Activation or tolerance of natural killer cells is modulated by ligand quality in a nonmonotonic manner

KIR2DS1 and KIR2DL1-C245 Dominantly Repress NK Cell Degranulation Triggered by Monoclonal or Bispecific Antibodies, whereas Education by Uptuning Inhibitory Killer Ig-related Receptors Exerts No Advantage in Ab-dependent Cellular Cytotoxicity

NK cell responsiveness to target cells is tuned by interactions between inhibitory NK cell receptors and their cognate HLA class I ligands in a process termed "NK cell education." Previous studies addressing the role for NK cell education in Ab-dependent cellular cytotoxicity (ADCC) show ambiguous results and do not encompass full educational resolution. In this study, we systematically characterized human NK cell CD16-triggered degranulation toward defined human tumor cell lines in the presence of either the mAb rituximab or a recently developed CD34xCD16 bispecific killer engager. Despite positive correlation between killer Ig-related receptor (KIR)-mediated education and CD16 expression, NK cells educated by one or even two inhibitory KIRs did not perform better in terms of ADCC than uneducated NK cells in either missing-self or KIR-ligand matched settings at saturating Ab concentrations. Instead, NKG2A+ NK cells consistently showed more potent ADCC in the missing-self context despite lower levels of CD16 expression. KIR2DS1+ NK cells demonstrated dampened ADCC in both the missing-self and KIR-ligand matched settings, even in the presence of its ligand HLA C2. The lower response by KIR2DS1+ NK cells was also observed when stimulated with a bispecific killer engager. Surprisingly, repression of ADCC was also observed by NKG2A+ NK cells coexpressing the inhibitory KIR2DL1-C245 receptor that confers weak education. In conclusion, our study suggests that NK cell education by inhibitory KIRs does not augment ADCC per se, whereas expression of KIR2DS1 and KIR2DL1-C245 dominantly represses ADCC. These insights add to the fundamental understanding of NK cells and may have implications for their therapeutic use.

BioNetGMMFit: estimating parameters of a BioNetGen model from time-stamped snapshots of single cells

Mechanistic models are commonly employed to describe signaling and gene regulatory kinetics in single cells and cell populations. Recent advances in single-cell technologies have produced multidimensional datasets where snapshots of copy numbers (or abundances) of a large number of proteins and mRNA are measured across time in single cells. The availability of such datasets presents an attractive scenario where mechanistic models are validated against experiments, and estimated model parameters enable quantitative predictions of signaling or gene regulatory kinetics. To empower the systems biology community to easily estimate parameters accurately from multidimensional single-cell data, we have merged a widely used rule-based modeling software package BioNetGen, which provides a user-friendly way to code for mechanistic models describing biochemical reactions, and the recently introduced CyGMM, that uses cell-to-cell differences to improve parameter estimation for such networks, into a single software package: BioNetGMMFit. BioNetGMMFit provides parameter estimates of the model, supplied by the user in the BioNetGen markup language (BNGL), which yield the best fit for the observed single-cell, time-stamped data of cellular components. Furthermore, for more precise estimates, our software generates confidence intervals around each model parameter. BioNetGMMFit is capable of fitting datasets of increasing cell population sizes for any mechanistic model specified in the BioNetGen markup language. By streamlining the process of developing mechanistic models for large single-cell datasets, BioNetGMMFit provides an easily-accessible modeling framework designed for scale and the broader biochemical signaling community.

