Understanding the HIV coreceptor switch from a dynamical perspective
- Christel Kamp^{1}Email author
https://doi.org/10.1186/1471-2148-9-274
© Kamp; licensee BioMed Central Ltd. 2009
Received: 3 February 2009
Accepted: 30 November 2009
Published: 30 November 2009
Abstract
Background
The entry of HIV into its target cells is facilitated by the prior binding to the cell surface molecule CD4 and a secondary coreceptor, mostly the chemokine receptors CCR5 or CXCR4. In early infection CCR5-using viruses (R5 viruses) are mostly dominant while a receptor switch towards CXCR4 occurs in about 50% of the infected individuals (X4 viruses) which is associated with a progression of the disease. There are many hypotheses regarding the underlying dynamics without yet a conclusive understanding.
Results
While it is difficult to isolate key factors in vivo we have developed a minimal in silico model based on the approaches of Nowak and May to investigate the conditions under which the receptor switch occurs. The model allows to investigate the evolution of viral strains within a probabilistic framework along the three stages of disease from primary and latent infection to the onset of AIDS with a a sudden increase in viral load which goes along with the impairment of the immune response. The model is specifically applied to investigate the evolution of the viral quasispecies in terms of R5 and X4 viruses which directly translates into the composition of viral load and consequently the question of the coreceptor switch.
Conclusion
The model can explain the coreceptor switch as a result of a dynamical change in the underlying environmental conditions in the host. The emergence of X4 strains does not necessarily result in the dominance of X4 viruses in viral load which is more likely to occur in the model after some time of chronic infection. A better understanding of the conditions leading to the coreceptor switch is especially of interest as CCR5 blockers have recently been licensed as drugs which suppress R5 viruses but do not seem to necessarily induce a coreceptor switch.
Keywords
Background
Although many studies aim to understand the evolution of the human immunodeficiency virus (HIV) in its host [1–3] there are still a lot of open questions related to the mechanisms driving the intra-host dynamics. One of these puzzles is associated with the HIV coreceptor switch [4]: To facilitate cell entry and subsequent replication HIV binds to the cell surface molecule CD4 as well as a chemokine coreceptor, most commonly the CCR5 or CXCR4 coreceptors. In most patients viruses that use the CCR5 coreceptor (R5 viruses) dominate in early stages of disease. This preference changes however in about 50% of patients during the course of disease towards viruses using the CXCR4 coreceptor (X4 viruses). This switch in coreceptor usage is associated with a worsened prognosis which makes a better understanding of the switch dynamics of direct clinical importance. At the same time there are several explanatory hypotheses but not yet a conclusive understanding of the underlying processes in terms of empirical evidence or model support. The transmission mutation hypothesis [4] assumes that R5 viruses are favoured in transmission and are in consequence more often found in early infection. X4 viruses with higher fitness can emerge from R5 viruses via intermediate mutants of lower fitness which will finally become dominant [4]. While there is some evidence for a fitness loss of intermediate mutants [5–7], this hypothesis strongly relies on the assumption that X4 viruses can hardly be transmitted in infectious doses as these would otherwise immediately become dominant due to their assumed selectional advantages in the host. There might be some strain selection during transmission but there is evidence that both R5 and X4 strains can be transmitted in infectious doses, however, with X4 viruses seemingly having a selectional disadvantage thereafter [8, 9]. The effective replication rate of X4 viruses exceeds R5 replication in some in vitro assays [10], there is, however, no conclusive picture of the in vivo situation with different environmental constraints [4, 11]. Taken this together [4, 12–14], it is highly questionable whether a fragile setting relying solely on the transmission mutation hypothesis adequately describes the major processes in place or robustly represents the observed dynamics.
An alternative and less fragile hypothesis assumes that environmental conditions in the host change during the course of disease in favour of X4 viruses. Such changes may be the consequence of immune pressure or the availability of adequate target cells for replication in terms of (co-)receptor expression or replication efficiency by stage of cell development (naive/memory cells) [4, 15]. In a recent study Sguanci and coworkers [16] investigate the changing environment by assuming that the susceptibility of target cells (or infectivity of viruses) increases in favour of X4 cells as soon as these emerge in an auto-catalytic fashion, however, staying with the assumption that the initial infection is with R5 virus only. Earlier work by Wodarz et al. [17] and Callaway et al. [18] allows more realistically for a mixed infection and investigates how the shift in target cells and HIV specific T cells can dynamically induce a shift from R5 to X4 viruses. In a similar fashion, Ribeiro et al. investigate how the coreceptor switch can be facilitated due to preferential replication of X4 and R5 viruses in naive and memory T cells, respectively, with differences in efficiency of viral reproduction [15]. These approaches support the hypothesis of environmental change as a driving force to the coreceptor switch but do not consider the stochastic nature of virus evolution and the emergence of new strains and viral diversity which have been associated with the coreceptor switch. While the relative importance of the above hypotheses for the coreceptor switch can ultimately only be answered experimentally we will here focus on the more robust hypothesis of environmental change for further analysis. We want to identify key processes resulting in the observed phenomenon by a restriction to a minimal model representing the dynamics of the immune system and HIV [19]. Therefore, we develop an approach based on the well studied model of Nowak and May [20] that allows to study the dynamical processes associated with the HIV coreceptor switch in combination with viral evolution. While we do not go yet into the details of cellular subpopulations whose roles are empirically still under debate we rather take advantage of the relative simplicity of the model to study the effects of the stochastic nature of HIV evolution neglected in the models outlined above. The stochastic framework will allow to analyse the distribution of evolutionary pathways associated with the coreceptor switch as well as survival times in a more systematic way.
