copy number in the genome (STAT6) used for the relative quantification of the KIR gene copy number23. This assay has been successfully used in studies involving large population panels and disease cohorts on infectious diseases such as HCV, autoimmune conditions like type 1 diabetes, and pregnancy disorders such as preeclampsia, as well as providing a genetic underpinning to studies aimed at understanding the NK cell function1,4,24,25,26.

Protocol

1. Preparation and Plating out of DNA

  1. Accurately quantify the gDNA concentration using a spectrophotometric or fluorometric instrument.
  2. Dilute DNA to 4 ng/µL on a 96-well deep-well plate. Include at least one control gDNA sample with a known copy number and one non-template control.
  3. Centrifuge the 96-well plates at 450 x g for 2 min.
  4. Using a liquid handling instrument, dispense each sample in quadruplicate onto 384-well qPCR plates so that every well has 10 ng of DNA (2.5 µL/well). Prepare at least ten 384-well plates, one for each qKAT reaction.
  5. If gDNA is being dispensed from more than one 96-well plate, perform a full-volume wash with 2% bleach and ultrapure water to clean the needles of the liquid handling system between each 96-well plate of gDNA samples.
  6. Air-dry the DNA by incubating the 384-well plates in a clean area at room temperature for at least 24 h.

2. Preparation of the Primers and Probes

NOTE: qKAT consists of ten multiplex reactions. Each reaction includes three primer pairs and three fluorescence-labeled probes that specifically amplify two KIR genes and one reference gene. The probes that were published in Jiang et al.27 were modified so that the oligonucleotides are now labeled with ATTO dyes since they offer improved photostability and long signal lifetimes. Pre-aliquoted primer combinations are commercially available (see Table of Materials).

  1. Prepare primer combinations for each reaction as per the dilutions given in Table 1.
  2. Prepare probe combinations for each reaction as per Table 1. Test each individual probe prior to making the combination.

3. Preparation of the Master Mix

NOTE The volumes mentioned below are for performing one qKAT reaction on a set of 10x 384-well plates.

  1. Ensure that the gDNA samples plated on the 384-well plates are completely dry. Conduct all steps on ice and keep the reagents covered from exposure to light as much as possible since the fluorescence-labeled probes are photo- and thermo-sensitive.
  2. Defrost the qPCR buffer, primer, and probe aliquots at 4 °C.
  3. On ice, prepare a master mix for 10x 384-well plates by adding 18.86 mL of ultrapure water, 20 mL of qPCR buffer, 1,000 µL of preprepared primer combination, and 180 µL of preprepared probe combination (Table 2).
  4. Distribute the master mix evenly across a 96-deep well plate using a multi-channel pipette, pipetting 415 µL into each well. Keep this plate in an ice box covered from light.
  5. Using a liquid handling instrument, dispense 9.5 µL of the master mix into each well of the 384-well plate with dried gDNA. Seal the plate with a foil and immediately place it at 4 °C. Repeat this process for the remaining plates, ensuring that the needles of the liquid handling system are washed with water between each plate.
  6. Centrifuge the 384-well plates at 450 x g for 3 min and incubate them at 4 °C overnight or between 6 - 12 h to resuspend the DNA and to dissipate any air bubbles.

4. qPCR Assay

  1. Following the overnight incubation, centrifuge at 450 x g for 3 min to dissipate any remaining air bubbles.
  2. For purposes of automation, connect the qPCR machine (e.g., LightCycler 480) to a microplate handler (see Table of Materials). Program the microplate handler to place the plates into the qPCR machine from a cooled storage dock that is protected from light.
    NOTE The assays should, in theory, work on other qPCR machines with compatible optic settings.
  3. Use the following cycling conditions: 95 °C for 5 min followed by 40 cycles of 95 °C for 15 s and 66 °C for 50 s, with data collection at 66 °C.
  4. Once the run is complete, have the robot collect the plate from the qPCR machine and place it in the discard dock.