Profiling of Natural Killer Interactions With Cancer Cells Using Mass Cytometry

We developed a comprehensive method for functional assessment of the changes in immune populations and killing activity of peripheral blood mononuclear cells after cocultures with cancer cells using mass cytometry. In this study, a 43-marker mass cytometry panel was applied to a coculture of immune cells from healthy donors' peripheral blood mononuclear cells with diverse cancer cell lines. DNA content combined with classical CD45 surface staining was used as gating parameters for cocultures of immune cells (CD45high/DNAlow) with hematological (CD45low/DNAhigh) and solid cancer cell lines (CD45neg/DNAhigh). This strategy allows for universal discrimination of cancer cells from immune populations without the need for a specific cancer cell marker and simultaneous assessment of phenotypical changes in both populations. The use of mass cytometry allows for simultaneous detection of changes in natural killer, natural killer T cell, and T cell phenotypes and degranulation of immune populations upon target recognition, analysis of target cells for cytotoxic protein granzyme B content, and cancer cell death. These findings have broad applicability in research and clinical settings with the aim to phenotype and assess functional changes following not only NK-cancer cell interactions but also the effect of those interactions on other immune populations.

McSNAC: A software to approximate first-order signaling networks from mass cytometry data

Background

Mass cytometry (CyTOF) gives unprecedented opportunity to simultaneously measure up to 40 proteins in single cells, with a theoretical potential to reach 100 proteins. This high-dimensional single-cell information can be very useful in dissecting mechanisms of cellular activity. In particular, measuring abundances of signaling proteins like phospho-proteins can provide detailed information on the dynamics of single-cell signaling processes. However, computational analysis is required to reconstruct such networks with a mechanistic model.

Methods

We propose our Mass cytometry Signaling Network Analysis Code (McSNAC), a new software capable of reconstructing signaling networks and estimating their kinetic parameters from CyTOF data. McSNAC approximates signaling networks as a network of first-order reactions between proteins. This assumption often breaks down as signaling reactions can involve binding and unbinding, enzymatic reactions, and other nonlinear constructions. Furthermore, McSNAC may be limited to approximating indirect interactions between protein species, as cytometry experiments are only able to assay a small fraction of protein species involved in signaling.

Results

We carry out a series of in silico experiments here to show (1) McSNAC is capable of accurately estimating the ground-truth model in a scalable manner when given data originating from a first-order system; (2) McSNAC is capable of qualitatively predicting outcomes to perturbations of species abundances in simple second-order reaction models and in a complex in silico nonlinear signaling network in which some proteins are unmeasured.

Conclusions

These findings demonstrate that McSNAC can be a valuable screening tool for generating models of signaling networks from time-stamped CyTOF data.

Improving NK cell function in multiple myeloma with NKTR-255, a novel polymer-conjugated human IL-15

Multiple myeloma (MM) is characterized by an immunosuppressive microenvironment that enables tumor development. One of the mechanisms of immune evasion used by MM cells is the inhibition of natural killer (NK) cell effector functions; thus, the restoration of NK cell antitumor activity represents a key goal to increase tumor cell recognition, avoid tumor escape and potentially enhancing the effect of other drugs. In this study, we evaluated the ability of the investigational medicine NKTR-255, an IL-15 receptor agonist, to engage the IL-15 pathway and stimulate NK cells against MM cells. We observed that incubation with NKTR-255 was able to tilt the balance toward an activated phenotype in NK cells isolated from peripheral blood mononuclear cells of patients with MM, with increased expression of activating receptors on the surface of treated NK cells. This resulted in an enhanced degranulation, cytokine release, and anti-tumor cytotoxicity when the NK cells were exposed to both MM cell lines and primary MM cells. We further evaluated the in vivo effect of NKTR-255 in fully humanized immunocompetent mice subcutaneously engrafted with H929 MM cells. Compared with placebo, weekly injection of the mice with NKTR-255 increased the number of circulating NK cells in peripheral blood and delayed tumor growth. Finally, we observed that combination of NKTR-255 with the anti-CD38 antibody, daratumumab, was effective against MM cells in vitro and in vivo. Taken together, our data suggest a significant impact of NKTR-255 in inducing NK cell function against MM cells with important translational implications.

