1. Computational and Systems Biology
  2. Microbiology and Infectious Disease
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Thresholds for post-rebound SHIV control after CCR5 gene-edited autologous hematopoietic cell transplantation

  1. E Fabian Cardozo-Ojeda  Is a corresponding author
  2. Elizabeth R Duke
  3. Christopher W Peterson
  4. Daniel B Reeves
  5. Bryan T Mayer
  6. Hans-Peter Kiem
  7. Joshua T Schiffer  Is a corresponding author
  1. Vaccine and Infectious Disease Division, University of Washington, United States
  2. Department of Medicine, University of Washington, United States
  3. Clinical Research Division, Fred Hutchinson Cancer Research Center, United States
  4. Stem Cell and Gene Therapy Program, Fred Hutchinson Cancer Research Center, United States
  5. Department of Pathology, University of Washington, United States
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Cite this article as: eLife 2021;10:e57646 doi: 10.7554/eLife.57646

Abstract

Autologous, CCR5 gene-edited hematopoietic stem and progenitor cell (HSPC) transplantation is a promising strategy for achieving HIV remission. However, only a fraction of HSPCs can be edited ex vivo to provide protection against infection. To project the thresholds of CCR5-edition necessary for HIV remission, we developed a mathematical model that recapitulates blood T cell reconstitution and plasma simian-HIV (SHIV) dynamics from SHIV-1157ipd3N4-infected pig-tailed macaques that underwent autologous transplantation with CCR5 gene editing. The model predicts that viral control can be obtained following analytical treatment interruption (ATI) when: (1) transplanted HSPCs are at least fivefold higher than residual endogenous HSPCs after total body irradiation and (2) the fraction of protected HSPCs in the transplant achieves a threshold (76–94%) sufficient to overcome transplantation-dependent loss of SHIV immunity. Under these conditions, if ATI is withheld until transplanted gene-modified cells engraft and reconstitute to a steady state, spontaneous viral control is projected to occur.

Introduction

The major obstacle to HIV-1 eradication is a latent reservoir of long-lived, infected cells (Chun et al., 1997; Chun et al., 1995; Finzi et al., 1997). Cure strategies aim to eliminate all infected cells or permanently prevent viral reactivation from latency. The only two known cases of HIV cure, the ‘Berlin Patient’ and ‘London Patient’, resulted from allogeneic hematopoietic stem cell (HSC) transplant with homozygous CCR5Δ32 donor cells (Allers et al., 2011; Hütter et al., 2009; Gupta et al., 2019; Gupta et al., 2020), a mutation that makes cells resistant to CCR5-tropic HIV-1. The Berlin Patient was diagnosed with HIV in 1995 and received total body irradiation and allo-HSC transplantation for the treatment of his acute myeloid leukemia in 2007 and 2008. On the day of his first transplantation, antiretroviral therapy (ART) was interrupted, and HIV viremia never returned (Allers et al., 2011; Hütter et al., 2009; Peterson and Kiem, 2019). In 2019, an HIV-1 remission for more than 18 months was reported in the London Patient as part of the IciSTEM cohort (Gupta et al., 2019). The London Patient underwent one allo-HSC transplantation for treatment of Hodgkin Lymphoma in 2016, but with a less aggressive conditioning compared to the Berlin patient without irradiation (Gupta et al., 2019). This individual stopped ART 17 months after transplantation and as of March, 2020 his viremia remains suppressed, representing a possible case of HIV-1 cure (Gupta et al., 2020). The success of the allo-HSC transplantation is likely multifactorial—in part attributable to HIV resistance of the transplanted cells, the conditioning regimen that facilitates engraftment and eliminates infected cells, graft-versus-host effect against residual infected cells, and immunosuppressive therapies for graft-versus-host disease (Henrich et al., 2016; Henrich et al., 2014; Henrich et al., 2013; Salgado et al., 2018).

We are interested in recapitulating this method of cure but with reduced toxicity. Specifically, we are investigating the use of autologous transplantation following ex vivo inactivation of the CCR5 gene with gene-editing (Tebas, 2014; Peterson et al., 2016). This procedure is safe and feasible in pigtail macaques infected with simian-HIV (SHIV) (Peterson et al., 2016; Peterson et al., 2017; Peterson et al., 2018) and is currently being investigated in a Phase I clinical trial in suppressed, HIV-1-infected humans (NCT02500849). Also, this approach is more broadly applicable because an allogeneic CCR5-negative donor is not needed. However, current data suggests that protocols do not achieve sufficient fractions of genetically modified HIV-resistant hematopoietic stem and progenitor cells (HSPCs). In contrast, in allogeneic transplant, nearly 100% of circulating immune cells after engraftment consist of donor-derived CCR5Δ32 cells. This leads to a key question: what threshold percentage of CCR5-edited, autologous HSPCs is necessary for the cure/long-term remission observed in the Berlin and London patients?

To answer this question, we developed a mathematical model that predicts the minimum threshold of gene-modified cells necessary for functional cure. First, we modeled the kinetics of CD4+CCR5+, CD4+ CCR5-, and CD8+ T cell reconstitution after autologous transplantation. Then, we modeled SHIV kinetics during acute infection and rebound following ATI to identify the degree of loss of anti-HIV cytolytic immunity following transplantation as presented before but including some additional data (Peterson et al., 2017; Reeves et al., 2017). Finally, we applied our models to predict the proportion of gene-modified cells, the dose of these cells relative to the intensity of the preparative conditioning regimen (total body irradiation, TBI), and the levels of SHIV-specific immunity required to maintain virus remission following ATI. Results from this three-part modeling approach support strategies that (1) increase stem cell dose, (2) enhance potency of conditioning regimen to reduce the number of endogenous HSPCs that compete with transplanted CCR5-edited HSPCs, (3) increase the fraction of gene-modified SHIV-resistant cells, (4) extend periods between HSPC transplantation and ATI with tracking of CCR5- cell recovery and/or (5) augment anti-HIV immunity to achieve sustained HIV remission.

Results

Study design and mathematical modeling

We analyzed data from 22 juvenile pig-tailed macaques that were intravenously challenged with 9500 TCID50 SHIV1157ipd3N4 (SHIV-C) (Figure 1A). After 6 months of infection, the macaques received combination ART that included tenofovir (PMPA), emtricitabine (FTC), and raltegravir (RAL). When on ART, 17/22 received total body irradiation (TBI) followed by the transplantation of autologous HSPCs with (n = 12) or without (n = 5) CCR5 gene editing (ΔCCR5 and WT groups, respectively). A control group (n = 5) did not receive TBI or HSPC transplantation. 14 of the animals underwent ATI approximately 1 year after ART initiation. The remaining eight animals were necropsied at an earlier time relative to the other animals’ ATI (see Materials and methods for details).

Study design and mathematical modeling.

(A) Twenty-two pig-tailed macaques were infected with SHIV and suppressed with ART. Next, 17/22 underwent hematopoietic stem and progenitor cell (HSPC) transplantation following myeloablative conditioning (TBI), including 12 animals that received CCR5-edited products and five that received non-edited products (ΔCCR5 and WT groups, respectively). A control group (n = 5) did not receive TBI or HSPC transplantation. Fourteen animals underwent ATI approximately 1 year after ART initiation, while the remaining eight animals were necropsied prior to ATI (see Materials and methods for details). (B) We first developed mathematical models for T cell dynamics and reconstitution following transplant and before ATI (purple), assuming that low viral loads on ART do not affect cell dynamics. After validation of that model, we introduced viral dynamics and fit those to the T cell, primary infection, and viral rebound dynamics from the animals pre- and post-ATI (blue).

To analyze the data and estimate thresholds for viral control under this approach, we used ordinary differential equation models. We performed multi-stage modeling (Figure 1B). First, we modeled the kinetics of CD4+ and CD8+ T cell subsets after autologous HSPC infusion following transplant and before ATI, assuming that ART suppression decouples SHIV-dynamics from cellular dynamics. After validation of the first-stage model, we introduced a second-stage of modeling to (1) explain virus and T cell kinetics during primary infection and ATI and to (2) identify the degree of loss of anti-HIV cytolytic immunity due to the preparative conditioning. Then, we used the final validated model to project SHIV kinetics assuming different transplantation conditions.

CD4+CCR5+ and CD8+ T cells recover more rapidly than CD4+CCR5- T cells after HSPC transplantation

We analyzed the kinetics of peripheral blood CD4+CCR5+ and CD4+CCR5- T cells, and total, Tnaive, TCM, and TEM CD8+ T cells in macaques after HSPC transplantation.

In untransplanted controls, levels of CD4+ and CD8+ T cells oscillated around a persistent set point (blue data-points in Figure 2A). Also, CD4+ CCR5+ T cell levels were ~100 cells/μL and were uniformly lower than the CD4+CCR5- T counts (each ~1000 cells/μL) (Figure 2—figure supplement 1A). Finally, total CD8+ T cell levels in the control group were ~1400 cells/μL with a greater contribution from TEM (73%) than TN+TCM (27%) (based on median values, Figure 2—figure supplement 1).

Figure 2 with 1 supplement see all
Post-transplantation, pre-ATI CD4+ and CD8+ T cell dynamics.

(A) Empirical data for peripheral CD4+ CCR5+ (top row), CD4+CCR5- (middle row), and CD8+ T cell counts (bottom row) for control (blue), wild-type (red), and ΔCCR5 (green) transplantation groups. Each data point shape and color is a different animal sampled over time. (B) Distributions of blood CD4+ and CD8+ T cell counts for weeks 0, 10, and 25 after transplantation (p-values calculated with pairwise Mann-Whitney test with Bonferroni correction comparing control group with transplant groups. *p<0.05, **p<0.01 and ***p<0.001). (C) Expansion-rate estimates of CD4+CCR5+, CD4+CCR5-, and CD8+ T cells (p-values calculated with paired Mann-Whitney test with Bonferroni correction comparing expansion rates of CD4+CCR5- with CD4+CCR5+ and CD8+ in transplant groups. **p<0.01 for both). Colors for boxplots in B and C are matched to A (blue: control, red: wild-type-transplantation, and green: ΔCCR5-transplantation groups).

In the transplant groups, post-TBI levels of CD4+CCR5+, CD4+CCR5-, and CD8+ T cells were significantly lower than in the control group but expanded at different rates during the following weeks (Figure 2A–C). The levels of CD4+CCR5+ T cells started at 1–10 cells/μL and reconstituted to levels similar to the control group over 5–10 weeks (Figure 2A–B). CD4+CCR5- T cells remained at higher levels (~100 cells/μL) than CD4+CCR5+ T cells after TBI but expanded more slowly and did not reach the values of the control group after 25 weeks (Figure 2A–B). The CD4+CCR5+ T cell compartment expanded eightfold more rapidly than the CD4+CCR5- compartment (p=0.008, paired Mann-Whitney test, Figure 2C). CD8+ T cells decreased to levels between 10 and 100 cells/μL after TBI but recovered to levels just below the control group in 5 weeks (Figure 2A–B); CD8+ T cells recovered as rapidly as the CD4+CCR5+ population (Figure 2C).