5. Post-run Analysis

  1. After amplification, calculate the quantification cycle (Cq) values using either the second derivative maximum method or the Fit Points method with the software of the qPCR machine (see Table of Materials), following the steps below.
  2. Open the qPCR software and, in the Navigator tab, open the saved reaction experiment file for one plate.
  3. For the analysis using the second derivative maximum method, select the Analysis tab, and create a new analysis using Abs Quant/Second Derivative Max method.
    1. In the Create new analysis window, select analysis type: Abs Quant/Second Derivative Max method, subset: All Samples, program: Amplification, name: Rx-DFO (where x is the reaction number).
    2. Select Filter Comb and choose VIC/HEX/Yellow555 (533-580). This ensures that the data collected for STAT6 is selected.
    3. Select Colour Compensation for VIC/HEX/Yellow555(533-580). Click Calculate. Repeat this for Fam (465-510) and Cy5/Cy5.5(618-660). Click Save file.
  4. For the analysis using the Fit Points method, select Abs Quant/Fit Points in the Analysis tab.
    1. In the Create new analysis window, select analysis type: Abs Quant/Fit Points method, subset: All Samples, program: Amplification, name: RxF-DFO (where x is the reaction number).
    2. Select the correct filters and color compensations for STAT6 and each of the KIR genes (Fam/Cy5). In the Noiseband tab, set the noise band to exclude the background noise.
    3. In the Analysis tab, set the fit points to 3 and select Show fit points. Click Calculate. Click Save file.

6. Export of the Results

  1. In the qPCR software, open the Navigator tab. Select Results Batch Export.
  2. Open the folder in which the experiment files are saved and transfer the files into the right-hand side section of the window. Click Next. Select the name and the location of the export file.
  3. Select Analysis type Abs Quant/Second Derivative Max method or Abs Quant/Fit Points. Click Next. Check that the name of the file, the export folder, and the analysis type are correct and click Next to start the export process.
  4. Wait until the Export Status is Ok. The screen will automatically move to the next step. Check that all selected files have been exported successfully so that the number of files failed = 0. Click Done.
  5. Use scripts split_file.pl and roche2sds.pl to split the exported plates into individual reactions for each plate.
    NOTE The scripts are provided on request/GitHub.

7. Copy Number Calculations

  1. Open the copy number analysis software (e.g., CopyCaller). Select Import real-time PCR results file and load text files created by roche2sds.pl.
  2. Select Analyze and conduct the analysis by either selecting calibrator sample with known copy number or by selecting most frequent copy number. See Table 5 for the most frequent copy number of KIR genes typically observed in European-origin populations.

8. Data-quality Checks

  1. Use R script KIR_CNVdata_analysis_for_Excel_ver020215.R to combine copy number data from all the plates into a spreadsheet.
    NOTE The scripts are provided on request/GitHub.
  2. Recheck the raw data on the copy number analysis software for samples that do not conform to the known linkage disequilibrium (LD) for KIR genes (Table 6).

Representative Results

Copy number analysis can be carried out by exporting the files to the copy number analysis software, which provides the predicted and estimated copy number based on the ΔΔCq method.

The copy number can be predicted either based on the known copy number of control DNA samples on the plate or by inputting the most frequent gene copy number (Table 5). Figure 1 shows the results of a plate for a reaction that targets KIR2DL4 and KIR3DS1, as well as the reference gene STAT6. The most frequent copy number for KIR2DL4, a framework gene in the KIR locus, is two copies, whereas the most frequent copy number for KIR3DS1, an activating gene, is one copy. The results in the figure show the PCR amplification plots observed on the qPCR software and the copy number data generated from the qPCR data. As shown, the assay is able to distinguish between 0, 1, 2, 3, and 4 KIR gene copy numbers. The copy number analysis software also enables a viewing of the distribution of the copy number across the plate as a pie chart or a bar graph. The efficacy of the copy number prediction is lower for samples with a higher copy number.

The quality of all the materials used in the reactions, gDNA, buffer, primers, and probes, can affect the accuracy of the results obtained. However, discordance in results is most likely to be caused due to variation in the concentration of DNA across a plate. The purity of the extracted gDNA, which can be measured using the 260/280 and 260/230 ratios, can also have an effect on the quality. A 260/280 ratio of 1.8 - 2 and a 260/230 ratio of 2 - 2.2 are desirable. An uneven range of DNA concentrations across a plate can lead to a high variability in the threshold cycle (Ct) between samples and discordance in the range of the estimated copy number. The results in Figure 2 show the effect the disparity between the Ct values across a plate can have on the accuracy in the prediction of the copy number. The red line indicates the range of the estimated copy number for a sample and, ideally, should be as close to an integer as possible.

