Computation, prediction, and experimental tests of fitness for bacteriophage T7 mutants with permuted genomes

Abstract

We created a simulation based on experimental data from bacteriophage T7 that computes the developmental cycle of the wildtype phage and also of mutants that have an altered genome order. We used the simulation to compute the fitness of more than 10 5 mutants. We tested these computations by constructing and experimentally characterizing T7 mutants in which we repositioned gene 1, coding for T7 RNA polymerase. Computed protein synthesis rates for ectopic gene 1 strains were in moderate agreement with observed rates. Computed phage-doubling rates were close to observations for two of four strains, but significantly overestimated those of the other two. Computations indicate that the genome organization of wild-type T7 is nearly optimal for growth: only 2.8% of random genome permutations were computed to grow faster, the highest 31% faster, than wild type. Specific discrepancies between computations and observations suggest that a better understanding of the translation efficiency of individual mRNAs and the functions of qualitatively ''nonessential'' genes will be needed to improve the T7 simulation. In silico representations of biological systems can serve to assess and advance our understanding of the underlying biology. Iteration between computation, prediction, and observation should increase the rate at which biological hypotheses are formulated and tested.genetic networks ͉ evolution ͉ optimization ͉ expression regulation R esearch over the last century has produced a wealth of information on the mechanisms and rates for the processes that constitute biological systems. Integration of this information to create quantitative, high-resolution, system-scale models has begun more recently and reflects the necessity of having sufficient information before construction of constrained models. Examples include models for the growth of a single bacterial cell (1, 2), the regulation of genetic circuits (3, 4) and the cell cycle (5, 6), signal transduction (7-9), and metabolic pathways (10). A numerical model of a biological system is a complex hypothesis that can be used to compute the behavior of the system it represents (11). Such a model has heuristic value (12) when used to predict the effects of experimentally uncharacterized perturbations to the system, and the predicted perturbations then are compared with laboratory observations to refine the hypothesis instantiated by the model.Here, we use a computer simulation for bacteriophage T7 development (13) in conjunction with laboratory experiments to compute, predict, and observe the effect of genome reorganization on T7 development. The simulation treats T7 at the logical scale of a self-replicating unit, from genome entry to production of progeny phage, and is resolved at the level of chemical species. Genome organization in T7 directly regulates the timing and level of gene expression during phage development. Reorganization of the T7 genome therefore should change the timing and level of gene expression and, presumably, the entire process of phage development....

Predicting evolution from genomics: experimental evolution of bacteriophage T7

“…As a test of the T7 virtual model, several constructs were made in which the RNAP gene was moved further from the left end of the genome. The most extreme shift was to 66-72% from the left end, and the predicted delays in life cycle were qualitatively, albeit not quantitatively, matched by the observed delays (Endy et al, 2000).…”

“…In addition, two compensatory mutations evolved before, and 15 (affecting 10 genes) and 1 promoter change evolved subsequent to the recombination, but there is no basis for understanding the relevance of any of these mutations to the reordered genome. Reflecting our lack of complete understanding of T7, the virtual model v2.5 (Endy et al, 2000) predicted fitness loss due to the observed final gene order to be much smaller than actually observed.…”

“…Two promoters, one at each end of the genome are thought to be used in cutting unit-length genomes or as replication origins and are thus not primarily involved in gene expression. This expression system, combined with many in vitro studies of transcription from the different promoters, has led to a strong sense that the T7 regulatory network is well understood, culminating in an empirically parameterized virtual model that qualitatively captures many aspects of the infection cycle (v2.5; Endy et al, 2000). The high fitness and the relatively small 40 kb dsDNA genome of T7 further suits it to a combination of genomic manipulations, easy propagation and fullgenome sequencing to determine all adaptive changes.…”

“…The position of the RNAP gene in the early region of the genome is therefore critical to its early expression. Escherichia coli RNAP is necessary to transcribe the phage RNAP gene, and shifting the latter's location away from the entering end of the genome (referred to as the genetic 'left' end) delays the life cycle and correspondingly reduces fitness (Endy et al, 2000).…”

Rewiring the severe acute respiratory syndrome coronavirus (SARS-CoV) transcription circuit: Engineering a recombination-resistant genome

“…In the 3Ј end of the mouse hepatitis virus genome, gene order rearrangements reduce the number of recombinant viruses recovered (24). New regulatory circuits have been designed to control the lysogenic and lytic phases of infection in phage (25,26), and genome order has been remodeled in some phage and mammalian viruses (27,28). However, complete redesign of a virus transcription circuit has not been reported.…”