It has specifically become of major importance to understand the processes leading to the dominance of X4 viruses since new drugs have been licensed that block the CCR5 receptor [12, 21–23] and in consequence can shift the selective advantage towards X4 viruses [24], although not necessarily inducing a coreceptor switch. Considering the association between X4 viruses and a worsened prognosis [4, 25] it is of vital importance to understand whether X4 can only flourish in the environment of a patient weakened by side effects of a chronic infection or whether X4 viruses themselves destabilise the patient and cause the worsened prognosis [13].
Results and Discussion
The Model
Variables and parameters in equations (1)
v _{ i } | viral load from strain i |
x _{ i } | specific immune response to strain i |
z | cross reactive immune response |
r _{ i } | growth rate of virus i |
p_{ i }, q_{ i } | effect of specific and cross reactive immune response on strain i |
c_{ i }, k_{ i } | growth of specific and cross reactive immune response induced by and directed against strain i |
b | natural decay of the immune response |
u _{ i } | decay of immune response induced by strain i. |
The viral load of strain i grows at a rate r_{ i }and is diminished by the specific and cross-reactive immune response at a rate -p_{ i }x_{ i }- q_{ i }z. Vice versa, a specific immune response is stimulated by each strain as is the cross-reactive immunity by the total viral load. Immune responses decay at a rate b and further at a rate proportional to the viral load which is to account for the fact that HIV infected T helper cells are depleted (and in consequence impair the immune response mediated by other T and B cells). Among the 2^{ n }possible equilibrium solutions of a set of equations (1) for n strains at most one is stable [20], that is, for any number of strains the viral load either diverges or converges to a well defined equilibrium viral load. Strains are distinguished by their sensitivity towards the specific immune response, i.e. several related genomic sequences might represent the same epitope and consequently correspond to one strain in the model. The model does not yet address the question of different immune cell populations as for example CD4 vs. CD8 cells, naive vs. memory cells or resting vs. replicating cells. The distinction between x_{ i }and z takes only into account that there is a strain specific and an unspecific component of the immune response. The latter can be a composition of the response towards conserved parts of the virus, of cross-reactivity from earlier specific immune responses or innate immunity. Bare of any strain-specific responsiveness, z is a measure for the general activation of the immune system going along with an overall turnover of CD4 cells (irrespective of their specificity) seen in HIV infections [27, 28].
Parameter settings
parameter | R5 virus | X4 virus |
---|---|---|
number of strains | n ^{R5} | n ^{X4} |
viral growth rate r_{ i } | r (2d^{-1}) | r(1 + αz) ((2 + z)d^{-1}) |
effect of specific immune response p_{ i } | p ^{R5} (2d^{-1}) | p^{X4 }>p^{R5} (20d^{-1}) |
effect of cross-reactive immunity q_{ i } | q (1.86d^{-1}) | |
stimulation of specific and cross-reactive immunity c_{ i }, k_{ i } | c, k (0.1d^{-1}V L^{-1}) | |
natural and virus induced depletion of immune response b, u_{ i } | b, u (0.02d^{-1}, 0.1d^{-1}V L^{-1}) | |
probability to generate a new strain p_{ m }per day and viral load | (0.005d^{-1}V L^{-1}) |
- 1.
ru <kq, i.e. the viral load is fully controlled by the cross reactive immune response z alone or
- 2.
that is, only as long as the numbers of R5 and X4 virus strains do not exceed the critical numbers given by condition (3) the immune system can control the infection.
The first inequality in (4) implies that the cross-reactive immune response cannot fully control the viral infection as can be seen from equation (2). This means that each viral strain needs to be suppressed both by a specific and a cross-reactive immune response. The second inequality implies that the immune system can control only a limited number of strains, i.e. the limitations of the cross-reactive immune response can be compensated by the specific immune response if the critical number of strains given by equation (3) is not exceeded. As soon as this number of strains is exceeded this will lead to an uncontrolled increase in viral load in the model corresponding to the onset of AIDS in the patient [20]. The detailed dynamics in this regime are not longer described by equations (1) as saturation effects have to be considered due to limited resources.
