Fraser, Christophe; Ferguson, Neil M.; Ghani, Azra C.; Prins, Jan M.a; Lange, Joep M. A.a; Goudsmit, Jaapb; Anderson, Roy M.; de Wolf, Frankb
Although antiretroviral therapy has been shown to be largely effective in inhibiting HIV-1 replication [1–4] and its pathogenic consequences [5–8], early predictions that treatment might eliminate virus from an infected patient in 2–3 years  now seem optimistic. Viral elimination is slowed by populations of infected long-lived quiescent CD4 T-cells, which are capable, on activation, of producing replication-competent virus [10–16]. The half-life of these infected cells has been recently estimated to be from 6 months to 40–50 months or more [17,18].
It has been suggested that this half-life could be dramatically shortened, and viral elimination thereby hastened, by supplementing highly active antiretroviral therapy (HAART) with drugs intended to activate the resting T-cell pool. Such activation would be expected to induce viral production in these cells and thus their more rapid clearance. Optimistically, it might be hoped that once the infected CD4 T-cell pool had been sufficiently reduced, HAART could be discontinued and the immune system left to eliminate or control any remaining reservoirs of virus [19,20].
In this paper we use a mathematical framework, incorporating key mechanisms underlying T-cell homeostasis in the HIV-1 infected individual, to analyse the results of a study of immune activation treatment (IAT). A 5-day course of the anti-CD3 murine monoclonal antibody OKT3 [21,22] in combination with recombinant human (rh) interleukin (IL)-2 [23,24] was given to three patients who had previously spent at least 38 weeks on a particularly potent five antiretroviral drug combination . Plasma HIV-RNA levels were suppressed to less than 5 copies/ml for at least 30 weeks. The treatment was repeated in one patient. Using the mathematical model, we compare the outcome in the patients to results from studies of repeated short courses of IL-2 alone [26–29]. Our results provide insights into T-cell homeostasis, HIV viral replication, and thus how IAT strategies might be optimized.
Patients and treatment
Three patients (002, 008, 010) were treated with 2.5 mg OKT3 (Jansen-Cilag BV, Tilburg, The Netherlands) on the first day and 5 mg per day on the following consecutive 4 days. OKT3 was given as a 2-h continuous infusion. From day 2 to day 6, 4.5 MIU rhIL-2 (Chiron, Amsterdam, The Netherlands) bi-daily subcutaneously was added. Antiretroviral therapy, started 9–15 months before was continued during the OKT3/IL-2 course. It consisted of zidovudine (300 mg twice daily), lamivudine (150 mg twice daily), abacavir (300 mg twice daily), nevirapine (200 mg twice daily) indinavir (800–1000 mg twice daily) and ritonavir (100 mg twice daily). Patient 010 stopped abacavir and nevirapine because of hypersensitivity reactions and hydroxyurea (500 mg twice daily) was given instead until 10 weeks before OKT3/IL-2 treatment. Plasma HIV-RNA levels were < 5 copies/ml in all patients for at least 30 weeks before the start of the OKT3/IL-2 course. Because of side-effects experienced by the first patient (008) treated, the IL-2 dosage was decreased to 2 MIU twice daily in patients 002 and 010. Patient 010 did receive a second course of OKT3/IL-2 two weeks after the first.
Lymphocyte immunotyping was performed by flow cytometry using dual staining.
Lymph node biopsies
Lymph node biopsies were obtained by surgical excision 4–6 weeks prior to the OKT3/IL-2 treatment and on the fourth day of the first (02) or the second (10) OKT3/IL-2 cycle. Part of the lymph node was fixed in Parafix and embedded in paraffin. Another part was used to tease out lymph node mononuclear cells in Iscove's modified Dulbelco's medium supplemented with 20% foetal calf serum.
HIV-RNA in plasma
Quantification of HIV-RNA in plasma was performed using the NucliSens HIV-1 QT assay (Organon Teknika, Boxtel, The Netherlands), with a lower limit of quantification of 400 copies/ml. When levels decreased below this limit, an ultrasensitive protocol adaptation was used with a lower quantification limit of 50 copies/ml . When the limit of 50 copies/ml was reached, an initial input volume of 2 ml plasma was used in this ultrasensitive protocol, resulting in a lower quantification limit of 5 copies/ml.
