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KIR genes are known to be polymorphic in terms of gene content, copy number variation, and number of alleles. To ensure the reliability and specificity of our method, genomic DNA from well-characterized cell lines were used; high-resolution KIR typing results obtained were then compared to those previously reported.
Our results demonstrated successful long-range KIR gene amplifications of all reference samples using intergenic KIR primers. Hematopoietic stem cell transplantation HSCT provides a curative therapy for many patients with hematological malignancies 1. However, substantial risks of morbidity and mortality caused by disease relapse 2graft-vs-host-disease GvHD 3and infection 4 are still prevalent after related, or unrelated HSCT.
NK cells are able to recognize the missing-self via killer cell immunoglobulin-like receptors KIRs 6. These genes are located within the leukocyte receptor cluster found on chromosome 19q Within a population, the genotypic diversity of KIR genes occurs at different levels. KIR haplotypes are classified into group A and group B The group A haplotype is defined by a fixed set of nine KIR genes: Second, a variable number of copies [copy number variation CNV ] of the gene generated by recombination and replication have also been described for some KIR genes particularly those of the B haplotype 15 — Overall, various KIR genotypes can be observed in a population.
KIR allele polymorphisms need to be investigated throughout the exon and the intron regions, and regulatory regions as shown for KIR3DL1 Taking these points into account, it is therefore necessary to thoroughly investigate the phenotypic and functional impact of KIR allele polymorphisms. Until recently, several standard methods are used to type KIR genes at allelic level. In addition, the constant increase in the number of KIR alleles described generates more and more ambiguous KIR typing in heterozygous samples since KIR polymorphism can extend over the entire gene.
The dA-tailed DNA fragments of each sample were then subjected to adapter ligation in 9. The first cleaning and size selecting of the samples after adapter ligation were performed in a 0.
PCR cycling legranr performed as follows: A second DNA cleaning and size selecting was performed in a 0. Quality control procedure for the library preparation included verification of fragment size before and after purification by using the QiAxcel Advanced Lehrand QiAgen, Courtaboeuf, France. The pooled and barcoded libraries were denaturated with 0. The quality of the Illumina raw data sequences obtained was monitored by using the Sequencing Analysis Viewer Illumina software. In parallel, raw KIR sequences were aligned and visualized using the Profiler software version 1.
A flowchart for data legran using the Profiler software is provided in Figure S1 in Supplementary Material. The first step of analysis consists by merging R1 and R2 sequences to each other when at least 10 complementary bases were found between R1 and R2 of the same cluster.
During this phase, for each inconsistency of base calling, the quality value was used to select the best assignment. All sequences issued from a cluster for which we could not determine complementary between R1 and R2 remained unchanged.
The second legrwnd of analysis consists of leggand each of legrxnd sequences obtained in step one by using Blast algorithm. The third step of analysis consists by merging all sequences together using Blast information. In lergand step, the depth for each position and the number of sequences for each allele were calculated.
The first three steps are managed in a Linux environment. The last step is presentation and assignment of each construction based on database information in a friendly interface for user, all mismatches and differences to the database are extracted and presented to the user. For KIR allele assignment, a manual bioinformatic pipeline was first used in the absence of available softwares. Then, two different bioinformatics algorithms were used: First, raw sequences from fastq files are processed through cutadapt v1.
Coverage mean is calculated on each gene position, and a threshold of 10 is applied in order to ascertain its absence or presence.
A python script is then used to reformat the multipage alignments in order to have one allele alignment per line. 0415 second python script is utilized to extract all variations from the default reference allele and map the exon position number of these variations to the chromosome position.
A file is created for each gene listing all the variations found for every allele. Bases at these positions are then called using SAMTools v1.
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Finally, KIR alleles are determined by calculating the percentage of nucleotide matches between the base calls and the allele variations for each KIR allele, the highest percentage giving the most confident allele. After the gene name, an asterisk is used as a separator before a numerical allele designation. The first three digits of the numerical designation are used to indicate alleles that differ in the sequences of their encoded proteins.
The next two digits are used to distinguish alleles that only differ by synonymous non-coding differences within the coding sequence. The final two digits are used to distinguish alleles that only differ by substitutions in an intron, promoter, or other non-coding region of the sequence.
