T Cell Turnover in SIV Infection-Reference-Cited by-同舟云学术

T Cell Turnover in SIV Infection

Published:1999-04-23 Issue:5414 Volume:284 Page:555-555
ISSN:0036-8075
Container-title:Science
language:en
Short-container-title:Science

Author:

Grossman Zvi¹,Herberman Ronald B.²,Dimitrov Dimiter S.³

Affiliation:

1. Office of AIDS Research, National Institutes of Health, Bethesda, MD 20892, USA E-mail:

2. University of Pittsburgh Cancer Institute, Pittsburgh, PA 15213, USA

3. Laboratory of Experimental and Computational Biology, DBS, National Cancer Institute, Frederick Cancer Research and Development Center, National Institutes of Health, Frederick, MD 21702, USA

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Reference14 articles.

1. Rapid Turnover of T Lymphocytes in SIV-Infected Rhesus Macaques

2. In Fig. 1 R represents the pool of resting T cells (either CD4 + or CD8 + cells) that are asynchronously activated (specifically or as “bystanders”) at an average rate a over the time of the experiment. The pool regenerates at a rate p R ; the death rate is d R . R 0 denotes the pool of resting memory and naı̈ve T cells that are not subject to immune activation during the time of the experiment. (The partition into R and R 0 is not essential for the present simulation. It becomes more relevant when the turnover of cells with particular phenotypes related to the cells' activation history is being considered.) A 1 A 2 and A 3 are activated cells at different states of activation and maturation (the partition into three classes is arbitrary). c i (i = 1 2 or 3) denote the maturation rates p i the proliferation rates and b i are the back-flow rates of memory cells returning to the resting compartment. E are nondividing fully activated (“effector”) cells that die at a rate d E . (More generally cells in E may also divide and give rise to memory cells at some rates.) The system is assumed to be at steady state leading to the following conditions: F R = ( p R − d R − a ) R + b 1 A 1 + b 2 A 2 + b 3 A 3 = 0; F 1 = ( p 1 − c 1 − b 1 ) A 1 + aR = 0; F 2 = ( p 2 − c 2 − b 2 ) A 2 + c 1 A 1 = 0; F 3 = ( p 3 − c 3 − b 3 ) A 3 + c 2 A 2 = 0; c 3 A 3 − d E E = 0; p 0 = d 0 . The equations for the labeled cells in the different compartments are dR L / dt = F R L + 2 p R ( R − R L ) S 21 ; dA 1 L / dt = F 1 L + 2 p 1 ( A 1 − A 1 L ) S 21 ; dA 2 L / dt = F 2 L + 2 p 2 ( A 2 − A 2 L ) S 21 ; dA 3 L / dt = F 3 L + 2 p 3 ( A 3 − A 3 L ) S 21 ; dE L / dt = c 3 A 3 L − d E E L ; dR 0 L / dt = 2 p 0 ( R 0 − R 0 L ) S 21 . Here F i L is the same as F i ( i = R 1 2 or 3) with the variables on the right-hand side of F i replaced with their labeled counterparts. S is a step-function of time t : S 21 = 1 if t is smaller than or equal to 21 days; S 21 = 0 for t > 21. S allows acquisition of BrdU by unlabeled cells to occur during the first 3 weeks but not later.

3. The equations listed in (2) were used as shown in Fig. 2 to fit data reproduced from the report by Mohri et al. [figure 1A upper-left panel in (1)]: the changes in fraction of cells that are BrdU + in the CD4 + lymphocyte subpopulation of a highly infected macaque (square points) and of an uninfected macaque (triangles) [animals # RH-1316 and # RH-1372 respectively in (1)]. Mohri et al. also used their model to fit CD4-turnover data from a few other macaques but these two examples are representative. In Fig. 2 of our comment the smooth curves demonstrate consistency of our equations with the data after fitting the parameters. For the uninfected animal the data are compatible with R = 0 (no activation) and p 0 = d 0 = 0.0035 day −1 . The set of parameters that fit the labeling-delabeling kinetics in the infected macaque is not unique and we have made no effort to optimize our choice either biologically or numerically. The set used in Fig. 2 is R 0 = 0.5 (half the total number); p R = 0.002 d R = 0.0035 a = 0.0044 p 1 = p 2 = p 3 = 1 c 1 = 3.9 c 2 = 1.5 c 3 = 1.1 b 1 = b 2 = b 3 = 0.07 d E = 0.1 p 0 = d 0 = 0.0035 day −1 . We have assumed for simplicity the turnover of nonactivated CD4 cells in the infected macaque to be the same as that in the uninfected macaque. This may well be an overestimate: As we have argued in the past chronically elevated immune activation may actually lead to inhibition of regenerative proliferation in the resting cell population (5). The size of this population might consequently be reduced. On the other hand the total number of all other cells belonging to the same T cell subset may increase or decrease depending on the average expansion of activated clones and on the rate of memory cell back-flow relative to the activation rate of resting cells. These considerations underscore the complexity of the relationship between immune activation and homeostasis (5).

4. In note 11 in (1) Mohri et al. indicate that “Although the source may represent cells being exported from the thymus or extrathymic tissues … it could also include a population of resting or slowly dividing T cells which upon activation would undergo rapid clonal expansion and enter the subpopulation of cells acquiring label during the experiment.” However “resting T cells” are themselves dynamic part of the population of cells acquiring label during their regeneration and also through the conversion of immune activated cells into resting memory cells. Furthermore the expansion of immune-activated cells alone irrespective of the “source ” more than compensates for their subsequent contraction. Outflow of memory cells from the activated pool into the resting pool may actually exceed the inflow of activated cells. Thus although activated clones continuously replace each other the activation of resting cells can hardly be seen as a homeostatic mechanism in SIV-infected macaques or in HIV-infected humans.

5. Grossman Z., Herberman R. B., Nature Med. 3, 486 (1997).

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