On pseudo-values for regression analysis in competing risks models

For regression on state and transition probabilities in multi-state models Andersen et al. (Biometrika 90:15-27, 2003) propose a technique based on jackknife pseudo-values. In this article we analyze the pseudo-values suggested for competing risks models and prove some conjectures regarding their asymptotics (Klein and Andersen, Biometrics 61:223-229, 2005). The key is a second order von Mises expansion of the Aalen-Johansen estimator which yields an appropriate representation of the pseudo-values. The method is illustrated with data from a clinical study on total joint replacement. In the application we consider for comparison the estimates obtained with the Fine and Gray approach (J Am Stat Assoc 94:496-509, 1999) and also time-dependent solutions of pseudo-value regression equation

Estimating the in vivo killing efficacy of cytotoxic T lymphocytes across different peptide-MHC complex densities

Cytotoxic T lymphocytes (CTLs) are important agents in the control of intracellular pathogens, which specifically recognize and kill infected cells. Recently developed experimental methods allow the estimation of the CTL's efficacy in detecting and clearing infected host cells. One method, the in vivo killing assay, utilizes the adoptive transfer of antigen displaying target cells into the bloodstream of mice. Surprisingly, killing efficacies measured by this method are often much higher than estimates obtained by other methods based on, for instance, the dynamics of escape mutations. In this study, we investigated what fraction of this variation can be explained by differences in peptide loads employed in in vivo killing assays. We addressed this question in mice immunized with lymphocytic choriomeningitis virus (LCMV). We conducted in vivo killing assays varying the loads of the immunodominant epitope GP33 on target cells. Using a mathematical model, we determined the efficacy of effector and memory CTL, as well as CTL in chronically infected mice. We found that the killing efficacy is substantially reduced at lower peptide loads. For physiological peptide loads, our analysis predicts more than a factor 10 lower CTL efficacies than at maximum peptide loads. Assuming that the efficacy scales linearly with the frequency of CTL, a clear hierarchy emerges among the groups across all peptide antigen concentrations. The group of mice with chronic LCMV infections shows a consistently higher killing efficacy per CTL than the acutely infected mouse group, which in turn has a consistently larger efficacy than the memory mouse group. We conclude that CTL killing efficacy dependence on surface epitope frequencies can only partially explain the variation in in vivo killing efficacy estimates across experimental methods and viral systems, which vary about four orders of magnitude. In contrast, peptide load differences can explain at most two orders of magnitude

Influence of the Fibroblastic Reticular Network on Cell-Cell Interactions in Lymphoid Organs

Secondary lymphoid organs (SLO), such as lymph nodes and the spleen, display a complex micro-architecture. In the T cell zone the micro-architecture is provided by a network of fibroblastic reticular cells (FRC) and their filaments. The FRC network is thought to enhance the interaction between immune cells and their cognate antigen. However, the effect of the FRC network on cell interaction cannot be quantified to date because of limitations in immunological methodology. We use computational models to study the influence of different densities of FRC networks on the probability that two cells meet. We developed a 3D cellular automaton model to simulate cell movements and interactions along the FRC network inside lymphatic tissue. We show that the FRC network density has only a small effect on the probability of a cell to come into contact with a static or motile target. However, damage caused by a disruption of the FRC network is greatest at FRC densities corresponding to densities observed in the spleen of naïve mice. Our analysis suggests that the FRC network as a guiding structure for moving T cells has only a minor effect on the probability to find a corresponding dendritic cell. We propose alternative hypotheses by which the FRC network might influence the functionality of immune responses in a more significant way

Theoretical analysis of the evolution of immune memory

<p>Abstract</p> <p>Background</p> <p>The ability of an immune system to remember pathogens improves the chance of the host to survive a second exposure to the same pathogen. This immunological memory has evolved in response to the pathogen environment of the hosts. In vertebrates, the memory of previous infection is physiologically accomplished by the development of memory T and B cells. Many questions concerning the generation and maintenance of immunological memory are still debated. Is there a limit to how many memory cells a host can generate and maintain? If there is a limit, how should new cells be incorporated into a filled memory compartment? And how many different pathogens should the immune system remember?</p> <p>Results</p> <p>In this study, we examine how memory traits evolve as a response to different pathogen environments using an individual-based model. We find that even without a cost related to the maintenance of a memory pool, the positive effect of bigger memory pool sizes saturates. The optimal diversity of a limited memory pool is determined by the probability of re-infection, rather than by the prevalence of a pathogen in the environment, or the frequency of exposure.</p> <p>Conclusions</p> <p>Relating immune memory traits to the pathogen environment of the hosts, our population biological framework sheds light on the evolutionary determinants of immune memory.</p

