Quantification of the Relative Importance of CTL, B Cell, NK Cell, and Target Cell Limitation in the Control of Primary SIV-Infection

CD8+ cytotoxic T lymphocytes (CTLs), natural killer (NK) cells, B cells and target cell limitation have all been suggested to play a role in the control of SIV and HIV-1 infection. However, previous research typically studied each population in isolation leaving the magnitude, relative importance and in vivo relevance of each effect unclear. Here we quantify the relative importance of CTLs, NK cells, B cells and target cell limitation in controlling acute SIV infection in rhesus macaques. Using three different methods, we find that the availability of target cells and CD8+ T cells are important predictors of viral load dynamics. If CTL are assumed to mediate this anti-viral effect via a lytic mechanism then we estimate that CTL killing is responsible for approximately 40% of productively infected cell death, the remaining cell death being attributable to intrinsic, immune (CD8+ T cell, NK cell, B cell) -independent mechanisms. Furthermore, we find that NK cells have little impact on the death rate of infected CD4+ cells and that their net impact is to increase viral load. We hypothesize that NK cells play a detrimental role in SIV infection, possibly by increasing T cell activation

Human neutrophil kinetics: modeling of stable isotope labeling data supports short blood neutrophil half-lives.

Human neutrophils have traditionally been thought to have a short half-life in blood; estimates vary from 4-18 hours. This dogma was recently challenged by stable isotope labeling studies with heavy water which yielded estimates in excess of 3 days. To investigate this disparity we generated new stable isotope labeling data in healthy adult subjects using both heavy water (n=4) and deuterium-labeled glucose (n=9), a compound with more rapid labeling kinetics. To interpret results we developed a novel mechanistic model. We applied this model to both previously-published (n=5) and newly-generated data. We initially constrained the ratio of the blood neutrophil pool to the marrow precursor pool (R=0.26, from published values). Analysis of heavy water datasets yielded turnover rates consistent with a short blood half-life, but parameters, particularly marrow transit-time, were poorly-defined. Analysis of glucose-labeling data yielded more precise estimates of half-life, 0.79 ± 0.25 days (19 hours), and marrow transit-time, 5.80 ± 0.42 days. Substitution of this marrow transit-time in the heavy water analysis gave a better-defined blood half-life, 0.77 ± 0.14 days (18.5 hours), close to glucose-derived values. Allowing R to vary yielded a best-fit value, R=0.19. Reanalysis of the previously-published model and data also revealed the origin of their long estimates for neutrophil half-life, an implicit assumption that R is very large, which is physiologically untenable. We conclude that stable isotope labeling in healthy humans is consistent with a blood neutrophil half-life of less than one day

Why Don't CD8+ T Cells Reduce the Lifespan of SIV-Infected Cells In Vivo?

In January 2010 two groups independently published the observation that the depletion of CD8+ cells in SIV-infected macaques had no detectable impact on the lifespan of productively infected cells. This unexpected observation led the authors to suggest that CD8+ T cells control SIV viraemia via non-lytic mechanisms. However, a number of alternative plausible explanations, compatible with a lytic model of CD8+ T cell control, were proposed. This left the field with no consensus on how to interpret these experiments and no clear indication whether CD8+ T cells operated primarily via a lytic or a non-lytic mechanism. The aim of this work was to investigate why CD8+ T cells do not appear to reduce the lifespan of SIV-infected cells in vivo

Reconciling Estimates of Cell Proliferation from Stable Isotope Labeling Experiments.