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DNA integrity, checked by loading each sample on an agarose gel, confirmed high quality for all samples data not shown. A Six intergenic KIR primers four forward primers: H 2 O, negative control. The sequencing of all amplicons yielded a total of 6. For all genes, the depth of coverage varies most at the llegrand and legrnd the end of the amplicons, but all key regions were sufficiently covered.
In particular, we observed that mean coverage ranged from Indeed, the lower the genomic length, the higher the mean coverage is as illustrated for the KIR3DP1 gene. The mean percentage of mapping, established by the coverage of amplicon, ranged from legrajd Full sequencing of all killer cell immunoglobulin-like receptor KIR genes. Reads were mapped to the human genome reference sequence hg19 using the Burrows—Wheeler Aligner Memory Efficient Mapping tool.
Coverage and percentage of mapping obtained for killer cell immunoglobulin-like receptor KIR genes. B Bars representing the percentage of mapping of each centromeric and telomeric KIR gene present for all IHW samples determined using Profiler software.
Results are presented according to the centromeric or telomeric localization of KIR genes on human genome. For each KIR allele, only the first three digits were taken into account for the assessment of concordance. Each bar represents one specific centromeric or telomeric KIR gene. Concordant one KIR allele matched for homozygous typing or two KIR alleles matched for heterozygous typingsemi-discordant one KIR allele mismatched for heterozygous typingand discordant KIR allele typing one KIR allele mismatched for homozygous typing or two KIR alleles mismatched for heterozygous typing were highlighted by a specific color code.
Concordances are highlighted in bold and discordances in italics. Discordant killer cell immunoglobulin-like receptor KIR typing of International Histocompatibility Workshop samples observed between typing obtained by exome capture a 47 and those obtained by next generation sequencing NGS in this study. Discrepancies are shown in italics for each KIR locus concerned.
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Allelic typing in bold represent concordant alleles. Our method of typing all KIR genes at high resolution provides an alternative, easily implemented method practice, to study the KIR allele polymorphisms. It may be a cheaper method than exome capture This tool is currently adapted to the KIR gene large-scale analysis. High quality DNA samples, high fidelity of enzyme polymerase, and a reliable library preparation were needed since evaluation legranv different Taq polymerase enzymes and library preparation kits gave conflicting results data not shown.
Our study evaluated the performance of different algorithms for KIR allele assignment.
Since all coding, non-coding, and regulatory regions were explored, one could expect that a lot of new KIR alleles will soon be described. Analysis with Profiler consists of two distinct parts. The first part is performed in three steps in a Linux environment: There are two interests in this step: The second step corresponds to the phasing of each sequence based on KIR databases using Blast algorithm.
Third, the data file from Blast was used to merge all sequences together to construct each allele. In this step, calculation for depth of each position and the number of sequences used for each allele are determined. The second part is done on a Windows environment. A friendly interface presents graphics of all sequences for all studied loci.
Assignment of all sequences is done using a database, highlighting all mismatches compared to reference and also differences between KIR alleles selected. Each allele is scored for quality control as per the European Federation for Immunogenetics guideline. However, many allelic ambiguities remained when this pipeline was used alone data not shown.
It is likely that this is due to the fact that only coding regions CDS were taken into account for allele variation comparison. Due to the time-consuming nature of manual KIR allele assignment, two different legrwnd are needed to ensure the reliability of NGS-based typing methods for the identification of KIR allele polymorphisms.
The implementation of our suitable NGS. KIR method will enable analysis of all allelic polymorphism within KIR genes extending to all coding, non-coding, and regulatory regions.
We speculate that KIR allelic lerand may affect not only the distribution and function of these gene products but also the licensing of Legran subpopulations as described for HLA class I molecules 57 MA upgraded Profiler software integrating a KIR module, analyzed data, and commented on the manuscript.
UG developed a pipeline for KIR allele assignment, analyzed data, and commented on the manuscript. EC supervised the development of a pipeline for KIR allele assignment, provided a bioinformatic help on KIR read mapping, and commented on the manuscript.
AW provided advices on the library construction setting and commented on the manuscript. CR designed the study, analyzed and interpreted data, commented on the manuscript, and contributed to writing the manuscript. KG designed the study, analyzed data, and wrote the paper. All the authors have approved the manuscript for publication.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors are most grateful to Audrey Donnart, Dr. The authors would also like to thank Dr.
The Supplementary Material for this article can be found online at http: