Natural T cells participate in cross talks between members of the innate and the adaptive immune systems by deploying cytokine/chemokine messengers. Current data suggest that natural T cells on activation in vivo promptly secrete several cytokines/chemokines. Of these cytokines/chemokines, IL-4 can skew the differentiation of CD4+ T cells toward the Th2 phenotype. GM-CSF, MIP-1α, and MIP-1β recruit, activate, and differentiate macrophages and dendritic cells resulting in the production of IL-12 and possibly other factors. IL-12, in turn, stimulates NK cells and possibly natural T cells to secrete IFN-γ. Along with IL-12, IFN-γ can polarize the differentiation of antigen-activated CD4+ T cells toward the Th1 phenotype. Thus IL-4, GM-CSF, MIP-1α, and MIP-1β can be thought of as primary cytokines, and IFN-γ can be thought of as a secondary cytokine of natural T cells. The Th1 cytokines can counterbalance Th2 and vice versa. How this counterbalance is accomplished under conditions where both Th1 and Th2 cytokines elicited by natural T cells are present simultaneously remains unclear. (See text for references.)
CTLs express activating T cell receptors (TCR) while NK cells express both inhibitory and activating receptors. No activation is triggered if the TCR recognize a self-peptide laden HLA class I molecule (a). TCR can trigger cytolysis if it detects a viral peptide loaded in the groove of HLA class I molecules of infected cells (b). Contrarily, the inhibitory receptors of NK cell recognize HLA class I molecules and trigger signals that stop spontaneous lytic activity of NK cells (c). By expressing normal levels of HLA class I molecules, the healthy cells are tolerant to NK cell lysis. Downregulation of HLA class I expression due to tumor transformation or viral infection relieves the inhibitory influence on NK cells, permitting NK cells to lyse the unhealthy target cells (d). The NK cell lysis can be augmented by further interactions between the activating receptors and putative ligands expressed upon infection or transformation (e).
KIR genes are encoded within a 150 Kb stretch of the 1 Mb long extended LRC on chromosome 19. The extended LRC also contains the genes encoding DAP adaptor proteins, CD66 antigens as well as SIGLEC, FcGRT, LILR, LAIR, FcAlphaR and NCR1 receptors. A prototypical group A KIR haplotype is shown in the right portion of the figure, where blue boxes indicate framework genes, purple boxes pseudogenes (KIR3DP1 is also a framework gene), red boxes indicate inhibitory KIR and green boxes represent activating KIR genes.
KIR genes sharing similar structural organisation have been grouped accordingly, while KIR genes with structural peculiarities are shown on their own. The coding regions of the exons are represented as blue boxes, their size in base pairs is shown in digits above them. The pseudoexon 3 and the deleted KIR3DP1 exon 2 is shown in red. The brackets at the bottom of the diagram illustrate the way in which the exons code for each protein domain and region.
The structural characteristics of two and three Ig-like domain KIR proteins are shown. The association of activating KIR to adaptor molecules is shown in green, whereas the ITIM of inhibitory KIR are shown as red boxes.
The main structural characteristics of KIR proteins are shown where the domains and regions are represented as boxes of different colours according to the key at the bottom of the figure. The length of each domain or region is shown in digits above their corresponding box.
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.
Schematic diagram of the impact of the areas in which KIR diversity may influence NK cell function. This can occur as the result of differences in gene content and allelic diversity at the KIR locus. This may result in different individuals having different numbers of activating and inhibitory KIR. These KIR are expressed stochastically on NK cells to generate a ‘KIR’ repertoire, which differs among different individuals. The presence or absence of human leucocyte antigen (HLA) class I ligands for these KIR may further impact on this repertoire, and on the functionality of these Schematic diagram of the impact of the areas in which KIR diversity may influence NK cell function. This can occur as the result of differences in gene content and allelic diversity at the KIR locus. This may result in different individuals having different numbers of activating and inhibitory KIR. These KIR are expressed stochastically on NK cells to generate a ‘KIR’ repertoire, which differs among different individuals. The presence or absence of human leucocyte antigen (HLA) class I ligands for these KIR may further impact on this repertoire, and on the functionality of these KIR-expressing subpopulations in different individuals.
Examples of the diverse array of molecules that generate diversity in the control of NK cell function are shown. This can occur at the level of the receptor, the ligand or the adaptor molecule. The signalling motifs that control these functions are illustrated: ITIM, immunoreceptor tyrosine-based inhibitory motif; ITAM, immunoreceptor tyrosine-based activating motif; ITSM, immunoreceptor tyrosine-based switch motif and the YINM motif of DAP10.
