Quantifying the Turnover of Transcriptional Subclasses of HIV-1-Infected Cells

HIV-1-infected cells in peripheral blood can be grouped into different transcriptional subclasses. Quantifying the turnover of these cellular subclasses can provide important insights into the viral life cycle and the generation and maintenance of latently infected cells. We used previously published data from five patients chronically infected with HIV-1 that initiated combination antiretroviral therapy (cART). Patient-matched PCR for unspliced and multiply spliced viral RNAs combined with limiting dilution analysis provided measurements of transcriptional profiles at the single cell level. Furthermore, measurement of intracellular transcripts and extracellular virion-enclosed HIV-1 RNA allowed us to distinguish productive from non-productive cells. We developed a mathematical model describing the dynamics of plasma virus and the transcriptional subclasses of HIV-1-infected cells. Fitting the model to the data allowed us to better understand the phenotype of different transcriptional subclasses and their contribution to the overall turnover of HIV-1 before and during cART. The average number of virus-producing cells in peripheral blood is small during chronic infection. We find that a substantial fraction of cells can become defectively infected. Assuming that the infection is homogenous throughout the body, we estimate an average in vivo viral burst size on the order of 104 virions per cell. Our study provides novel quantitative insights into the turnover and development of different subclasses of HIV-1-infected cells, and indicates that cells containing solely unspliced viral RNA are a good marker for viral latency. The model illustrates how the pool of latently infected cells becomes rapidly established during the first months of acute infection and continues to increase slowly during the first years of chronic infection. Having a detailed understanding of this process will be useful for the evaluation of viral eradication strategies that aim to deplete the latent reservoir of HIV-1.


Calculation of the proportion of cell types
After infection of target cells, infected cells I 1 can either become defectively infected (D), latently infected (L 1 ), persistently infected (M 1 ) or activated virus-producing cells (I 6 ) or die during the intracellular eclipse phase. The proportions of cells that end up in a particular subpopulation were calculated as follows: where π D , π L , π M , π I and π δ are the proportions of cells that become defectively infected, latently infected, persistently infected or activated virus-producing cells or die, respectively.

Alternative models
The results from the main text were based on our default model but we also constructed 12 alternative models (Table S1) that differ in one or more characteristics of the HIV-1 life cycle: • No CD4 + : We did not include the number of CD4 + T cells per µl in the model fits.
• Direct: The fate of an infected cell is determined directly after infection and not sequentially during the intracellular eclipse phase. This means that target cells (T ) can become defectively infected (D), latently infected (L 1 ) and persistently infected (M 1 ) directly after infection and not sequentially during the intracellular eclipse phase.
• No eclipse: There is no intracellular eclipse phase, i.e., no stages I 1 to I 5 .
• Hom. 1: Latently infected cells were assumed to be a homogeneous cell population (no L 2 ).
• Hom. 2: Persistently infected cells were assumed to be a homogeneous cell population (no M 1 ).
• Prod./death 2: Persistently infected cells have a different death rate (δ M 2 = δ T ) than activated, virus-producing cells (δ I 6 ), but the same viral burst size N .
• Prod./death 3: Persistently infected cells (M 2 ) and activated, virus-producing cells (I 6 ) have a both a different viral burst size and death rate than activated, virus-producing cells.
• Burst 1: Transcriptional bursts in latently and persistently infected cells last for one hour on average.
• Burst 2: Transcriptional bursts in latently and persistently infected cells last for one week on average. Table S1 provides an overview of all models together with the sum of squared residuals (SSR) from fitting them to the data of the five patients. The estimated parameters of the HIV-1 dynamics for all models are given in Table S2.
We did not include the outlier in plasma HIV-RNA at 308 days after start of treatment in the model fit because treatment had to be interrupted due to side effects.