For those states in which the critical number of strains given by equation (3) or a maximal viral load v_{ max }is exceeded, i.e. at the onset of AIDS, the last term in equation (5) is defined to vanish making these states absorbing states of the Markov process. v_{ max }limits the diverging viral load v(n^{R5}, n^{X4}) as the total viral load in the patient is limited. The distribution of times until absorption, i.e. the survival distributions, are described by phase type distributions [36].
The course of disease
The system shows a period of low viral load after the initial phase of disease with is interspersed with small outbreaks when a new viral mutant occurs - until eventually the viral load rises again. The system shows a quasi-stable behaviour as long as inequality (3) can be fulfilled for a non-zero but limited number of strains, i.e. if inequality (4) holds.
X4 viruses are underrepresented in viral load in the early stages of disease as the model assumes that they are more strongly suppressed by the specific immune response than R5 viruses (p^{X4 }> p^{R5}) [31] while having initially identical growth rates [11]. On the other hand the X4 replication rate in the model increases proportional to the overall activation of the immune system represented by the cross-reactive immune response. This shifts fitness advantages from R5 viruses to X4 viruses resulting in the phenomenology of a coreceptor switch.
The model captures the association between ongoing immune activation and the apparent shift in evolutionary advantages from R5 to X4 viruses during an HIV infection. There is however still controversy about the mechanisms underlying this association. Earlier assumptions about a shift in target cells favouring X4 over R5 viruses [4] are now questioned [37]. An alternative hypothesis links the efficiency of viral production to the division rate of target cells [15]. With the differential increase in target cell division rates during chronic infection [32] viral replication rates are differentially increased which eventually shifts the selectional advantage from R5 viruses to X4 viruses [15, 27, 28]. Another interpretation of the model equations is that X4 viruses experience a stronger specific immune response than R5 viruses but that they face a reduced cross-reactive immune response. In consequence X4 viruses will be more difficult to control as the number of strains grows. Any of these hypotheses allows for a dynamical change in environmental conditions in the host that become more favourable for X4 viruses after chronic infection while selection is in favour of R5 viruses in early stages of disease. Although, there is some evidence [4, 11, 15, 31, 32] in accordance with the model's assumptions on parameter settings it is difficult to get conclusive results from currently available empirical data. Many studies with one or only a few HIV or SIV cases point in different directions [5, 6, 7, 38]: Any comparative measurement of growth rates and the effect of an immune response among strains in vivo assesses quantities derived from a large variety of underlying processes that might vary among cases. Therefore, a more conclusive picture can only be expected with the integration of more data describing the interactions between HIV and the immune system in many patients. The observation that some viral strains may be present in a patient for a long time before they appear in detectable numbers favours the idea of a dynamical change in the environmental conditions in favour of X4 viruses [39, 40] at a higher level of abstraction. Reflecting these findings, the current model provides a coarse grained picture of possible scenarios for environmental shifts in the patient for further investigation. While details of these dynamics will have to be addressed within a refined model the current model has the advantage to be accessible to further analysis of the basic underlying stochastic processes as demonstrated in the following sections.
The routes of evolution
The probabilistic framework complementarily allows to study the probability to find a setting with n^{R5 }R5 strains and n^{X4 }X4 strains in a patient at any time during the course of disease - including the probability to have reached the final stage of disease, i.e. having exceeded the critical number of strains.
The coreceptor switch
Note that this situation is only observed in the latent phase defined by equation (3) if X4 viruses gain sufficiently fast replicative fitness, i.e. .
The results from the probabilistic approach considering equilibrium viral load according to equation (5) are compared with survival curves sampled from simulations of equations (1) based on the actual viral load. The good agreement shows that the flow of mutants is strongly determined by the equilibrium viral load (cf. Additional file 2 and 3, Figure S2 and S3).
As the average time to the onset of AIDS as well as the mean residence times in a given stage decrease with the accumulation of mutant strains (cf. equation (16)) patients with dominance in X4 viral load have a worsened prognosis in this respect. The higher burden in viral load associated with the higher number of strains leads to an accelerated progression of disease (cf. equation (16) and Figs. 2 and 3).
Note that suppression of R5 viruses as facilitated by coreceptor blockers [12, 21–23] does not only reduce R5 viral load in the model but also diminishes the X4 viral load due to reduced immune activation (cf. Additional file 4, Figure S4).