A wall-time minimizing parallelization strategy for approximate Bayesian computation

Approximate Bayesian Computation (ABC) is a widely applicable and popular approach to estimating unknown parameters of mechanistic models. As ABC analyses are computationally expensive, parallelization on high-performance infrastructure is often necessary. However, the existing parallelization strategies leave computing resources unused at times and thus do not optimally leverage them yet. We present look-ahead scheduling, a wall-time minimizing parallelization strategy for ABC Sequential Monte Carlo algorithms, which avoids idle times of computing units by preemptive sampling of subsequent generations. This allows to utilize all available resources. The strategy can be integrated with e.g. adaptive distance function and summary statistic selection schemes, which is essential in practice. Our key contribution is the theoretical assessment of the strategy of preemptive sampling and the proof of unbiasedness. Complementary, we provide an implementation and evaluate the strategy on different problems and numbers of parallel cores, showing speed-ups of typically 10-20% and up to 50% compared to the best established approach, with some variability. Thus, the proposed strategy allows to improve the cost and run-time efficiency of ABC methods on high-performance infrastructure.</jats:p

Can Non-lytic CD8+T Cells Drive HIV-1 Escape?

The CD8+ T cell effector mechanisms that mediate control of HIV-1 and SIV infections remain poorly understood. Recent work suggests that the mechanism may be primarily non-lytic. This is in apparent conflict with the observation that SIV and HIV-1 variants that escape CD8+ T cell surveillance are frequently selected. Whilst it is clear that a variant that has escaped a lytic response can have a fitness advantage compared to the wild-type, it is less obvious that this holds in the face of non-lytic control where both wild-type and variant infected cells would be affected by soluble factors. In particular, the high motility of T cells in lymphoid tissue would be expected to rapidly destroy local effects making selection of escape variants by non-lytic responses unlikely. The observation of frequent HIV-1 and SIV escape poses a number of questions. Most importantly, is the consistent observation of viral escape proof that HIV-1- and SIV-specific CD8+ T cells lyse infected cells or can this also be the result of non-lytic control? Additionally, the rate at which a variant strain escapes a lytic CD8+ T cell response is related to the strength of the response. Is the same relationship true for a non-lytic response? Finally, the potential anti-viral control mediated by non-lytic mechanisms compared to lytic mechanisms is unknown. These questions cannot be addressed with current experimental techniques nor with the standard mathematical models. Instead we have developed a 3D cellular automaton model of HIV-1 which captures spatial and temporal dynamics. The model reproduces in vivo HIV-1 dynamics at the cellular and population level. Using this model we demonstrate that non-lytic effector mechanisms can select for escape variants but that outgrowth of the variant is slower and less frequent than from a lytic response so that non-lytic responses can potentially offer more durable control

The population context is a driver of the heterogeneous response of epithelial cells to interferons

Isogenic cells respond in a heterogeneous manner to interferon. Using a micropatterning approach combined with high-content imaging and spatial analyses, we characterized how the population context (position of a cell with respect to neighboring cells) of epithelial cells affects their response to interferons. We identified that cells at the edge of cellular colonies are more responsive than cells embedded within colonies. We determined that this spatial heterogeneity in interferon response resulted from the polarized basolateral interferon receptor distribution, making cells located in the center of cellular colonies less responsive to ectopic interferon stimulation. This was conserved across cell lines and primary cells originating from epithelial tissues. Importantly, cells embedded within cellular colonies were not protected from viral infection by apical interferon treatment, demonstrating that the population context-driven heterogeneous response to interferon influences the outcome of viral infection. Our data highlights that the behavior of isolated cells does not directly translate to their behavior in a population, placing the population context as one important factor influencing heterogeneity during interferon response in epithelial cells.Synopsis The heterogeneous response to interferons in epithelial cells is determined by the population context (i.e. a cell’s positions within a population) through cell polarization and basolateral localization of the interferon receptor. Isogenic epithelial cells respond in a heterogeneous manner to type I and type III interferons. The population context (position of a cell within a population) drives the heterogeneous response to interferons, with cells located at the edge of a population being more responsive than cells located in the center of a population. Cells in the center of a population are refractive to interferon treatment from their apical side due to the polarized basolateral distribution of the interferon receptors. By controlling the response to interferon treatment, the population context affects susceptibility to viral infection.The heterogeneous response to interferons in epithelial cells is determined by the population context (i.e. a cell’s positions within a population) through cell polarization and basolateral localization of the interferon receptor.Deutsche Forschungsgemeinschaft (DFG)http://dx.doi.org/10.13039/501100001659Chica and Heinz Schaller Foundation (CHS-Stiftung)http://dx.doi.org/10.13039/501100012284Deutscher Akademischer Austauschdienst (DAAD)http://dx.doi.org/10.13039/501100001655UF | UF Health | College of Medicine, University of Florida (UF College of Medicine)http://dx.doi.org/10.13039/10000827

