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Antimicrobial Agents and Chemotherapy, March 2004, p. 1017-1020, Vol. 48, No. 3
0066-4804/04/$08.00+0     DOI: 10.1128/AAC.48.3.1017-1020.2004
Copyright © 2004, American Society for Microbiology. All Rights Reserved.

Quantitative Analysis of a Parasitic Antiviral Strategy

Hwijin Kim and John Yin^*

Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706-1607

Received 31 July 2003/ Returned for modification 25 September 2003/ Accepted 2 December 2003

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We extended a computer simulation of viral intracellular growth to study a parasitic antiviral strategy that diverts the viral replicase toward parasite growth. This strategy inhibited virus growth over a wide range of conditions, while minimizing host cell perturbations. Such parasitic strategies may inhibit the development of drug-resistant virus strains.

Top Abstract Introduction References

 
Although significant progress has been made in the last decade toward developing and implementing new antiviral therapeutics (4), a major challenge remains: to prevent or reduce the emergence of drug-resistant virus strains. While the new drugs have targeted virus molecular functions with exquisite specificity, they have at the same time created new conditions for the outgrowth of resistant virus strains (11, 12, 14, 15). Most current antiviral strategies aim to directly block virus-related functions. Examples include small molecules, such as nucleosides and amino acids and their analogues, that bind and adversely perturb the functions of key viral components and enzymes (6, 7, 9, 12). Such small-molecule drugs are designed to bind with high affinity to their targets, creating a selective advantage for virus strains that attenuate the drug-target interaction through mutation. As an alternative approach, we propose an indirect or parasitic antiviral strategy that diverts essential virus functions away from a productive infection. Since this strategy aims to divert—rather than inactivate—viral functions, it is not so obvious how resistant virus strains may arise.

To gain preliminary insight into the performance of parasitic antiviral strategies, we present here a computational study of such a strategy applied to the infection of Escherichia coli by the bacteriophage Qß, a biochemically and biophysically well-characterized system (8, 17, 19). We have previously developed a detailed kinetic model for the intracellular growth of bacteriophage Qß in E. coli (H. Kim and J. Yin, submitted for publication), and we extend the model here to incorporate a parasitic RNA (p-RNA) that competes with viral RNA for the viral replicase, as shown in Fig. 1. As a molecular parasite we employed minivariant 11, known as MNV11(+), one of several short-chain RNAs derived from phage Qß genomic RNA (1, 2, 16). MNV11(+) consists of 87 nucleotides (about 1/50 the length of the Qß genome), and it can replicate itself using the viral replicase. However, unlike the viral RNA, it lacks ribosome and coat protein binding sites. We assumed that the constant for the binding of MNV11(+) to replicase was the same as that for a viral RNA, though short-chain replicator RNAs often exhibit higher binding affinities for the replicase (16, 18).

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FIG. 1. Phage Qß intracellular infection cycle and antiviral strategies. Boldface solid lines, parasite antiviral strategy; p-RNA(-) and p-RNA(+), p-RNAs that serve as templates for each other's synthesis; boldface dotted line, regulation of replicase translation by coat protein. The antisense RNA targets the same step as the coat protein (not shown).

We first investigated the effect of the parasitic strategy on viral growth. In all strategies studied, whether parasitic or not, we assumed the therapeutic agents were present in the host cell prior to infection, supplied in a controlled manner by, for example, recombinant DNA technologies (13, 16). Figure 2a shows the simulated intracellular kinetics of viral RNA and viral progeny in the absence and presence of the parasite. In the absence of the parasite, viral RNA was rapidly amplified between 5 and 15 min postinfection, and over 10,000 progeny viruses appeared shortly thereafter. However, in the presence of the parasite neither viral RNA nor progeny viruses were produced. Instead, the parasite was amplified more than 10,000-fold during the first 10 min postinfection, indicating that the parasite effectively diverted all viral replicase activity toward its own growth. The parasite was so effective because its relatively short chain length allowed it to replicate much faster than the viral genome. Moreover, the parasite is not burdened by time-consuming interactions with ribosomes and viral coat proteins, which place the viral RNA at a further disadvantage against the parasite. These simulation results are consistent with preliminary experiments where genetically engineered E. coli cells producing MNV11(+) did not support phage Qß growth and where, moreover, the cells essentially prevented the emergence of antiviral escape mutants (16).

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FIG. 2. Effect of antiviral therapies on viral growth. (a) Effect of parasite drug therapy on viral growth dynamics. Solid and dotted lines, growth profiles of virus and its RNA in the absence (- parasite) and presence (+ parasite) of the molecular parasite MNV11(+), respectively. The growth curve of the parasite [p-RNA(+) alone] is also shown. Both the viral multiplicity of infection and initial parasite concentration are five molecules per cell. (b) Effects of coat protein level and target affinity on viral yield. The binding affinity of the drug to its target, shown quantitatively as the log10 of the ratio of the binding constant for the recombinant coat protein (kcoat) to the respective wild-type value (kcoat.wt), is plotted on the x axis. Here, the binding constants range from 0.01 to 100 times the respective wild-type values. The numbers on the plot denote viral yield, where the lowest and wild-type yields are <5,000 and 25,000 progeny, respectively. Boldface arrow, hypothetical path that a drug-sensitive virus strain (point A) could take toward becoming drug resistant (point B), achieved by mutations that weaken the binding of coat protein to its target.

