Rational Design and Adaptive Management of Combination Therapies for Hepatitis C Virus Infection

Complete genome analysis reveals evolutionary history and temporal dynamics of Marek’s disease virus

Marek’s disease has caused enormous losses in poultry production worldwide. However, the evolutionary process and molecular mechanisms underlying Marek’s disease virus (MDV) remain largely unknown. Using complete genomic sequences spanning an unprecedented diversity of MDVs, we explored the evolutionary history and major patterns in viruses sampled from 1964 to 2018. We found that the evolution of MDV strains had obvious geographical features, with the Eurasian and North American strains having independent evolutionary paths, especially for Asian strains. The evolution of MDVs generally followed a clock-like structure with a relatively high evolutionary rate. Asian strains had evolved at a faster rate than European strains, with most genetic mutations occurring in Asian strains. Our results showed that all recombination events occurred in the UL and US subregions. We found direct evidence of a closer correlation between Eurasian strains, related to a series of reorganization events represented by the European strain ATE2539. We also discovered that the vaccine strains had recombined with the wild virulent strains. Base substitution and recombination were found to be the two main mechanisms of MDV evolution. Our study offers novel insights into the evolution of MDVs that could facilitate predicting the spread of infections, and hence their control.

Quantifying the Evolutionary Constraints and Potential of Hepatitis C Virus NS5A Protein

RNA viruses, such as hepatitis C virus (HCV), influenza virus, and SARS-CoV-2, are notorious for their ability to evolve rapidly under selection in novel environments. It is known that the high mutation rate of RNA viruses can generate huge genetic diversity to facilitate viral adaptation. However, less attention has been paid to the underlying fitness landscape that represents the selection forces on viral genomes, especially under different selection conditions. Here, we systematically quantified the distribution of fitness effects of about 1,600 single amino acid substitutions in the drug-targeted region of NS5A protein of HCV. We found that the majority of nonsynonymous substitutions incur large fitness costs, suggesting that NS5A protein is highly optimized. The replication fitness of viruses is correlated with the pattern of sequence conservation in nature, and viral evolution is constrained by the need to maintain protein stability. We characterized the adaptive potential of HCV by subjecting the mutant viruses to selection by the antiviral drug daclatasvir at multiple concentrations. Both the relative fitness values and the number of beneficial mutations were found to increase with the increasing concentrations of daclatasvir. The changes in the spectrum of beneficial mutations in NS5A protein can be explained by a pharmacodynamics model describing viral fitness as a function of drug concentration. Overall, our results show that the distribution of fitness effects of mutations is modulated by both the constraints on the biophysical properties of proteins (i.e., selection pressure for protein stability) and the level of environmental stress (i.e., selection pressure for drug resistance).

IMPORTANCE Many viruses adapt rapidly to novel selection pressures, such as antiviral drugs. Understanding how pathogens evolve under drug selection is critical for the success of antiviral therapy against human pathogens. By combining deep sequencing with selection experiments in cell culture, we have quantified the distribution of fitness effects of mutations in hepatitis C virus (HCV) NS5A protein. Our results indicate that the majority of single amino acid substitutions in NS5A protein incur large fitness costs. Simulation of protein stability suggests viral evolution is constrained by the need to maintain protein stability. By subjecting the mutant viruses to selection under an antiviral drug, we find that the adaptive potential of viral proteins in a novel environment is modulated by the level of environmental stress, which can be explained by a pharmacodynamics model. Our comprehensive characterization of the fitness landscapes of NS5A can potentially guide the design of effective strategies to limit viral evolution.

Resist or perish: Fate of a microbial population subjected to a periodic presence of antimicrobial

The evolution of antimicrobial resistance can be strongly affected by variations of antimicrobial concentration. Here, we study the impact of periodic alternations of absence and presence of antimicrobial on resistance evolution in a microbial population, using a stochastic model that includes variations of both population composition and size, and fully incorporates stochastic population extinctions. We show that fast alternations of presence and absence of antimicrobial are inefficient to eradicate the microbial population and strongly favor the establishment of resistance, unless the antimicrobial increases enough the death rate. We further demonstrate that if the period of alternations is longer than a threshold value, the microbial population goes extinct upon the first addition of antimicrobial, if it is not rescued by resistance. We express the probability that the population is eradicated upon the first addition of antimicrobial, assuming rare mutations. Rescue by resistance can happen either if resistant mutants preexist, or if they appear after antimicrobial is added to the environment. Importantly, the latter case is fully prevented by perfect biostatic antimicrobials that completely stop division of sensitive microorganisms. By contrast, we show that the parameter regime where treatment is efficient is larger for biocidal drugs than for biostatic drugs. This sheds light on the respective merits of different antimicrobial modes of action.