HIV-1 RNA and DNA in peripheral blood mononuclear cells and lymph node mononuclear cells
HIV-1 RNA and DNA from mononuclear cells were isolated using TRIzol as recommended by the manufacturer (Gibco BRL, Life Technologies Inc., Grand Island, Maryland, USA). Briefly, at least 1 × 106 cells were suspended in TRIzol and an internal RNA control (MS2 RNA) was added. After addition of chloroform and centrifugation, the solution was separated into an aqueous phase containing the cellular RNA and an organic phase containing DNA and proteins. RNA was further recovered by isopropanol precipitation and re-suspended in water. HIV-RNA was quantified using the NucliSens HIV-1 QT assay with a lower quantification limit of 400 copies/ml. Control of the quality of the RNA was performed by PCR amplification of the MS2 RNA, as well as visualization of the isolated RNA on agarose gel. Cellular DNA was further recovered by ethanol precipitation and re-suspended in NaOH. Prior to amplification, DNA was incubated at 95°C. Subsequently the DNA was aliquoted and mutant plasmid was added (400, 80, 16 and 3 copies). A competitive nested PCR was performed on the HIV-1 pol region. After gel electrophoresis signals of mutant and wild-type were analysed and the HIV-1 DNA copy number was calculated per μg of DNA analysed. The DNA copy number was expressed as copy number per 106 CD4 T cells present in peripheral blood mononuclear cells.
Activation and T-cell homeostasis
OKT3 is a potent immune activant, inducing the release of a variety of cytokines, notably IL-2  and causing enhanced IL-2 receptor expression on memory T-cells . The effect of treatment with OKT3 in combination with rhIL-2 on absolute CD3 T-cell numbers and the CD4/CD8 T-cell ratio in three HIV-infected patients is shown in Fig. 1. Plasma HIV-RNA levels in these patients were suppressed to < 5 copies/ml 34 to 59 weeks before the start of the OKT3/IL-2 course by a treatment with a combination of four to six antiretroviral drugs (see methods). A substantial increase in CD8 and a corresponding fall in CD4 T-cell numbers occurred following OKT3/IL-2 treatment. These results are consistent with the long-lasting reduction of the CD4/CD8 T-cell ratios found in a 5-year follow-up study of HIV-uninfected renal allograft recipients treated with OKT3 . Figure 1 also demonstrates another key effect of OKT3 treatment – the transient disappearance of T-cells from peripheral blood. This transient lymphocytopenia is thought to be a result of increased expression of adhesion molecules on the lymphocyte membrane and subsequently the transport of T-cells to the vascular endothelium and the extravascular space .
These data give new insight into T-cell dynamics and homeostasis. A rapid change in the CD4/CD8 T-cell ratio was induced while the total T-cell pool size remained approximately constant. This provides new evidence for the conjecture that the quiescent CD4 and CD8 T-cell pools are co-regulated by the same density-dependent control mechanism – the hypothesis of `blind homeostasis' [34–37]. Further evidence for this conjecture is the long-term persistence of this change in CD4/CD8 T-cell ratio, as independent regulatory mechanisms would eventually correct any transient fluctuation in relative CD4 and CD8 T-cell numbers. Co-regulation is therefore key to understanding IAT, as it implies that different T-cell subpopulations proliferating at different rates will compete against each other much as species compete for space and resources in a finite ecosystem.
Within the competitive context of blind T-cell homeostasis, the reduction of the CD4/CD8 T-cell ratio caused by OKT3/IL-2 implies that the net proliferation rate of highly active CD8 T-cells must be greater than that of CD4 T-cells. While this cannot as yet be measured directly using techniques as in Hellerstein et al., this is supported indirectly by results from animal models of antigen clearance responses, where the antigen-specific CD8 cytotoxic T lymphocyte response is observed to be faster and greater than the corresponding CD4 T-helper response [39,40]. Furthermore, CD8 T-cell re-population is observed to be substantially faster initially than that of CD4 T-cells in patients who have suffered chemotherapy-induced immune-depletion .