Spatially resolved in silico modeling of NKG2D signaling kinetics suggests a key role of NKG2D and Vav1 Co-clustering in generating natural killer cell activation

Natural Killer (NK) cells provide key resistance against viral infections and tumors. A diverse set of activating and inhibitory NK cell receptors (NKRs) interact with cognate ligands presented by target host cells, where integration of dueling signals initiated by the ligand-NKR interactions determines NK cell activation or tolerance. Imaging experiments over decades have shown micron and sub-micron scale spatial clustering of activating and inhibitory NKRs. The mechanistic roles of these clusters in affecting downstream signaling and activation are often unclear. To this end, we developed a predictive in silico framework by combining spatially resolved mechanistic agent based modeling, published TIRF imaging data, and parameter estimation to determine mechanisms by which formation and spatial movements of activating NKG2D microclusters affect early time NKG2D signaling kinetics in a human cell line NKL. We show co-clustering of NKG2D and the guanosine nucleotide exchange factor Vav1 in NKG2D microclusters plays a dominant role over ligand (ULBP3) rebinding in increasing production of phospho-Vav1(pVav1), an activation marker of early NKG2D signaling. The in silico model successfully predicts several scenarios of inhibition of NKG2D signaling and time course of NKG2D spatial clustering over a short (~3 min) interval. Modeling shows the presence of a spatial positive feedback relating formation and centripetal movements of NKG2D microclusters, and pVav1 production offers flexibility towards suppression of activating signals by inhibitory KIR ligands organized in inhomogeneous spatial patterns (e.g., a ring). Our in silico framework marks a major improvement in developing spatiotemporal signaling models with quantitatively estimated model parameters using imaging data.

Natural Killer cells are lymphocytes of our innate immunity and provide important resistance against viral infections and tumors. NK cells scan the local environment with diverse activating and inhibitory NK cell receptors (NKRs) and remain tolerized or lyse target cells expressing cognate ligands to NKRs. NKRs have been found to form micron sized clusters (or microclusters) as they interact with cognate ligands, and mechanisms regarding how the formation and movements of these microclusters influence NK cell signaling and activation, specifically related to activating NKRs, are often unclear. To this end, we develop a predictive spatially resolved early-time NK cell signaling model to study the interplay between membrane-proximal biochemical signaling events and the kinetics of microclusters of activating NKG2D and inhibitory KIR2DL2 receptors. We used published TIRF imaging data to validate our in silico models and estimate model parameters. Predictions from multiple in silico models are tested against a variety of data obtained from published imaging experiments and immunoassays. Our analysis suggests co-clustering of NKG2D and the guanosine nucleotide exchange factor Vav1 in the microclusters plays a major role in enhancing downstream activating signals. The developed framework can be extended to describe spatiotemporal signaling for other activating NKRs including CD16.

Inhibition of SHP-1 activity by PKC-θ regulates NK cell activation threshold and cytotoxicity

Natural killer (NK) cells play a crucial role in immunity, killing virally infected and cancerous cells. The balance of signals initiated upon engagement of activating and inhibitory NK receptors with cognate ligands determines killing or tolerance. Nevertheless, the molecular mechanisms regulating rapid NK cell discrimination between healthy and malignant cells in a heterogeneous tissue environment are incompletely understood. The SHP-1 tyrosine phosphatase is the central negative NK cell regulator that dephosphorylates key activating signaling proteins. Though the mechanism by which SHP-1 mediates NK cell inhibition has been partially elucidated, the pathways by which SHP-1 is itself regulated remain unclear. Here, we show that phosphorylation of SHP-1 in NK cells on the S591 residue by PKC-θ promotes the inhibited SHP-1 ‘folded’ state. Silencing PKC-θ maintains SHP-1 in the active conformation, reduces NK cell activation and cytotoxicity, and promotes tumor progression in vivo. This study reveals a molecular pathway that sustains the NK cell activation threshold through suppression of SHP-1 activity.

Dynamic variability in SHP-1 abundance determines natural killer cell responsiveness

[Figure: see text].

