Application of optimized protocol to <i>in vivo</i> titrations.

<p>The procedure outlined in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1005100#ppat.1005100.g002" target="_blank">Fig 2</a> was used to titrate two different SIV strains <i>in vivo</i>. For both strains, challenges and outcomes are represented pictorially, with statistical analyses tabulated to the right. Estimates of AID₅₀ and its precision were done using the first order kinetics model described in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1005100#ppat.1005100.g001" target="_blank">Fig 1</a> (“FOK”), or by the Logit model online calculator (“Logit”) [<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1005100#ppat.1005100.ref025" target="_blank">25</a>]. (A) Titration of SIV FL14-TR. (B) Titration of SIV FL14-AK. Note that the protocol was terminated with one animal still uninfected in phase 2; further challenges will not substantively change the estimate of AID₅₀.</p

Parsimonious Determination of the Optimal Infectious Dose of a Pathogen for Nonhuman Primate Models

<div><p>The nonhuman primate (NHP) model is often the best experimental model for testing interventions designed to block infection by human pathogens, such as HIV, tuberculosis, and malaria. A physiological model may require the use of a limiting dose of the infectious agent, where only a fraction of animals become infected upon any given challenge. Determining the challenge dose of the pathogen in such experiments is critical to the success of the experiment: using too-high or too-low a challenge dose may lead to false negative results and an excessive use of animals. Here I define an optimized protocol for defining the dose of pathogen that infects 50% of the time (AID₅₀); other challenge doses, e.g. AID₈₀, can be easily calculated from the same data. This protocol minimizes the number of animals, as well as resources and procedures, while providing an estimate of the AID₅₀ within 1.5-fold of the true value.</p></div

Flow chart of overall procedure.

<p>The procedure is divided into two phases. First, an initial dose ranging study that uses the same cohort size (N_C) at each challenge, where the dose D is refined following an initial guess (D₀) after each challenge. Once the accuracy of D is within the desired bounds (σ_T), remaining uninfected animals are moved to phase 2, undergoing challenges until all are infected. The AID₅₀ is estimated at each time based on a least-squares regression to the single-parameter equation in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1005100#ppat.1005100.g001" target="_blank">Fig 1A</a>, using data from all challenges in phases 1 and 2. n = total number of animals enrolled. Shown in red are the parameters optimized in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1005100#ppat.1005100.g003" target="_blank">Fig 3</a>.</p

Infection probability as a function of challenge dose.

<p>(A) Modeling the probability of infection (P) as a function of the pathogen challenge dose (D). At D = 1, P = 0.5 (i.e., AID₅₀, the dose that will infect 50% of the time). The curves for linear kinetics (solid; m = 1), third order kinetics (long dashes; m = 3), and mixed kinetics (short dashes, m = 0.5) are shown on linear (left) and logarithmic (right) scaling. The inset table on the right illustrates some values for P at different doses, for linear kinetics. (B) Five groups of NHP were challenged with a moderate dose of SIVsmE660 (a dose that gave 30% infection in unvaccinated animals, or approximately 0.4 AID₅₀). Data are taken from a published study [<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1005100#ppat.1005100.ref015" target="_blank">15</a>]. Env immunized groups were partially protected, showing lower infection rates overall. Shown is the fitted infection rate (linear least squares, with shaded confidence intervals), at each challenge point (i.e., among animals remaining uninfected following prior exposures). There is no decline in infection rate over time, indicating no acquisition of immunity to infection nor selection for innately-resistant animals.</p

Example results from optimization of procedural parameters.