To put the parasitic strategy in perspective, we compared its performance with two alternative strategies: deliberate application of viral regulatory molecules at the wrong stage of the viral growth cycle (10) and expression of antisense RNA directed at a viral function (5). For phage Qß, its coat proteins play diverse roles at different times, including repression of translation of replicase and viral assembly. Figure 2b shows the simulated effects of different binding affinities and levels of recombinant coat protein (drug) on viral yield. The yields are lowest (less than 5,000 progeny) when there is high affinity of the drug for its target and high levels of drug, and they reach a high plateau (20,000 to 25,000 progeny) for low binding affinities. There is also a region where yields would be above wild type (greater than 25,000 progeny) that corresponds to the range of optimal mutation for the wild-type virus against the selection pressure of the therapy (13). This optimality arises due to the combined activating and inhibitory involvement of the coat protein in multiple reaction steps. When the molecular parasite was tested over the same range of binding affinities and levels as the recombinant coat protein, no viral progeny were formed, indicating the remarkable potency of the parasite strategy.

The potency landscape of Fig. 2b can be used to anticipate how a therapeutic strategy may create a selection for drug-resistant virus strains. Here any change in the binding constant from the wild-type value can be interpreted as a potential evolutionary trajectory for the virus population. Our results suggest that antiviral coat protein strategies may be easily thwarted either by the selection of a lower-binding-affinity variant among an existing quasispecies virus distribution or by mutation to create a lower binding variant. For example, when 9,000 coat proteins were applied, the wild-type Qß growth was significantly reduced because yield was less than 5,000 progeny (point A, Fig. 2b). However, the wild-type virus could significantly increase its yield, to greater than 20,000 progeny (point B, Fig. 2b) by a mutation or mutations that reduced its binding affinity by less than a factor of 10. We found a similar landscape and escape path for an antisense RNA therapy that targeted the ribosome binding site of the replicase cistron, interfering with the same developmental step as the coat protein therapy (not shown). Experimental results for antiviral escapes qualitatively support our observation. For example, Qß mutants escape from the premature translation repression of replicase by coat proteins (13), and mutant phage SP escapes from antisense RNA that targets the phage (3). In contrast to these findings, it is challenging to anticipate how viruses might escape from the parasite strategy. To attenuate the drug-target interaction, the replicase would be under selective pressure to reduce its binding to the parasitic RNA. However, such mutations would also likely reduce binding of the replicase to phage genomic and antigenomic RNA, creating a selection against mutations that allow viral escape from the parasite.

A potential drawback of the parasitic strategy is the negative effect it may have on the host cell. To test how different antiviral strategies might impact the host cell, we defined an extent of resource depletion (ERD). Since the depletion of host nucleoside triphosphate and amino acid pools for phage growth is coupled with quantifiable bioenergetic costs (Kim and Yin, submitted), we defined ERD as the amount of energy resources consumed by viral growth, estimated by simulation, divided by the total capacity of resources a host cell could supply the virus (Kim and Yin, submitted). Hence, ERD ranges from 0 to 1, where we expect ERDs near 0 to correlate with minimal utilization of the cell's resources, while ERDs near 1 reflect a high resource burden created by viral growth or a given antiviral strategy. For example, in Fig. 2b virus yields at or above 20,000 progeny correspond to ERDs near 1, while virus yields at or below 5,000 progeny correspond with ERDs less than 0.1. For the parasite antiviral strategy, tested over a broad range of parameter values, including those used to generate Fig. 2a, we determined an ERD of 0.21, indicating a low, but not negligible, burden of this strategy on the host cell resources.

Finally, we tested the effects of a preventative cocktail therapy on viral yield and ERD, combining parasite and cross-protective strategies. As shown in Fig. 3a, viral growth essentially stopped over the entire range, even with fewer than 10 parasite molecules. Moreover, as the number of coat proteins increased at a fixed number of parasites, the ERD was reduced, as shown in Fig. 3b, without a loss in antiviral performance. Here, the ERD was lower than that observed for other single- or multiple-drug therapies that we tested (not shown). Its low value could be attributed to the coat-mediated repression of replicase translation, which caused overall reductions in the replication rates of both the viral RNA and the parasite.

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FIG. 3. Effect of preventative cocktail drug therapy of molecular parasite and coat protein on viral growth and host cell physiology. Viral yield (a) and ERD (b) were computed. Each binding constant is the same as its basal value. An ERD of 1, which occurs when no parasite is added, indicates that host resources are fully depleted, whereas an ERD of 0 indicates that host resources remain essentially intact. Very similar results were obtained for the cocktail therapy of molecular parasite and antisense RNA, but antisense RNA therapy showed slightly higher ERD than coat protein therapy (not shown).

In this work we showed how a parasite antiviral strategy could outperform other strategies. Our simulations considered only a single intracellular cycle of viral growth and ignored any dynamics associated with population level virus-host interactions. Although we demonstrated the parasite strategy by using the Qß-E. coli system, the strategy holds potential for the treatment of a wide range of viruses, with an emphasis on those that replicate their genomes in an autonomous or autocatalytic manner.

 
This work was supported by the National Science Foundation and Merck Research Laboratories.

 
* Corresponding author. Mailing address: 3633 Engineering Hall, 1415 Engineering Dr., Madison, WI 53706-1607. Phone: (608) 265-3779. Fax: (608) 262-5434. E-mail: yin{at}engr.wisc.edu.

Top Abstract Introduction References

 

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Antimicrobial Agents and Chemotherapy, March 2004, p. 1017-1020, Vol. 48, No. 3
0066-4804/04/$08.00+0     DOI: 10.1128/AAC.48.3.1017-1020.2004
Copyright © 2004, American Society for Microbiology. All Rights Reserved.

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This Article Abstract Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Reprints and Permissions Copyright Information Books from ASM Press MicrobeWorld Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Kim, H. Articles by Yin, J. Search for Related Content PubMed PubMed Citation Articles by Kim, H. Articles by Yin, J.