Morphology-dependent entry kinetics and spread of influenza A virus

Influenza A viruses (IAV) display a broad variety of morphologies ranging from spherical to long filamentous virus particles. These diverse phenotypes are believed to allow the virus to overcome various immunological and pulmonary barriers during entry into the airway epithelium, and to influence the viral entry pathway. Notably, laboratory-adapted IAV strains predominantly adopt a spherical form, yet the factors driving this preference as well as the factors favoring filamentous morphology in physiological settings remain unclear. To address this, we generated fluorescent reporter viruses with identical surface glycoproteins but distinct morphologies and developed a correlative light and scanning electron microscopy workflow. This enabled us to investigate the impact of viral morphology on spread, and to identify conditions favoring either form. Our findings demonstrate that filamentous IAV spread significantly slower in various cell lines, consistent with delayed entry kinetics and in-cell cryo-electron tomography, explaining the predominance of spherical forms in laboratory-adapted strains. Cellular junction integrity, neuraminidase activity, and mucin do not inhibit IAV spread in a morphology-dependent manner. However, filamentous virions confer a selective advantage under neutralizing-antibody pressure against hemagglutinin.Synopsis Morphology of influenza A virus (IAV) particles has been shown to affect viral spread and is regulated by selective pressure in the host. Here, correlative light and scanning electron microscopy enables investigation of the effect of host barriers and drugs on viral spread kinetics and selective pressure induced morphological changes. Filamentous IAV particles exhibit slower spread in cell culture compared to spherical particles, consistent with delayed entry kinetics into the host cell. Neutralizing anti-hemagglutinin antibodies inhibit the entry and spread of spherical IAV particles better than that of filamentous viruses. Both viral morphologies remain structurally stable throughout serial passaging under antibody pressure in vitro.Neutralizing antibodies exert selective pressure favoring filamentous IAV by inhibiting cell entry and transmission of spherical particles.Deutsche Forschungsgemeinschaft (DFG)http://dx.doi.org/10.13039/501100001659Chica and Heinz Schaller Foundation (CHS-Stiftung)http://dx.doi.org/10.13039/501100012284EC | European Research Council (ERC)http://dx.doi.org/10.13039/501100000781Bundesministerium für Bildung und Forschung (BMBF)http://dx.doi.org/10.13039/501100002347Daimler und Benz Stiftung (Daimler and Benz Foundation)http://dx.doi.org/10.13039/50110000297

Investigating CTL Mediated Killing with a 3D Cellular Automaton

Cytotoxic T lymphocytes (CTLs) are important immune effectors against intra-cellular pathogens. These cells search for infected cells and kill them. Recently developed experimental methods in combination with mathematical models allow for the quantification of the efficacy of CTL killing in vivo and, hence, for the estimation of parameters that characterize the effect of CTL killing on the target cell populations. It is not known how these population-level parameters relate to single-cell properties. To address this question, we developed a three-dimensional cellular automaton model of the region of the spleen where CTL killing takes place. The cellular automaton model describes the movement of different cell populations and their interactions. Cell movement patterns in our cellular automaton model agree with observations from two-photon microscopy. We find that, despite the strong spatial nature of the kinetics in our cellular automaton model, the killing of target cells by CTLs can be described by a term which is linear in the target cell frequency and saturates with respect to the CTL levels. Further, we find that the parameters describing CTL killing on the population level are most strongly impacted by the time a CTL needs to kill a target cell. This suggests that the killing of target cells, rather than their localization, is the limiting step in CTL killing dynamics given reasonable frequencies of CTL. Our analysis identifies additional experimental directions which are of particular importance to interpret estimates of killing rates and could advance our quantitative understanding of CTL killing