Overall, these results show that after transplantation CD4+CCR5+ and CD8+ T cells recover faster than CD4+CCR5- cells. This suggests that each cell subset may have different and/or complementary mechanisms that drive their expansion. To explore these mechanisms, we analyzed the data with a mechanistic mathematical model of cellular dynamics.

Lymphopenia-induced proliferation drives early CD4+CCR5+ and CD8+ T cell reconstitution after HSPC transplantation

To identify the main drivers of T cell reconstitution after transplant, we developed a mathematical model that considered plausible mechanisms underlying reconstitution of distinct T cell subsets following autologous transplantation (Figure 3A). We assumed that T cell reconstitution may have two main drivers: (1) lymphopenia-induced proliferation of mature cells that persist through myeloablative TBI (Jameson, 2002; Schluns et al., 2002; Schluns et al., 2000; Goldrath et al., 2004; Voehringer et al., 2008) and (2) differentiation from naive cells from progenitors in the thymus (from transplanted CD34+ HSPCs (Douek et al., 2000; Douek et al., 1998) or residual endogenous CD34+ HSPCs that persist following TBI) and further differentiation to an activated effector state (Voehringer et al., 2008; Bender et al., 1999; Kieper and Jameson, 1999; Sallusto et al., 2004; Le Saout et al., 2008; Sprent and Surh, 2011). We also assumed the infused product dose D contains a fraction fp of transplanted, gene-edited HSPCs that do not express CCR5 (see Figure 3—source data 1 for individual values of  D and fp). Thus, in our model, ΔCCR5-gene-modified CD4+ T cells differentiating from these modified HSPCs are a subset of the total CD4+CCR5- cell compartment (Figure 3A).

Figure 3 with 4 supplements see all
Mathematical model of T cell reconstitution after hematopoietic stem and progenitor cell (HSPC) transplantation.

(A) Schematics of the model. Each circle represents a cell compartment: T represents the HSPCs from the transplant; P, the progenitor cells in bone marrow (BM) and thymus; S and N, CD4+CCR5+ and CD4+CCR5- T cells, respectively; Tp, the protected (ΔCCR5), gene-modified cells from transplant; Pp, protected (ΔCCR5) progenitor cells in BM/thymus; Np1 and Np2 the protected (ΔCCR5) CD4+ T cells; M the CD8+ T cells with naive and central memory phenotype and E CD8+ T cells with effector memory phenotype. The initial fraction of protected cells in the product is represented by the parameter fp. Gray panels represent mature blood CD4+ and CD8+ T cells, and the green panel all ΔCCR5 cells in the model. Red, dashed arrows represent discarded terms after model selection and validation (see text for details). (B) Model predictions using the maximum likelihood estimation of the population parameters (solid black lines) for all blood T cell subsets before ATI for all animals in the transplant groups using model with ΔAIC = 0 (Figure 3—source datas 23). Each gray line is one animal. (C) Model predictions of the total concentration of CD4+CCR5- T cells generated by CCR5 downregulation (dashed line) or thymic export (solid line), and of the total concentration of CD4+CCR5+ T cells generated by proliferation (solid line) or by upregulation of CCR5 (dashed line) over time using the maximum likelihood estimation of the population parameters.

Figure 3—source code 1

Best model file for T cell reconstitution in Monolix format.

https://cdn.elifesciences.org/articles/57646/elife-57646-fig3-code1-v1.zip
Figure 3—source code 2

R code for plots in Figure 3.

https://cdn.elifesciences.org/articles/57646/elife-57646-fig3-code2-v1.zip
Figure 3—source data 1

Values of the fraction of protected cells in transplant product fp, dose or number of hematopoietic stem and progenitor cell (HSPCs) in transplant product D and time of transplantation tx of each animal for model fitting and projections.

We assumed animal weight of 5 Kg.

https://cdn.elifesciences.org/articles/57646/elife-57646-fig3-data1-v1.docx
Figure 3—source data 2

Competing models for fitting T cell reconstitution with respective AIC values.

Best fit in bold-red (lowest AIC). The AIC values presented for each statistical assumption is the lowest of 10 runs of the SAEM algorithm with different randomly selected initial guesses.

https://cdn.elifesciences.org/articles/57646/elife-57646-fig3-data2-v1.docx
Figure 3—source data 3

Population parameter estimates for the best fits of the model in Equation 2 in the main text (lowest AIC in Figure 3—source data 2) to the T cell reconstitution dynamics.

RSE: relative standard error. Empty fields represent a standard deviation of random effects, σψ, fixed to zero. Values of ψ¯ for Kp,N(t0),S(t0),M(t0), and E(t0) shown here are in log10 cell counts/μL assuming a blood volume of of 3 × 105 μL (calculated assuming blood:weight ratio of 60 mL/kg and body weight of 5 kg). Red values represent an RSE greater than 100% implying that the number of data points may not be enough to estimate the respective parameter.

https://cdn.elifesciences.org/articles/57646/elife-57646-fig3-data3-v1.docx
Figure 3—source data 4

Individual parameter estimates for the best fits of the model in Equation 2 in the main text (lowest AIC in Figure 3—source data 2) to the T cell reconstitution dynamics.

Values obtained for N(t0),S(t0),M(t0), and E(t0) shown here are in log10 cell counts/μL assuming a blood volume of of 3 × 105 μL (calculated assuming blood:weight ratio of 60 mL/kg and body weight of 5 kg). Initial values for the control group where obtained assuming steady state.

https://cdn.elifesciences.org/articles/57646/elife-57646-fig3-data4-v1.docx
Figure 3—source data 5

Population parameter estimates for the best fits used in the R code for Figure 3.

https://cdn.elifesciences.org/articles/57646/elife-57646-fig3-data5-v1.zip

We built 24 versions of the model by assuming that one or multiple mechanisms are absent, or by assuming certain mechanisms have equivalent or differing kinetics (Figure 3—source data 2). Using model selection theory, we identified the most parsimonious model that reproduced the data (schematic in Figure 3A without red-dashed lines). The best model predictions for each cell subset using maximum likelihood estimates of the population parameters (Figure 3—source data 3) are presented in Figure 3B. Individual fits are visualized in Figure 3—figure supplement 13 and parameter estimates are collected in Figure 3—source data 4.

Model selection illuminated several likely biological phenomena: (1) CD4+CCR5+ T cell reconstitution after transplant is determined by cell proliferation and to a minor degree by upregulation of CCR5 (Figure 3C); (2) CD4+CCR5- T cell expansion is driven primarily by new naive cells from the thymus and to a lesser extent by CCR5 downregulation (Figure 3C); and (3) thymic export is not significantly different for CD4+ or CD8+ T cells (Figure 3—source data 2). However, model selection could not distinguish between the two models where ΔCCR5-gene-modified CD4+ T cells have the kinetics of both non-modified CD4+CCR5+ and CD4+CCR5- versus only the kinetics of non-modified CD4+CCR5- (i.e. having compartment Np2 or not in Figure 3A). Regardless, these two best models were identical in all other respects (Figure 3—source datas 2 and 3).

This first-stage modeling suggested additional testable biological predictions. First, the estimated CD4+CCR5+ T cell proliferation rate (~0.1/day) far exceeds the estimated CCR5 upregulation (~0.004/day) and thymic export rates (~0.002/day). Therefore, 1 month after transplantation, the total concentration of CD4+CCR5+ T cells generated by proliferation is predicted to be 40-fold higher than the concentration generated by upregulation of CCR5 (Figure 3C). Second, the CD8+ TEM cells comprise the majority of the total CD8+ T cell compartment (Figure 3B) with a proliferation rate up to 10-fold higher than the CD8+ TCM cell differentiation rate (Figure 3—figure supplement 4). In this way, CD8+ T cells follow a similar pattern to CD4+CCR5+ T cells (Figure 3B).

In summary, following autologous HSPC transplant: (1) thymic export and downregulation of CCR5 drive a modest expansion of CD4+CCR5- T cells, whereas (2) rapid lymphopenia-induced proliferation after TBI is the main driver for CD4+CCR5+ and CD8+ T cell expansion, which are derived from both the transplanted HSPC product and residual endogenous cells that persisted through the myeloablative conditioning regimen.

Plasma virus and blood CD4+CCR5+ dynamics are heterogenous among transplanted, SHIV-infected animals

To build a mathematical model for the virus and T cell dynamics, we analyzed plasma viral load kinetics and CD4+CCR5+/CCR5- T cell subset dynamics after ATI with respect to kinetics pre-ART (Peterson et al., 2017; Reeves et al., 2017). Figure 4A presents the plasma viral loads and the blood CD4+CCR5+ and CD4+CCR5- T cell kinetics before and after transplantation in the three groups.

Figure 4 with 1 supplement see all
Plasma viral load and CD4+ T cell kinetics after ATI.

(A) Empirical data for viral load (top row) and peripheral T cell counts (middle and bottom rows) for control (blue), wild-type (red) and ΔCCR5 (green) transplantation groups. Each data point shape and color represent a different animal sampled over time. (B) Distributions of the ratio at ATI vs pre-ART of final, nadir, and median viral load. Dotted horizontal lines represent a ratio equal to one (or no difference between ATI vs nadir).

We calculated the ratio of the viral load at necropsy versus at initiation of ART (Figure 4B) and the ratio of the nadir and median viral load after ATI versus pre-ART (Figure 4C–D). In general, the viral burden after ATI compared to pre-ART was slightly lower for the control group. However, for transplanted animals the viral load changes were heterogeneous, having much higher ratios for the wild-type (WT) group and slightly higher for CCR5-edited (ΔCCR5) group. For the three computed ratios, the viral load change after ATI was between 10- and 100-fold for the wild-type group (Figure 4B–D).

During ATI, CD4+CCR5+ T cells declined heterogeneously in the transplanted groups (Figure 4A), but CD4+CCR5+ T cell nadirs in the transplanted groups were consistently lower than those of control animals whose CD4+CCR5+ T cell levels did not decrease (Figure 4—figure supplement 1A). On the other hand, blood CD4+CCR5- T cell levels decreased to a similar nadir in all groups during ATI (Figure 4A and Figure 4—figure supplement 1B).

To summarize, SHIV viral load and CD4+CCR5+ dynamics are heterogeneous among transplanted animals. Higher ATI versus pre-ART viral load ratios in transplanted animals suggest that transplantation affects the host response against SHIV-replication, but this damage to host response may be mitigated somewhat when transplantation includes CCR5-edition.