<p>The procedure outlined in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1005100#ppat.1005100.g002" target="_blank">Fig 2</a> was evaluated by Monte Carlo simulation (350–1000 simulations for each set of values). The parameters modeled were: (1) the starting dose (D₀), ranging from 1/27^th to 27x AID₅₀; the number of animals challenged at each dose during the dose ranging (group size: N_C); the target standard error on the estimate of AID₅₀ (σ_T); the maximum number of exposures (E_MAX) of any single animal; and the order of the kinetics of infection (m). Shown in red are the suggested values of these parameters, i.e., those that resulted in the fewest animals, the fewest rounds, and the most accurate estimate of AID₅₀. Where not graphed, parameter values were held constant, as follows: N_C = 8, σ_T = 0.5, D₀ = 1/3, m = 1, E_MAX = unlimited. (A) The number of animals required is not strongly dependent upon the group size, except in cases where the starting estimate of D₀ is greater than 1 AID₅₀. Thus, in the case where D₀ is low, the number of rounds required decreases with increasing group size, to a point. (B) The number of animals required increases when D₀ goes above 1, but the number of rounds (shown for phase 1) is not strongly impacted by D₀. (C) The number of animals required decreases as the target standard error (σ_T) on the final estimate of AID₅₀ decreases (i.e., more precision requires more data). (D) Decreasing E_MAX increases the number of animals required, particularly at low D₀. If D₀ is close to 1, then a limit of three exposures performs equally to no limit. (E) When E_MAX is limited, then the number of animals required increases when D₀ is far from AID₅₀ in either direction. (F) Example outcomes on parameters when using a infection kinetics with nonlinear order (m = 3 or m = 0.5). The pattern of results is largely similar to first-order kinetics, with similar optima. Bar and whisker plots show the median, interquartile range, and full range excluding outliers.</p

Surface phenotype of <i>in vivo</i> SIV-infected CD4 T cells.

<p><b>(A)</b> Memory differentiation status of <i>tat/rev</i>⁺ infected cells. CD28 and CD95 surface protein expression by <i>tat/rev</i>⁺ cells (green) is overlaid atop the profile of SIV negative cells (gray). The percentage of cells negative for CD28 is indicated for each population. Inset pie charts depict the proportion of <i>tat/rev</i>⁺ cells that are central (CM; white) and effector memory (EM; black). See also <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006445#ppat.1006445.s011" target="_blank">S4 Table</a>. <b>(B)</b> Surface staining for activation markers, CD38 and CD69, is shown for <i>tat/rev</i>⁺ cells. <b>(C)</b> Surface protein expression distribution of CD95, ICOS, CD38, CD69, and HLA-DR by uninfected (gray), <i>gag</i>^<i>+</i> and/or <i>LTR</i>^<i>+</i> (orange; early, latent or abortive infection), and <i>tat/rev</i>^<i>+</i> (green) cells. Significant differences were determined by ANOVA across the three populations (p<0.05) followed by Student’s t-test between populations (p<0.05, *). Experiments were performed once for each specimen (n = 6) and all data is shown.</p

<i>in vivo</i> SIV-infected cell frequency and single-cell viral RNA expression.

<p>SIV-infected macaque specimens from 14 animals were surveyed for spliced and unspliced nucleic acid positive memory (CD95+) CD4 T-cells by limiting dilution FACS sorting and qPCR. Animals are described in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006445#ppat.1006445.s008" target="_blank">S1 Table</a>. <b>(A)</b> Schematic of the experimental workflow. <b>(B)</b> The frequency of <i>tat/rev+</i> and <i>gag+</i> cells are plotted alongside (left) and against each other (right), against total proviral DNA (<i>gag</i>) in bulk sorted memory CD4 T-cells <b>(C)</b>, and against concurrent viremia <b>(D)</b>. <i>gag</i> DNA⁺ cell frequency, determined by excluding reverse transcriptase (“RT–“), is also shown for a subset in (B); bars represent means. In bivariate plots, each animal is represented by a unique symbol and chronic infection by circles. <b>(E)</b> Single-cell SIV transcript levels within memory CD4 T cells from d10 SIVmac251 (AY69, n = 3 tissues) and d14 SIVsmE660 (PBMC, n = 3 animals) infected rhesus macaques (subset from B-D). The relative (Et) and absolute quantity of <i>env</i> and <i>tat/rev</i> (top) and <i>gag</i> and <i>LTR</i> (bottom) transcripts per cell is shown. Undetectable mRNA is plotted as a scatter near the origin for visualization. Symbol color corresponds to the number and type of viral transcripts detected in each cell; number of cells analyzed is provided in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006445#ppat.1006445.s010" target="_blank">S3 Table</a>. <b>(F)</b> Pictorial of viral life cycle with corresponding combination of viral genes present and symbol colors used herein. See also <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006445#ppat.1006445.s001" target="_blank">S1 Fig</a>. <b>(G)</b> Pie charts depict the proportion of <i>tat/rev</i>^<i>+</i> cells expressing additional viral genes. Experiments were performed once for each animal and all data is shown.</p

Post-transcriptional CD4 downregulation and MHC class I preservation on <i>in vivo</i> infected T cells.