An optimal control approach for enhancing natural killer cells' secretion of cytolytic molecules

Natural killer (NK) cells are immune effector cells that can detect and lyse cancer cells. However, NK cell exhaustion, a phenotype characterized by reduced secretion of cytolytic models upon serial stimulation, limits the NK cell's ability to lyse cells. In this work, we investigated in silico strategies that counteract the NK cell's reduced secretion of cytolytic molecules. To accomplish this goal, we constructed a mathematical model that describes the dynamics of the cytolytic molecules granzyme B (GZMB) and perforin-1 (PRF1) and calibrated the model predictions to published experimental data using a Bayesian parameter estimation approach. We applied an information-theoretic approach to perform a global sensitivity analysis, from which we found that the suppression of phosphatase activity maximizes the secretion of GZMB and PRF1. However, simply reducing the phosphatase activity is shown to deplete the cell's intracellular pools of GZMB and PRF1. Thus, we added a synthetic Notch (synNotch) signaling circuit to our baseline model as a method for controlling the secretion of GZMB and PRF1 by inhibiting phosphatase activity and increasing production of GZMB and PRF1. We found that the optimal synNotch system depends on the frequency of NK cell stimulation. For only a few rounds of stimulation, the model predicts that inhibition of phosphatase activity leads to more secreted GZMB and PRF1; however, for many rounds of stimulation, the model reveals that increasing production of the cytolytic molecules is the optimal strategy. In total, we developed a mathematical framework that provides actionable insight into engineering robust NK cells for clinical applications.

Enhancing network activation in natural killer cells: predictions from in silico modeling

Natural killer (NK) cells are part of the innate immune system and are capable of killing diseased cells. As a result, NK cells are being used for adoptive cell therapies for cancer patients. The activation of NK cell stimulatory receptors leads to a cascade of intracellular phosphorylation reactions, which activates key signaling species that facilitate the secretion of cytolytic molecules required for cell killing. Strategies that maximize the activation of such intracellular species can increase the likelihood of NK cell killing upon contact with a cancer cell and thereby improve efficacy of NK cell-based therapies. However, due to the complexity of intracellular signaling, it is difficult to deduce a priori which strategies can enhance species activation. Therefore, we constructed a mechanistic model of the CD16, 2B4 and NKG2D signaling pathways in NK cells to simulate strategies that enhance signaling. The model predictions were fit to published data and validated with a separate dataset. Model simulations demonstrate strong network activation when the CD16 pathway is stimulated. The magnitude of species activation is most sensitive to the receptor's initial concentration and the rate at which the receptor is activated. Co-stimulation of CD16 and NKG2D in silico required fewer ligands to achieve half-maximal activation than other combinations, suggesting co-stimulating these pathways is most effective in activating the species. We applied the model to predict the effects of perturbing the signaling network and found two strategies that can potently enhance network activation. When the availability of ligands is low, it is more influential to engineer NK cell receptors that are resistant to proteolytic cleavage. In contrast, for high ligand concentrations, inhibiting phosphatase activity leads to sustained species activation. The work presented here establishes a framework for understanding the complex, nonlinear aspects of NK cell signaling and provides detailed strategies for enhancing NK cell activation.

Data analysis to modeling to building theory in NK cell biology and beyond: How can computational modeling contribute?

The use of mathematical and computational tools in investigating Natural Killer (NK) cell biology and in general the immune system has increased steadily in the last few decades. However, unlike the physical sciences, there is a persistent ambivalence, which however is increasingly diminishing, in the biology community toward appreciating the utility of quantitative tools in addressing questions of biological importance. We survey some of the recent developments in the application of quantitative approaches for investigating different problems in NK cell biology and evaluate opportunities and challenges of using quantitative methods in providing biological insights in NK cell biology.