A reduction in SHIV-specific immunity leads to higher viral rebound set points following ATI in transplanted animals

We simultaneously analyzed the viral and T cell subset data using mechanistic mathematical models in order to recapitulate the heterogeneity of plasma viral load and CD4+CCR5+ T cell kinetics and how transplantation may modify the immune response during ATI compared to the pre-ART stage. We extended our T cell reconstitution model to include SHIV infection of CD4+CCR5+ T cells (Figure 5A and Methods) and used this second-stage model to analyze virus and T cell dynamics during primary SHIV-infection, ART, transplant, and ATI.

Figure 5 with 3 supplements see all
Mathematical model of virus and T cell dynamics following ATI.

(A) Model: Susceptible cells, S, are infected by the virus, V, at rate β. Ip represents the fraction τ of the infected cells that produce virus, and, Iu, the other fraction that becomes unproductively infected. Total CD4+CCR5+ T cell count is given by the sum of S, Ip and Iu. All infected cells die at rate δI. IP cells arise from activation of latently infected cells at rate ξL¯ and produce virus at a rate π. Virus is cleared at rate γ. CD8+ M cells proliferate in the presence of infection with rate ω8 from which a fraction f become SHIV-specific CD8+ effector T cells, Eh, that are removed at a rate dh. These effector cells reduce virus production (π) by 1/ (1+θEh). Non-susceptible CD4+ T cells that were not CCR5-edited upregulate CCR5 in the presence of infection and replenish the susceptible pool at rate ω4. Gray panels represent mature blood CD4+ and CD8+ T cells, and the green panel represents ΔCCR5 cells. (B) Individual fits of the model (black lines) to SHIV RNA (left column), blood CD4+CCR5+ T cells (middle column), and CD4+CCR5- T cells (right column) for one animal in the control (top row), wild type (middle row), and ΔCCR5 groups (bottom row). Shaded areas represent time during ART and dashed-point line, the time of transplantation. (C–D) Scatterplots of observed ATI/pre-ART ratio of the (C) nadir viral load, and the median viral load ratio versus the SHIV-specific CD8+ T immunity ATI/pre-ART ratio:ω8ATI/dhATIω8preART/dhpreART (p-values calculated by Pearson’s correlation test); a higher ratio means a better immune response post-ATI. (D) Individual estimates of the SHIV-specific CD8+ T immunity ATI/preART ratio. Blue: control, red: wild type, and green: ΔCCR5 transplant group.

Figure 5—source code 1

Best model file for T cell and virus dynamics from acute infection after ATI in Monolix format.

https://cdn.elifesciences.org/articles/57646/elife-57646-fig5-code1-v1.zip
Figure 5—source code 2

R code for plots in Figure 5B.

https://cdn.elifesciences.org/articles/57646/elife-57646-fig5-code2-v1.zip
Figure 5—source code 3

R code for plots and tests in Figure 5C–D.

https://cdn.elifesciences.org/articles/57646/elife-57646-fig5-code3-v1.zip
Figure 5—source data 1

Competing models for fitting T cell and viral dynamics (Equations 2-3 in main text) using the best model in Figure 3—source data 2 and fixing parameter values as in Figure 3—source data 3, with AIC values.

Best fit in bold-red (lowest AIC).

https://cdn.elifesciences.org/articles/57646/elife-57646-fig5-data1-v1.docx
Figure 5—source data 2

Population parameter estimates for the fits of the model with lowest AIC in Figure 5—source data 1 to the T cell and virus dynamics.

RSE: relative standard error. Empty fields represent cases when the standard deviation of random effects, σψ, was fixed to zero. Values of ψ¯ for β,ω4,ω8, and I50 shown here are transformed assuming a blood volume of 3 × 105 μL (calculated assuming blood:weight ratio of 60 mL/kg and body weight of 5 kg). Red values represent an RSE greater than 100% implying that the number of data points may not be enough to estimate the respective parameter.

https://cdn.elifesciences.org/articles/57646/elife-57646-fig5-data2-v1.docx
Figure 5—source data 3

Individual parameter estimates for the fits of the model in Equations 2-3 in main text (lowest AIC in Figure 5—source data 1) to the T cell and virus dynamics.

Values of ψ¯ for β,ω4,ω8, and I50 shown here are transformed assuming a blood volume of 3 × 105 μL (calculated assuming blood:weight ratio of 60 mL/kg and body weight of 5 kg). Shown are individual estimates for animals that continued study after ATI.

https://cdn.elifesciences.org/articles/57646/elife-57646-fig5-data3-v1.docx
Figure 5—source data 4

Individual parameter estimates obtained from Monolix for the best fits used in the R code for Figure 5.

https://cdn.elifesciences.org/articles/57646/elife-57646-fig5-data4-v1.zip

Again, following model selection theory based on AIC, we compared six mechanistic models and found a parsimonious model to explain the data (Figure 5A, Figure 5—source data 1). This model simultaneously recapitulates plasma viral load and the kinetics of CD4+ CCR5+ and CCR5- T cells as shown in Figure 5B and Figure 5—figure supplements 13 with corresponding estimated parameters in Figure 5—source datas 2 and 3. In the best fitting model, parameters related to immune response against infection: the SHIV-specific CD8+ T cell proliferation (ω8), saturation (I50), and death rates (dh) were different during ATI and the pre-ART stage (see Figure 5—source data 1; Reeves et al., 2017). SHIV-specific CD8+ effector cells reduce virus production rather than killing infected cells (Elemans et al., 2011; Klatt et al., 2010; Wong et al., 2010), possibly by secretion of HIV-antiviral factors (Shridhar et al., 2014; Blazek et al., 2016; Zhang et al., 2002)—not explicitly included in the model. The model also suggests that infection enhances upregulation of CD4+CCR5- T cells. This upregulation transiently reduces the CD4+CCR5- compartment and replenishes CD4+CCR5+ T cells after ATI (Douek et al., 2003; Okoye et al., 2007; Okoye et al., 2012). Finally, in this model, some of the ΔCCR5-gene-modified CD4+ T cells also have kinetics similar to non-modified CD4+CCR5+ cells (i.e. it includes the compartment Np2 as in Figure 5A), whereas this was not able to be differentiated in the first-stage modeling.

We used our model to compute the SHIV-specific CD8+ T cell turnover rates after ATI and during pre-ART as measures of SHIV-specific immunity (SI) for each stage, that is, SIATI=ω8ATIdhATI and SIpreART=ω8preARTdhpreART, respectively. We found that the SHIV immunity ATI/pre-ART ratio (SIATISIpreART) correlated negatively with the ATI/pre-ART ratio of the observed nadir and median viral loads (Figure 5C–D). In this sense, the viral burden increase during ATI (viral burden ratio >1) in animals in the transplant groups might be due to the underlying loss of the immune response to the virus (SIATISIpreART<1, Figure 5C–D). Similarly, decrease in viral burden during ATI (viral burden ratio <1) in animals in control and ΔCCR5 groups might be due immune response memory or its recovery, respectively (SIATISIpreART>1, Figure 5C–D).

In conclusion, we developed a second-stage model that simultaneously recapitulates viral and T cell dynamics from SHIV-infected animals receiving autologous HSPC transplantation. The model suggests that transplant may reduce host T-cell immunity resulting in higher viral loads after ATI compared to the pre-ART stage. However, SHIV immunity might be recovered if CCR5 disruption is added in the transplant resulting in lower viral loads after ATI.

Post-ATI viral control requires a large HSPC dose containing a high fraction of CCR5-edited cells

An important advantage of our model is the ability to calculate the conditions required for post-ATI viral control (viral load set point <30 copies/ml) after CCR5-edited autologous transplant. To this end, we used our second-stage model to approximate an effective reproductive ratio Reff to describe the ability of the virus to sustain infection after ATI in transplanted animals (see Materials and methods):

(1) Reff=RT(1fpDD+Pr).

Here,  fp describes the fraction of protected HSPCs in the transplant product, D the dose or total number of infused HSPCs, and Pr the number of residual endogenous HSPCs after conditioning (variable  P at time of transplant, Figure 3A). RT is the approximate number of new infections caused by one infected cell after T cell complete reconstitution post-conditioning as defined in Equation 4 (see Materials and methods) and is inversely related to the anti-SHIV immune response at the time of ATI. Post-ATI viral control depends on the fraction of protected HSPCs in the body immediately after transplant that are protected from SHIV infection, or (fpDD+Pr).

To estimate the values of fp, D, and Pr needed for viral control, we first estimated RT for each animal based on individual parameter estimates pertaining to SHIV virulence and anti-SHIV immunity. We then simulated the model for each animal using varying values of fp from zero to one (0–100% CCR5-edited HSPCs), values of D from 106 to 109 HSPCs, and values of Pr from zero to 107 HSPCs. As an illustration, Figure 6A depicts projections of the model for ΔCCR5-transplanted animal A11219 for a range of values of fp when D=107 HSPCs/kg and Pr=6×106 HSPCs. When fp, D and Pr resulted in Reff1, plasma virus was not controlled following viral rebound post-ATI. When Reff < 1, post-rebound control was observed, but only at weeks 40–60 post-ATI, following an initial decrease in viral loads beginning 30–40 weeks after ATI. In this case, post-rebound control occurred concomitantly with ΔCCR5 CD4+ T cell complete reconstitution relative to non-edited CCR5+/- CD4+ T cells (Figure 6B). Lower values of Reff resulted in earlier post-rebound control (earliest ~40 weeks).

Figure 6 with 2 supplements see all
Model predictions of factors governing post-rebound viral control after CCR5 gene-edited hematopoietic stem and progenitor cell (HSPC) transplant.

(A) Predictions for plasma viral loads post-ATI using the optimized mathematical model. Here, Reff=RT(1fpDD+Pr) and is the composite determinant of viral control. Parameter estimates for animal A11219 (Figure 5—source data 3) were used to compute the effective reproductive ratio RT . Higher values of RT  imply poorer anti-SHIV immunity and high virulence (see Equation 4 in Materials and methods). We varied values of the fraction of HSPCs in transplant fp, the stem cell dose D as shown, and fixed the remaining number of HSPCs after TBI before transplant Pr=6×106. Reff<1 predicted spontaneous viral control 40–60 weeks after ATI. (B) A simulation with Reff=0.7 demonstrates CCR5-edited CD4+ T cell recovery is concurrent with viral control. (C) Model predictions of the fraction of protected HSPCs in the transplant fp (y-axis) and the ratio of transplanted HSPCs to total infused plus remaining post-TBI HSPCs D:Pr (x-axis) required for spontaneous viral control. The heatmap shown corresponds to animal A11200 which has RT=4 , the lowest predicted fp (76%) and D:Pr (~5) required for post-ATI viral control (heatmaps for other animals in Figure 6—figure supplement 2). Blue shaded region represents the parameter space with post-ATI viral control or Reff<1. Yellow-to-red region represent the parameter space with no control or Reff>1. Data points represent the individual values of fp and D:Pr  from each transplanted animal in the study. (D) Model predictions of the minimum fraction of protected HSPCs in the body fp for viral control (y-axis) for each animal given their calculated values for RT (x-axis). In all cases, the minimum fp corresponded to DPr>100 (Figure 6—figure supplement 2). Each color is an animal, and A11200 is the red diamond with the lowest value of min fp. p-Value calculated using Pearson’s correlation test.

