T-cell dynamics during acute SIV infection
Mattapallil, Joseph Ja; Letvin, Norman Lb; Roederer, Marioa
From the aVaccine Research Center, NIAID, NIH, Bethesda, Maryland and the bBeth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
Requests for reprints to: M. Roederer, Room 5509, 40 Convent Dr, Bethesda, MD 20895, USA.
Received: 6 February 2003; revised: 17 April 2003; accepted: 7 May 2003.
Objectives: To delineate T-cell dynamics during acute SIV infection, particularly of phenotypically defined memory T cell subsets.
Design: T cells are a heterogeneous mix of naive and memory subsets delineated by simultaneously measuring CD4, CD8, CD45RA/RO, CD11a, CD28, and CD27. The effects of SIV infection on these subsets was measured to evaluate the impact of changes in functionally distinct cell types during pathogenesis.
Methods: Peripheral blood was obtained from six SIV-infected macaques at multiple times before and after SIV infection and analyzed using 12-color flow cytometry.
Results: Acute infection was characterized by an initial lymphopenia caused by a decline in B cells. Total T-cell counts remained steady during the early acute phase; however, CD4 cell counts declined while CD8 T cells increased. The decline in CD4 T cells was a result of a decline in both naive and memory cells. CCR5+ or CD103+ subsets of CD4 T cells were depleted but only partially accounted for the decline of CD4 memory T cells, suggesting that acute infection was associated with a rapid redistribution of T cells from the periphery. Naive CD8 cell counts declined while memory CD8 cell counts increased. The increase coincided with declines in plasma viremia and was made up initially of CD27−CD28− (effector) cells; subsequently, the predominant phenotype became CD27+CD28−, akin to central memory cells.
Conclusions: A complete understanding of the T-cell dynamics during acute SIV or HIV infection requires the simultaneous evaluation of a broad spectrum of T-cell subsets. Changes in homeostasis and associated immunopathogenesis can no longer be accurately described simply by measuring naive and memory T-cell subsets.
HIV infection is characterized by a progressive decline of CD4 T cells. Numerous studies [1–7] have reported changes in the phenotype and functions of T cells during various stages of HIV infection. Given the heterogeneity of T-cell subsets, both phenotypically and functionally, it is likely that immunological changes observed during HIV infection are part of a highly complex process in which selective changes in T-cell subsets may presage the course of disease.
In healthy individuals, T cell subsets exist in a homeostatic balance that is maintained by a constant turnover and replenishment of T cells. During the acute infection phase, this balance is significantly altered by competing forces: viral-induced killing of CD4 T cells, expansion of virus-specific memory cells, and altered homing patterns induced by generalized activation. The rhesus macaque model can be used to study the changes that accompany primary SIV infection in a multitude of T cell subsets. Previous studies have shown that SIV causes an AIDS-like disease in rhesus macaques that is similar to HIV disease .
A critical tool for such a study is our recently developed multiparameter flow cytometer, capable of independently measuring 14 different parameters on each cell, including 12 fluorescent markers (each of which can be used to identify a cell-associated antigen). Using a combination of markers, such as for CD3, CD4, CD8, CD20, CD45RA or CD45RO, CD11a, CD27 and CD28, simultaneously, the representations of numerous subsets within naive and memory T cells can be delineated; for example, functionally distinct memory CD4 subsets can be discriminated based on the expression of CD27 and CD28 [9,10]. Recent studies showed that naive CD4 T cells are lost during later stages of chronic HIV infection [11–15], whereas an early loss of CD4 memory T cells has been demonstrated during acute HIV infection [16–18].
Hamann et al.  have used functional assays to delineate three subsets of memory CD8 T cells, namely CD45RA+CD27−, CD45RA−CD27− and CD45RA−CD27+. The CD45RA+CD27− subset of memory CD8 T cells were found to have cytolytic activity in vivo whereas the CD45RA−CD27+ subset contained antigen-specific precursor cytotoxic T lymphocytes. HIV-associated CD8 T-cell dysfunction has been attributed to a downmodulation of CD28 expression during early HIV infection [5–7] and Appay et al.  have shown decreased expression of CD27 on HIV-specific CD8 T cells during acute HIV infection. To date, only limited studies have been performed to delineate these ‘fine’ subsets of naive and memory T cells in rhesus macaques. Pitcher et al.  have delineated rhesus macaque T cells into naive and memory subsets based on the expression of CD45RA and one of CD11a, integrin β7, CD95 or CD28. However, the effects of primary infection on the nature and dynamics of various T-cell subsets have not been quantified.
