Life cycle synchronization is a viral drug resistance mechanism

Viral infections are one of the major causes of death worldwide, with HIV infection alone resulting in over 1.2 million casualties per year. Antiviral drugs are now being administered for a variety of viral infections, including HIV, hepatitis B and C, and influenza. These therapies target a specific phase of the virus’s life cycle, yet their ultimate success depends on a variety of factors, such as adherence to a prescribed regimen and the emergence of viral drug resistance. The epidemiology and evolution of drug resistance have been extensively characterized, and it is generally assumed that drug resistance arises from mutations that alter the virus’s susceptibility to the direct action of the drug. In this paper, we consider the possibility that a virus population can evolve towards synchronizing its life cycle with the pattern of drug therapy. The periodicity of the drug treatment could then allow for a virus strain whose life cycle length is a multiple of the dosing interval to replicate only when the concentration of the drug is lowest. This process, referred to as “drug tolerance by synchronization”, could allow the virus population to maximize its overall fitness without having to alter drug binding or complete its life cycle in the drug’s presence. We use mathematical models and stochastic simulations to show that life cycle synchronization can indeed be a mechanism of viral drug tolerance. We show that this effect is more likely to occur when the variability in both viral life cycle and drug dose timing are low. More generally, we find that in the presence of periodic drug levels, time-averaged calculations of viral fitness do not accurately predict drug levels needed to eradicate infection, even if there is no synchronization. We derive an analytical expression for viral fitness that is sufficient to explain the drug-pattern-dependent survival of strains with any life cycle length. We discuss the implications of these findings for clinically relevant antiviral strategies

Inflammation and Metabolism in Cancer Cell—Mitochondria Key Player

Cancer metabolism is an essential aspect of tumorigenesis, as cancer cells have increased energy requirements in comparison to normal cells. Thus, an enhanced metabolism is needed in order to accommodate tumor cells' accelerated biological functions, including increased proliferation, vigorous migration during metastasis, and adaptation to different tissues from the primary invasion site. In this context, the assessment of tumor cell metabolic pathways generates crucial data pertaining to the mechanisms through which tumor cells survive and grow in a milieu of host defense mechanisms. Indeed, various studies have demonstrated that the metabolic signature of tumors is heterogeneous. Furthermore, these metabolic changes induce the exacerbated production of several molecules, which result in alterations that aid an inflammatory milieu. The therapeutic armentarium for oncology should thus include metabolic and inflammation regulators. Our expanding knowledge of the metabolic behavior of tumor cells, whether from solid tumors or hematologic malignancies, may provide the basis for the development of tailor-made cancer therapies

Stochastic competition between viral strains with different life cycle times.

<p>Viral dynamics were simulated under periodic antiviral therapy given by the simple on-off model with a period (<i>T</i>) of 2 days and varying drug efficacy (<i>f</i>). The fixation probability (heat map color) is measured as the fraction of simulations in which a strain was the last surviving in the population and continued on to reach a steady state. <b>(A)</b> Results with <i>n</i> = 10 maturation steps. <b>(B)</b> Results with fixed maturation time <i>τ</i> = 1/<i>m</i>. The white dotted lines show where the average maturation time is equal to an integer multiple of the drug period. For all shown simulations, we assume the death rate of immature cells to be zero (<i>d</i>_<i>w</i> = 0). 500 simulations were run for each drug efficacy level. Data shown for 21 different drug efficacies between <i>f</i> = 0.8 and <i>f</i> = 1.0, for competitions between 126 strains with average maturation times between 1 and 6 days. A version of the results for all drug efficacies is in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005947#pcbi.1005947.s006" target="_blank">S4 Fig</a>, and a version with <i>d</i>_<i>w</i> > 0 is in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005947#pcbi.1005947.s007" target="_blank">S5 Fig</a>.</p

Modified basic reproductive ratio , unlike the time-averaged , accurately predicts infection outcome under a periodic drug treatment.

<p><b>(A)</b> Time course of infection levels (concentration of mature infected cells, <i>y</i>(<i>t</i>)) for an unsynchronized strain (<i>T</i> = 2 days and <i>τ</i> = 5 days). The maturation time is fixed and drug levels are modeled as a periodic step function. Unsynchronized strains are more exposed to the drug effects, as they overlap less with the off-windows of the drug treatment. <b>(B)</b> Synchronized strains (<i>τ</i> = 2) are less exposed to the drug effects, as they overlap with the off-windows of the drug treatment. The off-windows in the drug treatment are represented by blue shading. In this example, we use drug period <i>T</i> = 2 days and drug efficacy <i>f</i> = 0.75. <b>(C)</b> The time-averaged basic reproductive ratio minus one () is plotted versus the maturation time (<i>τ</i>) for a fixed drug efficacy and the simple on-off model of drug levels (black line). is independent of maturation time when immature cells do not die (<i>d</i>_<i>w</i> = 0 here) and weakly dependent for <i>d</i>_<i>w</i> ≪ <i>m</i>. versus is unable to explain the observed persistence versus extinction of viral strains (e.g. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005947#pcbi.1005947.g002" target="_blank">Fig 2</a>). We derived a new quantity, (blue line), which works in the presence of synchronization to describe the observed behavior. was obtained via numerical solution of Equation (S.99) for and substitution of into Equation (S.100). The equilibrium infection level for a single strain (red line) is scaled to match at <i>τ</i> = 1.8. The drug efficacy is set to <i>f</i> = 0.9, and the death rate of immature infected cells is zero (<i>d</i>_<i>w</i> = 0). <b>(D)</b> Same as (C), except the drug efficacy is set to <i>f</i> = 0.5, and the death rate of immature infected cells is non-zero (<i>d</i>_<i>w</i> = 0.1). The equilibrium infection level is scaled to match at <i>τ</i> = 0. Connecting lines between points are drawn as a guide for the eye.</p

Parameter values used in the viral dynamics and drug treatment models.