The copy number data, once analyzed, can be exported as a spreadsheet file in a 96-well format. We used an R script (available on request) to combine the copy number data of all 10 plates that are run as a set into one spreadsheet. Published data about KIRs from mostly European-origin populations enables the prediction of LD rules that exist between various genes in the KIR complex1. These predictions are used to conduct downstream checks on the copy number results obtained (Table 6). Samples that do not conform to the predicted LD between the genes might contain unusual polymorphism or haplotypic structural variations. A flowchart describing the protocol is shown in Figure 3.

A tool called KIR Haplotype Identifier (http://www.bioinformatics.cimr.cam.ac.uk/haplotypes/) was developed to facilitate the imputation of haplotypes from the data set. The imputation works on the basis of a list of reference haplotypes observed in a European-origin population1. However, the tool also allows for a custom set of reference haplotypes to be used instead. Three separate files are generated; the first file lists all haplotype combinations for a sample, the second file provides a trimmed list of the haplotypes combinations that have the highest combined frequencies, and the third file lists the samples that cannot be assigned haplotypes. Non-assignment of haplotypes could be used as an indicator of novel haplotypes.

Figure 1
Figure 1: Representative results of a plate for reaction number 5. (A) This panel shows amplification plots. (B) This panel shows copy number plots. (C) This panel shows the copy number distribution. Please click here to view a larger version of this figure.

Figure 2
Figure 2: Representative results of a plate with a variable DNA concentration for reaction number 5. (A) This panel shows amplification plots. (B) This panel shows copy number plots. Please click here to view a larger version of this figure.

Figure 3
Figure 3: Flowchart of the qKAT protocol. Please click here to view a larger version of this figure.

Assay Genes Forward Primers Concentration (nM) Reverse Primers Concentration (nM) Probes Concentration (nM)
No 1 3DP1 A4F 250 A5R 250 P4a 150
2DL2 2DL2F4 400 C3R2 600 P5b 150
STAT6 STAT6F 200 STAT6R 200 PSTAT6 150
No 2 2DS2 A4F 400 A6R 400 P4a 200
2DL3 D1F 400 D1R 400 P9 150
STAT6 STAT6F 200 STAT6R 200 PSTAT6 150
No 3 3DL3 A8F 500 A8R 500 P4a 150
2DS4Del 2DS4Del 250 2DS4R2 250 P5b 150
STAT6 STAT6F 200 STAT6R 200 PSTAT6 150
No 4 3DL1e4 B1F 250 B1R 125 P4b 150
3DL1e9 D4F 250 D4R2 500 P9 150
STAT6 STAT6F 200 STAT6R 200 PSTAT6 150
No 5 3DS1 B2F 250 B1R 250 P4b 150
2DL4 C1F 200 C1R 200 P5b-2DL4 150
STAT6 STAT6F 200 STAT6R 200 PSTAT6 150
No 6 2DL1 B3F 500 B3R 125 P4b 150
2DP1 D3F 250 D3R 500 P9 150
STAT6 STAT6F 200 STAT6R 200 PSTAT6 150
No 7 2DS1 B4F 500 B4R 250 P4b 150
2DL5 D2F 500 D2R 500 P9 150
STAT6 STAT6F 200 STAT6R 200 PSTAT6 150
No 8 2DS3 B5F 250 B5R 250 P4b 150
3DL2e9 D4F 250 D5R 125 P9 150
STAT6 STAT6F 200 STAT6R 200 PSTAT6 150
No 9 3DL2e4 A1F 200 A1R 200 P4a 150
2DS4FL 2DS4FL 250 2DS4R2 500 P5b 150
STAT6 STAT6F 200 STAT6R 200 PSTAT6 150
No 10 2DS5 B6F2 200 B6R3 200 P4b 150
2DS4 C5F 250 C5R 250 P5b 150
STAT6 STAT6F 200 STAT6R 200 PSTAT6 150

Table 1: Combination and concentration of primers and probes used in each qKAT reaction27.