The objective of this study was to characterize the heterogeneity of lymphocyte subsets in peripheral blood of rhesus macaques and determine the effects of primary SIV infection on these subsets. It was hypothesized that lymphocytes in rhesus macaques comprise heterogeneous phenotypes that exhibit differential changes during acute SIV infection. It is likely that these changes, some of which are reversed during the resolution of the acute phase while others are not, may presage the immunodeficiency that accompanies the chronic phase of the infection. Understanding the dynamics of these functionally diverse subsets will allow for the more complete interpretation of the changing functional state of the immune system after lentiviral infections.
Materials and methods
Animals, infection and samples
Six colony-bred healthy rhesus macaques (Macaca mulatta) housed at Bioqual Inc. (Maryland, USA) were used in this longitudinal study. The animals were housed in accordance with American Association for Accreditation of Laboratory Animal Care guidelines and were seronegative for simian retrovirus 1 and simian T cell leukemia virus type 1. Animals were infected with 1000 TCID50 (tissue culture median infective dose) of uncloned pathogenic SIVmac251 intravenously. Peripheral blood samples were collected from each animal in vacutainer tubes containing acid citrate dextrose before and at various times after infection. Peripheral blood mononuclear cells (PBMC) were isolated by centrifugation over a Ficoll–Hypaque density gradient. After washing, freshly isolated cells were used for flow cytometric and ELISPOT (Hitech Instruments, Edgement, Pennsylvania, USA) assays. Plasma samples were collected for determining viral loads using the branched DNA (bDNA; Chiron, Emeryville, California, USA) assay. Absolute cell counts were determined using complete blood counts and flow cytomteric data.
Antibodies and flow cytometry
All antibodies were purchased from PharMingen (San Diego, California, USA), either conjugated or unconjugated and derivatized in our laboratory, with the exception of CD103 (Coulter Immunotech, Miami, Florida, USA). All reagents were validated and titrated using rhesus macaque PBMC. Freshly isolated PBMC were labeled simultaneously with the following combinations of antibodies: (a) CD3–fluorescein (FITC), CD4–Cy5.5-phycoerythrin (PE), CD8–Texas Red–phycoerythrin (TRPE), CD45RA–Cy7PE, CCR5–allophycocyanin (APC), CD11a–Cy7APC, CD62L–Cy5.5APC, CD27–Alexa594, CD28–PE, CD20–Alexa430, and HLA-DR–Cascade Blue (CB); and (b) CD103–FITC, CD4–Cy5.5PE, CD8–TRPE, CD45RA–Cy7PE, CCR5–APC, CD11a–Cy7APC, CD62L–Cy5.5APC, CD27–Alexa594, CD28–PE, CD20–Alexa430, and CD3–CB. Labeled cells were fixed with 0.5% paraformaldehyde and analyzed using a modified Becton Dickinson Digital Vantage SE (Becton Dickinson, San Jose, California, USA) equipped with three lasers (488 nm argon, 406 nm krypton and a 595 nm dye laser) capable of simultaneously measuring 14 parameters. At least 1 × 106 total events were collected and analyzed using FlowJo (version 4.3; Tree Star Inc., San Carlos, California, USA).
The wells of 96-well multiscreen plates were coated overnight with 100 μl/well of 10 μg/ml anti-human interferon γ [IFNγ (B27); Becton Dickinson] in endotoxin-free Dulbecco's phosphate-buffered saline (D-PBS, Life Technologies, Gaithersburg, Maryland, USA). The plates were then washed three times with D-PBS containing 0.25% Tween-20 (D-PBS/Tween), blocked for 2 h with D-PBS containing 5% fetal bovine serum at 37°C, washed three times with D-PBS/Tween, and rinsed with RPMI 1640 containing 10% fetal bovine serum to remove the Tween-20. PBMC were plated in triplicate at 2 × 105/well in 100 μl final volume with either medium alone or 2 μg/ml SIVmac239 Env or Gag peptide pools. The peptide pools consisted of overlapping 15-mer peptides spanning the SIVmac239 Gag or Env protein and were used such that each peptide was present at a concentration of 2 μg/ml. Following an 18-h incubation at 37°C, the plates were washed nine times with D-PBS/Tween and once with distilled water. The plates were then incubated with 2 μg/ml biotinylated rabbit anti-human IFNγ (Biosource, Camarillo, California, USA) for 2 h at room temperature, washed six times with Coulter Wash (Beckman Coulter, Miami, Florida, FL), and incubated for 2.5 h with a 1:500 dilution of streptavidin–AP (Southern Biotechnology, Birmingham, Alabama, USA). Following five washes with Coulter Wash and one with PBS, the plates were developed with NBT/BCIP (nitroblue tetrazolium/5-bromo-4-chloro-3- indolyl phosphate) chromogen (Pierce, Rockford, Illinois, USA), stopped by washing with tap water, air dried, and read using an ELISPOT reader (Hitech Instruments, Edgemont, Pennsylvania, USA). The mean number of spots from triplicate wells was calculated for each animal and adjusted to represent the mean number of spots per 106 PBMC. Data are presented as the sum total of the mean number of Gag and Env specific spots per 106 PBMC from six monkeys.