<p>Parameter values used in the viral dynamics and drug treatment models.</p

Competition between viral strains with different life cycle times.

<p>Viral dynamics were simulated under periodic antiviral therapy given by the simple on-off model with a drug efficacy of 85% (<i>f</i> = 0.85). The infection level (y-axis) is measured as the concentration of mature infected cells (<i>y</i>) once a steady-state has been reached. Each simulation included a collection of viral strains with the full range of maturation times shown. Each strain is labeled by its average maturation time 1/<i>m</i> (maturation rate of <i>nm</i> for each stage). <b>A-C</b> Fixed maturation time, varying drug dosing period (<i>T</i>). <b>(A)</b> Drug dosing period <i>T</i> = 4 days. <b>(B)</b> Drug dosing period <i>T</i> = 3 days. <b>(C)</b> Drug dosing period <i>T</i> = 1 day. <b>D-F</b> Drug dosing period (<i>T</i>) of 2 days, varying distribution of maturation times. <b>(D)</b> Results with <i>n</i> = 1 maturation step. <b>(E)</b> Results with <i>n</i> = 10 maturation steps. <b>(F)</b> Results with fixed maturation time <i>τ</i> = 1/<i>m</i>. For all shown simulations, we assume the death rate of immature cells to be zero (<i>d</i>_<i>w</i> = 0). Data shown for competitions between 57, 85, 165, 501, 501, and 108 different strains, for panels from <b>A</b> to <b>F</b> respectively, with average maturation times between 1 and 6 days.</p

Equilibrium infection level for the single-strain deterministic model, as a function of the average maturation time.

<p>Viral dynamics were simulated under periodic antiviral therapy given by the simple on-off model with a period (<i>T</i>) of 2 days and varying drug efficacy (<i>f</i>). The infection level (heat map color) is measured as the concentration of mature infected cells (<i>y</i>) once a steady-state has been reached. Each calculation included only a single virus strain with average maturation time 1/<i>m</i> (maturation rate of <i>nm</i> for each stage). <b>(A)</b> Results with <i>n</i> = 1 maturation steps. <b>(B)</b> Results with <i>n</i> = 10 maturation steps. <b>(C)</b> Results with <i>n</i> = 25 maturation steps. <b>(D)</b> Results with fixed maturation time <i>τ</i> = 1/<i>m</i>. The white dotted lines show where the average maturation time is equal to an integer multiple of the drug period. For all shown simulations, we assume the death rate of immature cells to be zero (<i>d</i>_<i>w</i> = 0). Results shown for 41 different drug efficacies between <i>f</i> = 0.6 and <i>f</i> = 1.0, for 101 different strains with average maturation times between 1 and 6 days. A version of the results with more resolution around the threshold drug efficacy (<i>f</i> = 0.9) is in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005947#pcbi.1005947.s004" target="_blank">S2 Fig</a> and a version with <i>d</i>_<i>w</i> > 0 is in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005947#pcbi.1005947.s005" target="_blank">S3 Fig</a>.</p

Competition between viral strains with different life cycle times when infected cells can die before maturing.

<p>Viral dynamics were simulated under periodic antiviral therapy given by the simple on-off model with a period (<i>T</i>) of 2 days, a drug efficacy of 85% (<i>f</i> = 0.85), and an immature cell death rate between 0 and <i>d</i>_<i>y</i> (mature infected cell death rate). The infection level (heat map color) is measured as the concentration of mature infected cells (<i>y</i>) once a steady-state has been reached. Each simulation included a collection of viral strains with the full range of maturation times shown, all with the same death rate of immature infected cells. Each strain is labeled by its average maturation time 1/<i>m</i> (maturation rate of <i>nm</i> for each stage). <b>(A)</b> Results with <i>n</i> = 1 maturation step. <b>(B)</b> Results with <i>n</i> = 10 maturation steps. Data shown for 110 different immature cell death rates between <i>d</i>_<i>w</i> = 0.001 and <i>d</i>_<i>w</i> = 1, for competitions between 501 different strains with average maturation times between 1 and 6 days.</p

Regulatory T lymphocytes in evaluation of the local protective cellular immune response to Mycobacterium tuberculosis in Romanian patients

Active suppression by Regulatory T lymphocytes (Treg) might be important in controlling immune responses against Mycobacterium tuberculosis (Mtb). Our aim was to evaluate the local cellular immune response to Mtb, by evaluation of Treg cells in pleural fluid (PF) compared to peripheral blood (PB) from patients with active Mtb infection and healthy Romanian subjects. Tregs were isolated using MACS CD4 +CD25 +CD127 dim/- (Miltenyi) and CD4 CD25 T cells and Foxp3 transcription factor were analyzed by flow cytometry. We found higher % of Tregs in PF compared to PB from patients or healthy Romanian subjects, which might explain the relatively effective local immune response against Mtb infection