Reaction  Primer Aliquots (µL) Probe Aliquots (µL)
R1 3DP1 A4F A5R 2DL2F4 C3R2 WATER STAT6F STAT6R P4A P5B PSTAT6
2DL2 100 100 160 240 200 80 80 60 60 60
R2 2DS2 A2F A6R D1F D1R WATER STAT6F STAT6R P4A P9 PSTAT6
2DL3 160 160 160 160 160 80 80 80 60 60
Note: need 20 µL less water in the MasterMix
R3 3DL3 A8F  A8FB A8R 2DS4DELF 2DS4R2 WATER STAT6F STAT6R P4A P5B PSTAT6
2DS4DEL 100  100 200 100 100 200 80 80 60 60 60
R4 3DL1E4 B1F B1R D4F D4R2 WATER STAT6F STAT6R P4B P9 PSTAT6
3DL1E9 100 50 100 200 350 80 80 60 60 60
R5 3DS1 B2F B1R C1F C1R WATER STAT6F STAT6R P4B P5B-2L4 PSTAT6
2DL4 100 100 80 80 440 80 80 60 60 60
R6 2DL1 B3F B3R D3F D3R WATER STAT6F STAT6R P4B P9 PSTAT6
2DP1 200 50 100 200 250 80 80 60 60 60
R7 2DS1 B4F B4R D2F D2R WATER STAT6F STAT6R P4B P9 PSTAT6
2DL5 200 100 200 200 100 80 80 60 60 60
R8 2DS3 B5F B5R D4F D5R WATER STAT6F STAT6R P4B P9 PSTAT6
3DL2E9 100 100 100 50 450 80 80 60 60 60
R9 3DL2E4 A1F A1R 2DS4WTF 2DS4R2 WATER STAT6F STAT6R P4A P5B PSTAT6
2DS4WT 80 80 100 200 340 80 80 60 60 60
R10 2DS5 B6F2 B6R3 C5F C5R WATER STAT6F STAT6R P4B P5B PSTAT6
2DS4TOTAL 80 80 100 100 440 80 80 60 60 60

Table 2: Volumes (µL) of 100 µM primer/probe stock solutions to make primer and probe combination aliquots.

Name Direction 5´ modification 3´ modification Sequence (5'→3') Length Tm GC% Exon Position
P4a Sense FAM BHQ-1 TCATCCTGC
AATGTTGGT
CAGATGTCA		 27 60 44.4 4 425-451
P4b Antisense FAM BHQ-1 AACAGAACC
GTAGCATCT
GTAGGTCCC
T		 28 62 50 4 576-603
P5b Sense ATTO647N BHQ-2 AACATTCCA
GGCCGACT
TTCCTCTG		 25 60 52 5 828-852
P5b-2DL4 Sense ATTO647N BHQ-2 AACATTCCA
GGCCGACT
TCCCTCTG		 25 61 56 5 828-852
P9 Sense ATTO647N BHQ-2 CCCTTCTCA
GAGGCCCA
AGACACC		 24 60 62.5 9 1246-1269
PSTAT6 ATTO550 BHQ-2 CTGATTCCT
CCATGAGCA
TGCAGCTT		 26 62 50

Table 3: List of probes used in qKAT1,27. The fluorescent dyes used at the 5' end of the oligo probes P5b, P5b-2DL4, P9, and PSTAT6 were modified to ATTO dyes.