<p><b>(A)</b> FACS staining distribution of surface CD4 protein on SIV <i>tat/rev</i>⁺ productively infected (green; n = number of cells analyzed) memory CD95⁺ CD4 T cells from animal AY69 PBMC, lymph node, and jejunum compared to uninfected cells (gray). <b>(B)</b> FACS surface staining for CD3 and CD4 on <i>tat/rev</i>^<i>+</i> cells from memory CD3^+/- CD4 T cells in PBMC from 3 additional animals. <b>(C)</b> FACS CD4 surface fluorescence is plotted against <i>tat/rev</i> mRNA copies. Dot colors correspond to <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006445#ppat.1006445.g001" target="_blank">Fig 1F</a>. <b>(D)</b> <i>tat/rev</i> (top) and <i>CD4</i> (bottom) mRNA copies per <i>tat/rev</i>⁺ cell is plotted by surface CD4 protein expression. <b>(E)</b> Surface CD4 protein (fluorescence) is plotted against <i>CD4</i> mRNA (left) or surface CD3 protein (right). Dot size corresponds to <i>tat/rev</i> mRNA copies per cell. FACS surface staining of surface MHC class I <b>(F)</b> and co-expression with surface CD4 <b>(G)</b> on <i>tat/rev</i>^<i>+</i> cells. Experiments were performed once for each specimen and all data is shown.</p

Differential host gene expression by <i>in vivo</i> SIV-infected T cells.

<p><b>(A)</b> Genes showing a change in expression level or proportion in single cells from one or more infection states compared to uninfected cells in PBMC from one or more animals (n = 4). Significant changes (FDR < 0.1) are indicated (*). Plots display the MAST estimates of average frequency of expression (x-axis) and average expression level in cells when a gene is expressed (y-axis) for each gene by cell infection state (ellipse). Boundaries reflect the 90% bivariate Wald test confidence interval (Chi-square, 2 degrees of freedom). Contours with minimal or no overlap are more likely to be associated with a significant difference. The number of cells analyzed from each infection state is shown in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006445#ppat.1006445.s010" target="_blank">S3 Table</a>. (<b>B</b>) PBMC Z-statistics (standardized model coefficients) show expression across animals and infection stages by combining (using Stouffer’s method) the frequency of expression and continuous expression. Expression is mean centered by row. (<b>C</b>) Venn diagram of DE genes identified by the hurdle likelihood ratio test, common to PBMC from multiple animals. (<b>D</b>) Estimated average expression of four significant genes from (A) by cellular infection state. For each animal, significant differences from uninfected cells (at the 10% level after multiple testing adjustment across 92 genes tested) are marked with an asterisk. Unadjusted 95% confidence bounds are shown for the expression level. (<b>E-G</b>) Analyses of animal AY69 lymph node and jejunum as in (A-C). (<b>H</b>) Venn diagram of DE genes common across tissues within animal AY69. Two genes common to all tissues are indicated. Experiments were performed once for each specimen and all data is shown.</p

Most CTL Clones Isolated from Endogenous Responses Are Efficient in Tumor Cell Lysis

<p>CTL clones derived from each patient were classified as “efficient” (greater than 40%), “intermediate” (between 10% and 40%), or “low/no” (less than 10%) in lysis of melanoma cells based on data displayed in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0010028#pmed-0010028-g002" target="_blank">Figure 2</a>. Each bar represents the portion of total clones from each patient with “efficient,” “intermediate,” or “low/no” melanoma lysis potential.</p