Roles for HLA and KIR polymorphisms in natural killer cell repertoire selection and modulation of effector function

Interactions between killer cell immunoglobulin-like receptors (KIRs) and human leukocyte antigen (HLA) class I ligands regulate the development and response of human natural killer (NK) cells. Natural selection drove an allele-level group A KIR haplotype and the HLA-C1 ligand to unusually high frequency in the Japanese, who provide a particularly informative population for investigating the mechanisms by which KIR and HLA polymorphism influence NK cell repertoire and function. HLA class I ligands increase the frequencies of NK cells expressing cognate KIR, an effect modified by gene dose, KIR polymorphism, and the presence of other cognate ligand–receptor pairs. The five common Japanese KIR3DLI allotypes have distinguishable inhibitory capacity, frequency of cellular expression, and level of cell surface expression as measured by antibody binding. Although KIR haplotypes encoding 3DL1*001 or 3DL1*005, the strongest inhibitors, have no activating KIR, the dominant haplotype encodes a moderate inhibitor, 3DL1*01502, plus functional forms of the activating receptors 2DL4 and 2DS4. In the population, certain combinations of KIR and HLA class I ligand are overrepresented or underrepresented in women, but not men, and thus influence female fitness and survival. These findings show how KIR–HLA interactions shape the genetic and phenotypic KIR repertoires for both individual humans and the population

HLA-C levels impact natural killer cell subset distribution and function

Differences in HLA-C expression are inversely correlated with HIV viral load set-point and slower progression to AIDS, linked to enhanced cytotoxic T cell immunity. Yet, beyond T cells, HLA-C serves as a dominant ligand for natural killer (NK) cell killer immunoglobulin-like receptors (KIR). Thus, we speculated that HLA-C expression levels may also impact NK activity, thereby modulating HIV antiviral control. Phenotypic and functional profiling was performed on freshly isolated PBMCs. HLA-C expression was linked to changes in NK subset distribution and licensing, particularly in HLA-C1/C1, KIR2DL3+2DL2-individuals. Moreover, high levels of HLA-C, were associated with reduced frequencies of anergic CD56neg NKs and lower frequencies of KIR2DL1/2/3+ NK cells, pointing to an HLA-C induced influence on the NK cell development in the absence of disease. In HIV infection, several spontaneous controllers, that expressed higher levels of HLA-C demonstrated robust NK-IFN-γ secretion in response to target cells, highlighting a second disease induced licensing phenotype. Thus this population study points to a potential role for HLA-C levels both in NK cell education and development

Exploiting glycan topography for computational design of Env glycoprotein antigenicity

Mounting evidence suggests that glycans, rather than merely serving as a “shield”, contribute critically to antigenicity of the HIV envelope (Env) glycoprotein, representing critical antigenic determinants for many broadly neutralizing antibodies (bNAbs). While many studies have focused on defining the role of individual glycans or groups of proximal glycans in bNAb binding, little is known about the effects of changes in the overall glycan landscape in modulating antibody access and Env antigenicity. Here we developed a systems glycobiology approach to reverse engineer the complexity of HIV glycan heterogeneity to guide antigenicity-based de novo glycoprotein design. bNAb binding was assessed against a panel of 94 recombinant gp120 monomers exhibiting defined glycan site occupancies. Using a Bayesian machine learning algorithm, bNAb-specific glycan footprints were identified and used to design antigens that selectively alter bNAb antigenicity as a proof-of concept. Our approach provides a new design strategy to predictively modulate antigenicity via the alteration of glycan topography, thereby focusing the humoral immune response on sites of viral vulnerability for HIV

T cell–dependent production of IFN-γ by NK cells in response to influenza A virus

The role of human NK cells in viral infections is poorly understood. We used a cytokine flow-cytometry assay to simultaneously investigate the IFN-γ response of NK and T lymphocytes to influenza A virus (fluA). When PBMCs from fluA-immune adult donors were incubated with fluA, IFN-γ was produced by both CD56(dim) and CD56(bright) subsets of NK cells, as well as by fluA-specific T cells. Purified NK cells did not produce IFN-γ in response to fluA, while depletion of T lymphocytes reduced to background levels the fluA-induced IFN-γ production by NK cells, which indicates that T cells are required for the IFN-γ response of NK cells. The fluA-induced IFN-γ production of NK cells was suppressed by anti–IL-2 Ab, while recombinant IL-2 replaced the helper function of T cells for IFN-γ production by NK cells. This indicates that IL-2 produced by fluA-specific T cells is involved in the T cell–dependent IFN-γ response of NK cells to fluA. Taken together, these results suggest that at an early stage of recurrent viral infection, NK-mediated innate immunity to the virus is enhanced by preexisting virus-specific T cells

Global glycan occupancy site utilization across 94 HIV gp120s.