Shows comparison of coding-sequence diversity in the genomes of two Asian individuals. For the four alleles of each gene the number of single nucleotide polymorphisms (SNP) normalized to the number of codons in the gene. These values are presented in a histogram (yellow bars) and a continuous distribution (blue line). The genes form a normal distribution, with KIR3DL1/S1 , HLA-A , B , and C , and HLA-DRB1 being outliers. For the named genes, the number of allotypes described worldwide is in parentheses (112, 53). Although KIR3DL2 and KIR3DL3 have many alleles, they differ by one or a few substitutions. Per-gene summary statistics were from Wang et al. 2008 (113) and Kim et al. 2009 (114), and analyzed using ‘Statistica software version 8’.
HIV infection and growth may be suppressed in a variety of ways. 1) ‘‘Traditional neutralization’’ where the antibodies bind to virions and inhibit virion attachment and/or fusion with target cells. 2) ADCC, in which antibodies bind to viral proteins on the surface of infected cells, enabling NK cells to engage and kill the target cells. 3) ADCVI, which includes not only ADCC, but also the secretion of cytokines and/or chemokines that inhibit infection. 4) Direct killing of infected cells triggered by downregulation of HLA_A and HLA_B, loss of inhibitory signaling, and subsequent activation of NK killing. Polymorphisms in the FccRIIIa may affect functions shown in 2 and 3; polymorphisms in KIR3D may affect NK cell function diagrammed in 4.
To date, it is not fully understood how natural killer (NK) cells recognize HIV-1-infected cells, and different mechanisms have been proposed. The expression of ligands for activating NK cells receptors on infected cells, such as NKG2D-ligands, results in the direct activation of NKG2D+ NK cells and target cell lysis (a). Changes in the epitopes presented by HLA class I molecules might allow for the engagement of activating killer inhibitory receptors (KIR) receptors, and resulting NK cell activation (b). Similarly, changes in HLA class I presented epitopes on HIV-1-infected cells can result in the disruption of the binding of inhibitory KIRs, leading to NK cell activation (c). Finally, antibodies binding to HIV-1-infected cells can crosslink CD16 and activate CD16+ NK cells (d).
A summary of the binding interactions of four KIR3DL1 allotypes with nine complexes of HLA class I and a viral peptide, as determined by Thananchai et al 2007 (90). Boxes shaded green denote significant binding. Under peptide the amino acid sequence of the peptide is given and the viral pathogen from which it derives: Human immunodeficiency virus (HIV), cytomegalovirus (CMV), Epstein-Barr virus (EBV). The relationship of the D0, D1, and D2 domains for each 3DL1 allotype is shown below; blue identical to 3DL1*005 and red identical to 3DL1*015.
ORs (circle) and 95% confidence intervals (dashes) are shown for C1C1/Bw4-80/(KIR2DS4/1D) separately. The referent groups, C1C1-/Bw4-80-/(2DS4/1D)- are those with which all other genotypically defined groups are compared, and the OR for referent group is set at 1. We listed the various genotypes separately and ordered by increasing ORs as a means to compare ORs of individual genotypes (table S3). Group C1C1-/Bw4-0+/(2DS4/1D)+ was not included because only 2 subjects in HCC group and 1 subject in control group carried this genotype.
Ab, Aboriginal; NAb, Non-aboriginal; VA, Venezuelan Amerindian; CA, Caucasoid. 3DL1*051, 3DS1*010 comparisons not available. aAllele frequencies derived from Allele frequency net (28). b2n = 460; c2n = 4056; d2n = 17,680. 6Frequencies derived from low-resolution data. 1Odds Ratio:infinite; 2Odds Ratio:5.460(CI95%:2.717–10.970); 3Odds Ratio:2.163(CI95%:1.019–4.593); 4For 2DL2, Odds Ratio:0.390(CI95%:0.169–0.902); For 2DL3, Odds Ratio:2.563(CI95%:1.109–5.923). Ab, Aboriginal; NAb, Non-aboriginal; VA, Venezuelan Amerindian; CA, Caucasoid. 3DL1*051, 3DS1*010 comparisons not available. aAllele frequencies derived from Allele frequency net (28). b2n = 460; c2n = 4056; d2n = 17,680. 6Frequencies derived from low-resolution data. 1Odds Ratio:infinite; 2Odds Ratio:5.460(CI95%:2.717–10.970); 3Odds Ratio:2.163(CI95%:1.019–4.593); 4For 2DL2, Odds Ratio:0.390(CI95%:0.169–0.902); For 2DL3, Odds Ratio:2.563(CI95%:1.109–5.923).