Given that CD8 T-cells out-compete CD4 T-cells at high levels of activation, it might then seem paradoxical that HIV-infected patients who were repeatedly treated with less potent IAT (IL-2 alone) in combination with two or three antiretroviral drugs [26–29,42–46] show significantly increased CD4/CD8 T-cell ratios compared with controls treated without IL-2. A recent trial of IL-2 on patients who had been on HAART alone for several months prior to administration of IL-2 confirms that this effect on homeostasis is caused by IL-2 itself and not by HAART . There are two potential explanations for this. The first explanation is that IL-2 preferentially stimulates CD4 T-cells while OKT3 preferentially stimulates CD8 T-cells. The second explanation is a dose dependence in the response to activation, namely that CD4 T-cells respond faster at low dose and CD8 T-cells respond faster at high dose.
Under the latter hypothesis, we can infer the forms of the response of CD4 and CD8 T-cells to different concentrations, s, of some T-cell activating agent. We use a simple functional form for the response, described in the methods section, and parameterized in terms of the concentration required to achieve a 50% increase in cell turnover for either cell pool (A50X and A50Z in the mathematical model; see Appendix). The form of these responses critically determine the effect of IAT on homeostasis, as for any drug concentration s, the cell type with the largest response will out-compete the other. The precise nature of the response will determine the magnitude of the window of concentrations in which CD4 T-cells are not out-competed by CD8 T-cells. Aiming within this window of drug concentration will result in a treatment that does not reduce the CD4/CD8 T-cell ratio.
Activation and viral replication
A number of studies [48–57] have shown that provoking immune responses to common vaccines for other diseases results in a transient rise (typically of 4–6 weeks duration) in plasma HIV viraemia several-fold over baseline levels. In separate work, we have demonstrated that these results are consistent with the hypothesis that increased cell proliferation results in an increased susceptibility to viral entry and enhanced viral production . This scaling is supported by the in vitro observation that activation of T-cells is required for integration of the provirus and thus production of new virus [59–62]. In addition, infected cell lifetime would also be expected to scale with cell turnover as cells will be at most risk of viral-induced death and immune clearance during the activated phase.
We assume that cell susceptibility for HIV infection, HIV production and infected cell lifetime will be affected by indiscriminate activation in the same manner as by antigen-specific activation. The results from the OKT3/IL-2 study provide some direct evidence for this. In one patient (008) plasma levels of HIV-RNA increased above detectable levels (Fig. 2a). In the other two patients plasma HIV-RNA levels remained below detectable, however total HIV-DNA in peripheral CD4 T-cells did not change (Fig. 2b) and total HIV-RNA in these cells and especially in CD4 lymph node cells tended to increase (Table 1). This is in accordance with the large rise in plasma viraemia observed for an untreated patient treated with another anti-CD3 monoclonal antibody (WT32) during renal transplantation .
In attempting to deplete the long-lived infected T-cell pool by activation, there is a trade-off between enhanced viral replication (and thus new cell infections) and increased clearance of infected cells in the quiescent cell pool. In the absence of HAART, it is likely that the former process will win, and a net increase in the proportion of infected cells will be seen during T-cell activation treatment. This is because analysis of the pattern of viral load decay following initiation of HAART suggests that whilst highly activated and rapidly dividing cells only form around 1% of the total CD4 T-cell pool, they are responsible for over 99% of viral production prior to initiation of HAART . This implies that increasing T-cell activity results in a net increase in viral production.