All animals exhibited peak plasma viremia at 2–3 weeks after infection (Fig. 1a). (Data from the same six animals are shown in all figures. Each animal is shown with a different symbol, which is consistent throughout all figures.) Viral loads declined to reach set points at 7–9 weeks after infection. Variations were observed in the set points with most of the animals having viral loads < 105 copies/ml of plasma. One animal continued to exhibit high viral set points throughout the course of the study, with a plasma viral set point at nearly 106 copies/ml. Though this animal had a higher viral set point, the platelet and red cell counts and hemoglobin and hematocrit levels following infection did not differ from those prior to infection.
Primary SIV infection was accompanied by a strong immune response. In order to examine if the dynamics of T-cell changes correlated with the generation of virus-specific immune responses, IFNγ responses by PBMC to SIV Gag and Env peptides were quantified using the ELISPOT assay (Fig. 1b). Strong ELISPOT responses were observed very early during primary infection and these coincided with the decline in peak viremia. The ELISPOT responses persisted during the course of the study.
Changes in peripheral blood lymphocyte subset representation during primary SIV infection
To quantify the effects of SIV infection on lymphocytes, panels of markers were used that could not only discriminate T cells from non-T cells but also simultaneously delineate the subsets of naive and memory T cells (Fig. 2). At all time points where lymphocyte phenotyping was performed, viral load, absolute cell counts, and SIV-specific immune responses were simultaneously determined (Fig. 1). In healthy rhesus macaques, B cells have a CD3−CD8−CD20+ phenotype , whereas most natural killer (NK) cells are CD3−CD8+CD20− . A subset of CD3−CD8− CD20− lymphocytes was also observed and could harbor additional NK cells .
The mean absolute numbers of lymphocytes (Fig. 1c) decreased at 1 week after infection then partially recovered and remained steady during the course of study. This initial decline was mostly a result of a decline in absolute numbers of CD3− lymphocytes (Fig. 1c). To determine which of the CD3− subsets contributed to this decline, changes in B- and NK-cell subsets were evaluated (Fig. 1c) prior to and following SIV infection. Absolute B-cell counts declined dramatically at 1 week after infection whereas the absolute cell counts of NK cells was the same before and after SIV infection. The B-cell counts did not recover during the course of the study. However, the absolute cell counts of the CD3−CD8−CD20− subset of lymphocytes increased following infection (Fig. 1c) and accounted for the increased numbers of CD3− lymphocytes observed during the later time points of the study. The implications of an increase in this subset of lymphocytes are difficult to determine at this point. It is possible that these subsets may harbor additional NK cells and this will need to be evaluated further.
The absolute CD3 T-cell counts remained steady during early SIV infection but gradually declined at 7 weeks after infection (Fig. 1c). However, as expected, this apparent lack of change in total T-cell counts masked a massive reorganization of the underlying subsets. Reciprocal changes were observed in the absolute cell counts of CD4 and CD8 T cells following infection (Fig. 1c). CD4 T-cell counts gradually declined following infection whereas CD8 T-cell counts increased gradually and stayed high until 7–9 weeks after infection. Following this expansion, during which viremia was controlled and viral loads reached set points, the absolute numbers of CD8 T cells declined to those observed preinfection.
Discrimination of T-cell subsets using multicolor flow cytometry
Among the CD3 T cells, naive and memory CD4 and CD8 T cells were discriminated based on the expression patterns of CD45RA and CD11a (Fig. 2). Previous studies  have shown that naive T are primarily CD45RA+CD11alo and do not respond to antigen stimulation, whereas memory T-cell subsets harbor cells responsive to antigen stimulation and have either CD45RA+CD11ahi (CD45RA+ memory) or CD45RA− phenotype (CD45RA− memory) with variable CD11a expression.
