Quantification of Unintegrated HIV-1 DNA at the Single Cell Level In Vivo

In the nucleus of HIV-1 infected cells, unintegrated HIV-1 DNA molecules exist in the form of one and two LTR circles and linear molecules with degraded extremities. In tissue culture they are invariably more numerous than the provirus, the relative proportion of integrated to unintegrated forms varies widely from ∼1∶1 to 1∶10 and even over 1∶100. In vivo, this ratio is unknown. To determine it, single nuclei from two infected patients with a known provirus copy number were microdissected, HIV DNA was amplified by nested PCR, cloned and individual clones sequenced. Given the extraordinary sequence complexity, we made the assumption that the total number of distinct sequences approximated to real number of amplifiable HIV-1 DNA templates in the nucleus. We found that the number of unintegrated DNA molecules increased linearly with the proviral copy number there being on average 86 unintegrated molecules per provirus

Examining the cooperativity mode of antibody and CD8+ T cell immune responses for vaccinology

A fundamental unsolved issue in vaccine design is how neutralizing antibodies and cytotoxic CD8+ T cells cooperate numerically in controlling virus infections. We hypothesize on a viewpoint for the multiplicative cooperativity between neutralizing antibodies and CD8+ T cells and propose how this might be exploited for improving vaccine-induced protective immunity.info:eu-repo/semantics/acceptedVersio

Markov Chain-Based Stochastic Modelling of HIV-1 Life Cycle in a CD4 T Cell

Replication of Human Immunodeficiency Virus type 1 (HIV) in infected CD4+ T cells represents a key driver of HIV infection. The HIV life cycle is characterised by the heterogeneity of infected cells with respect to multiplicity of infection and the variability in viral progeny. This heterogeneity can result from the phenotypic diversity of infected cells as well as from random effects and fluctuations in the kinetics of biochemical reactions underlying the virus replication cycle. To quantify the contribution of stochastic effects to the variability of HIV life cycle kinetics, we propose a high-resolution mathematical model formulated as a Markov chain jump process. The model is applied to generate the statistical characteristics of the (i) cell infection multiplicity, (ii) cooperative nature of viral replication, and (iii) variability in virus secretion by phenotypically identical cells. We show that the infection with a fixed number of viruses per CD4+ T cell leads to some heterogeneity of infected cells with respect to the number of integrated proviral genomes. The bottleneck factors in the virus production are identified, including the Gag-Pol proteins. Sensitivity analysis enables ranking of the model parameters with respect to the strength of their impact on the size of viral progeny. The first three globally influential parameters are the transport of genomic mRNA to membrane, the tolerance of transcription activation to Tat-mediated regulation, and the degradation of free and mature virions. These can be considered as potential therapeutical targets

Modeling of the HIV-1 Life Cycle in Productively Infected Cells to Predict Novel Therapeutic Targets

There are many studies that model the within-host population dynamics of Human Immunodeficiency Virus Type 1 (HIV-1) infection. However, the within-infected-cell replication of HIV-1 remains to be not comprehensively addressed. There exist rather few quantitative models describing the regulation of the HIV-1 life cycle at the intracellular level. In treatment of HIV-1 infection, there remain issues related to side-effects and drug-resistance that require further search “...for new and better drugs, ideally targeting multiple independent steps in the HIV-1 replication cycle” (as highlighted recently by Tedbury & Freed, The Future of HIV-1 Therapeutics, 2015). High-resolution mathematical models of HIV-1 growth in infected cells provide an additional analytical tool in identifying novel drug targets. We formulate a high-dimensional model describing the biochemical reactions underlying the replication of HIV-1 in target cells. The model considers a nonlinear regulation of the transcription of HIV-1 mediated by Tat and the Rev-dependent transport of fully spliced and singly spliced transcripts from the nucleus to the cytoplasm. The model is calibrated using available information on the kinetics of various stages of HIV-1 replication. The sensitivity analysis of the model is performed to rank the biochemical processes of HIV-1 replication with respect to their impact on the net production of virions by one actively infected cell. The ranking of the sensitivity factors provides a quantitative basis for identifying novel targets for antiviral therapy. Our analysis suggests that HIV-1 assembly depending on Gag and Tat-Rev regulation of transcription and mRNA distribution present two most critical stages in HIV-1 replication that can be targeted to effectively control virus production. These processes are not covered by current antiretroviral treatments

Monitoring CD27 expression to evaluate Mycobacterium tuberculosis activity in HIV-1 infected individuals in vivo.