Natural co-infection with two virulent wild strains of Marek's disease virus in a commercial layer flock

Marek's disease (MD) is a highly contagious lymphoproliferative poultry disease caused by the oncogenic herpesvirus, Marek's disease virus (MDV). MDV strains have shown a continued evolution of virulence leading to immune failure, and MD cases continue to occur. Co-infection of virulent MDV strains is an important factor leading to viral evolution and host immune failure. This study conducted a laboratory diagnosis and analysis of a MDV infected flock. Testing showed that all samples were MDV positive. PCR detection identified a variable 132-base pair repeat (132-bpr) sequence copy number. This indicated that two virulent strains of MDV were co-infecting the flock. Therefore, we performed homology, sequence alignment, and phylogenetic tree analysis of MDV variant genes including meq, pp38, and RLORF4. Two MDV strains had co-infected the flock; one was the 132bpr two-copy characteristic strain (AH2C) and the other was a 132bpr three-copy characteristic strain (AH3C). Specific mutations in AH3C were found, suggesting that it is a new variant strain. Furthermore, the viral load of the two strains in vivo indicated that both strains had high and similar replication ability. There was no significant difference in the proportion of positive samples of the two strains causing disease. In the whole flock, neither strain displayed an obvious advantage. However, there was a dominant strain in individual chickens, with the exception of one sample. This study reported the co-infection regularity of two virulent MDV strains in the same flock, and even in the same chicken in field conditions. In the context of overall epidemiology, this study is a useful reference.

Mutational pathway maps and founder effects define the within-host spectrum of hepatitis C virus mutants resistant to drugs

Knowledge of the within-host frequencies of resistance-associated amino acid variants (RAVs) is important to the identification of optimal drug combinations for the treatment of hepatitis C virus (HCV) infection. Multiple RAVs may exist in infected individuals, often below detection limits, at any resistance locus, defining the diversity of accessible resistance pathways. We developed a multiscale mathematical model to estimate the pre-treatment frequencies of the entire spectrum of mutants at chosen loci. Using a codon-level description of amino acids, we performed stochastic simulations of intracellular dynamics with every possible nucleotide variant as the infecting strain and estimated the relative infectivity of each variant and the resulting distribution of variants produced. We employed these quantities in a deterministic multi-strain model of extracellular dynamics and estimated mutant frequencies. Our predictions captured database frequencies of the RAV R155K, resistant to NS3/4A protease inhibitors, presenting a successful test of our formalism. We found that mutational pathway maps, interconnecting all viable mutants, and strong founder effects determined the mutant spectrum. The spectra were vastly different for HCV genotypes 1a and 1b, underlying their differential responses to drugs. Using a fitness landscape determined recently, we estimated that 13 amino acid variants, encoded by 44 codons, exist at the residue 93 of the NS5A protein, illustrating the massive diversity of accessible resistance pathways at specific loci. Accounting for this diversity, which our model enables, would help optimize drug combinations. Our model may be applied to describe the within-host evolution of other flaviviruses and inform vaccine design strategies.

A Bitter Pill to Swallow: Why Medication Safety Is Critical in Hepatitis C Treatment

PURPOSE

To provide medication safety tips to optimize the management of patients receiving treatment for chronic hepatitis C virus (HCV) infection.

SUMMARY

Ensuring safe medication use in patients who receive treatment for HCV infection is a crucial component in providing optimal patient care. Because of the complexity of available treatment options, numerous challenges exist in preventing medication errors with HCV therapies. This article will focus on the selection of appropriate treatment options along with proper dosing and duration, awareness of concomitant disease states and drug interactions, identifying adverse drug reactions (ADRs) and patient counseling points, the provision of adherence counseling and prevention of treatment interruptions, improving communication with patients and between pharmacies, and recognizing the importance of a multidisciplinary approach.

CONCLUSION

Maintaining awareness of medication safety strategies geared toward HCV pharmacotherapy is critical for providing optimal care for patients while minimizing the opportunity for errors.

Adaptive protocols based on predictions from a mechanistic model of the effect of IL7 on CD4 counts

In human immunodeficiency virus-infected patients, antiretroviral therapy suppresses the viral replication, which is followed in most patients by a restoration of CD4+ T cells pool. For patients who fail to do so, repeated injections of exogenous interleukin 7 (IL7) are experimented. The IL7 is a cytokine that is involved in the T cell homeostasis and the INSPIRE study has shown that injections of IL7 induced a proliferation of CD4+ T cells. Phase I/II INSPIRE 2 and 3 studies have evaluated a protocol in which a first cycle of three IL7 injections is followed by a new cycle at each visit when the patient has less than 550 CD4 cells/μL. Restoration of the CD4 concentration has been demonstrated, but the long-term best adaptive protocol is yet to be determined. A mechanistic model of the evolution of CD4 after IL7 injections has been developed, which is based on a system of ordinary differential equations and includes random effects. Based on the estimation of this model, we use a Bayesian approach to forecast the dynamics of CD4 in new patients. We propose four prediction-based adaptive protocols of injections to minimize the time spent under 500 CD4 cells/μL for each patient, without increasing the number of injections received too much. We show that our protocols significantly reduce the time spent under 500 CD4 over a period of two years, without increasing the number of injections. These protocols have the potential to increase the efficiency of this therapy.

