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

Jyothi Jayaraman; Vitalina Kirgizova; Da Di; Christopher Johnson; Wei Jiang; James A. Traherne

doi:10.3791/58646

JoVE Journal > Genetics

遗传学

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

Published: March 06, 2019

doi:

10.3791/58646

Jyothi Jayaraman^1,2,3,4, Vitalina Kirgizova¹, Da Di^1,5, Christopher Johnson^1,6, Wei Jiang^1,7, James A. Traherne¹

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

Accurately quantify the gDNA concentration using a spectrophotometric or fluorometric instrument.
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.
Centrifuge the 96-well plates at 450 x g for 2 min.
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.
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.
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).

Prepare primer combinations for each reaction as per the dilutions given in Table 1.
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.

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.
Defrost the qPCR buffer, primer, and probe aliquots at 4 °C.
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).
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.
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.
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

Following the overnight incubation, centrifuge at 450 x g for 3 min to dissipate any remaining air bubbles.
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.
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.
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

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.
Open the qPCR software and, in the Navigator tab, open the saved reaction experiment file for one plate.
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.
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

In the qPCR software, open the Navigator tab. Select Results Batch Export.
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.
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.
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.
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

Open the copy number analysis software (e.g., CopyCaller). Select Import real-time PCR results file and load text files created by roche2sds.pl.
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

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.
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 (C_t) between samples and discordance in the range of the estimated copy number. The results in Figure 2 show the effect the disparity between the C_t 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: 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: 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: 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	3DL2008, 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		3DL3054, 3DL300905.
	A8R	Reverse	AGTTGACCTGGG AACCCG	18	51	61.1		526-543		None
3DL1e4	B1F	Forward	CATCGGTCCCAT GATGCT	18	51	55.6	4	549-566	85	3DL100505, 3DL1006, 3DL1054, 3DL1086, 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	3DS1047; may pick up 3DL1054.
	B1R	Reverse	GGGAGCTGACAA CTGATAGG	20	52	55		614-633		None
2DL1	B3F	Forward	TTCTCCATCAGT CGCATGAC	20	52	50	4	544-563	96	2DL1020, 2DL1028
	B3R	Reverse	GTCACTGGGAGC TGACAC	18	50	61.1		622-639		2DL1023, 2DL1029, 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		2DL4018, 2DL4019
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		2DL3010, 2DL3017, 2DL301801 and 2DL301802
2DL5	D2F	Forward	CACTGCGTTTTC ACACAGAC	20	52	50	9	1214-1233	120	2DL5B011 and 2DL5B020
	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	3DL1061, 3DL1068
	D4R2	Reverse	CCGTGTACAAGA TGGTATCTGTA	23	53	43.5		1273-1295		3DL105901, 3DL105902, 3DL1060, 3DL1061, 3DL1064, 3DL1065, 3DL1094N, 3DL1098
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.