Stable isotope labeling is the state of the art technique for in vivo quantification of lymphocyte kinetics in humans. It has been central to a number of seminal studies, particularly in the context of HIV-1 and leukemia. However, there is a significant discrepancy between lymphocyte proliferation rates estimated in different studies. Notably, deuterated (2)H2-glucose (D2-glucose) labeling studies consistently yield higher estimates of proliferation than deuterated water (D2O) labeling studies. This hampers our understanding of immune function and undermines our confidence in this important technique. Whether these differences are caused by fundamental biochemical differences between the two compounds and/or by methodological differences in the studies is unknown. D2-glucose and D2O labeling experiments have never been performed by the same group under the same experimental conditions; consequently a direct comparison of these two techniques has not been possible. We sought to address this problem. We performed both in vitro and murine in vivo labeling experiments using identical protocols with both D2-glucose and D2O. This showed that intrinsic differences between the two compounds do not cause differences in the proliferation rate estimates, but that estimates made using D2-glucose in vivo were susceptible to difficulties in normalization due to highly variable blood glucose enrichment. Analysis of three published human studies made using D2-glucose and D2O confirmed this problem, particularly in the case of short term D2-glucose labeling. Correcting for these inaccuracies in normalization decreased proliferation rate estimates made using D2-glucose and slightly increased estimates made using D2O; thus bringing the estimates from the two methods significantly closer and highlighting the importance of reliable normalization when using this technique

The Efficiency of the Human CD8+ T Cell Response: How Should We Quantify It, What Determines It, and Does It Matter?

Multidisciplinary techniques, in particular the combination of theoretical and experimental immunology, can address questions about human immunity that cannot be answered by other means. From the turnover of virus-infected cells in vivo, to rates of thymic production and HLA class I epitope prediction, theoretical techniques provide a unique insight to supplement experimental approaches. Here we present our opinion, with examples, of some of the ways in which mathematics has contributed in our field of interest: the efficiency of the human CD8+ T cell response to persistent viruses

Heritable tumor cell division rate heterogeneity induces clonal dominance

<div><p>Tumors consist of a hierarchical population of cells that differ in their phenotype and genotype. This hierarchical organization of cells means that a few clones (i.e., cells and several generations of offspring) are abundant while most are rare, which is called <i>clonal dominance</i>. Such dominance also occurred in published <i>in vitro</i> iterated growth and passage experiments with tumor cells in which genetic barcodes were used for lineage tracing. A potential source for such heterogeneity is that dominant clones derive from cancer stem cells with an unlimited self-renewal capacity. Furthermore, ongoing evolution and selection within the growing population may also induce clonal dominance. To understand how clonal dominance developed in the iterated growth and passage experiments, we built a computational model that accurately simulates these experiments. The model simulations reproduced the clonal dominance that developed in <i>in vitro</i> iterated growth and passage experiments when the division rates vary between cells, due to a combination of initial variation and of ongoing mutational processes. In contrast, the experimental results can neither be reproduced with a model that considers random growth and passage, nor with a model based on cancer stem cells. Altogether, our model suggests that <i>in vitro</i> clonal dominance develops due to selection of fast-dividing clones.</p></div

Estimates of CTL killing of ag⁺ B cells in 6 BLV-infected sheep, k (d⁻¹) and their 95% confidence intervals.

<p>Three animals (BLV4–6) had large confidence intervals, partly due to lower proviral load in these animals. The median rate of killing is 1.98 d⁻¹; if we limit our analysis to killing rates we can estimate with reasonable confidence then the median rate of killing is 1.60 d⁻¹.</p

Parameters of the stochastic growth and passage models.

<p>Parameters of the stochastic growth and passage models.</p

ABM simulations describing evolution of division rate variability match results for polyclonal K562 cell line.

<p><b>A</b>–<b>B</b> Maximum Likelihood estimator (<i>ℓ</i>) based on the percentage of clones lost (<b>A</b>), on the Gini coefficient (<b>B</b>), and on both metrics (<b>C</b>) for a range of initial division rate SDs () and mutation SDs (). Note that we plot −<i>ℓ</i> in these plots and thus its minimum value is sought. <b>D</b>–<b>E</b> comparison of clone loss (<b>D</b>) and clonal dominance (<b>E</b>) observed in simulations with the best fitting parameter values for the Gini coefficient (red rectangle in <b>B</b>) and in the experiments with polyclonal K562 cells. <b>F</b> Comparison of the number of major clones, i.e. clones representing more than 1% of the population, developing in simulations with the parameter set highlighted by the red rectangle in <b>B</b> and in the experiments with polyclonal K562 cells. <b>G</b> Evolution of the mean division rate with the best fitting parameter values for the Gini coefficient. All simulation results are the mean of 10 simulations and the results for the polyclonal K562 cells are the mean of 3 replicates, with the error bars or colored areas representing the SD.</p