Activation and HAART: optimal treatment
Given this trade-off under T-cell activation, we find in the model that the key factor which determines the virological outcome of IAT is the extent to which co-administered HAART inhibits viral replication. If HAART were able to block all replication, IAT would always reduce the prevalence of quiescent infected cells. However, recent analysis indicates that although HAART reduces viral production below the critical threshold required for self-sustaining replication, it may achieve this with only a 50–90% decrease in average viral production per infected cell . It may seem surprising that the rapid clearance of HIV virus associated with HAART can be achieved with such a modest reduction. The rapid time-scale of viral decay is in fact mainly a consequence of the rapid turnover of virus within the activated CD4 T-cell population. The decay profile is to a lesser extent dependent on antiretroviral drug potency, provided that net viral production and re-infection is kept below the critical threshold required for the virus to sustain itself (the basic reproductive number R0 < 1 ). When antiretroviral drug potency is only just above this threshold, HAART alone is observed to be effective; however the addition of IAT can result in HIV viral population growth by increasing viral production back above the critical self-sustaining threshold. This type of threshold effect is typical of non-linear infection processes .
To gain a more quantitative appraisal of the key issues and thus inform the debate on what might constitute an optimal IAT, it is useful to incorporate biological models of T-cell homeostasis [34–37] and viral replication [14,62] into a mathematical framework. This is described in detail in the Appendix. A simpler form of this framework which excluded CD8 T-cell dynamics was used previously to explain simultaneously post-HAART trends in viral load and CD4 T-cell counts and the perturbations in viral load seen following tetanus immunization of untreated HIV-infected patients . We use parameter estimates consistent with that analysis in the present study.
A consequence of our biological model is that increasing the potency of HAART will offer a potential advantage in giving greater freedom for IAT to increase activation (and infected cell clearance rates) and yet keep viral replication below the critical threshold required for new infection of quiescent T-cells to dominate the dynamics. The criterion for HAART to succeed under IAT is more stringent than for success without IAT.
Figure 3a shows the simulated effect of high-level IAT on CD4 and CD8 T-cells and viral load. High-level activation (simulating 5 days' treatment with OKT3/IL-2 under antiretroviral therapy) causes rapid proliferation of both CD4 and CD8 T-cells. However, the higher proliferation rate of CD8 T-cells means that CD4 T-cells are rapidly depleted through competitive exclusion mediated by density-dependent clearance. Hence, total T-cell numbers remain approximately constant, but, as seen in the study data (Fig. 1), the CD4/CD8 T-cell ratio declines substantially. In addition, CD4 T-cell numbers may be reduced by HIV-induced death caused by enhanced viral replication. Depending on assumptions about naïve cell recruitment, this change, which is certainly long-term, may be permanent. Also, despite the use of highly potent five-drug HAART, high-level activation increases viral replication above the critical threshold and so the number of CD4 T-cells which are infected with replication competent virus increases.
Repeated low-level activation is somewhat more successful [26–29,42–47]. Simulation, matched to the results of the study by Simonelli et al., show that activation is marginally below the threshold at which CD8 proliferation rates exceed those of CD4 T-cells, and viral replication, while increased, is still controlled (Fig. 3b). Hence CD4 T-cell counts increase over the course of therapy, and infected T-cell prevalence falls.
Our analysis indicate that two distinct factors therefore need to be taken into account when measuring the success of IAT – the net changes in total and in infected CD4 T-cell numbers. Figure 4a shows the change in total numbers of HIV-infected CD4 T-cells caused by a single 5-day administration of IAT as a function of HAART potency and the level of activation achieved during IAT, while Fig. 4b shows the long-term effect on total CD4 T-cell numbers. The different shape of these two graphs indicates that for a given antiretroviral drug efficacy, therapy needs to be evaluated both in terms of the effect on T-cell homeostasis and on the infected quiescent T-cell pool, and may not be simultaneously optimal for both effects. Therefore in practice, IAT should aim to deplete the infected cell population as much as possible while not causing a fall in CD4 T-cells. Furthermore, for any antiretroviral drug efficacy, negative (rather than neutral) outcomes are always associated with over-stimulation.