As previous studies  had shown that memory T-cell subsets could be further delineated into numerous functionally distinct subsets based on the expression of CD27 and CD28, the expression these cell-surface proteins was also evaluated. Naive T cells express both CD27 and CD28 whereas memory T cells differentially express these markers . Our results (Fig. 2) confirm that in healthy rhesus macaques, essentially all naive CD4 and CD8 T cells expressed both CD27 and CD28. Within the naive T-cell gate based solely on CD45RA and CD11a, there is a small fraction of cells that do not express one or both of CD27 and CD28. These are memory T cells, as shown by de Rosa et al.  for human T cells; highly accurate discrimination of naive T cells requires the simultaneous measurement of at least five cell surface markers simultaneously.
Memory CD4 and CD8 T cells were found to express CD27 and CD28 differentially (Fig. 2). Approximately 50% of the CD45RA+ memory CD4 T cells expressed either CD27 or CD28 alone or both; the remaining cells expressed neither. In contrast, higher frequencies (approximately 90–100%) of CD45RA− memory CD4 T cells expressed both CD27 and CD28, with the remaining cells expressing neither marker or only one of these markers. A similar expression profile was observed in both the CD45RA+ and the CD45RA− memory CD8 T cells. However, unlike the CD45RA+ memory CD4 T cells, higher frequencies of CD45RA+ memory CD8 T cells were CD27+CD28+.
Dynamics of CD4 and CD8 T-cell subsets
Because SIV can infect and kill CD4 T cells, a differential effect of primary infection on CD4 and CD8 T cells would be expected. There might also be relative changes in naive versus memory CD4 T cells, given the apparent resistance of naive T cells to productive HIV infection [11,25–27]. As shown in Fig. 3a,b, there were complex changes in these major subsets of T cells. There are broad declines in the absolute cell counts of most T-cell subsets, with the exception of CD8 memory T cells. Both naive CD4 and naive CD8 T cells declined in absolute number, although the ratio of these two (Fig. 3c) indicated that CD8 naive T cells were, if anything, lost to a greater degree. The similar rates of loss of naive CD4 and CD8 T cells suggests that SIV cytolysis is not responsible for their decline.
CD4 memory T cells also declined in absolute numbers, as expected. There was a preferential loss of CD4 memory compared with CD4 naive cells, as the ratio of naive to memory cells changed from nearly 1:1 before infection to more than 2:1 by 24 weeks (data not shown), possibly as a result of SIV-induced infection and cytolysis. However, the total CD8 memory cell counts increased during the study. This increase in CD8 memory cells is perhaps more profound than the absolute numbers suggest (Fig. 3b), as it overcame the broad T-cell lymphopenia observed for the other T-cell compartments. Indeed, the ratio of CD8 naive to CD8 memory cells (data not shown), unlike that for CD4 T cells, decreased dramatically by 7 weeks, with partial recovery in some animals at later time points.
Dynamics of functionally distinct memory subsets
Subsets of memory T cells were further delineated based on the expression of markers such as CD27 and CD28 (which may define different stages of maturation [9,10,19]), CCR5, and CD103 (which marks mucosa-homing T cells).
There were similar changes in the CD27- and CD28-defined subsets of memory CD4 and CD8 T cells, showing a shift from less-differentiated to more differentiated cells (Fig. 4a,b). For both CD4 and CD8 memory T cells, there was a decline in the representation of cells coexpressing CD27 and CD28, although this was far more dramatic for CD8 T cells than it was for CD4 T cells. Indeed, the loss of CD27 and CD28 on CD8 memory T cells was dramatic even by week 1 after infection; this was among the earliest selective changes observed in the T cell compartment.
Among CD4 T cells (Fig. 4a), the disease was accompanied by an increase in the CD45RA−CD27− CD28+ memory T cells. However, among the much less numerous CD45RA+ memory subset, the shift was to an increase in the CD27−CD28− population. This subset represents perhaps the most differentiated CD4 memory subset.
