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Oncolytic virus (OV) is a replication competent virus that selectively invades cancer cells; as these cells die under the viral burden, the released virus particles proceed to infect other cancer cells. Oncolytic viruses are designed to also be able to stimulate the anticancer immune response. Thus, one may represent an OV by two parameters: its replication potential and its immunogenicity. In this paper we consider a combination therapy with OV and a checkpoint inhibitor, anti-PD-1. We evaluate the efficacy of the combination therapy in terms of the tumor volume at some later time, for example, 6 months from initial treatment. Since T cells kill not only virus-free cancer cells but also virus-infected cancer cells, the following question arises: Does increasing the amount of the checkpoint inhibitor always improve the efficacy? We address this question, by a mathematical model consisting of a system of partial differential equations. We use the model to construct, by simulations, an efficacy map in terms of the doses of the checkpoint inhibitor and the OV injection. We show that there are regions in the map where an increase in the checkpoint inhibitor actually decreases the efficacy of the treatment. We also construct efficacy maps with checkpoint inhibitor vs. the replication potential of the virus that show the same antagonism, namely, an increase in the checkpoint inhibitor may actually decrease the efficacy. These results have implications for clinical trials. [ABSTRACT FROM AUTHOR]

T-cells and antigen presenting cells are an essential part of the adaptive immune response system and how they interact is crucial in how the body effectively fights infection or responds to vaccines. Much of the experimental work studying interaction forces between cells has looked at the average properties of bulk samples of cells or applied microscopy to image the dynamic contact between these cells. In this paper we present a novel optical trapping technique for interrogating the force of this interaction and measuring relative interaction forces at the single-cell level. A triple-spot optical trap is used to directly manipulate the cells of interest without introducing foreign bodies such as beads to the system. The optical trap is used to directly control the initiation of cell-cell contact and, subsequently to terminate the interaction at a defined time point. The laser beam power required to separate immune cell pairs is determined and correlates with the force applied by the optical trap. As proof of concept, the antigen-specific increase in interaction force between a dendritic cell and a specific T-cell is demonstrated. Furthermore, it is demonstrated that this interaction force is completely abrogated when T-cell signalling is blocked. As a result the potential of using optical trapping to interrogate cellular interactions at the single cell level without the need to introduce foreign bodies such as beads is clearly demonstrated. [ABSTRACT FROM AUTHOR]

The spread of KPC-type carbapenemases is mainly attributed to the global dissemination of Klebsiella pneumoniae (KP) strains belonging to the clonal group (CG) 258, including sequence type (ST) 258 and other related STs. Two distinct clades of CG258-KP have evolved, which differ mainly for the composition of their capsular polysaccharides, and recent studies indicate that clade 1 evolved from an ancestor of clade 2 by recombination of a genomic fragment carrying the capsular polysaccharide (cps) locus. In this paper, we investigated the ability of two ST258-KP strains, KKBO-1 and KK207-1, selected as representatives of ST258-KP clade 2 and clade 1, respectively, to activate an adaptive immune response using ex vivo-stimulation of PBMC from normal donors as an experimental model. Our data showed that KKBO-1 (clade 2) induces a Th17 response more efficiently than KK207-1 (clade 1): the percentage of CD4+IL17+ cells and the production of IL-17A were significantly higher in cultures with KKBO-1 compared to cultures with KK207-1. While no differences in the rate of bacterial internalization or in the bacteria-induced expression of CD86 and HLA-DR by monocytes and myeloid dendritic cells were revealed, we found that the two strains significantly differ in inducing the production of cytokines involved in the adaptive immune response, as IL-1β, IL-23 and TNF-α, by antigen-presenting cells, with KKBO-1 being a more efficient inducer than KK207-1. The immune responses elicited by KK207-1 were comparable to those elicited by CIP 52.145, a highly virulent K. pneumoniae reference strain known to escape immune-inflammatory responses. Altogether, present results suggest that CG258-KP of the two clades are capable of inducing a different response of adaptive immunity in the human host. [ABSTRACT FROM AUTHOR]

Experimental and mathematical studies in immunology have revealed that the dynamics of the programmed T cell response to vigorous infection can be conveniently modelled using a sigmoidal or a discontinuous immune response function. This paper hypothesizes strong synergies between this existing work and the dynamical behaviour of engineering systems with a variable structure control (VSC) law. These findings motivate the interpretation of the immune system as a variable structure control system. It is shown that dynamical properties as well as conditions to analytically assess the transition from health to disease can be developed for the specific T cell response from the theory of variable structure control. In particular, it is shown that the robustness properties of the specific T cell response as observed in experiments can be explained analytically using a VSC perspective. Further, the predictive capacity of the VSC framework to determine the T cell help required to overcome chronic Lymphocytic Choriomeningitis Virus (LCMV) infection is demonstrated. The findings demonstrate that studying the immune system using variable structure control theory provides a new framework for evaluating immunological dynamics and experimental observations. A modelling and simulation tool results with predictive capacity to determine how to modify the immune response to achieve healthy outcomes which may have application in drug development and vaccine design. [ABSTRACT FROM AUTHOR]