Spatial Clustering of Receptors and Signaling Molecules Regulates NK Cell Response to Peptide Repertoire Changes

Natural Killer (NK) cell activation requires integration of inhibitory and activating signaling. Inhibitory signals are determined by members of the killer cell immunoglobulin-like receptor (KIR) family, which have major histocompatibility complex (MHC) class I ligands. Loss of this inhibitory signal leads to NK cell activation. Thus, down-regulation of MHC I during viral infection or cancer induces NK cell activation. However, NK cell activation in the presence of MHC-I has been demonstrated for HLA-C^*0102 through changes in its peptide content: "peptide antagonism." Here we identify an antagonist peptide for HLA-C^*0304 suggesting that peptide antagonism is a generalizable phenomenon and, using a combination of mathematical modeling, confocal imaging, and immune-assays, we quantitatively determine mechanisms that underlie peptide antagonism in inhibitory KIR2DL2/3 signaling. These data provide a mechanism for NK cell activation based on a reduction of inhibitory signaling in the presence of preserved levels of MHC class I.

Crystal Structures of Spleen Tyrosine Kinase in Complex with Two Novel 4-Aminopyrido[4,3-d] Pyrimidine Derivative Inhibitors

Spleen tyrosine kinase (SYK) is a cytosolic non-receptor protein tyrosine kinase. Because SYK mediates key receptor signaling pathways involving the B cell receptor and Fc receptors, SYK is an attractive target for autoimmune disease and cancer treatments. To date, representative oral SYK inhibitors, including fostamatinib (R406 or R788), entospletinib (GS-9973), cerdulatinib (PRT062070), and TAK-659, have been assessed in clinical trials. Here, we report the crystal structures of SYK in complex with two newly developed inhibitors possessing 4-aminopyrido[4,3-D]pyrimidine moieties (SKI-G-618 and SKI-O-85). One SYK inhibitor (SKI-G-618) exhibited moderate inhibitory activity against SYK, whereas the other inhibitor (SKI-O-85) exhibited a low inhibitory profile against SYK. Binding mode analysis indicates that a highly potent SYK inhibitor might be developed by modifying and optimizing the functional groups that interact with Leu377, Gly378, and Val385 in the G-loop and the nearby region in SYK. In agreement with our structural analysis, one of our SYK inhibitor (SKI-G-618) shows strong inhibitory activities on the β-hexosaminidase release and phosphorylation of SYK/Vav in RBL-2H3 cells. Taken together, our findings have important implications for the design of high affinity SYK inhibitors.

In silico modeling identifies CD45 as a regulator of IL-2 synergy in the NKG2D-mediated activation of immature human NK cells

Natural killer (NK) cells perform immunosurveillance of virally infected and transformed cells, and their activation depends on the balance between signaling by inhibitory and activating receptors. Cytokine receptor signaling can synergize with activating receptor signaling to induce NK cell activation. We investigated the interplay between the signaling pathways stimulated by the cytokine interleukin-2 (IL-2) and the activating receptor NKG2D in immature (CD56^bright) and mature (CD56^dim) subsets of human primary NK cells using mass cytometry experiments and in silico modeling. Our analysis revealed that IL-2 changed the abundances of several key proteins, including NKG2D and the phosphatase CD45. Furthermore, we found differences in correlations between protein abundances, which were associated with the maturation state of the NK cells. The mass cytometry measurements also revealed that the signaling kinetics of key protein abundances induced by NKG2D stimulation depended on the maturation state and the pretreatment condition of the NK cells. Our in silico model, which described the multidimensional data with coupled first-order reactions, predicted that the increase in CD45 abundance was a major enhancer of NKG2D-mediated activation in IL-2-treated CD56^bright NK cells but not in IL-2-treated CD56^dim NK cells. This dependence on CD45 was verified by measurement of CD107a mobilization to the NK cell surface (a marker of activation). Our mathematical framework can be used to glean mechanisms underlying synergistic signaling pathways in other activated immune cells.