The remodeling of the CD8 memory compartment was far more profound than that seen for CD4 T cells (Fig. 4b). There was an early and transient rise in the CD27−CD28− cell compartment, coinciding with the highest viral loads. Concomitant with the control of the viral loads, these terminally differentiated cells disappeared; however, the predominant CD8 memory subset became CD27+CD28− (as opposed to preinfection, for which the predominant population was CD27+CD28+). Similar patterns were seen for both the CD45RA+ and CD45RA− memory T cell subsets; note that the relative proportions of these major memory subsets are more evenly balanced than in the CD4 compartment.
Loss of CD4 T cells and CCR5 expression
Since SIV is known to use CCR5 as a coreceptor for infection, the changes in CCR5+CD4+ T cells during acute infection was evaluated (Fig. 5). Previous studies reported a decline in CCR5+CD4+ T cells in the periphery during early SIV infection ; our results are consistent with this. Most of the CCR5-expressing cells are within the CD45RA−CD11ahi subset of memory CD4 T cells, with the remainder in the CD45RA+CD11ahi subsets. As expected, essentially no naive CD4 T cells expressed CCR5. Following SIV infection, CCR5+CD4+ T cells declined sharply and stayed at a low level during the course of this study. However, since only a small fraction of CD4 memory T cells expressed CCR5, the loss of this subset can only partially account for the larger depletion of memory CD4 T cells.
Expression of the mucosa-homing marker CD103 on CD4 T cells
Veazey et al.  have reported a loss of mucosa-homing CD4 T cells from the peripheral blood during the chronic stage of SIV infection. As shown in Fig. 5a, CD103 (αEβ7) was primarily expressed on the memory CD4 T cells and these subsets sharply declined as early as 1 week after infection and stayed low throughout the course of this study (Fig. 5c). However, as for CCR5+CD4+ T cells, these cells represent only a minor fraction of memory CD4 T-cell subsets and do not account for the loss of memory CD4 T cells following infection. Interestingly, most of the CD103-expressing subsets of memory CD4 T cells were found to be CCR5− (Fig. 5a). As CD103 is primarily expressed by mucosa-homing T cells [30–37], these results suggest that homing of these subsets to mucosal tissues was a mechanism for the decline of this subset in the periphery.
Analyzing the dynamics of T-cell changes during HIV infection is complicated by the fact that this cell group contains numerous phenotypically and functionally distinct subsets that may display differential dynamics during the course of infection. Evaluating these changes at a ‘fine’ subset level provides a more complete understanding of the changes in the T-cell compartment during primary HIV infection. The use of multiparameter flow cytometry enables numerous subsets of T and non-T cells to be delineated simultaneously and the effects of primary SIV infection on these subsets to be assessed longitudinally. Our results demonstrate that the homeostatic make-up of both T and non-T cells was significantly altered during primary infection. A lymphopenia primarily resulting from a severe loss of B cells was observed 1 week after infection. This is in line with previous reports of an acute depletion of circulating CD20 B cells very early following infection with SIV . In contrast to non-T cells, CD3 T-cell counts remained steady for the first few weeks and then declined gradually to below that in controls after a few weeks following infection. CD4 T-cell counts gradually declined, accompanied by a corresponding increase in CD8 T-cell counts, which contributed to the steady CD3 cell counts observed during the first few weeks after infection. However, as the viral loads declined, CD8 T-cell counts decreased without a corresponding increase in the CD4 T cells, leading to an overall decline in absolute CD3 T-cell counts.
T cells are a heterogeneous mix of naive and memory subsets that are differentially affected during primary SIV infection. Our results demonstrate that naive CD4 T-cell counts gradually declined very early following infection. However, this decline was mirrored by a similar (if not more pronounced) decline in naive CD8 T cells. Hence, it is likely that this depletion of naive CD4 and CD8 T cells represents changes in homeostatic control mechanisms (i.e., redistribution, similar to that observed during the chronic phase of HIV infection in humans). These data support previous reports of innate resistance of naive CD4 T cells to productive (and cytolytic) HIV infection in vitro , showing that naive CD4 cells are resistant to SIV infection in vivo. It is unlikely that this resistance is solely a result of the lack of expression of CCR5, since naive T cells can be infected with CCR5-tropic HIV in vitro . Likewise, it is important to note that most memory CD4 T cells are depleted, despite lack of CCR5 expression.