Indeed, for all animals, post-treatment control occurred when values of fp, D, and Pr resulted in Reff<1 (Figure 6—figure supplement 1). Model predictions for animal A11200 demonstrate that regardless of the fraction of protected HSPCs in the transplant (fp), viral control is possible only when the ratio of HSPCs in the transplant to the residual endogenous HSPCs in the body post-TBI (D:Pr) is above 5 (Figure 6C). Moreover, if the ratio D:Pr is greater than 5, the minimum fraction of protected cells required is 76%, and further increasing D:Pr does not decrease  fp significantly. From all transplanted animals we found that the minimum fraction of protected cells in the transplant fp varied from 76% to 94% and was positively correlated with a weaker anti-SHIV immune response of the given animal defined by RT (Figure 6D and Figure 6—figure supplement 2). This is consistent with Equation 1 as ReffRT(1fp) when DPr. RT varied from 4 to 16 across animals using individual parameter estimates in Figure 5—source data 3. The required levels for fp are lower in the context of more intense anti-SHIV immunologic pressure and lower viral strength. This result argues for strategies that (1) augment anti-SHIV immunity despite conditioning (lower RT using SHIV-specific CAR T cells, therapeutic vaccination, etc.), (2) increase the stem cell dose relative to the residual endogenous stem cells (D:Pr) after transplant—perhaps by enhancing potency of the conditioning regimen, and (3) increase the fraction of gene-modified, SHIV-resistant cells (fp).

Based on the observation that viral control occurred when CD4+ T cell subsets approached a steady state in the simulations (Figure 6A–B), we simulated the model again to determine whether viral control might occur faster if ATI was postponed at a time when more mature, protected cells have expanded. As an illustration, we simulated animal A11219 under conditions that lead to viral control: fp=0.95, D=108 HSPCs and Pr=107 HSPCs with ATI occurring at 3, 14, 25, or 37 weeks after transplantation. Indeed, time to post-ATI viral control (shaded areas in Figure 7A) decreased as time to ATI was extended after transplant and as the difference between CD4+CCR5- cell density at ATI and its expected set point decreased (shaded areas in Figure 7B). In this case, ΔCCR5 CD4+ T cells comprised the majority of the CD4+CCR5- T cell compartment (Figure 7B). Further, we simulated increasing times of ATI using parameter estimates for all transplanted animals but under conditions that lead to viral control (fp>0.95, D=108 HSPCs and Pr=107 HSPCs). The model predicted the same decreasing pattern with times between transplant and ATI required to avoid viral rebound from 20 to 60 weeks (Figure 7C). This timeframe allowed all animals to achieve viral control due to CD4+CCR5- cell densities at ATI exceeding 60–90% of the ultimate steady state value (Figure 7D). As in Figure 7B for all animals ΔCCR5 CD4+ T cells comprised the majority of the CD4+CCR5- T cell compartment.

Model predictions of time to post-ATI viral control given varying times for the start of ATI.

(A-B) Examples of projected (A) viral load and (B) total, modified and unmodified CD4+ CCR5- (solid) and ΔCCR5 CD4+ T cells (dashed) from the model for animal A11219 when fp=0.95, D=108.5 HSPCs and Pr=107 HSPCs, for different times of ATI (tATI=3, 14, 25, and 37 weeks after transplantation). Dashed-dotted vertical lines represent time of transplant. Shaded areas between the dotted lines in (A) describe the time from ATI until spontaneous viral control. Shaded areas between the blue dashed lines in (B) represent the difference between the CD4+ CCR5- T cell concentration at ATI and the projected steady state. Dotted lines in (B) represent time of ATI. (C–D) Model predictions of the (C) time until viral control after ATI and (D) the fraction of total CD4+ CCR5- T cell concentration at ATI with respect to its steady state conditions given actual estimated parameter values for each transplanted macaque when fp=0.95, D=108.5 HSPCs and Pr=107 HSPCs.

Figure 7—source data 1

Results from all simulations varying time to ATI.

https://cdn.elifesciences.org/articles/57646/elife-57646-fig7-data1-v1.zip

In summary, our model predicts that post-ATI viral control during autologous HSPC transplantation is obtained when (1) the transplanted HSPC dose is significantly higher than the residual endogenous HSPCs that persist through myeloablative conditioning (in this case TBI) and (2) the fraction of protected (i.e. CCR5-edited) HSPCs in the transplant (fp) is sufficiently high to outcompete cells susceptible to infection and disrupt ongoing cycles of viral replication. Spontaneous post-rebound control occurs after CCR5- CD4+ T cells achieve a steady state approximately 1-year after transplantation. Hence, our model suggests that, under the two described conditions, prolonging time to ATI (at least 1-year post-transplantation) may increase the likelihood of rapid viral control post-ATI. Moreover, specifically tracking CD4+CCR5- (or CCR5-edited CD4+) T cell growth and waiting for steady-state could be used as a surrogate for the decision to undergo ATI.

Discussion

Here we introduce a data-validated mathematical model that, to our knowledge, is the first to simultaneously recapitulate viral loads as well as CD4+ and CD8+ T cell subset counts in a macaque model of suppressed HIV-1 infection. In addition, our model is the first to describe dynamics of CCR5+ and CCR5- T cells within the CD4 compartment. We performed extensive, systematic data fitting comparing 30 mechanistic models to arrive at a set of equations that most parsimoniously explains the available data. In multiple stages of modeling, we recapitulated (1) peripheral CD4+ and CD8+ T cell subset reconstitution dynamics following transplant and (2) T cell subset dynamics and SHIV viral rebound following ATI. Before ATI, all animals had suppressed plasma viral loads below the limit of detection, allowing analysis of T cell reconstitution dynamics independent of virus-mediated pressure. At each step, we applied model selection theory to select the simplest set of mechanisms capable of explaining the observed data (Burnham et al., 2002). Our model predicts that post-rebound viral control might be possible during autologous gene-edited HSPC transplantation if therapy achieves (1) a sufficient fraction of gene-protected, autologous HSPCs (2) a high dose of transplant product relative to a residual endogenous population of stem cells that persists following conditioning, and (3) enhancement of SHIV-specific immune responses following transplantation. Further, our model predicts, that under these conditions, spontaneous viral control after ATI is likely if ΔCCR5 T cells (tracked by CD4+CCR5- T cells) are allowed to reconstitute prior to ATI. These results are consistent with the cure achieved by the Berlin and London Patients who received a transplant with 100% HIV-resistant cells after intense conditioning (Allers et al., 2011; Hütter et al., 2009). In the autologous setting where 100% CCR5 editing may not be feasible, adjunctive measures that augment virus-specific immunity, such as therapeutic vaccination, infusion of HIV-specific chimeric antigen receptor (CAR) T cells or use of neutralizing antibodies, may synergize with HSPC transplantation to achieve post-treatment control (Haworth et al., 2017; Zhen et al., 2017).

Although the model predicts a potential benefit for more potent conditioning that favors engraftment of SHIV-resistant cells, a more aggressive conditioning regimen may also deplete SHIV-specific immune responses and lead to less favorable toxicity profiles. On the other hand, in the absence of conditioning, the number of endogenous HSPCs will remain too high. Thus, post-rebound control following ΔCCR5 transplant requires not only highly potent myeloablative conditioning, it also requires a higher percentage of gene-edited cells to counteract the loss of SHIV-specific immunity. Furthermore, due to the high levels of endogenous HSPCs and lack of an engraftment ‘niche’, the long-term persistence of transplanted, CCR5-edited HSPC would be exceedingly low. Alternatively, non-genotoxic conditioning regimens that target only HSPC for transplantation may prevent the loss of SHIV-specific immune cells (Palchaudhuri et al., 2016; Czechowicz et al., 2019; Srikanthan et al., 2020).

We previously demonstrated the link between disruption of the immune response during transplant and increased magnitude of viral rebound during treatment interruption (Peterson et al., 2017; Reeves et al., 2017). Here, we confirm that the increase of viral load median and nadir at ATI compare to the pre-ART stage is correlated with the reduction of the SHIV-specific immune response during transplant, but also predict that a reduction of viral load burden at ATI compared to pre-ART in animals receiving CCR5-edited cells in the transplant is correlated to a recovery of the SHIV-specific immunity.

Our results are somewhat limited by a small sample size of 22 animals, only 12 of which underwent ΔCCR5 transplant. For that reason, several model parameters were assumed to be the same among all animals (i.e. without random effects). However, the number of observations for each animal was large enough to discriminate among several different plausible model candidates. Due to the small sample size, we also performed projections by varying the parameters related to transplantation (i.e. dose, fraction of protected cells, and residual endogenous HSPCs) and using only the estimated individual parameters rather than sampling from estimated population distributions. Reassuringly, our results align with prior mechanistic studies of cellular reconstitution after stem cell transplantation (Jameson, 2002; Douek et al., 2000; Guillaume et al., 1998; Krenger et al., 2011; Roux et al., 2000). Our analysis also suggests that the majority of reconstituting CD4+CCR5- T cells do not proliferate and have slow rates consistent with estimates of thymic export from previous studies (Douek et al., 2000; Krenger et al., 2011; Roux et al., 2000).

Recent studies from our group and others make clear that although a preparative conditioning regimen (e.g. TBI) is essential to maximize engraftment of transplanted HSPCs, it does not clear 100% of host lymphocytes, especially those in tissues (Peterson et al., 2017; Peterson et al., 2018; Donahue et al., 2015; Radtke et al., 2017). The best fitting model predicts that incomplete elimination of lymphocytes by TBI prevents CD4+CCR5- cells from predominating post-transplant. We found that the rapid expansion of CD4+CCR5+ and CD8+ T cells during the first few weeks after HSPC transplantation is most likely due to lymphopenia-induced proliferation of residual endogenous cells after TBI rather than thymic reconstitution. CD4+CCR5- T cells arising from thymic export of both transplanted and remaining cells are overwhelmed by more rapidly populating CD4+CCR5+ T cells within weeks of transplantation. Going forward, we will need to identify anatomic sites (namely viral reservoir tissues such as spleen and lymph nodes) and associated mechanisms that allow activated CD4+CCR5+ to survive conditioning.

