Resistance of virus to extinction on bottleneck passages: study of a decaying and fluctuating pattern of fitness loss

Abstract

RNA viruses display high mutation rates and their populations replicate as dynamic and complex mutant distributions, termed viral quasispecies. Repeated genetic bottlenecks, which experimentally are carried out through serial plaque-to-plaque transfers of the virus, lead to fitness decrease (measured here as diminished capacity to produce infectious progeny). Here we report an analysis of fitness evolution of several low fitness foot-and-mouth disease virus clones subjected to 50 plaque-to-plaque transfers. Unexpectedly, fitness decrease, rather than being continuous and monotonic, displayed a fluctuating pattern, which was influenced by both the virus and the state of the host cell as shown by effects of recent cell passage history. The amplitude of the fluctuations increased as fitness decreased, resulting in a remarkable resistance of virus to extinction. Whereas the frequency distribution of fitness in control (independent) experiments follows a log-normal distribution, the probability of fitness values in the evolving bottlenecked populations fitted a Weibull distribution. We suggest that multiple functions of viral genomic RNA and its encoded proteins, subjected to high mutational pressure, interact with cellular components to produce this nontrivial, fluctuating pattern.

Fig. 1.

Schematic representation of virus platings as carried out in this article. (A) Serial transfers. Clones , and (described in ref. and in Materials and Methods) were diluted and plated for isolation of virus from an individual plaque (upper plates) and for titration of infectious particles (lower plates). Each viral population was derived from a plaque of the previous plating, and the serial plaque-to-plaque transfers were repeated a total of 50 times. (B) Control platings. C-S8c1, p50, MARLS, and RGG were repeatedly plated to determine the influence of the cells on virus titer. Each titration was a dead end for these control platings of nonevolving virus. A total of 50 platings were carried out with aliquots of the same population (indicated as 1). Procedures for virus plating and plaque development are detailed in Materials and Methods.

Fig. 2.

Infectious units produced by sequential or repetitive platings of FMDV clones and populations. (A) Logarithm of the number of infectious virus progeny per ml per plaque as a function of passage number of clones , and . The inset in each section corresponds to the infectious units per ml produced per plaque (plaque transfers 40–50) by using cells with identical passage history (serial 1:6 dilutions). The origin of the clones is described in ref. and in Materials and Methods, and the experimental procedure is schematically depicted in Fig. 1A. All main plots share the scale in both axes. The scale for insets is also shared, as represented in Lower Right.(B) Control (nonevolving) populations C-S8c1, p50, MARLS, and RGG were repeatedly plated as schematically depicted in Fig. 1B. The procedures used are described in Materials and Methods and references therein. All four sections have the same scale as in A.

Fig. 3.

Infectious units per ml per plaque produced during 100 plaque-to-plaque passages of clone . After the initial decrease in fitness we observe that the system settles into a statistically stationary state with large fluctuations. After plaque transfer 60, the cells have an identical passage history (1:6).

Fig. 4.

Amplitude of the fluctuations of the number of infectious progeny quantified through the logarithm of infectious units per ml per viral plaque at each passage (iu at p_i+1) relative to the logarithm of infectious units per ml per viral plaque at the previous passage (iu at p_i). The experimental design is depicted in Fig. 1, and the origin of viruses and methods for plating on cell monolayers are detailed in Materials and Methods.

Fig. 5.

Representation of the values of infectious units per ml per plaque (iu/plaque) as a function of the logarithm of the rank. The data series were ordered by decreasing values and raised to the power α that was obtained from the fitting of the frequency values to a Weibull distribution (ref. , see Table 2).