Killer Cell Immunoglobulin-Like Receptor (KIR) Genes and Association with Human Viral Infections
Killer Cell Immunoglobulin-Like Receptor (KIR) Genes and Association with Human Viral Infections Mirko Spiroski, MD, PhDInstitute of Immunobiology and Human Genetics, Faculty of Medicine,University "Ss. Cyril and Methodius", Skopje, Republic of Macedonia http://www.iibhg.ukim.edu.mk/ e-mail: email@example.com FB: mirko.spiroski
At a glance ...• Natural killer cells• Leukocyte Receptor Complex• KIR genes• KIR genes diversity• Determination of KIR genes• Diversity of KIR genes• Association of KIR genes with viruses 2
A model for natural T cells role within the immune system 3
Natural killer (NK) cells and cytotoxic T cells (CTL) are professional killer cells and share several common featuresbut differ by their HLA class I-specific receptors that are used to distinguish unhealthy targets from the healthy host cells 4
The extended Leukocyte Receptor Complex(19q13.4) and a KIR haplotype 5
KIR gene frequencies inMacedonian population 13
KIR genotypesin Macedonianpopulation (n = 214) 14
France Argentina IrelandNorthern Italy BelgiumKIRPopKIR genetic Lebanon Reunion SwedenVasterbottenthree of 29 Macedonia SpainGranada world MexicoVeraCruzMestizos EnglandWestMidlandsCaucasian EnglandWestMidlandsIndianAsianpopulations Basque Africans EnglandWestMidlandsAfroCaribbean Guadeloupe Senegal Koreans Japanese Finland Mestizo Tarahumara Huichol Purepecha IndiaNorthHindus SouthAsians PakistanKarachi BrazilSouthEastCaucasian 0.020 0.015 0.010 0.005 150.000
Interaction between KIR genes and their ligand (HLA) 16
Killer cell immunoglobulin-like receptor(KIR) diversity can impact natural killer (NK) cells at different levels 17
Natural killer (NK) cells are diverse at the level of receptors, ligands and signalling adaptors 18
Diversity of KIR genes and their ligandsheredited in offspring from the parents with different KIR and HLA genotypes ... 19
... Diversity of KIR genes and their ligandsheredited in offspring from the parents with different KIR and HLA genotypes ... 20
KIR3DL1/S1, like HLA-A, B, C and HLA-DRB1, is one of the most highly polymorphichuman genes 21
Association of KIR with viruses• HIV-1• HCV• HBV• (H1N1)pdm90• West Nile Virus 22
Model for the engagement of NK cells to mediate HIV inhibition 23
NK cell-mediated recognition of HIV-1 infected cells 24
KIR3DL1 polymorphism affects specificity for HLA class I 25
KIR gene frequencies in control and patients with chronic hepatitis C viral infection 26
Combined effect HLA-C1C1/Bw4-80/(KIR2DS4/1D) on hepatocellular carcinoma(HCC) occurrence in HBV-infected patients 27
Comparison of KIR3DL1/S1 allele frequencies between (H1N1)pdm09 ICUpatients and analogous world populations 28
Percentages of carriers of inhibitory, activating, and pseudogene KIR genes in a control Gabonese population and in contacts (IgG+), survivors, and fatalities of Ebola virus infection 29
Comparison of the observed and estimated KIRgene frequencies for critically ill Macedonian patients with pandemic influenza A (H1N1)pdm09 infection (N = 63) and healthy Macedonians (N=214)N, number of individuals; F, observed frequency was obtained by direct counting; GF, genefrequencies were calculated using the formula GF=1-√(1-F); p, statistical significance; &,cannot be calculated because expected <5, χ2 test; OR, Odds ratio; CI,confidence interval. 30
KIR locus haplogroups, genotypes ID and genotype frequency of critically ill Macedonian patients with pandemic influenza A (H1N1)pdm09 infection (N = 63) and corresponding frequencies in healthy Macedonians (n=214)KIR Genotype [1=Positive, 0=negative] 31
Comparison of the observed and estimated KIR gene frequencies for Macedonian patients with West Nile Virus (WNV) infection (N = 4) and healthy Macedonians (N=214)N, number of individuals; F, observed frequency was obtained by direct counting; GF, genefrequencies were calculated using the formula GF=1-(1-F); p, statistical significance; &, cannot becalculated because expected <5, 2 test; OR, Odds ratio; CI, confidence interval. 32
KIR genotype frequencies in Macedonianpatients with West Nile Virus (WNV) infection(N = 4) and healthy Macedonians (N=214) N, number of individuals displaying certain Bx KIR genotype; F, frequency of KIR genotype; CI, confidence interval. &, cannot be calculated. 33
Conclusions• Associations of KIR genes, alleles, genotypes and haplotypes are found with some viral infections (HIV-1, HCV, HBV, (H1N1)pdm09, Ebola, and WNV).• We expect intensive investigations about the susceptibility and/or resistance to viral infections connected with KIR genes and their ligands. 34
AcknowledgementAssociate Professor Dr. Dejan TrajkovAssistant Dr. Aleksandar PetlichkovskiAssistant Dr. Slavica HristomanovaAssistant Dr. Meri KirijasAssistant Dr. Aleksandar SenevBiotechnologist Olivija Efinska-MladenovskaMolecular biolog Olgica SibinovskaLaboratory technician Elena Cvetkovska 35