Gene Primers Direction Sequence (5´-3´) Length Tm GC% Exon Position Amplicon (bp) Alleles might be missed
3DL2e4 A1F Forward GCCCCTGCTGAA
ATCAGG		 18 52 61.1 4 399-416 179 3DL2*008, *021, *027, *038.
A1R Reverse CTGCAAGGACAG
GCATCAA		 19 53 52.6 559-577 3DL2*048 
3DP1 A4F Forward GTCCCCTGGTGA
AATCAGA		 19 49 52.6 4 398-416 112 None
A5R Reverse GTGAGGCGCAAA
GTGTCA		 18 52 55.6 492-509 None
2DS2 A2F Forward GTCGCCTGGTGA
AATCAGA		 19 49 52.6 4 398-416 111 None
A6R Reverse TGAGGTGCAAAG
TGTCCTTAT		 21 51 42.9 488-508 None
3DL3 A8Fa Forward GTGAAATCGGGA
GAGACG		 18 50 55.6 4 406-423 139 None
A8Fb Forward GGTGAAATCAGG
AGAGACG		 19 50 52.6 405-423 3DL3*054, 3DL3*00905.
A8R Reverse AGTTGACCTGGG
AACCCG		 18 51 61.1 526-543 None
3DL1e4 B1F Forward CATCGGTCCCAT
GATGCT		 18 51 55.6 4 549-566 85 3DL1*00505, 3DL1*006, 3DL1*054, 3DL1*086, 3DL1*089
B1R Reverse GGGAGCTGACAA
CTGATAGG		 20 52 55 614-633 3DL1*00502
3DS1 B2F Forward CATCGGTTCCAT
GATGCG		 18 51 55.6 4 549-566 85 3DS1*047; may pick up 3DL1*054.
B1R Reverse GGGAGCTGACAA
CTGATAGG		 20 52 55 614-633 None
2DL1 B3F Forward TTCTCCATCAGT
CGCATGAC		 20 52 50 4 544-563 96 2DL1*020, 2DL1*028
B3R Reverse GTCACTGGGAGC
TGACAC		 18 50 61.1 622-639 2DL1*023, 2DL1*029, 2DL1*030
2DS1 B4F Forward TCTCCATCAGTC
GCATGAA		 19 51 47.4 4 545-563 96 2DS1*001
B4R Reverse GGTCACTGGGAG
CTGAC		 17 49 64.7 624-640 None
2DS3 B5F Forward CTCCATCGGTCG
CATGAG		 18 53 61.1 4 546-563 96 None
B5R Reverse GGGTCACTGGGA
GCTGAA		 18 51 61.1 624-641 None
2DS5 B6F2 Forward AGAGAGGGGACG
TTTAACC		 19 50 52.6 4 475-493 173 None
B6R3 Reverse TCCAGAGGGTCA
CTGGGC		 18 53 66.7 630-647 2DS5*003
2DL4 C1F Forward GCAGTGCCCAGC
ATCAAT		 18 52 55.6 5 808-825 83 None
C1R Reverse CCGAAGCATCTG
TAGGTCT		 19 52 52.6 872-890 2DL4*018, 2DL4*019
2DL2 2DL2F4 Forward GAGGTGGAGGCC
CATGAAT		 19 52 57.9 5 778-796 151 2DL2*009; 782G changed to A.
C3R2 Reverse TCGAGTTTGACC
ACTCGTAT		 20 51 45 909-928 None
2DS4 C5F Forward TCCCTGCAGTGC
GCAGC		 17 57 70.6 5 803-819 120 None
C5R Reverse TTGACCACTCGT
AGGGAGC		 19 52 57.9 904-922 2DS4*013
2DS4Del 2DS4Del Forward CCTTGTCCTGCA
GCTCCAT		 19 54 57.9 5 750-768 203 None
2DS4R2 Reverse TGACGGAAACAA
GCAGTGGA		 20 53 50 933-952 None
2DS4FL 2DS4FL Forward CCGGAGCTCCTA
TGACATG		 19 53 57.9 5 744-762 209 None
2DS4R2 Reverse TGACGGAAACAA
GCAGTGGA		 20 53 50 933-952 None
2DL3 D1F Forward AGACCCTCAGGA
GGTGA		 17 48 58.8 9 1180-1196 156 None
D1R Reverse CAGGAGACAACT
TTGGATCA		 20 50 45 1316-1335 2DL3*010, 2DL3*017, 2DL3*01801 and 2DL3*01802
2DL5 D2F Forward CACTGCGTTTTC
ACACAGAC		 20 52 50 9 1214-1233 120 2DL5B*011 and 2DL5B*020
D2R Reverse GGCAGGAGACAA
TGATCTT		 19 49 47.4 1315-1333 None
2DP1 D3F Forward CCTCAGGAGGTG
ACATACGT		 20 53 55 9 1184-1203 121 None
D3R Reverse TTGGAAGTTCCG
TGTACACT		 20 50 45 1285-1304 None
3DL1e9 D4F Forward CACAGTTGGATC
ACTGCGT		 19 52 52.6 9 1203-1221 93 3DL1*061, 3DL1*068
D4R2 Reverse CCGTGTACAAGA
TGGTATCTGTA		 23 53 43.5 1273-1295 3DL1*05901, 3DL1*05902, 3DL1*060, 3DL1*061, 3DL1*064, 3DL1*065, 3DL1*094N, 3DL1*098
3DL2e9 D4F Forward CACAGTTGGATC
ACTGCGT		 19 52 52.6 9 1203-1221 156 None
D5R Reverse GACCTGACTGTG
GTGCTCG		 19 54 63.2 1340-1358 None
STAT6 STAT6F Forward CCAGATGCCTAC
CATGGTGC		 20 54 60 129
STAT6R Reverse CCATCTGCACAG
ACCACTCC		 20 54 60

Table 4: Sequences of the primers used in qKAT1,27.

KIR gene 3DL3 2DS2 2DL2 2DL3 2DP1 2DL1 3DP1 2DL4 3DL1
EX9		 3DL1
EX9		 3DS1 2DL5 2DS3 2DS5 2DS1 2DS4
Total		 2DS4
FL		 2DS4
DEL		 3DL2
ex4		 3DL2
EX9
Most frequent copy number 2 1 1 2 2 2 2 2 2 2 1 1 1 1 1 2 1 1 2 2

Table 5: Most frequent copy number for KIR genes commonly observed in European-origin samples.

Linkage disequilibrium rules for qKAT based on European populations Copy number check
1 KIR3DL3, KIR3DP1,KIR2DL4 and KIR3DL2 are framework genes present on both haplotypes. KIR3DL3, KIR3DP1,KIR2DL4 and KIR3DL2 = 2
2 KIR2DS2 and KIR2DL2 are in LD with each other 2DS2=2DL2
3 KIR2DL2 and KIR2DL3 are alleles of the same gene 2DL2+2DL3=2
4 KIR2DP1 and KIR2DL1 are in LD with each other 2DP1=2DL1
5 Exon 4 of KIR3DL1 and KIR3DL2 is equal to exon 9 of KIR3DL1 and KIR3DL2 respectively. 3DL1ex4=3DL1ex9 AND 3DL2ex4=3DL2ex9
6 KIR3DL1 and KIR3DS1 are alleles 3DL1+3DS1=2
7 KIR2DS3 and KIR2DS5 are in LD with KIR2DL5 2DS3+2DS5=2DL5
8 KIR3DS1 and KIR2DS1 are in LD 3DS1=2DS1
9 Presence of  KIR2DS1 and KIR2DS4Total is mutually exclusive on a haplotype 2DS1+2DS4TOTAL=2
10 KIR2DS4FL and KIR2DS4del are variants of KIR2DS4TOTAL 2DS4FL+2DS4DEL=2DS4TOTAL

Table 6: Linkage disequilibrium between KIR genes commonly observed in European-origin populations can be used to check copy number data1,27.

Discussion

We described a novel semi-automated high-throughput method, called qKAT, which facilitates copy number typing of KIR genes. The method is an improvement over conventional methods like SSP-PCR, which are low-throughput and can only indicate the presence or absence of these highly polymorphic genes.

The accuracy of the copy number data obtained is dependent on multiple factors, including the quality and concentration-uniformity of the gDNA samples and the quality of the reagents. The quality and accuracy of the gDNA samples across a plate are extremely important since variations in concentration across the plate can result in errors in the calculation of the copy number. Since the assays were validated using European-origin sample sets, data from cohorts from other parts of the world require more thorough checks. This is to ensure that instances of allele dropout or non-specific primer/probe binding are not misinterpreted as copy number variation.