Elimination of reservoirs of infected cells is a long-term goal of treatment of HIV-infected patients on antiretroviral therapy. Immune activation has the potential to help achieve that goal by accelerating the clearance of infected slow-turnover quiescent CD4 T-cells. In this paper we have shown that the outcome of such therapy is critically dependent on two factors: the potency or dose of the immune activating agent administered, and the potency of co-administered HAART. Immune activation that causes non-specific activation of a large proportion of the T-cell pool has the risk of radically depleting both infected and healthy CD4 T-cell populations. Furthermore, as viral production increases as a function of cell turnover, high-level activation may overwhelm the ability of co-administered HAART to control viral replication. In that case, the outcome of therapy might be doubly negative: a net decrease in the size of the CD4 T-cell pool and an increase in the proportion of cells infected.
Some insight can be gained by comparing long-term changes to T-cell homeostasis in HIV-negative kidney transplantation patients treated with OKT3  with changes in the HIV-infected patients treated with OKT3/IL-2 presented here. Results from Müller et al. need to be interpreted cautiously as they report the changes averaged over 13 patients rather than individual patient data. The dose used, 5 mg daily, was the same as in our study, but OKT3 was administered for longer (> 13 days average) and in combination with cyclosporine and prednisolon. The long-term reduction in the CD4/CD8 T-cell ratio was less pronounced in these HIV-negative patients (approximately 40% versus 60% in our HIV-infected patients). As the total T-cell counts remain constant, this suggests that the reduction in CD4 T-cell numbers was 50% larger in HIV-positive patients than in HIV-negative patients, despite the shorter duration of the treatment period. Comparing our model results with those of Müller et al., a daily dose of 5 mg of OKT3 corresponds to a level of T-cell activation where s ≅ 0.35–0.45. This does not take into the account the effect of cyclosporine and prednisolon which could result in a modified value for this parameter. At this dose, the changes to the total CD4 T-cell pool in HIV-positive patients can be accounted for provided that antiretroviral efficacy is approximately 90% (see Fig. 4b). Thus, the discrepancy between HIV-positive and HIV-negative patients may be the result of direct cytopathic effects of residual HIV viral replication under HAART, enhanced by T-cell activation. This interpretation also suggests (from Fig. 4b) that the long-lived T-cell pool infected with replication competent virus was increased several fold by the treatment. This is consistent with our finding that viral RNA in lymph node biopsies tended to increase.
Our findings and that of others [16,18] that the total amount of HIV-DNA per 1 × 106 CD4 T-cells does not change are interpreted as indicating that after more than 1 year of HAART proviral HIV-DNA is no longer a measure of the replication competent HIV pool. This is consistent with recent findings on the effect of IL-2 alone [46,47]. Our conclusions appear to be independent of the details of the model framework or parameters used. They are, however, reliant on the assumption that OKT3 does not have an intrinsically different mode of action in HIV-positive patients.
More optimistically, our model suggests that IAT does have the potential to both systematically deplete infected cell reservoirs and even accelerate CD4 T-cell recovery if less potent (e.g., IL-2) immune activants are used [26–29,42–47,65], or the more potent ones (e.g., OKT3 plus IL-2) are administered at lower doses. When more moderate activation is induced, the risks of viral replication exceeding the threshold that can be controlled by HAART is reduced, together with the ferocity of the competition between CD4 and CD8 T-cell pools. Indeed, it appears that subtle differences in the response of these two cell pools to activation may give a window of drug concentrations for which IAT will result in a net increase in the CD4/CD8 ratio. A further clinical advantage of using less potent immune activants is improved tolerability and reduced risk of serious side-effects.
In conclusion, the quantitative analyses presented here predict that optimal viral elimination therapy should consist of the most potent HAART available coupled with extended periodic (or even continuous) low-level IAT. In the longer term, development of tolerable CD4-specific activants may further improve the effectiveness of this type of therapy, offering the potential of not only causing depletion of infected cell reservoirs, but also of restoring CD4/CD8 T-cell ratios to normal levels. However, caution is still required in all use of IAT, as the effect of activants on immune system function is still unknown. Even if IAT were capable of restoring CD4 T-cell populations, memory cell repertoires are still likely to be limited for patients who had reached late-stage disease, and IAT-induced anergy poses a potential risk.