<p>(a) The heat map represents the N-linked glycosylation site occupancy profiles of 94 distinct recombinant gp120 proteins. Site utilization was determined by mass spectrometry, and the frequency of utilized sites at each potential glycosylation site (columns) is presented using a yellow-to-black gradient. The gray boxes depict the absence of a sequon (N-X-S/T, X≠P) at that specific site within that sequence. The right panel shows the average glycosylation site occupancy per protein. N-glycan sites were aligned based on the HXB2 sequence. Canonical N-glycan sites were designated based on the aligned sequence. Non-canonical N-glycan sites, which are not present in the HXB2 sequence, are shown in decimal numbers, based on the previously aligned N-glycan site. (b)-(e) The bar graphs show (b) the frequency of sequons present at each potential N-glycan site across all strains; (c) the mean (± standard deviation) glycan occupancy; (d) the variance of the glycosylation site occupancy (dotted line represents the top 15th percentile). (e) The N-glycan sites with the top 15% highest variance were mapped onto the BG505.SOSIP crystal structure (PDB #: 4NCO) highlighted as red. The approximate binding epitopes of various bNAbs on the Env structure are labeled in hatched circles.</p

Defining the glycosylation site determinants that shape bNAb binding profiles.

<p>(a)-(d) Four different Bayesian MCMC-SVR models were evaluated for their respective abilities to predict PGT121 binding to the 94 proteins. The models include a Bayesian MCMC-SVR model based on: (a) sequon presence (Figure A in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006093#pcbi.1006093.s001" target="_blank">S1 File</a>); (b) protein sequence; (c) glycosylation site occupancy; or (d) glycosylation site occupancy and sequence combined. Cross validation (100-iterative 10-fold) was used to evaluate model performance. Goodness-of-fit was assessed and is reported as the mean squared error (MSE) between predicted and ELISA-measured binding. (e) Heat map shows the binding signatures of individual Abs (rows), where the selected glycan sites (determinants) that mediate effects on Ab binding are highlighted. NAbs that share similar glycan determinants are grouped by hierarchical clustering. (f) The significant glycan site determinants for PGT121, PGT128, and VRC01 are plotted onto a 3-dimensional gp120 monomer structure using the same directional color coding as the heat-map. Additionally, the critical protein residues predicted by our model are shaded in yellow on the same 3D structure. Finally, broad Ab-binding sites were highlighted for each bNAb in hatched circles. (g) Agonistic and antagonistic glycan site determinants and critical protein residues for PGT122 are projected on the BG505 SOSIP.664-PGT122 co-crystal structure (PDB #: 4NCO) with the same color coding. The V3 loop is highlighted in light green shading.</p

Proof-of-concept glycoengineering of gp120 antigens to selectively enhance antigenicity.

<p>(a) Cartoon depicts the overall <i>de novo</i> antigen optimization design approach. (b) The heat maps, as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006093#pcbi.1006093.g003" target="_blank">Fig 3E</a>, depict the glycosylation site determinant profiles preferred by PGT121 and PGT128 including directional glycan coloring across all N-glycan sites (columns). (c) The top heat map represents the original wild-type MG535.W0M.ENV.D11 gp120 sequon site profile (yellow = sequon site absent and black = sequon site present); middle and bottom heat maps indicated the introduced point mutations (brown = sequon site knock-in, light blue = sequon site knock-out) for the gp120s engineered to have increased binding to PGT121 (+PGT121), PGT128 (+PGT128), and both PGT121 and PGT128 (+PGT121+PGT128); also, the gp120s engineered to selectively bind PGT121 but not PGT128 (+PGT121-PGT128), or PGT128 but not PGT121 (-PGT121+PGT128 and -PGT121+PGT128 2nd). (d) The bar graph depicts comparison of the predicted binding (beige = PGT121 and brown = PGT128) and ELISA-determined binding (light blue = PGT121, dark blue = PGT128, and grey = VRC01 binding) to the wildtype and engineered gp120s. ELISA binding activity was determined as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006093#pcbi.1006093.g002" target="_blank">Fig 2A</a>. In order to compare the model predictions to the experimental results, both the model and actual ELISA values were normalized to wild-type binding values, which were set to 1. Error bars indicate the standard deviation from six replicates. (e) The bar graph shows the degree of steric hindrance found on each antigen by summing all steric glycan site pairs (Figure J in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006093#pcbi.1006093.s001" target="_blank">S1 File</a>), if any site in the pair was considered essential for predicting Ab binding. Pink highlighted region denotes the average and range of the degree of steric hindrance across all the 94 recombinant gp120 proteins.</p