Evolution at increased error rate leads to the coexistence of multiple adaptive pathways in an RNA virus

Abstract

Background: When beneficial mutations present in different genomes spread simultaneously in an asexual population, their fixation can be delayed due to competition among them. This interference among mutations is mainly determined by the rate of beneficial mutations, which in turn depends on the population size, the total error rate, and the degree of adaptation of the population. RNA viruses, with their large population sizes and high error rates, are good candidates to present a great extent of interference. To test this hypothesis, in the current study we have investigated whether competition among beneficial mutations was responsible for the prolonged presence of polymorphisms in the mutant spectrum of an RNA virus, the bacteriophage Qβ, evolved during a large number of generations in the presence of the mutagenic nucleoside analogue 5-azacytidine.

Results: The analysis of the mutant spectra of bacteriophage Qβ populations evolved at artificially increased error rate shows a large number of polymorphic mutations, some of them with demonstrated selective value. Polymorphisms distributed into several evolutionary lines that can compete among them, making it difficult the emergence of a defined consensus sequence. The presence of accompanying deleterious mutations, the high degree of recurrence of the polymorphic mutations, and the occurrence of epistatic interactions generate a highly complex interference dynamics.

Conclusions: Interference among beneficial mutations in bacteriophage Qβ evolved at increased error rate permits the coexistence of multiple adaptive pathways that can provide selective advantages by different molecular mechanisms. In this way, interference can be seen as a positive factor that allows the exploration of the different local maxima that exist in rugged fitness landscapes.

Figure 1

Scheme showing the serial transfers experienced by bacteriophage Qβ. a) Populations obtained in our previous work [31] that have also been used in the current work. b) Progression of the transfers series to obtain the new population Qβ-AZC(t90). The procedure describing how transfers were carried out is described in Methods. Populations were named Qβ-AZC(tx) or Qβ-control(tx) , where x indicates the number of transfer at which they were isolated. The mutations fixed and the number of polymorphic mutations at the end of each transfer series are also indicated. Boxes filled in yellow enclose populations where both the mutant spectrum and the consensus sequence have been analyzed. Non-filled boxes enclose populations analyzed only at the level of consensus sequence. Consensus sequences were analyzed from nucleotide 180 to 4180. Sequences from individual viruses spanned from nucleotide 1485 to 4028.

Figure 2

Competition between different bacteriophage Qβ virus clones. a) Qβ_wt and Qβ_{A1746U+U3989C}. b) Qβ_A1746U and Qβ_{A1746U+U3989C}. The experiment was carried out as described in Methods. The populations obtained after the number of transfers indicated were sequenced to determine whether one of the competitor viruses had become dominant.

Figure 3

Phylogenetic analysis of the virus genomes isolated from bacteriophague Qβ populations evolved in the presence of AZC. Different colours are used to distinguish the genomes from each population included in the analysis: green for Qβ-AZC(t60), pink for Qβ-AZC(t70), and orange for Qβ-AZC(t90). Virus genomes corresponded to those shown in Table  1 and Additional file 1, and are identified using the same notation. The tree was derived by maximum likelihood methods (PhyML, program seaview 4) [39] using the sequence of the wild type virus to root the tree. Numbers at each node represent the bootstrap value (carried out with 100 replicates). Clusters described in the main text are highlighted, and the mutations common to all the genomes included in each one are also indicated.