A final important observation from our model is that CD4+ T cell kinetics conducive to viral control may not be reached until 20–60 weeks after transplant. Therefore, our model suggests that ATI should be delayed until CD4+CCR5- T cells reconstitute (as a proxy for ΔCCR5 CD4+ T cell reconstitution) to their natural steady state. Furthermore, optimized timing of ATI would ideally be based on reconstitution of all CD4+ and CD8+ T cell subsets ensuring approximately steady state levels before discontinuing ART.

In conclusion, our mathematical model recapitulates, to an unprecedented degree of accuracy and detail, the complex interplay between reconstituting SHIV-susceptible CD4+ T cells, SHIV-resistant CD4+ T cells, infected cells, virus-specific immune cells, and replicating virus following autologous, CCR5-edited HSPC transplantation. Our results illustrate the capabilities of mathematical models to glean insight from preclinical animal models and highlight that modeling will be required to optimize strategies for HIV cure.

Materials and methods

Study design

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We employed a multi-stage approach using ordinary differential equation models of cellular and viral dynamics to analyze data from SHIV-infected pig-tailed macaques that underwent autologous HSPC transplantation during ART and to find conditions for post-rebound control when gene-edited cells were included in the transplant product. First, we modeled T cell dynamics and reconstitution following transplant and before ATI, assuming that low viral loads during suppressive ART do not affect cell dynamics (Figure 1B). In the second stage, we added viral load data during primary infection and after ATI and fit models to the T cell and viral dynamics simultaneously from data pre- and post-ATI (Figure 1C). We then used the most parsimonious model, as determined by AIC, to perform simulated experiments for different transplant conditions, focusing on variables including fraction of protected cells, dose, depletion of HSPCs after conditioning, and time of ATI after transplant to find thresholds for viral control post-ATI.

Experimental data

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Twenty-two juvenile pigtail macaques were intravenously challenged with 9500 TCID50 SHIV-1157ipd3N4 (SHIV-C) (Peterson et al., 2017; Peterson et al., 2018). After 6 months, the macaques received combination antiretroviral therapy (ART): tenofovir (PMPA), emtricitabine (FTC), and raltegravir (RAL). After ~30 weeks on ART, 17 animals received total body irradiation (TBI) followed by transplantation of autologous HSPCs. In 12/17 animals the transplant product included CCR5 gene-edited HSPCs (ΔCCR5 group); HSPC products in 5/17 animals were not edited (WT group). After an additional 25 weeks following transplant, when viral load was well suppressed, animals underwent ATI (Peterson et al., 2017). A control group of five animals did not receive TBI or HSPC transplantation and underwent ATI after ~50 weeks of treatment. One and six of the animals in the WT and ΔCCR5 groups, respectively, were necropsied before ATI. One of the animals in the control group was necropsied before ATI (Figure 1A). Plasma viral loads and absolute peripheral T-cell counts from CD4+CCR5-, CD4+CCR5+ and total CD8+ and subsets (naive, central memory [TCM], and effector memory [TEM]) were measured for the control and WT group as described previously (Peterson et al., 2017). We analyzed peripheral T cell counts and plasma viral load from infection until 43 weeks post-transplant (~25 weeks pre-ATI and ~20 weeks post-ATI).

Mathematical modeling of T cell reconstitution dynamics

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We modeled the kinetics of CD4+ and CD8+ T cell subsets in blood including residual endogenous, transplanted cells that home to the BM, and progenitor cells in the BM/thymus both from transplant and residual endogenous. We included CD8+ T cells in the model because CD8+ and CD4+ T cells may arise from new naïve cells from the thymus and compete with each other for resources that impact clonal expansion and cell survival (Jameson, 2002; Mehr and Perelson, 1997; Margolick and Donnenberg, 1997). We assumed that expansion of CD4+ and CD8+ T cells in the blood derives from: (1) export of naïve cells differentiated from a progenitor compartment in the BM/Thymus (Guillaume et al., 1998; Spits, 2002) ([either from transplanted (Douek et al., 2000; Douek et al., 1998)] or residual endogenous CD34+ HSPCs) and further differentiation to an activated effector state (Voehringer et al., 2008; Bender et al., 1999; Kieper and Jameson, 1999; Sallusto et al., 2004; Le Saout et al., 2008; Sprent and Surh, 2011; Buchholz et al., 2013; Farber et al., 2014; Kaech et al., 2002), or (2) lymphopenia-induced division of mature, residual endogenous cells that persist through myeloablative TBI (Jameson, 2002; Schluns et al., 2002; Schluns et al., 2000; Goldrath et al., 2004; Voehringer et al., 2008) as factors that drive T cell proliferation are more accessible (i.e. self-MHC molecules on antigen-presenting cells [Bender et al., 1999; Kieper and Jameson, 1999; Tanchot, 1997] and γ-chain cytokines such as IL-7 and IL-15 [Schluns et al., 2002; Schluns et al., 2000; Goldrath et al., 2004; Tan et al., 2001]). However, as they grow, cells compete for access to these resources, limiting clonal expansion (Jameson, 2002) such that logistic growth models are appropriate (Mehr and Perelson, 1997).

In our mathematical model, transplanted HSPCs T home to the bone marrow at a rate ke. We assumed a single-cell compartment for T cell progenitors in the bone marrow (BM)/thymus represented by variable P. We assumed that P renew logistically with maximum rate rp, differentiate into naïve CD4+ and CD8+ T cells at rates λf and λe, respectively, or are cleared at rate dp (Stiehl and Marciniak-Czochra, 2011; Stiehl et al., 2014; Stiehl and Marciniak-Czochra, 2017). We assumed two CD4+ T cell compartments: SHIV-non-susceptible, i.e. CD4+ T cells that do not express CCR5 (CD4+CCR5- T cells) N, and a SHIV-susceptible compartment, S (CD4+CCR5+ T cells). Only the N compartment includes CD4+ naïve cells migrating from the thymus (Bleul et al., 1997; Zaitseva et al., 1998; Berkowitz et al., 1998) at an input rate λfP cells per day (Douek et al., 2000; McCune, 1997). N cells grow with maximum rate rn, upregulate CCR5 (27) at rate λn, and are cleared from the periphery at rate dn. The S compartment does not have a thymic input but can grow with maximum division rate rs, downregulate CCR5 (27) at a rate λs, and are cleared at rate ds. We model CD8+ T cell reconstitution assuming a compartment for naïve and central memory cells, M, and a compartment for the effector memory subset, E. We assumed that M cells have thymic input of λeP cells per day, grow logistically with maximum division rate rm, differentiate to effector memory at rate λm, and are cleared at rate dm. The E compartment grows with maximum division rate re and is cleared at rate de. We added variables Tp, Pp, Np1 and Np2, representing CCR5 gene-modified- transplanted HSPCs, T cell progenitor cells in BM/thymus, and blood CD4+CCR5- T cells with CD4+CCR5- and CD4+CCR5+ kinetics, respectively. These compartments have the same structure as T, P, N and S, but with two differences. First, the value of Tp at transplantation is a fraction fp of the total number of infused cells. Second, the Np1 cell compartment do not upregulate CCR5 when transitioning to Np2. We model the competition of CD4+ and CD8+ T cells for resources that allow cell division using a logistic equation that depends on the difference between the total number of competing cells, i.e. A = Np1+Np2+N+S+M+E, and a carrying capacity K (Jameson, 2002). Under these assumptions we constructed the following model form:

(2) dTpdt=keTpdPpdt=keTp+r^P(1AKp)PpdNp1dt=λfPp+r^n(1AKn)Np1+λsNp2dNp2dt=r^s(1AKs)Np2+λnNp1dTdt=keTdPdt=keT+r^p(1AKp)PdNdt=λfP+r^n(1AKn)N+λsSdSdt=r^s(1AKs)S+λnNdMdt=λe(P+Pp)+r^m(1AKm)MdEdt=λmM+r^e(1Ake)E,

where r^p=rp(λf+λe+dp), r^n=rn(λn+dn), r^s=rs(λs+ds), r^m=rm(λm+dm), r^e=rede, as well as Kw=Kr^wrw for each model variable w{ p,n,s,m,e }. We did this re-parameterization to have compound parameters that were identifiable.

When simulating the model, we assumed t0 as the time of transplantation. For the transplant groups the system is in a transient stage due to conditioning (TBI) at t0, therefore initial values cannot be obtained from steady state equations. Transplantation is modeled as T(t0)=(1fp)D and Tp(t0)=fpD. For the control group we used t0 at a similar time relative to the transplant groups. Since the control group did not have any transplantation or TBI, we assumed T(t0)=Tp(t0)=Pp(t0)=Np(t0)=0. Other initial values were calculated assuming steady state: P(t0)=q2q3q4Kp(q1+1)q3q4+q2(q4+1), N(t0)=q1q3q4Kp(q1+1)q3q4+q2(q4+1), S(t0)=q3q4Kp(q1+1)q3q4+q2(q4+1), M(t0)=q2q4Kp(q1+1)q3q4+q2(q4+1) and E(t0)=q2Kp(q1+1)q3q4+q2(q4+1). Here q1=r^s λn(KpKs1), q2=r^n λf(q1(KpKn1)λs), q3=r^m λe(KpKm1) and q4=r^e λm(KpKe1). A parsimonious, curated version of this model was selected from a series of models with varying mechanistic and statistical complexity (Figure 3—source data 2).