Enhancement or inhibition of cytokine signaling and corresponding immune cells responses are critical factors in various disease treatments. Cytokine signaling may be inhibited by cytokine-neutralizing antibodies (CNAs), which prevents further activation of cytokine receptors. However, CNAs may result in enhanced—instead of inhibitory—cytokine signaling (an “agonistic effect”) in various in vitro and in vivo experiments. This may lead to lack of efficacy or adverse events for cytokine-inhibiting based medicines. Alternatively, cytokine-antibody complexes may produce stronger signaling vs. cytokine alone, thereby increasing the efficacy of stimulating cytokine-based drugs, at equal or lower cytokine doses. In this paper, the effect of cytokine signaling enhancement by a CNA was studied in a generic mathematical model of interleukin-4 (IL-4) driven T-cell proliferation. The occurrence of the agonistic effect depends upon the antibody-to-cytokine binding affinity and initial concentrations of antibody and cytokine. Model predictions were in agreement with experimental studies. When the cytokine receptor consists of multiple subunits with substantially differing affinities (e.g., IL-4 case), the choice of the receptor chain to be blocked by the antibody is critical, for the agonistic effect to appear. We propose a generic mechanism for the effect: initially, binding of the CNA to the cytokine reduces free cytokine concentration; yet, cytokine molecules bound within the cytokine-CNA complex—and released later and over time—are “rescued” from earlier clearance via cellular internalization. Hence, although free cytokine-dependent signalling may be less potent initially, it will also be more sustained over time; and given non-linear dynamics, it will lead ultimately to larger cellular effector responses, vs. the same amount of free cytokine in the absence of CNA. We suggest that the proposed mechanism is a generic property of {cytokine, CNA, receptor} triads, both in vitro and in vivo, and can occur in a predictable fashion for a variety of cytokines of the immune system. [ABSTRACT FROM AUTHOR]

The study of the initial phase of the adaptive immune response after first antigen encounter provides essential information on the magnitude and quality of the immune response. This phase is characterized by proliferation and dissemination of T cells in the lymphoid organs. Modeling and identifying the key features of this phenomenon may provide a useful tool for the analysis and prediction of the effects of immunization. This knowledge can be effectively exploited in vaccinology, where it is of interest to evaluate and compare the responses to different vaccine formulations. The objective of this paper is to construct a stochastic model based on branching process theory, for the dissemination network of antigen-specific CD4+ T cells. The devised model is validated on in vivo animal experimental data. The model presented has been applied to the vaccine immunization context making references to simple proliferation laws that take into account division, death and quiescence, but it can also be applied to any context where it is of interest to study the dynamic evolution of a population. [ABSTRACT FROM AUTHOR]

Background: T-cell epitopes play the important role in T-cell immune response, and they are critical components in the epitope-based vaccine design. Immunogenicity is the ability to trigger an immune response. The accurate prediction of immunogenic T-cell epitopes is significant for designing useful vaccines and understanding the immune system. Methods: In this paper, we attempt to differentiate immunogenic epitopes from non-immunogenic epitopes based on their primary structures. First of all, we explore a variety of sequence-derived features, and analyze their relationship with epitope immunogenicity. To effectively utilize various features, a genetic algorithm (GA)-based ensemble method is proposed to determine the optimal feature subset and develop the high-accuracy ensemble model. In the GA optimization, a chromosome is to represent a feature subset in the search space. For each feature subset, the selected features are utilized to construct the base predictors, and an ensemble model is developed by taking the average of outputs from base predictors. The objective of GA is to search for the optimal feature subset, which leads to the ensemble model with the best cross validation AUC (area under ROC curve) on the training set. Results: Two datasets named ‘IMMA2’ and ‘PAAQD’ are adopted as the benchmark datasets. Compared with the state-of-the-art methods POPI, POPISK, PAAQD and our previous method, the GA-based ensemble method produces much better performances, achieving the AUC score of 0.846 on IMMA2 dataset and the AUC score of 0.829 on PAAQD dataset. The statistical analysis demonstrates the performance improvements of GA-based ensemble method are statistically significant. Conclusions: The proposed method is a promising tool for predicting the immunogenic epitopes. The source codes and datasets are available in . [ABSTRACT FROM AUTHOR]