The level of bacterial activity is only poorly defined during asymptomatic Mycobacterium tuberculosis (MTB) infection. The objective was to study the capacity of a new biomarker, the expression of the T cell maturation marker CD27 on MTB-specific CD4 T cells, to identify active tuberculosis (TB) disease in subjects from a MTB and HIV endemic region. The frequency and CD27 expression of circulating MTB-specific CD4 T cells was determined in 96 study participants after stimulation with purified protein derivative (PPD) using intracellular cytokine staining for IFNgamma (IFNγ). Subjects were then stratified by their TB and HIV status. Within PPD responders, a CD27(-) phenotype was associated with active TB in HIV(-) (p = 0.0003) and HIV(+) (p = 0.057) subjects, respectively. In addition, loss of CD27 expression preceded development of active TB in one HIV seroconverter. Interestingly, in contrast to HIV(-) subjects, MTB-specific CD4 T cell populations from HIV(+) TB-asymptomatic subjects were often dominated by CD27(-) cells. These data indicate that down-regulation of CD27 on MTB-specific CD4 T cell could be used as a biomarker of active TB, potentially preceding clinical TB disease. Furthermore, these data are consistent with the hypothesis that late, chronic HIV infection is frequently associated with increased mycobacterial activity in vivo. The analysis of T cell maturation and activation markers might thus be a useful tool to monitor TB disease progression

Numerical modelling of label-structured cell population growth using CFSE distribution data

<p>Abstract</p> <p>Background</p> <p>The flow cytometry analysis of CFSE-labelled cells is currently one of the most informative experimental techniques for studying cell proliferation in immunology. The quantitative interpretation and understanding of such heterogenous cell population data requires the development of distributed parameter mathematical models and computational techniques for data assimilation.</p> <p>Methods and Results</p> <p>The mathematical modelling of label-structured cell population dynamics leads to a hyperbolic partial differential equation in one space variable. The model contains fundamental parameters of cell turnover and label dilution that need to be estimated from the flow cytometry data on the kinetics of the CFSE label distribution. To this end a maximum likelihood approach is used. The Lax-Wendroff method is used to solve the corresponding initial-boundary value problem for the model equation. By fitting two original experimental data sets with the model we show its biological consistency and potential for quantitative characterization of the cell division and death rates, treated as continuous functions of the CFSE expression level.</p> <p>Conclusion</p> <p>Once the initial distribution of the proliferating cell population with respect to the CFSE intensity is given, the distributed parameter modelling allows one to work directly with the histograms of the CFSE fluorescence without the need to specify the marker ranges. The label-structured model and the elaborated computational approach establish a quantitative basis for more informative interpretation of the flow cytometry CFSE systems.</p

Saccharomyces cerevisiae: a versatile eukaryotic system in virology

The yeast Saccharomyces cerevisiae is a well-established model system for understanding fundamental cellular processes relevant to higher eukaryotic organisms. Less known is its value for virus research, an area in which Saccharomyces cerevisiae has proven to be very fruitful as well. The present review will discuss the main achievements of yeast-based studies in basic and applied virus research. These include the analysis of the function of individual proteins from important pathogenic viruses, the elucidation of key processes in viral replication through the development of systems that allow the replication of higher eukayotic viruses in yeast, and the use of yeast in antiviral drug development and vaccine production

Numbers Game and Immune Geography as Determinants of Coronavirus Pathogenicity

info:eu-repo/semantics/publishedVersio

Metabolite profiling studies in Saccharomyces cerevisiae: an assisting tool to prioritize host targets for antiviral drug screening

Background: The cellular proteins Pat1p, Lsm1p, and Dhh1p are required for the replication of some positive-strand viruses and therefore are potential targets for new antiviral drugs. To prioritize host targets for antiviral drug screening a comparative metabolome analysis in Saccharomyces cerevisiae reference strain BY4742 Matα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0 and deletion strains pat1Δ, lsm1Δ and dhh1Δ was performed. Results: GC/MS analysis permitted the quantification of 47 polar metabolites and the identification of 41 of them. Metabolites with significant variation between the strains were identified using partial least squares to latent structures discriminate analysis (PLS-DA). The analysis revealed least differences of pat1Δ to the reference strain as characterized by Euclidian distance of normalized peak areas. The growth rate and specific production rates of ethanol and glycerol were also most similar with this strain. Conclusion: From these results we hypothesize that the human analog of yeast Pat1p is most likely the best drug target candidate