Mathematical modeling of SHIV infection and T cell response dynamics

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We next adapted the curated T cell reconstitution model by combining several adaptations of the canonical model of viral dynamics (Reeves et al., 2017; Perelson, 2002; Perelson et al., 1997; Hill et al., 2018; Borducchi et al., 2016; De Boer, 2007; Wodarz and Nowak, 1999; Pandit and de Boer, 2016). Here, virus V infects only CD4+CCR5+ T cells (Ho et al., 2009) S at rate β. We modeled ART by reducing the infection rate to zero. A fraction τ of the infected cells produce virus, Ip, and the other fraction become unproductively infected, Iu (Reeves et al., 2017; Doitsh et al., 2010; Matrajt et al., 2014). IP cells arise only from activation of a persistent set of latently infected cells at rate ξL¯. We modeled ATI by assuming infection β is greater than zero after some delay following ATI. We approximate this delay as the sum of the time of ART to washout (~3 days) and the time of successful activation (tsa) of a steady set of latently infected cells. For simplicity, we assumed that ξL¯=1tsa and assumed that tsa has lognormal distribution among the animal population with high variance (Conway et al., 2019; Hill et al., 2014; Prague et al., 2019). All infected cells die at rate δI (Reeves et al., 2017). IP cells produce virus at a rate π per cell, that is cleared at rate γ. CD8+ M cells proliferate in the presence of infection with maximum rate ω8. A fraction f of these cells become SHIV-specific CD8+ effector T cells, Eh, that are removed at a rate dh (De Boer, 2007; Wodarz and Nowak, 1999; Wodarz et al., 2000). These effector cells may reduce virus production (π) or increase infected cell clearance (δI) by 1/ (1+θEh) or by (1+κEh), respectively (Elemans et al., 2011; Klatt et al., 2010; Wong et al., 2010; Borducchi et al., 2016; Cardozo et al., 2018). We assumed that non-susceptible CD4+ T cells may upregulate CCR5 and replenish the susceptible pool during infection (Okoye et al., 2007; Okoye et al., 2012; Okoye and Picker, 2013) with rate ω4. For cell growth the total number of competing cells is given by A =Np1+Np2+N+S+Ip+Iu+M+E+Eh. The model in Equation 2 is modified to include:

(3) dNdt=λfP+r^n(1AKn)N+λsSω4Ip+Iu1+Ip+IuI50NdSdt=r^s(1AKs)S+λnNβVS+ω4Ip+Iu1+Ip+IuI50NdIpdt=τβVSδI(1+κEh)Ip+ξL¯dIudt=(1τ)βVSδI(1+κEh)IudVdt=11+θEhπIpγVdMdt=λe(P+Pp)+r^m(1AKm)M+ω8(12f)Ip+Iu1+Ip+IuI50MdEhdt=ω8fIp+Iu1+Ip+IuI50MdhEh.

When simulating this model, we assume t0=0 as the moment of SHIV challenge, and tx as the moment of transplantation after challenge. We modeled conditioning by: (1) adding a term kTC in all blood cell compartments C{ N,S,Ip,Iu,M,E,Eh } and (2) the term  kHP for the HSPC compartment P. kT and kH are different than zero only during the 2 days before transplant (tx2 t<tx). Transplantation is modeled as an input only when t=tx to cell compartments T and Tp with amounts (1fp)D and fpD, respectively. A parsimonious version of this model was selected from a series of models with varying mechanistic and statistical complexity (Figure 3—source data 2).

Nonlinear mixed-effects modeling

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To fit our models (Equations 2, 3) to the transplant data, we used a nonlinear mixed-effects modeling approach (Lavielle, 2014). Within this approach, we modeled a state variable vector v with observations at time  i for each animal j as log10vij=fv(tij,Ψj)+ϵv. Here, fv describes the solution of the nonlinear models in Equations 2 or 3 for the state variable vector v at observation time tij with animal-specific parameter set Ψj. The distribution of measurement noise is assumed as ϵv𝒩(0,σv2).

In the mixed-effects model, it is assumed that for an animal j each single parameter ψjΨj is drawn from a probability distribution across the population. This distribution includes the fixed effects ψ¯ representing the median value over the population, and the random effects ηj representing its variability in the population, assumed to be normally distributed with standard deviation σψ, that is ηj𝒩(0,σψ2). We assumed that the random effects of the parameters  ηj might not be independent. In that case, the vector of random effects ηj  follows a multinormal distribution: η𝒩(0,Ω), being Ω the variance-covariance matrix based on the values σψ and correlations between the individual parameters in η.

We fit each model to all data points from all animals simultaneously using a maximum likelihood approach. We assumed that individual observations of each state variable vij for each animal j at each time point tij are independent. For each model, we obtained the Maximum Likelihood Estimation (MLE) of the standard deviation of the measurement error for the observations σv, and each parameter fixed effects ψ¯ and standard deviation of the random effects σψ (or elements in matrix Ω when applicable) using the Stochastic Approximation of the Expectation Maximization (SAEM) algorithm embedded in the Monolix software (http://www.lixoft.eu).

Fitting T cell reconstitution before ATI

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We first fit the observed blood T cell kinetics after HSPC transplantation and before analytical treatment interruption (ATI) using the model in Equation 2. During this procedure, we defined the vector v(1) to model the log10 of the observed blood CD4+CCR5-, CD4+CCR5+, total CD8+, CD8+ TN + TCM, and CD8+ TEM cell counts which are represented in Equation 2 by the variables { N+Np1+Np2,S,C,M,E }, respectively with C=M+E and solution f(1).

We defined the statistical form of each parameter in Ψ(1) in the following form: parameters r^pj, r^mj,r^ej,λfj,λej,λnj,λsj,λmj were modeled as ψj=ψ¯enj; parameter Kpj was modeled as ψj=10ψ¯+nj; Knj,Ksj,Kmj,Kej were modeled as ψj=10Kpjψ¯enj; and initial values in the transplant group: Nj(t0),Sj(t0),Mj(t0)  and Ej(t0) had the model ψj=10ψ¯+nj. We explored the possibility that rn=0, in that case we assumed d^nj=λnj(1+ψ¯enj). We fixed the HSPC homing rate ke = 1/day (Lapidot et al., 2005; Chute, 2006), and fp and D as described in Figure 3—source data 1. Since at t0 the system is in a transient stage due to conditioning (TBI), we estimated blood cell concentrations at t0, but fixed the number of HSPCs that remained in the BM/thymus P(t0) to 6×106 based on the estimated minimum number of infused HSPCs needed for engraftment in the same animal model (Radtke et al., 2017).

We fit instances of models with varying statistical and mechanistic complexity in Equation 2 to blood T cell counts during transplant and before ATI (Figure 1B) assuming that one or multiple mechanisms are absent, or that certain mechanisms have equal kinetics (Figure 3—source data 2 includes all 24 competing models with the different statistical assumptions).

Fitting T cell and viral load dynamics before and after ATI

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Next, we fit the model in Equations 2-3 to the pre- and post-ATI blood T cell counts and plasma viral loads (Figure 1B). Here, we defined v(2) for variables { N+Np1+Np2,R,C4,C8,M,E,V } with V indicating the observed plasma viral load, N+Np1+Np2 indicating the observed blood CD4+CCR5- T cell concentration, R=S+Ip+Iu the observed blood CD4+CCR5+ T cell concentration, C8 the total CD8+ T cell concentration, C4=R+N+Np1+Np2 the total CD4+ T cell concentration and the others state-variables as specified for v(1). We included C4 because we had total CD4+ T cell counts, but CD4+ T subset counts during the primary infection stage were not available in many of the animals. For this model, we defined the parameter set Ψ(2) by adding to the parameters in the previous section the parameters relative to virus dynamics (i.e. Ψ(2)={Ψ(1),κj,θj,βj,πj,ω4j,ω8j,I50j,dhj,tsaj} but fixing the values in Ψ(1) to the MLE values using Figure 3—source data 3). For parameters κj,θj, βj,πj, ω4j, ω8j, I50j , we used a model with form ψj=10ψ¯+nj, and for dhj and tsaj we used ψj=ψ¯enj. We included the possibility that immunity might be different at ATI compared to pre-ART by assuming the forms ψj,ATI=10ψ¯+nj+ςψ,ATI for ω8j  and I50j, and ψj,ATI=ψ¯enj+ςψ,ATI for dhj during ATI. We evaluated single or combination of mechanistic hypotheses along with different statistical assumptions as listed in Figure 5—source data 1 using AIC. V(0) was fixed to a small value below the limit of detection, and Ip(0) and Iu(0) were calculated as τcV(0)/π and (1τ)cV(0)/π, respectively. We fixed the following parameters: γ=23/day (Ramratnam et al., 1999), δI=1/day (Markowitz et al., 2003; Cardozo et al., 2017), τ=0.05 (Doitsh et al., 2010), and f= 0.9 (Borducchi et al., 2016). The value of kh was constrained to obtain a value of the HSPCs after conditioning P(tx)=Pr=6×106 (Radtke et al., 2017). We fixed values of tx, fp and D as described in Figure 3—source data 1.

We fit several instances of the model in Equation 3 to pre- and post-ATI blood T cell counts and plasma viral loads (Figure 1B) using the best model obtained for Equation 2 (Figure 5—source data 1 includes all four competing models and respective statistical assumptions). At the time of SHIV infection, values for the cell compartments were calculated from steady state equations with the same form as for the group without transplantation (‘control’) in the previous section.

Model selection

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To determine the best and most parsimonious model among the instances, we computed the log-likelihood (log L) and the Akaike Information Criteria (AIC=-2log L+2 m, where m is the number of parameters estimated) (Burnham et al., 2002). We assumed a model has similar support from the data if the difference between its AIC and the best model (lowest) AIC is less than two (Burnham et al., 2002).

Effective reproductive ratio when rn=0 and κ=0

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We calculated an approximate effective reproductive ratio Reff for our model (Equations 2, 3) by computing the average number of offspring produced by one productively infected cell Ip at ATI assuming all cell compartments have reached steady state after transplantation during ART. This number is the product of the average lifespan of one Ip, the virus production rate by this latently infected cell, the lifespan of produced virions from this cell, the rate at which each virion infects the pool of susceptible cells at steady state, the fraction of these infections that become productive and the reduction of virus production, cell infection, and cell death by SHIV-specific immune cells at ATI. Using this approach, we obtain that Reff=τβS¯πγδI(1+θE¯h), with S¯λfP¯d^nr^sλn(KpKs1)λs and E¯hfω8λeKpadhδItsar^m(KpKm1) the steady state values of variables S (SHIV-susceptible cells) and Eh (SHIV-specific effector cells) during ART, with a=λer^m(KpKm1)+λfλnd^nr^s(KpKs1)λsλn+λfr^s(KpKs1)d^nr^s(KpKs1)λsλn+λeλmr^m(KpKm1)r^e(KpKe1)+fω8λedhδItsar^m(KpKm1). By assuming that the total amount of infused cells (dose D and fraction of CCR5-editing fp) home to the BM/Thymus rapidly, and that the amount of remaining HSPCs after TBI and immediately before transplant is P(tx)=Pr, the approximate steady state for P is P¯Kpa(1fp)D+PrD+Pr=Kpa(1fpDD+Pr). Together this gives the following expression for the effective reproductive ratio:

(4) Reff=RT(1fpDD+Pr),withRT=τβπλeKpaγδI[d^nr^sλn(KpKs1)λs](1+θE¯h)

Here, RT then represents the effective reproductive ratio during transplant in the absence of gene-editing when cells have reached steady state.

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Decision letter

  1. Miles P Davenport
    Reviewing Editor; University of New South Wales, Australia
  2. Aleksandra M Walczak
    Senior Editor; École Normale Supérieure, France

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

The search for an HIV 'cure' includes a number of approaches. These include eradication of the latent virus, as well as strategies to prevent viral replication. The known clinical examples of HIV 'cure' have occurred in patients that have received bone marrow transplants where the new donor marrow lacked the CCR5 gene that encodes an HIV co-receptor molecule. Thus, understanding how reduced CCR5 expression affects viral replication is an important question. In this study, the authors investigate viral control in SHIV infected macaques following autologous CCR5 gene-edited transplantation. To this end, they combine experimental data on SHIV-infected macaques with mathematical models describing viral and immune cell dynamics using a stepwise approach. This as a very innovative interdisciplinary study that provides important insights to inform potential future treatment regimens against HIV.

