A practical method for simultaneously determining the effective burst sizes and cycle times of viruses

New mathematical approaches to quantify human infectious viruses from environmental media using integrated cell culture-qPCR

Quantifying infectious viruses by cell culture depends on visualizing cytopathic effect, or for integrated cell culture-PCR, attaining confidence a PCR-positive signal is the result of virus growth and not inoculum carryover. This study developed mathematical methods to calculate infectious virus numbers based on viral growth kinetics in cell culture. Poliovirus was inoculated into BGM cell monolayers at 10 concentrations from 0.001 to 1000 PFU/ml. Copy numbers of negative-strand RNA, a marker of infectivity for single-stranded positive RNA viruses, were measured over time by qRT-PCR. Growth data were analyzed by two approaches. First, data were fit with a continuous function to estimate directly the initial virus number, expressed as genomic copies. Such estimates correlated with actual inoculum numbers across all concentrations (R(2)=0.62, n=17). Second, the length of lag phase appeared to vary inversely with inoculum titers; hence, standard curves to predict inoculum virus numbers were derived based on three definitions of lag time: (1) time of first detection of (-)RNA, (2) second derivative maximum of the fitted continuous function, and (3) time when the fitted curve crossed a threshold (-)RNA concentration. All three proxies yielded standard curves with R(2)=0.69-0.90 (n=17). The primary advantage of these growth kinetics approaches is being able to quantify virions that are unambiguously infectious, a particular advantage for viruses that do not produce CPE.

A statistical method for comparing viral growth curves

Viral replication is often analyzed by growth curves, in which viral multiplication in the presence of host cells is measured as a function of time. Comparing growth curves is one of the most sensitive ways of comparing viral growth under different conditions or for comparing replication of different viral mutants. However, such experiments are rarely analyzed in a statistically rigorous fashion. Here a statistical method is described for comparing curves, using replication of HIV in the presence of an integrase inhibitor as an example. A complication in the analysis arises due to the fact that sequential measurements of virus accumulation are not independent, which constrains the choice of statistical method. In the recommended approach, the values for virus accumulation over time are fitted to an exponential equation, then the means of the extracted growth rates compared using a nonparametric test, either the Mann-Whitney U-test for two samples or the Kruskal-Wallis test for multiple samples. A web-based tutorial for implementing this method is available at http://microb230.med.upenn.edu/tutorials/wangTutorial.