Lack of immunogenicity of cancer cells has been considered a major reason for their failure in induction of a tumor specific T cell response. In this paper, we present evidence that decitabine (DAC), a DNA methylation inhibitor that is currently used for the treatment of myelodysplastic syndrome (MDS), acute myeloid leukemia (AML) and other malignant neoplasms, is capable of eliciting an anti-tumor cytotoxic T lymphocyte (CTL) response in mouse EL4 tumor model. C57BL/6 mice with established EL4 tumors were treated with DAC (1.0 mg/kg body weight) once daily for 5 days. We found that DAC treatment resulted in infiltration of IFN-γ producing T lymphocytes into tumors and caused tumor rejection. Depletion of CD8+, but not CD4+ T cells resumed tumor growth. DAC-induced CTL response appeared to be elicited by the induction of CD80 expression on tumor cells. Epigenetic evidence suggests that DAC induces CD80 expression in EL4 cells via demethylation of CpG dinucleotide sites in the promoter of CD80 gene. In addition, we also showed that a transient, low-dose DAC treatment can induce CD80 gene expression in a variety of human cancer cells. This study provides the first evidence that epigenetic modulation can induce the expression of a major T cell co-stimulatory molecule on cancer cells, which can overcome immune tolerance, and induce an efficient anti-tumor CTL response. The results have important implications in designing DAC-based cancer immunotherapy. [ABSTRACT FROM AUTHOR]

Agent-based simulation is a powerful method for investigating the complex interplay of the processes occurring in a lymph node during an adaptive immune response. We have previously established an agent-based modeling framework for the interactions between T cells and dendritic cells within the paracortex of lymph nodes. This model simulates in three dimensions the "random-walk" T cell motility observed in vivo, so that cells interact in space and time as they process signals and commit to action such as proliferation. On-lattice treatment of cell motility allows large numbers of densely packed cells to be simulated, so that the low frequency of T cells capable of responding to a single antigen can be dealt with realistically. In this paper we build on this model by incorporating new numerical methods to address the crucial processes of T cell ingress and egress, and chemotaxis, within the lymph node. These methods enable simulation of the dramatic expansion and contraction of the T cell population in the lymph node paracortex during an immune response. They also provide a novel probabilistic method to simulate chemotaxis that will be generally useful in simulating other biological processes in which chemotaxis is an important feature. [ABSTRACT FROM AUTHOR]

Recent mathematical models have been developed to study the dynamics of chronic myelogenous leukemia (CML) under imatinib treatment. None of these models incorporates the anti-leukemia immune response. Recent experimental data show that imatinib treatment may promote the development of anti-leukemia immune responses as patients enter remission. Using these experimental data we develop a mathematical model to gain insights into the dynamics and potential impact of the resulting anti-leukemia immune response on CML. We model the immune response using a system of delay differential equations, where the delay term accounts for the duration of cell division. The mathematical model suggests that anti-leukemia T cell responses may play a critical role in maintaining CML patients in remission under imatinib therapy. Furthermore, it proposes a novel concept of an ''optimal load zone'' for leukemic cells in which the anti-leukemia immune response is most effective. Imatinib therapy may drive leukemic cell populations to enter and fall below this optimal load zone too rapidly to sustain the anti-leukemia T cell response. As a potential therapeutic strategy, the model shows that vaccination approaches in combination with imatinib therapy may optimally sustain the anti-leukemia T cell response to potentially eradicate residual leukemic cells for a durable cure of CML. The approach presented in this paper accounts for the role of the anti-leukemia specific immune response in the dynamics of CML. By combining experimental data and mathematical models, we demonstrate that persistence of anti-leukemia T cells even at low levels seems to prevent the leukemia from relapsing (for at least 50 months). As a consequence, we hypothesize that anti-leukemia T cell responses may help maintain remission under imatinib therapy. The mathematical model together with the new experimental data imply that there may be a feasible, low-risk, clinical approach to enhancing the effects of imatinib treatment. [ABSTRACT FROM AUTHOR]

Background: Regulatory T cells are central actors in the maintenance of tolerance of self-antigens or allergens and in the regulation of the intensity of the immune response during infections by pathogens. An understanding of the network of the interaction between regulatory T cells, antigen presenting cells and effector T cells is starting to emerge. Dynamical systems analysis can help to understand the dynamical properties of an interaction network and can shed light on the different tasks that can be accomplished by a network. Methodology and Principal Findings: We used a mathematical model to describe a interaction network of adaptive regulatory T cells, in which mature precursor T cells may differentiate into either adaptive regulatory T cells or effector T cells, depending on the activation state of the cell by which the antigen was presented. Using an equilibrium analysis of the mathematical model we show that, for some parameters, the network has two stable equilibrium states: one in which effector T cells are strongly regulated by regulatory T cells and another in which effector T cells are not regulated because the regulatory T cell population is vanishingly small. We then simulate different types of perturbations, such as the introduction of an antigen into a virgin system, and look at the state into which the system falls. We find that whether or not the interaction network switches from the regulated (tolerant) state to the unregulated state depends on the strength of the antigenic stimulus and the state from which the network has been perturbed. Conclusion/Significance: Our findings suggest that the interaction network studied in this paper plays an essential part in generating and maintaining tolerance against allergens and self-antigens. [ABSTRACT FROM AUTHOR]