Decision letter after peer review:

Thank you for submitting your article "Thresholds for post-rebound SHIV control after CCR5 gene-edited autologous hematopoietic cell transplantation" for consideration by eLife. Your article has been reviewed by Aleksandra Walczak as the Senior Editor, a Reviewing Editor, and two reviewers. The reviewers have opted to remain anonymous.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

As the editors have judged that your manuscript is of interest, but as described below that additional work is required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv (if it is not already there) along with this decision letter and a formal designation that the manuscript is "in revision at eLife". Please let us know if you would like to pursue this option. (If your work is more suitable for medRxiv, you will need to post the preprint yourself, as the mechanisms for us to do so are still in development.)

Summary:

In this study, the authors investigate the requirements for viral control in SHIV infected macaques under CCR5 gene-edited HSPC transplantation treatment. To this end, they combine experimental data on SHIV-infected macaques with mathematical models describing viral and immune cell dynamics using a stepwise approach. The Materials and methods and Results are presented in a thorough way. The article utilizes a robust data set and also uses detailed mathematical models and extensive analyses to capture the relevant dynamics.

Essential revisions:

1) The authors perform model selection based on the AIC values to determine the appropriate mathematical model describing the observed dynamics. However, the reliability of the obtained AIC values also depends on the identifiability of the estimated model parameters. Can you say anything about the extent to which you are overfitting in these models? The number of parameters is quite large and you see some quite substantial correlations (Supplementary file 3).

2) Conditioning and CCR5-deleted autologous HSCT depletes existing infected cells and pre-existing immunity to SHIV and ultimately increases the proportion of infection-resistant cells after reconstitution. It's therefore not obvious that extensive conditioning is necessarily the best strategy, and you have shown this in Peterson et al., 2017 and Reeves et al., 2017. Could this be worth highlighting early on? It may help to increase the impact of the insights you derive from the modeling.

3) When modeling reconstitution in deltaCCR5 individuals – you assume that the CCR5- cells all behave like CCR5- cells in WT transplants. This doesn't seem like a valid assumption; a proportion of CCR5-/- cells will become activated (as they do in WT transplants) and so the CCR5-/- cell kinetics should be a mixture of the CCR5- and CCR5+ kinetics. The same parameters could be used in the two groups. How sensitive are your conclusions to this issue?

4) Subsection “A reduction in SHIV-specific immunity leads to higher viral rebound set points and CD4+CCR5+ T cell depletion following ATI in transplanted animals” and Figure 5E – this section needs more interpretation/discussion. It's surprising that the only difference in parameters between the control and treatment groups was the time to virus rebound; so presumably differences across groups derive from the steady state sizes of the different populations? Are latently infected cells depleted in the transplant groups? And it's puzzling that delayed rebound in transplant groups is taken to imply more depleted immunity. Shouldn't it imply less depletion? Also – elevated virus load after ATI only occurs in the WT transplant group, not both. Please clarify and expand this section.

5) Subsection “Post-ATI viral control requires a large HSPC dose containing a high fraction of CCR5-edited cells”: Are strategies 1 and 2 not in conflict? Or does more potent conditioning somehow not necessarily imply a greater reduction in anti-SHIV immunity?

In summary, the reviewers acknowledged the analysis of an important topic by a unique dataset. However, they identified some major aspects that addressed the parameter identifiability within the model estimates as well as identifying the role of conditioning and dynamics of CCR5- cells. Therefore, we would like to ask you to analyze/comment on parameter identifiability for individual model estimates and validate the robustness of parameter estimates and, hence, model selection. This will be subject to re-review to determine if these issues have been satisfactorily addressed.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Thresholds for post-rebound SHIV control after CCR5 gene-edited autologous hematopoietic cell transplantation" for further consideration by eLife. Your revised article has been evaluated by Aleksandra Walczak (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but the reviewers have identified some remaining issues that need to be addressed before acceptance, as outlined below:

1) Figure 2—figure supplement 1, Panel B: The statistical comparison total vs. TN+TCM does not make sense. As I assume that Total=TN+TCM+TEM, "Total" does not contain additional information when there is already a comparison TN+TCM vs. TEM.

2) The manuscript contains a lot of typos and needs proofreading.

3) The Berlin and London patients are still not described.

4) The authors should provide all data and code used to perform the analyses.

https://doi.org/10.7554/eLife.57646.sa1

Author response

Essential revisions:

1) The authors perform model selection based on the AIC values to determine the appropriate mathematical model describing the observed dynamics. However, the reliability of the obtained AIC values also depends on the identifiability of the estimated model parameters. Can you say anything about the extent to which you are overfitting in these models? The number of parameters is quite large and you see some quite substantial correlations (Supplementary file 3).

We carefully took into account the following factors to reduce overfitting of the model and unidentifiability of the parameters as much as possible:

1) We divided the fits in two steps: (1) T cell reconstitution following TBI during virus suppression while on ART and (2) T cell and virus dynamics for the whole study period. The first step modeling fits to the isolated kinetics of T cells: expansion, overshoot and steady state. This method allows identification of parameters related to T cell reconstitution that do not correlate with parameters of virus dynamics. In the second step we fit to the same T cell subsets and to viral load data from acute infection and during ART interruption, fixing the parameters unrelated to virus infection and response from the first step. By using the two steps, parameters of the T cell dynamics related and unrelated to the virus dynamics are separately estimated.

2) The data during step (1) was frequently sampled for multiple T cell subsets allowing parameter identification related to each subset that were unrelated to virus dynamics (which is normally difficult when fitting models to viral loads only).

3) We transformed the model for step (1) as presented in the Materials and methods section after Equation 2 to ensure structural identifiability. Specifically, we combined several parameters, such as proliferation (ri), transition/differentiation and death rates (di) into compound parameters and estimated the compound parameters. Whereas the individual rates were not identifiable, compound rates (capturing correlated behaviors) were.

4) The reparameterization shows that some different differentiation parameters (λi) cannot be equal to zero, but others can be. For those that could take on a value of zero, we performed model selection to obtain a better fit and avoid overfitting.

5) We did careful model selection with reasonable models for step (1) (Figure 3—source data 2) using AIC to reject the most complex models and thus avoid overfitting (given the large amount of data compared to the number of parameters, AICc did not result in different outcomes). For each model we were careful in evaluating which parameters might not be practically identifiable and which ones correlated using the software Monolix:

a) For practical identifiability we used the relative standard error (standard error/population parameter value). Since the standard error is based on the diagonal of the inverse of the Fisher information matrix, it describes how much information each parameter gives to calculate the likelihood, and therefore gives information on how identifiable a parameter can be relative to the available data. We made sure the best model had small % relative standard error (%RSE<=40) for most of the estimated parameters (Figure 3—source data 3). For some parameters, the standard deviation of the random effects were estimated to be close to zero but with very high %RSE values. We therefore assume those parameters have no random effects (standard deviation equal to zero), i.e. all animals have the same estimated individual value for that parameter.

b) For correlations among parameters Monolix also uses the inverse of the Fisher Information Matrix to give information about those correlations. For those with high correlation we fit the model again but explicitly including the statistical relationship between the parameters in the variance-covariance matrix Ω of the mixed-effects model (corr≠0 in Figure 3—source data 2 and Figure 5—source data 1 means that the correlations were explicitly part of Ω in the mixed-effect model—therefore they were estimated parameters also included in the AIC). We made sure that for the best model the estimated correlation was identifiable (i.e. low %RSE in Figure 3—source data 3 and Figure 5—source data 2). This means that when drawing individual parameter values from the distribution in the mixed-effect model, the estimated correlation was also taken into account.

6) Because of the possibility of lack of convergence by the SAEM fitting algorithm in Monolix, we repeated the fitting procedure for each model 10 times having randomly selected initial guesses for the parameters. From the 10 attempts we selected the case with the highest likelihood.

7) When fitting the model for step (2) we fixed all the estimated parameters in step one, including those with correlations. For practical identifiability and correlations of the parameters related with virus dynamics and response to the virus we followed the same procedure as in item 5 (all summarized in the “statistical assumptions” in Figure 5—source data 1. See also the %RSE for best model estimates in Figure 5—source data 2 – most of them with %RSE <=40%).

8) For both steps, we fixed some parameters based on estimation from previous studies.

We recognized the possibility of model overfitting and unidentifiability of parameters, but we believe that the careful steps we took reduced those two problems significantly.

2) Conditioning and CCR5-deleted autologous HSCT depletes existing infected cells and pre-existing immunity to SHIV and ultimately increases the proportion of infection-resistant cells after reconstitution. It's therefore not obvious that extensive conditioning is necessarily the best strategy, and you have shown this in Peterson et al., 2017 and Reeves et al., 2017. Could this be worth highlighting early on? It may help to increase the impact of the insights you derive from the modeling.

We have included a sentence in the Introduction explaining that extensive conditioning is a necessary condition (despite the loss of pre-existing immunity) to reduce the number of endogenous HSCs so that transplanted CCR5-deleted HSCs can outcompete with them.

We also added a sentence in the Discussion saying that in the absence of conditioning the number of endogenous HSPCs will remain too high. In this case, the amount that would have to be taken from the animals for ex-vivo CCR5 deletion and transplantation for SHIV remission would be unrealistic, and their engraftment potential would be low.

3) When modeling reconstitution in deltaCCR5 individuals – you assume that the CCR5- cells all behave like CCR5- cells in WT transplants. This doesn't seem like a valid assumption; a proportion of CCR5-/- cells will become activated (as they do in WT transplants) and so the CCR5-/- cell kinetics should be a mixture of the CCR5- and CCR5+ kinetics. The same parameters could be used in the two groups. How sensitive are your conclusions to this issue?

We agree with the reviewers that the CCR5-/- cell kinetics could be a mixture of the CCR5- and CCR5+ kinetics. We added models that include this feature (12 new mechanistic models for T cell reconstitution part, and 4 new mechanistic ones for the section including viral dynamics) and found that the results/conclusions of the paper are not sensitive to this change:

1) We modified Equation 2 and Figure 3A to account for the possibility that CD4+CCR5-disrupted cells can have kinetics of CCR5- and CCR5+ cells, Np1 and Np2 compartments in the model, respectively.

a) We therefore added 12 more models including Np2 (models 13-24 in Figure 3—source data 2) with the same statistical assumptions for the models without Np2.

b) We fit the new 12 models to the T cell reconstitution data and found that the best model with compartment Np2 has almost the same AIC (Figure 3—source data 2 -models 11 and 23) and parameter estimates (Figure 3—source data 3) that the best model without Np2.

c) From this part we deduced that:

i) T cell reconstitution data could not be used to distinguish if CD4+CCR5-disrupted cells have a mixture of the CCR5- and CCR5+ kinetics.

ii) But regardless, the model with mixture of the CCR5- and CCR5+ kinetics didn’t differ at all from a model without that mixture.

