doi: 10.1038/srep18088.
HIV-1 CCR5 gene therapy will fail unless it is combined with a suicide gene
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- PMID:26674113
- PMCID: PMC4682191
- DOI: 10.1038/srep18088
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HIV-1 CCR5 gene therapy will fail unless it is combined with a suicide gene
Aridaman Pandit et al. Sci Rep..
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Abstract
Highly active antiretroviral therapy (ART) has successfully turned Human immunodeficiency virus type 1 (HIV-1) from a deadly pathogen into a manageable chronic infection. ART is a lifelong therapy which is both expensive and toxic, and HIV can become resistant to it. An alternative to lifelong ART is gene therapy that targets the CCR5 co-receptor and creates a population of genetically modified host cells that are less susceptible to viral infection. With generic mathematical models we show that gene therapy that only targets the CCR5 co-receptor fails to suppress HIV-1 (which is in agreement with current data). We predict that the same gene therapy can be markedly improved if it is combined with a suicide gene that is only expressed upon HIV-1 infection.
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(a) Schematic of thetypical HIV model. The uninfected CD4+ T cells can self-renew (logistic growth), die at rateδT day−1, and get infected at rateβ particle−1 day−1. To model the influx from the naive compartment, uninfected T cells additionally get replenished at rateλ cells day−1. Infected cells producep virus particles cell−1 day−1 and die at rateδI day−1. Viruses are cleared at ratec day−1 (see section Models). (b) The infection rate in thetypical HIV model (β) was reduced, and is expressed as a percentage of the initial infection rate (β0). Only once the infection rate is reduced below a certain threshold (β < β*; vertical gray dashed line) will the viral load start to decrease. Several HIV-1 models let uninfected T cells be replenished at a constant rate from thymus, bone marrow, naive, and memory T cells. A quantitavily similar model with a constant rate of replenishment (i.e.λ = 14.3 andr = 0 in Eq. 1) also has the property thatβ has to be decreased markedly (here more than 50%) to have a significant effect on the viral load (black dashed line). Modeling oneμl of blood, the following parameter values were used:r = 0.06 day−1,K = 1500 cells,δT = 0.02 day−1,β0 = 3.6 × 10−6 particle−1 day−1,δI = 1 day−1,p = 2.14 × 104 virus particles cell−1 day−1,c = 23 day−1 (see section Models). The values ofλ, r, andK were chosen to have 1000 T cells perμl blood in the virus-free steady state. (c) For theGT model, we model two populations of uninfected T cells (normal and genetically modified) sharing logistic growth. Normal uninfected T cells additionally get replenished at rateλ cells day−1. Normal and genetically modified T cells get infected at rateβn andβg particle−1 day−1 (whereβg ≤ βn), respectively. Infected cells die at rateδn andδg day−1.
We kept the infection rate of unmodified target cells constant (i.e.βn = 3.6 × 10−6) and varied the infection rate of the genetically modified cells (βg). The value ofβg is given as a percentage of theβn value, where 100% means thatβg = βn and 20% means thatβg = 0.2βn. The effect of a change in the death rate of genetically modified cells (δg) is shown by different lines. Black lines represent the current CCR5 gene therapies (withδg = δn). The value forδg was changed as a fold increase ofδn. (a) Decreasing the infection rate (βg) of genetically modified cells increases the total T cell count (black line). (b) Decreasing the infection rate (βg) of genetically modified cells decreases the normal uninfected T cell count (black line). Decreasing the infection rate (βg) below a threshold rescues the normal uninfected T cell count. (c) Decreasing the infection rate (βg) of genetically modified cells can increase the viral load slightly for a mildly effective gene therapy. A suicide gene (to increaseδg) expressed in infected genetically modified cells increases the total T cell count (a), rescues the normal uninfected T cell count (b), and decreases the viral load (c) indicated by different colors (see legend in Fig. 2a). To initialize theGT model, we replaced 10% of theTn cells byTg cells in the infected steady state and we ran the model until the steady state is approached. Parameters: thymic influx forTn (λ) = 1 cellsμl−1 day−1, logistic growth parameter forTn andTg (r) = 0.057 day−1, and death rate ofIg (δg); the other parameters remain the same (Fig. 1). The values forr andλ were chosen to haveTn = 1000 cells perμl blood as virus-free steady state using parameter values given in. (d) The effect of variation ofβg along withδg inGT model (see section Models). The colors indicate the steady state viral load.
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