Prenatal Allospecific NK Cell Tolerance Hinges on Instructive Allorecognition through the Activating Receptor during Development

Little is known about how the prenatal interaction between NK cells and alloantigens shapes the developing NK cell repertoire toward tolerance or immunity. Specifically, the effect on NK cell education arising from developmental corecognition of alloantigens by activating and inhibitory receptors with shared specificity is uncharacterized. Using a murine prenatal transplantation model, we examined the manner in which this seemingly conflicting input affects NK cell licensing and repertoire formation in mixed hematopoietic chimeras. We found that prenatal NK cell tolerance arose from the elimination of phenotypically hostile NK cells that express an allospecific activating receptor without coexpressing any allospecific inhibitory receptors. Importantly, the checkpoint for the system appeared to occur centrally within the bone marrow during the final stage of NK cell maturation and hinged on the instructive recognition of allogeneic ligand by the activating receptor rather than through the inhibitory receptor as classically proposed. Residual nondeleted hostile NK cells expressing only the activating receptor exhibited an immature, anergic phenotype, but retained the capacity to upregulate inhibitory receptor expression in peripheral sites. However, the potential for this adaptive change to occur was lost in developmentally mature chimeras. Collectively, these findings illuminate the intrinsic process in which developmental allorecognition through the activating receptor regulates the emergence of durable NK cell tolerance and establishes a new paradigm to fundamentally guide future investigations of prenatal NK cell-allospecific education.

The limits of chemosensation vary across dimensions

Many biological processes rely on the ability of cells to measure local ligand concentration. However, such measurements are constrained by noise arising from diffusion and the stochastic nature of receptor–ligand interactions. It is thus critical to understand how accurately, in principle, concentration measurements can be made. Previous theoretical work has mostly investigated this in 3D under the simplifying assumption of an unbounded domain of diffusion, but many biological problems involve 2D concentration measurement in bounded domains, for which diffusion behaves quite differently. Here we present a theory of the precision of chemosensation that covers bounded domains of any dimensionality. We find that the quality of chemosensation in lower dimensions is controlled by domain size, suggesting a general principle applicable to many biological systems. Applying the theory to biological problems in 2D shows that diffusion-limited signalling is an efficient mechanism on time scales consistent with behaviour.

Theoretical studies on chemosensation often invoke a model of three dimensional unbounded diffusion, but many biological problems involve two-dimensional diffusion in a bounded domain. Here Bicknell et al. present a model for chemosensation that covers bounded domains of any dimension, and apply it to biological problems in two dimensions.

Controlling natural killer cell responses: integration of signals for activation and inhibition

Understanding how signals are integrated to control natural killer (NK) cell responsiveness in the absence of antigen-specific receptors has been a challenge, but recent work has revealed some underlying principles that govern NK cell responses. NK cells use an array of innate receptors to sense their environment and respond to alterations caused by infections, cellular stress, and transformation. No single activation receptor dominates; instead, synergistic signals from combinations of receptors are integrated to activate natural cytotoxicity and cytokine production. Inhibitory receptors for major histocompatibility complex class I (MHC-I) have a critical role in controlling NK cell responses and, paradoxically, in maintaining NK cells in a state of responsiveness to subsequent activation events, a process referred to as licensing. MHC-I-specific inhibitory receptors both block activation signals and trigger signals to phosphorylate and inactivate the small adaptor Crk. These different facets of inhibitory signaling are incorporated into a revocable license model for the reversible tuning of NK cell responsiveness.

Understanding natural killer cell regulation by mathematical approaches

The activity of natural killer (NK) cells is regulated by various processes including education/licensing, priming, integration of positive and negative signals through an array of activating and inhibitory receptors, and the development of memory-like functionality. These processes are often very complex due to the large number of different receptors and signaling pathways involved. Understanding these complex mechanisms is therefore a challenge, but is critical for understanding NK cell regulation. Mathematical approaches can facilitate the analysis and understanding of complex systems. Therefore, they may be instrumental for studies in NK cell biology. Here we provide a review of the different mathematical approaches to the analysis of NK cell signal integration, activation, proliferation, and the acquisition of inhibitory receptors. These studies show how mathematical methods can aid the analysis of NK cell regulation.