2) When fitting viral load, we also tried to fit the model using the compartment Np2 (Models 5-8 in Supplementary file 5)

a) From this step we found that the model with mixture of the CCR5- and CCR5+ for disrupted cells had a significantly lower AIC (Figure 5—source data 1).

b) However model fits and comparisons between parameters and viral load steady state did not change (Figure 5, Figure 5—figure supplement 1, Figure 5—figure supplement 2, Figure 5—figure supplement 3, Figure 5—source data 2 and Figure 5—source data 3).

c) When using the best model with corresponding parameter estimates the model projections using different fp, Dose and Pr did not change either (Figure 6A-B). Minimum fp and Dose:Pr were very close to values obtained before: 76%-94%, and D:Pr>5, respectively (Figure 6C). Also, the relation of immune response (RT) and minimum fp was the same (Figure 6D).

d) Finally, when using the model for projections of viral control for different times of ATI, we obtain the same decrease pattern of time to control.

We edited the text to include these analyses.

4) Subsection “A reduction in SHIV-specific immunity leads to higher viral rebound set points and CD4+CCR5+ T cell depletion following ATI in transplanted animals” and Figure 5E – this section needs more interpretation/discussion. It's surprising that the only difference in parameters between the control and treatment groups was the time to virus rebound; so presumably differences across groups derive from the steady state sizes of the different populations? Are latently infected cells depleted in the transplant groups? And it's puzzling that delayed rebound in transplant groups is taken to imply more depleted immunity. Shouldn't it imply less depletion? Also – elevated virus load after ATI only occurs in the WT transplant group, not both. Please clarify and expand this section.

Since the best model we obtained allowed distinct SHIV-specific CD8s parameter values for the ATI and pre-ART stages, we re-analyzed the data and model results using a different approach to address what drives different kinetics between the control and transplant groups.

1) We looked first at how viral load burden differed during ATI compared to pre-ART using three summary statistics: median, nadir and final viral loads from viral peak onwards for each stage (ATI vs pre-ART). Figure 4B-D now presents that result.

2) We compared each of these summary statistics per group and found that in the three summary statistics viral burden during ATI is maintained or slightly decreased for the control group, increased for the WT-transplant group (no CCR5-edition), and is increased for the CCR5-edited group but not nearly to the degree seen in the WT group.

3) Then, we compared how the model predicts that SHIV-specific immunity changed during ATI with respect to pre-ART using the parameters directly related to SHIV-specific CD8+ T cells (since we can only measure this with the model and not in the data). We found that SHIV-immunity change at ATI compared to pre-ART was correlated to the change in viral burden between ATI and pre-ART (median and nadir viral load during ATI:pre-ART). These results are now presented in Figure 5C-D. SHIV-immunity during ATI slightly increased for the control group (ratio over 1), decreased for the WT-transplant group, and was recovered for the CCR5-edited group

We have clarified and expanded these analyzes in the viral and T cell dynamics modeling result section.

Regarding if latently infected cells are depleted in the transplant groups, Peterson et al., 2017 and Reeves et al., 2017 support that conclusion. However, answering this question from a mathematical modeling perspective was beyond the scope of this study. For that reason, we did not explicitly have a model for the SHIV-latently infected cell compartment, or how they could have been depleted during conditioning (as we did for all other cell compartments). We only added a constant parameter ξL¯ to account for their reactivation. In the model, time to rebound was only related to latent cell reactivation (tsa=1ξL¯), but viral load kinetics after peak was related to SHIV-immunity as described above. It is possible that delayed rebound is a response to some depletion of latent cells during conditioning, and therefore, less reactivated latent cells. In that sense there is a connection of delayed rebound and depletion of SHIV-immunity during conditioning. However, exploring this relationship was beyond the scope of this study. Therefore, we have taken out the figure presenting how time to rebound was different among groups and its discussion from the manuscript to avoid confusion to the message we are presenting. Also, this result has already been presented in Peterson et al., 2018.

5) Subsection “Post-ATI viral control requires a large HSPC dose containing a high fraction of CCR5-edited cells”: Are strategies 1 and 2 not in conflict? Or does more potent conditioning somehow not necessarily imply a greater reduction in anti-SHIV immunity?

We believe they are not in conflict in the sense that we don’t mean in strategy 1 to reduce conditioning, but to find ways to augment anti-SHIV immunity despite conditioning. Several examples could include anti-SHIV CAR T cells and/or immunotherapies based on broadly neutralizing antibodies to specifically target the persistent viral reservoir but not virus-specific immune cells, and/or conditioning strategies that are targeted more specifically to HSPCs, without impacting differentiated immune subsets. In that sense we have changed the wording of strategy 1 as “augment anti-SHIV immunity despite conditioning”.

In summary, the reviewers acknowledged the analysis of an important topic by a unique dataset. However, they identified some major aspects that addressed the parameter identifiability within the model estimates as well as identifying the role of conditioning and dynamics of CCR5- cells. Therefore, we would like to ask you to analyze/comment on parameter identifiability for individual model estimates and validate the robustness of parameter estimates and, hence, model selection. This will be subject to re-review to determine if these issues have been satisfactorily addressed.

We appreciate the reviewer’s comments and acknowledgements and believe we have addressed to the best of our abilities the major aspects regarding parameter identifiability/model selection, role of conditioning and dynamics of CCR5- cells.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The manuscript has been improved but the reviewers have identified some remaining issues that need to be addressed before acceptance, as outlined below:

1) Figure 2—figure supplement 1, Panel B: The statistical comparison total vs. TN+TCM does not make sense. As I assume that Total=TN+TCM+TEM, "Total" does not contain additional information when there is already a comparison TN+TCM vs. TEM.

We have changed Figure 2—figure supplement 1, Panel B: taking out the statistical comparison for total vs. TN+TCM

2) The manuscript contains a lot of typos and needs proofreading.

We have proofread the manuscript and took care of the typos we found.

3) The Berlin and London patients are still not described.

We have included a description of the Berlin and London patients in the Introduction.

4) The authors should provide all data and code used to perform the analyses.

Besides of the code already submitted we have provided the code for analyses in Figure 2, Figure 3, Figure 4, Figure 5 and Figure 6.

https://doi.org/10.7554/eLife.57646.sa2

Article and author information

Author details

  1. E Fabian Cardozo-Ojeda

    Vaccine and Infectious Disease Division, University of Washington, Seattle, United States
    Contribution
    Software, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing, Developed the mathematical models, wrote all code, performed all calculations and derivations, ran the models in Monolix and analyzed output data
    For correspondence
    ecojeda@fredhutch.org
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8690-9896
  2. Elizabeth R Duke

    1. Vaccine and Infectious Disease Division, University of Washington, Seattle, United States
    2. Department of Medicine, University of Washington, Seattle, United States
    Contribution
    Visualization, Methodology, Writing - review and editing, Development of mechanistic mathematical models
    Competing interests
    No competing interests declared
  3. Christopher W Peterson

    1. Department of Medicine, University of Washington, Seattle, United States
    2. Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, United States
    3. Stem Cell and Gene Therapy Program, Fred Hutchinson Cancer Research Center, Seattle, United States
    Contribution
    Data curation, Writing - review and editing
    Competing interests
    No competing interests declared
  4. Daniel B Reeves

    Vaccine and Infectious Disease Division, University of Washington, Seattle, United States
    Contribution
    Methodology, Writing - review and editing, Development of mechanistic mathematical models
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5684-9538
  5. Bryan T Mayer

    Vaccine and Infectious Disease Division, University of Washington, Seattle, United States
    Contribution
    Methodology, Writing - review and editing, Contributed ideas and support for statistical models and analyses
    Competing interests
    No competing interests declared
  6. Hans-Peter Kiem

    1. Department of Medicine, University of Washington, Seattle, United States
    2. Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, United States
    3. Stem Cell and Gene Therapy Program, Fred Hutchinson Cancer Research Center, Seattle, United States
    4. Department of Pathology, University of Washington, Seattle, United States
    Contribution
    Data curation, Funding acquisition, Writing - review and editing
    Competing interests
    H.-P.K has served on advisory boards for Rocket Pharmaceuticals, Homology medicines and CSL for research unrelated to this manuscript.
  7. Joshua T Schiffer

    1. Vaccine and Infectious Disease Division, University of Washington, Seattle, United States
    2. Department of Medicine, University of Washington, Seattle, United States
    3. Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, United States
    Contribution
    Conceptualization, Supervision, Funding acquisition, Methodology, Writing - original draft, Writing - review and editing
    For correspondence
    jschiffe@fredhutch.org
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2598-1621

Funding

National Institute of Allergy and Infectious Diseases (UM1 AI126623)

  • E Fabian Cardozo-Ojeda
  • Christopher W Peterson
  • Hans-Peter Kiem
  • Joshua T Schiffer

National Institute of Allergy and Infectious Diseases (R01 AI150500)

  • E Fabian Cardozo-Ojeda
  • Joshua T Schiffer

National Center for Advancing Translational Sciences (KL2 TR002317)

  • Elizabeth R Duke

Center for AIDS Research (New Investigator Award P30 AI027757)

  • Daniel B Reeves

Washington Research Foundation (Postdoctoral Fellowship)

  • Daniel B Reeves

National Institutes of Health (P51 OD010425)

  • Hans-Peter Kiem

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This study was supported by grants from the National Institutes of Health, National Institute of Allergy and Infectious Diseases (UM1 AI126623, R01 AI150500). ERD is supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number KL2 TR002317. DBR is supported by a Washington Research Foundation postdoctoral fellowship, and a CFAR NIA P30 AI027757. NHP studies were supported by NIH P51 OD010425. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the Washington Research Foundation.

Ethics

Animal experimentation: The data used in this work were collected in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. The study protocol was approved by the Institutional Animal Care and Use Committees (IACUC) protocols (#3235-03) of the Fred Hutchinson Cancer Research Center and the University of Washington.

Senior Editor

  1. Aleksandra M Walczak, École Normale Supérieure, France

Reviewing Editor

  1. Miles P Davenport, University of New South Wales, Australia

Publication history

  1. Received: April 7, 2020
  2. Accepted: December 27, 2020
  3. Version of Record published: January 12, 2021 (version 1)

Copyright

© 2021, Cardozo-Ojeda et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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