Insight into treatment of HIV infection from viral dynamics models

The odds of living a long and healthy life with HIV infection have dramatically improved with the advent of combination antiretroviral therapy. Along with the early development and clinical trials of these drugs, and new field of research emerged called viral dynamics, which uses mathematical models to interpret and predict the time-course of viral levels during infection and how they are altered by treatment. In this review, we summarize the contributions that virus dynamics models have made to understanding the pathophysiology of infection and to designing effective therapies. This includes studies of the multiphasic decay of viral load when antiretroviral therapy is given, the evolution of drug resistance, the long-term persistence latently infected cells, and the rebound of viremia when drugs are stopped. We additionally discuss new work applying viral dynamics models to new classes of investigational treatment for HIV, including latency-reversing agents and immunotherapy.

Quantifying the impact of a periodic presence of antimicrobial on resistance evolution in a homogeneous microbial population of fixed size

The evolution of antimicrobial resistance generally occurs in an environment where antimicrobial concentration is variable, which has dramatic consequences on the microorganisms' fitness landscape, and thus on the evolution of resistance. We investigate the effect of these time-varying patterns of selection within a stochastic model. We consider a homogeneous microbial population of fixed size subjected to periodic alternations of phases of absence and presence of an antimicrobial that stops growth. Combining analytical approaches and stochastic simulations, we quantify how the time necessary for fit resistant bacteria to take over the microbial population depends on the alternation period. We demonstrate that fast alternations strongly accelerate the evolution of resistance, reaching a plateau for sufficiently small periods. Furthermore, this acceleration is stronger in larger populations. For asymmetric alternations, featuring a different duration of the phases with and without antimicrobial, we shed light on the existence of a minimum for the time taken by the population to fully evolve resistance. The corresponding dramatic acceleration of the evolution of antimicrobial resistance likely occurs in realistic situations, and may have an important impact both in clinical and experimental situations.

Superinfection and cure of infected cells as mechanisms for hepatitis C virus adaptation and persistence

RNA viruses exist as a genetically diverse quasispecies with extraordinary ability to adapt to abrupt changes in the host environment. However, the molecular mechanisms that contribute to their rapid adaptation and persistence in vivo are not well studied. Here, we probe hepatitis C virus (HCV) persistence by analyzing clinical samples taken from subjects who were treated with a second-generation HCV protease inhibitor. Frequent longitudinal viral load determinations and large-scale single-genome sequence analyses revealed rapid antiviral resistance development, and surprisingly, dynamic turnover of dominant drug-resistant mutant populations long after treatment cessation. We fitted mathematical models to both the viral load and the viral sequencing data, and the results provided strong support for the critical roles that superinfection and cure of infected cells play in facilitating the rapid turnover and persistence of viral populations. More broadly, our results highlight the importance of considering viral dynamics and competition at the intracellular level in understanding rapid viral adaptation. Thus, we propose a theoretical framework integrating viral and molecular mechanisms to explain rapid viral evolution, resistance, and persistence despite antiviral treatment and host immune responses.

Beneficial coinfection can promote within-host viral diversity

In many viral infections, a large number of different genetic variants can coexist within a host, leading to more virulent infections that are better able to evolve antiviral resistance and adapt to new hosts. But how is this diversity maintained? Why do faster-growing variants not outcompete slower-growing variants, and erode this diversity? One hypothesis is if there are mutually beneficial interactions between variants, with host cells infected by multiple different viral genomes producing more, or more effective, virions. We modelled this hypothesis with both mathematical models and simulations, and found that moderate levels of beneficial coinfection can maintain high levels of coexistence, even when coinfection is relatively rare, and when there are significant fitness differences between competing variants. Rare variants are more likely to be coinfecting with a different variant, and hence beneficial coinfection increases the relative fitness of rare variants through negative frequency dependence, and maintains diversity. We further find that coexisting variants sometimes reach unequal frequencies, depending on the extent to which different variants benefit from coinfection, and the ratio of variants which leads to the most productive infected cells. These factors could help drive the evolution of defective interfering particles, and help to explain why the different segments of multipartite viruses persist at different equilibrium frequencies.