Loss of CD4 T-cell subsets from the periphery could also result from preferential homing of these cells to secondary lymphoid tissues. Mucosal tissues harbor the majority of the lymphocytes in the body, and numerous studies have identified mucosa-homing T cells in the periphery [30–35,37]. CD103 (αEβ7) along with α4β7 are preferentially expressed by mucosa-homing T cells. Our results demonstrated that almost all the CD103+CD4+ T cells had a memory phenotype and were completely lost from the periphery very early during SIV infection. This is in line with previous reports demonstrating a loss of mucosa-homing CD4 T cells from the periphery during SIV infection . CD103 subsets are memory CD4 T cells that are primarily CCR5−, suggesting that either CCR5- independent infection occurred or that redistribution was the primary cause of their disappearance from the periphery. The significant loss of CCR5−CD4+ memory T cells supports the hypothesis that the early loss of CD4 memory T cells from the periphery during primary SIV infection is primarily a redistribution phenomenon.
Studies in HIV-infected subjects have suggested that HIV infection is characterized by a defect in T cell differentiation. Appay et al.  demonstrated that HIV infection is accompanied by an incomplete differentiation of CD8 T cells, with most of the CD8 memory T cells expressing a CD28−CD27+ phenotype. Primary SIV infection led to slightly different changes in CD8 T-cell memory differentiation. A transient expansion of fully differentiated CD27−CD28− subsets of CD8 memory T cells occurred early following infection. However, these subsets declined a few weeks later and were replaced by CD27+CD28− subsets. These changes were more dramatic within the CD45RA+ subset of CD8 memory T cells. Previous studies had shown that cytolytic activity was associated with CD45RA+CD27− subsets of memory CD8 T cells , whereas HIV-specific CD8 T cells had a CD45RA−CD27+CD28− phenotype [39–41] suggesting that these expanded subsets contribute to viral control during early SIV infection. Our results also demonstrate that during primary SIV infection, unlike HIV infection , CD8 T-cell maturation was not a strictly linear process since the undifferentiated CD27+CD28− subsets emerged after the decline of differentiated CD27−CD28− subsets. Previous studies in humans have shown that the CD27+CD28+ subsets of memory T cells are precursors or early differentiated cells whereas CD27−CD28− subsets are fully or late differentiated cells [19,42]. Cells expressing either one of these markers are in the intermediate stages of differentiation. These results suggest that any defect that might be present in CD8 memory T cells necessarily emerges later during the course of primary infection with the appearance of CD27+CD28− subsets.
Our results demonstrate that primary SIV infection is characterized by significant changes in both T and non-T lymphocyte subsets and that it is likely that multiple mechanisms (cytolytic and non-cytolytic) account for these changes. It is particularly important that studies of lymphocyte dynamics report changes in both absolute cell counts and representation (percentages), since these provide very different view of the dynamics. Unfortunately, such data retrieval has additional complexity because representation must be analyzed not only as a percentage of total lymphocytes but also as a fraction of any given ‘parent’ subset. For example, the dramatic changes in representation of CD27- and CD28-defined subsets of CD8 T cells shown in Fig. 4b are much more apparent when shown as a fraction of CD8 T cells than as a fraction of lymphocytes.
Our study extends previous observations that CD4 and CD8 memory T cells show differential dynamics. We show that the first week of SIV infection is accompanied by a broad (but minor) T-cell lymphopenia; the only selective change is a shift in the apparent differentiation stage of memory CD8 T cells (i.e., loss of CD27 and CD28 expression). Major expansions in late-differentiation stages of both CD4 and CD8 T cells accompanied the large increase in viral burden during the subsequent 2 weeks. Then, concomitant with partial control of virus, the T-cell compartments equilibrated, but with significant increases compared with preinfection values for CD27−CD28+CD4+ cells and CD27+CD28−CD8+ cells. Finally, the study shows a complete depletion of mucosa-homing CD4 T cells from the periphery. These observations demonstrate that a full understanding of the T-cell dynamics that accompany SIV (or HIV) disease requires the simultaneous evaluation of a broad spectrum of T-cell subsets; changes in homeostasis and the associated immunopathogenesis can no longer be accurately described simply by measuring naive and memory T-cell subsets.
We thank Stephen De Rosa, Steve Perfetto, Joanne Yu and other members of the VRC ImmunoTechnology Section for advice and technical help; Marsha Sowers and Marisa St Claire at Bioqual Inc., Rockville, Maryland for expert assistance with the animals; Kristin Beaudry, Sampa Santra, Michael Seaman and Maggie Beddall at the Beth Israel Deaconness Medical Center, Harvard Medical School for their valuable assistance during the course of this study.
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