Acknowledgements

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

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/

References

Jiang, W., et al. Copy number variation leads to considerable diversity for B but not A haplotypes of the human KIR genes encoding NK cell receptors. Genome Research. 22, 1845-1854 (2012).
Nemat-Gorgani, N., et al. Different Selected Mechanisms Attenuated the Inhibitory Interaction of KIR2DL1 with C2 + HLA-C in Two Indigenous Human Populations in Southern Africa. The Journal of Immunology. 200, 2640-2655 (2018).
Norman, P. J., et al. Co-evolution of human leukocyte antigen (HLA) class I ligands with killer-cell immunoglobulin-like receptors (KIR) in a genetically diverse population of sub-Saharan Africans. PLoS Genetics. 9, e1003938 (2013).
Nakimuli, A., et al. Killer cell immunoglobulin-like receptor (KIR) genes and their HLA-C ligands in a Ugandan population. Immunogenetics. 65, 765-775 (2013).
Bontadini, A., et al. Distribution of killer cell immunoglobin-like receptors genes in the Italian Caucasian population. Journal of Translational Medicine. 4, 1-9 (2006).
Graef, T., et al. KIR2DS4 is a product of gene conversion with KIR3DL2 that introduced specificity for HLA-A*11 while diminishing avidity for HLA-C. The Journal of Experimental Medicine. 206, 2557-2572 (2009).
Béziat, V., Hilton, H. G., Norman, P. J., Traherne, J. A. Deciphering the killer-cell immunoglobulin-like receptor system at super-resolution for natural killer and T-cell biology. Immunology. 150, 248-264 (2017).
Blokhuis, J. H., et al. KIR2DS5 allotypes that recognize the C2 epitope of HLA-C are common among Africans and absent from Europeans. Immunity, Inflammation and Disease. 5, 461-468 (2017).
Martin, M. P., et al. Epistatic interaction between KIR3DS1 and HLA-B delays the progression to AIDS. Nature Genetics. 31, 429-434 (2002).
Khakoo, S. I., et al. HLA and NK cell inhibitory receptor genes in resolving hepatitis C virus infection. Science. 305, 872-874 (2004).
van Bergen, J., et al. KIR-ligand mismatches are associated with reduced long-term graft survival in HLA-compatible kidney transplantation. American Journal of Transplantation. 11, 1959-1964 (2011).
Hiby, S. E., et al. Association of maternal killer – cell immunoglobulin-like receptors and parental HLA – C genotypes with recurrent miscarriage. Human Reproduction. 23, 972-976 (2008).
Nakimuli, A., et al. A KIR B centromeric region present in Africans but not Europeans protects pregnant women from pre-eclampsia. Proceedings of the National Academy of Sciences. 112, 845-850 (2015).
van Bergen, J., et al. HLA reduces killer cell Ig-like receptor expression level and frequency in a humanized mouse model. The Journal of Immunology. 190, 2880-2885 (2013).
Bachanova, V., et al. Donor KIR B Genotype Improves Progression-Free Survival of Non-Hodgkin Lymphoma Patients Receiving Unrelated Donor Transplantation. Biology of Blood and Marrow Transplantation. 22, 1602-1607 (2016).
Cooley, S., et al. Donor selection for natural killer cell receptor genes leads to superior survival after unrelated transplantation for acute myelogenous leukemia. Blood. 116, 2411-2419 (2010).
Barani, S., Khademi, B., Ashouri, E., Ghaderi, A. KIR2DS1, 2DS5, 3DS1 and KIR2DL5 are associated with the risk of head and neck squamous cell carcinoma in Iranians. Human Immunology. 79, 218-223 (2018).
Vilches, C., Castaño, J., Gómez-Lozano, N., Estefanía, E. Facilitation of KIR genotyping by a PCR-SSP method that amplifies short DNA fragments. Tissue Antigens. 70, 415-422 (2007).
Ashouri, E., Ghaderi, A., Reed, E. F., Rajalingam, R. A novel duplex SSP-PCR typing method for KIR gene profiling. Tissue Antigens. 74, 62-67 (2009).
Martin, M. P., Carrington, M. KIR locus polymorphisms: genotyping and disease association analysis. Methods in Molecular Biology. , 49-64 (2008).
Crum, K. A., Logue, S. E., Curran, M. D., Middleton, D. Development of a PCR-SSOP approach capable of defining the natural killer cell inhibitory receptor (KIR) gene sequence repertoires. Tissue Antigens. 56, 313-326 (2000).
Houtchens, K. A., et al. High-throughput killer cell immunoglobulin-like receptor genotyping by MALDI-TOF mass spectrometry with discovery of novel alleles. Immunogenetics. 59, 525-537 (2007).
Livak, K. J., Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2-ΔΔCT method. Methods. 25, 402-408 (2001).
Traherne, J. A., et al. KIR haplotypes are associated with late-onset type 1 diabetes in European-American families. Genes and Immunity. 17, 8-12 (2016).
Hydes, T. J., et al. The interaction of genetic determinants in the outcome of HCV infection: Evidence for discrete immunological pathways. Tissue Antigens. 86, 267-275 (2015).
Dunphy, S. E., et al. 2DL1, 2DL2 and 2DL3 all contribute to KIR phenotype variability on human NK cells. Genes and Immunity. 16, 301-310 (2015).
Jiang, W., et al. qKAT: A high-throughput qPCR method for KIR gene copy number and haplotype determination. Genome Medicine. 8, 1-11 (2016).

Play Video

PDF

DOI

DOWNLOAD MATERIALS LIST

Cite This Article

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).