Christian L Althaus | Search bioRxiv

bioRxiv2015

Time-kill curve analysis and pharmacodynamic functions for in vitro evaluation of antimicrobials against Neisseria gonorrhoeae

Gonorrhea is a sexually transmitted infection caused by the Gram-negative bacterium Neisseria gonorrhoeae. Resistance to first-line empirical monotherapy has emerged, so robust methods are needed to appropriately evaluate the activity of existing and novel antimicrobials against the bacterium. Pharmacodynamic functions, which describe the relationship between the concentration of antimicrobials and the bacterial net growth rate, provide more detailed information than the MIC only. In this study, a novel standardized in vitro time-kill curve assay was developed. The assay was validated using five World Health Organization N. gonorrhoeae reference strains and various concentrations of ciprofloxacin, and then the activity of nine antimicrobials with different target mechanisms were examined against a highly susceptible clinical wild type isolate (cultured in 1964). From the time-kill curves, the bacterial net growth rates at each antimicrobial concentration were estimated. Finally, a pharmacodynamic function was fitted to the data, resulting in four parameters that describe the pharmacodynamic properties of each antimicrobial. Ciprofloxacin resistance determinants shifted the pharmacodynamic MIC (zMIC) and attenuated the bactericidal effect at antimicrobial concentrations above the zMIC. Ciprofloxacin, spectinomycin and gentamicin had the strongest bactericidal effect during the first six hours of the assay. Only tetracycline and chloramphenicol showed a purely bacteriostatic effect. The pharmacodynamic functions differed between antimicrobials, showing that the effect of the drugs at concentrations below and above the MIC vary widely. In conclusion, N. gonorrhoeae time-kill curve experiments analyzed with pharmacodynamic functions have potential for in vitro evaluation of new and existing antimicrobials and dosing strategies to treat gonorrhea.

Microbiology↗

bioRxiv2013

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 (25.7 cells ml-1). We find that 14.0%, 0.3% and 21.2% of infected cells become defectively, latently and persistently infected cells, respectively. Assuming that the infection is homogenous throughout the body, we estimate an average in vivo viral burst size of 2.1 x 104 virions per cell. Our study provides novel quantitative insights into the turnover and development of different subclasses of HIV-1-infected cells. The model predicts that 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.\n\nAuthor SummaryGaining a quantitative understanding of the development and turnover of different HIV-1-infected subpopulations of cells is crucial to improve the outcome of patients on combination antiretroviral therapy (cART). The population of latently infected cells is of particular interest as they represent the major barrier to a cure of HIV-1 infection. We developed a mathematical model that describes the dynamics of different transcriptionally active subclasses of HIV-1-infected cells and the viral load in peripheral blood. The model was fitted to previously published data from five chronically HIV-1-infected patients starting cART. This allowed us to estimate critical parameters of the within-host dynamics of HIV-1, such as the the number of virions produced by a single infected cell. The model further allowed investigation of HIV-1 dynamics during the acute phase. Computer simulations predict that latently infected cells become rapidly established during the first months of acute infection and continue to increase slowly during the first years of chronic infection. This illustrates the opportunity for strategies that aim to eradicate the virus during early cART as the pool of HIV-1 infected cells is substantially smaller during acute infection than during chronic infection.

Systems Biology↗