Conclusion
A mechanistic assessment of the processes leading to the coreceptor switch in HIV infected patients is difficult - and maybe even inadequate - due to the large variability and stochasticity of the underlying processes in vivo. Quantities such as viral growth or clearance rates are often only known in very specific settings without detailed knowledge of possible hidden dependencies from the underlying dynamical processes - and can in consequence point in contradictory directions. The observation that certain HIV strains may be present in a patient for a long time before they grow to a detectable viral load [39, 40] hints however towards a picture of a dynamical change in the environmental conditions in the host. This holds specifically for X4 viruses [40].
The current model provides a coarse grained, probabilistic picture of possible dynamical scenarios of the coreceptor switch without going yet into the details of virus replication and immune response. This allows to study the basic dynamical features and conditions for a coreceptor switch. In the model, the viral quasispecies [41] is generated by a probabilistic process under the pressure of the immune system. Its composition depends on the randomly chosen evolutionary path that may show some biases due to environmental conditions. These include the viral replication efficiencies based on adequate target cells as well as constraints of immune recognition or therapy. The answer to the question whether a coreceptor switch has occurred is determined by the composition of the viral quasispecies found at the time when the host's immune system loses control and the viral load diverges, i.e. at the onset of AIDS in the model system. One prediction is that the suppression of R5 viruses in the host will not necessarily result in an expansion of X4 viruses. The control of R5 viruses can even delay or prevent the emergence of considerable X4 viral load - which strongly depends on the constitution of the host, i.e. in biases in the choice of the possible evolutionary paths. This is well in accordance with the studies done for a newly licensed drug which blocks the CCR5 receptor [22, 23, 40]. The model provides a stochastic framework to investigate R5 and X4 virus evolution in a dynamic environment, however staying with a qualitative description of the cellular processes. This is a strong simplification of the complex interaction network that interlinks HIV and the hosts immune system. The growing knowledge about their interactions [15, 27, 32, 42] will be implemented within a refined model. This should combine the advantages of this stochastic approach with more details on the involved immune cells populations - both with regard to their role in the immune response and their contribution to persistent immune activation and viral replication - to allow for more quantitative predictions.
Methods
Virus evolution
The differential equation for the evolution of viral load and immune response (1) have been solved numerically with routine rk4 from the Math::RungeKutta module for PERL. The system is initialised with one R5 viral strain and one X4 viral strain with v^{R5}(0) = v^{X4}(0) = 1V L, i.e. one unit of viral load for each subtype. During each integration step of duration dt = 0.02d a new mutant arises with probability dt × p_{ m }× v = 0.02d × 0.01d^{-1}VL^{-1} × v proportional to the viral load v. R5 and X4 mutants occur with equal probability.
leading to equations (6) and (7) (v(n^{R5}, n^{X4}) = v^{R5}(n^{R5}, n^{X4}) + v^{X4}(n^{R5}, n^{X4})). Note that v^{R5}(n^{R5}, n^{X4}) < 0 corresponds to the case in which no R5 viruses can co-exist with X4 viruses, i.e. a total coreceptor switch [20].
Note that less R5 viruses can coexist with X4 viruses in case of r_{CCR5 }<r, i.e. neither R5 infection nor X4 infection (due to lack of immune activation) can progress as fast as without therapy.
Probabilistic approach
with p_{1} being the probability per time and viral load to mutate within the same subtype (R5 → R5, X4 → X4) and p_{2} being the probability per time and viral load to mutate between subtypes (R5 → X4, X4 → R5). For the data shown it holds p_{1} = p_{2} = = 0.005d^{-1}VL^{-1} (for p_{1} ≠ p_{2} cf. Additional file 1, Figure S1). The last term in equation (15) vanishes if the critical number of stains according to equation (3) or the maximal viral load v_{ max }= 500V L is exceeded leading to an absorbing boundary. The time evolution of P(n^{R5}, n^{X4}, t) was solved numerically with routine rk4 from the Math::RungeKutta module for PERL with the initial condition P(1, 1, 0) = 1 (zero otherwise) corresponding to the initialisation with one R5 and one X4 strain.
The mean waiting time until the onset of AIDS is derived in equation (16) by averaging the waiting times arising along all possible evolutionary paths starting form a configuration with n^{R5 }R5 virus strains and n^{X4 }X4 virus strains (cf. Additional file 5, Figure S5).
Declarations
Acknowledgements
I would like to thank B. Schnierle and B. Krause for many helpful discussions on HIV evolution during the preparation of the manuscript. Their feedback as well as that of colleagues from the section of Biostatistics at the Paul-Ehrlich-Institute, specifically of K.M. Hanschmann, have contributed much to the improvement of the manuscript. Finally I would like to thank the referees for their detailed remarks allowing me to elaborate the approach more transparently.
Authors’ Affiliations
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