While the assays were designed and optimized to run as high-throughput, they can be modified to run fewer samples. The confidence metric in the copy number analysis software is affected when analyzing fewer samples, but this can be improved if control genomic DNA samples with a known KIR gene copy number are included on the plate and additional sample replicates are included.

For laboratories without liquid/plate-handling robots, master mix can be dispensed using multi-channel pipettes and plates can be manually loaded into the qPCR instrument.

The main aim behind the development of qKAT was to create a simple, high-throughput, high-resolution, and cost-effective method to genotype KIRs for disease association studies. This was successfully achieved since qKAT has been employed in investigating the role of KIR in several large disease association studies, including a range of infectious diseases, autoimmune conditions, and pregnancy disorders4,24,25,26.

Disclosures

The authors have nothing to disclose.

Acknowledgments

The project received funding from the Medical Research Council (MRC), the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 695551) and the National Institute of Health (NIH) Cambridge Biomedical Research Centre and NIH Research Blood and Transplant Research Unit (NIHR BTRU) in Organ Donation and Transplantation at the University of Cambridge and in partnership with NHS Blood and Transplant (NHSBT). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, the Department of Health, or the NHSBT.

Materials

Name Company Catalog Number Comments
REAGENTS
Oligonucleotides Sigma Custom order SEQUENCES: Listed in Table 4
Probes labelled with ATTO dyes Sigma Custom order SEQUENCES: Listed in Table 3
SensiFAST Probe No-ROX Kit Bioline BIO-86020 −
MilliQ water − − −
Name Company Catalog Number Comments
EQUIPMENT
Centrifuge with a swinging bucket rotor Eppendorf(or equivalent) Eppendorf 5810R or equivalent system
NanoDrop Thermo Scientific ND-2000
OR
QuBit Fluorometer Life Technologies Q33216
Matrix Hydra Thermo Scientific 109611
LightCycler 480 II Instrument 384-well Roche 05015243001
Twister II Microplate Handler with MéCour Thermal Plate Stacker (MéCour) Caliper Life Sciences 204135
Vortex mixer Biosan BS-010201-AAA
Single-channel pipettes (volume range: 0.5–10 µL, 2–20 µL, 20–200 µL, 200–1,000 µL; 1-10 mL) Gilson(or equivalent) F144801, F123600, F123615, F123602, F161201
RNase- and DNase-free pipette tips filtered (10 µL, 20 µL, 200 µL, 1,000 µL, 10 mL) Starlab (or equivalent) S1111-3810, S1120-1810, S1120-8810, S1111-6810, I1054-0001
StarTub PS Reagent Reservoir, 55 mL STARLAB E2310-1010
50 mL Centrifuge Tube STARLAB E1450-0200
96-well deep well plate Fisher Scientific 12194162
LC480 384 Multi-well plates Roche 04729749001
LightCycler 480 Sealing Foil Roche 04729757001
Name Company Catalog Number Comments
SOFTWARE
Roche LightCycler 480 Software v1.5
Applied Biosystems CopyCaller Software v2.1 https://www.thermofisher.com/uk/en/home/technical-resources/software-downloads/copycaller-software.html
KIR haplotype identifier http://www.bioinformatics.cimr.cam.ac.uk/haplotypes/

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References

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Tags

QKAT Quantitative Semi-automated Typing Killer-cell Immunoglobulin-like Receptor Genes NK Cell Receptor Genetics Haplotype Diversity Disease Association Automation Process Data Analysis High-throughput Gene Copy Number Viral Infections HIV Hepatitis C Cancer Stem Cell Transplantation Pregnancy Disorders Population Genetics Expression Functional Interactions Exons Multiplex Reactions Specific Primers PCR Amplification Real Time Monitoring Copy Number Analysis
qKAT: Quantitative Semi-automated Typing of Killer-cell Immunoglobulin-like Receptor Genes
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Jayaraman, J., Kirgizova, V., Di,… More

Jayaraman, J., Kirgizova, V., Di, D., Johnson, C., Jiang, W., Traherne, J. A. qKAT: Quantitative Semi-automated Typing of Killer-cell Immunoglobulin-like Receptor Genes. J. Vis. Exp. (145), e58646, doi:10.3791/58646 (2019).

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