Why does drug resistance readily evolve but vaccine resistance does not?

Why is drug resistance common and vaccine resistance rare? Drugs and vaccines both impose substantial pressure on pathogen populations to evolve resistance and indeed, drug resistance typically emerges soon after the introduction of a drug. But vaccine resistance has only rarely emerged. Using well-established principles of population genetics and evolutionary ecology, we argue that two key differences between vaccines and drugs explain why vaccines have so far proved more robust against evolution than drugs. First, vaccines tend to work prophylactically while drugs tend to work therapeutically. Second, vaccines tend to induce immune responses against multiple targets on a pathogen while drugs tend to target very few. Consequently, pathogen populations generate less variation for vaccine resistance than they do for drug resistance, and selection has fewer opportunities to act on that variation. When vaccine resistance has evolved, these generalities have been violated. With careful forethought, it may be possible to identify vaccines at risk of failure even before they are introduced.

Peromyscus as a model system for human hepatitis C: An opportunity to advance our understanding of a complex host parasite system

Worldwide, there are 185 million people infected with hepatitis C virus and approximately 350,000 people die each year from hepatitis C associated liver diseases. Human hepatitis C research has been hampered by the lack of an appropriate in vivo model system. Most of the in vivo research has been conducted on chimpanzees, which is complicated by ethical concerns, small sample sizes, high costs, and genetic heterogeneity. The house mouse system has led to greater understanding of a wide variety of human pathogens, but it is unreasonable to expect Mus musculus to be a good model system for every human pathogen. Alternative animal models can be developed in these cases. Ferrets (influenza), cotton rats (human respiratory virus), and woodchucks (hepatitis B) are all alternative models that have led to a greater understanding of human pathogens. Rodent models are tractable, genetically amenable and inbred and outbred strains can provide homogeneity in results. Recently, a rodent homolog of hepatitis C was discovered and isolated from the liver of a Peromyscus maniculatus. This represents the first small mammal (mouse) model system for human hepatitis C and it offers great potential to contribute to our understanding and ultimately aid in our efforts to combat this serious public health concern. Peromyscus are available commercially and can be used to inform questions about the origin, transmission, persistence, pathology, and rational treatment of hepatitis C. Here, we provide a disease ecologist's overview of this new virus and some suggestions for useful future experiments.

Does High-Dose Antimicrobial Chemotherapy Prevent the Evolution of Resistance?

High-dose chemotherapy has long been advocated as a means of controlling drug resistance in infectious diseases but recent empirical studies have begun to challenge this view. We develop a very general framework for modeling and understanding resistance emergence based on principles from evolutionary biology. We use this framework to show how high-dose chemotherapy engenders opposing evolutionary processes involving the mutational input of resistant strains and their release from ecological competition. Whether such therapy provides the best approach for controlling resistance therefore depends on the relative strengths of these processes. These opposing processes typically lead to a unimodal relationship between drug pressure and resistance emergence. As a result, the optimal drug dose lies at either end of the therapeutic window of clinically acceptable concentrations. We illustrate our findings with a simple model that shows how a seemingly minor change in parameter values can alter the outcome from one where high-dose chemotherapy is optimal to one where using the smallest clinically effective dose is best. A review of the available empirical evidence provides broad support for these general conclusions. Our analysis opens up treatment options not currently considered as resistance management strategies, and it also simplifies the experiments required to determine the drug doses which best retard resistance emergence in patients.

The evolution of antimicrobial resistant pathogens threatens much of modern medicine. For over one hundred years, the advice has been to ‘hit hard’, in the belief that high doses of antimicrobials best contain resistance evolution. We argue that nothing in evolutionary theory supports this as a good rule of thumb in the situations that challenge medicine. We show instead that the only generality is to either use the highest tolerable drug dose or the lowest clinically effective dose; that is, one of the two edges of the therapeutic window. This approach suggests treatment options not currently considered, and simplifies the experiments required to identify the dose that best retards resistance evolution.

Resistance of Hepatitis C Virus to Inhibitors: Complexity and Clinical Implications

Selection of inhibitor-resistant viral mutants is universal for viruses that display quasi-species dynamics, and hepatitis C virus (HCV) is no exception. Here we review recent results on drug resistance in HCV, with emphasis on resistance to the newly-developed, directly-acting antiviral agents, as they are increasingly employed in the clinic. We put the experimental observations in the context of quasi-species dynamics, in particular what the genetic and phenotypic barriers to resistance mean in terms of exploration of sequence space while HCV replicates in the liver of infected patients or in cell culture. Strategies to diminish the probability of viral breakthrough during treatment are briefly outlined.