The authors thank S. Jurriaans for excellent technical support.
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To explore the effects of IAT for different doses of T-cell stimulant and anti-viral drug action, a mathematical framework based on coupled differential equations is defined to study the time-evolution of the different subpopulations of T-cells. We are interested in describing two main biological processes: the HIV replication cycle and the nature of T-cell homeostasis.
We call V the free HIV viral load and Zi, Xi and Yi the sizes of the CD8, susceptible CD4 and infected CD4 T-cell subpopulations respectively, where the subscript i takes the values 1 and 2 for quiescent and active T-cells respectively. Cell turnover is assumed to be solely due to division of existing cells rather than recruitment of new cells from precursor populations. It is straightforward to include recruitment terms, even in an age-dependent manner and we find that a low recruitment rate for adults provides a simple explanation for the persistence of changes to the CD4/CD8 balance caused by IAT. Indeed, we would predict that for cases where recruitment of new cells played a significant part in cell turnover, the recovery of the ratio could be substantially accelerated. The equations for the system are:EQUATION where V˙ =dV/dt and so on. The turnover of quiescent T-cells is independent of cell type such that μX1=μZ1=μ, while the turnover of active and proliferating CD8 T-cells μZ2 is somewhat larger than that of their CD4 counterparts μX2. The effect of T-cell activating agents is accounted for by the f functions. There is no effect on the active cell pools, so fX2=fZ2=1, while the effect on the quiescent cell pools is to continuously increase the baseline turnover from μ through to μX or μZ for CD4 and CD8 T-cells respectively. A suitable functional form for CD4 T-cells fX1=1+(μX/μ−1) s/(s +AX50) with an entirely analogous formula for fZi for CD8 T-cells. For low viral loads the force of infection takes the standard form Λ=βV, but to take into account that for high viral loads infection is limited by the complete viral generation time, including the infected cell lifetime, we use the saturating form Λ=μβV/(βV +μ). X̄ = ΣiμXifXiXi is the total turnover of the susceptible CD4 T-cell pool, and a similar definition holds for Ȳ for the infected pool. The functions D represent density dependent control of the T-cell pools. The quiescent T-cell pools are jointly regulated by blind homeostasis, so the functions are DX1=DZ1 =TQ/ NQ+C where TQ is the total pool size and NQ is the carrying capacity. The active T-cell pools are regulated specifically at the level of proliferation and so the functions are DX2=(X2+Y2)/ NAX +C, where NAX is the carrying capacity for active CD4 T-cells and DZ2=Z2/ NAZ +C, where NAT is the equivalent for CD8 T-cells. Crowding occurs when the total cell pool size T grows beyond Tmax and acts equally on all cell pools as C =ϕ(1+exp(Tmax/ T)). HAART is included in the equations by a reduction in β (cell infection) and a fraction of virus produced being defective.
Parameter values were determined by fitting the model to the HIV-RNA post-HAART decay curves across a wide spectrum of patients . The model was also shown to be consistent with transient increases in HIV-RNA observed following antigenic challenge of patients not on HAART with vaccines for pathogens other than HIV . Remaining parameters, especially those regarding CD8 T-cell dynamics were set to be consistent with the data on OKT3/IL-2 therapy  and IL-2 therapy alone , but were not fitted as there was not enough data to give meaningful estimates. The aim of the analysis was to illustrate the effect of changing the level of immune stimulation in therapy and also changing the HAART drug efficacy.
We deliberately do not include the transport of cells back and forth from lymph to peripheral blood in this model, as we are more interested in exploring the perturbations to T-cell population sizes during and following therapy than exactly reproducing results from IAT studies. This means that certain transient effects such as the initial rapid rise of CD4 T-cells following HAART observed in some patients as well as the complete peripheral lymphocytopenia observed immediately following OKT3 or high dose IL-2 administration are not captured by the model.
Table. The parameter...Image Tools
Further follow up of the three patients since this paper was submitted indicates a partial slow recovery of the CD4/CD4 T-cell ratio. Cited Here...
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